International Trade, Labor Flexibilization, and Wage Inequality: The Colombian Manufacturing Case

Yezid Hernández-Luna; Camila Salazar-Pérez; Jorge A. Herrera-Cuartas

doi:10.1515/jgd-2022-0017

Published by De Gruyter August 10, 2023

International Trade, Labor Flexibilization, and Wage Inequality: The Colombian Manufacturing Case

Yezid Hernández-Luna , Camila Salazar-Pérez and Jorge A. Herrera-Cuartas

From the journal Journal of Globalization and Development

https://doi.org/10.1515/jgd-2022-0017

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Abstract

This article delivers empirical evidence for the Colombian manufacturing sectors (during 1993–2012 and the subperiod 2000–2012) regarding the relationship between trade integration after the openness policy and the sector skill intensity over the skill premia, which depends on the Skill Biased Technical Change. We find that international trade reduces the positive sector skill intensity effect on wage inequality, moderately benefiting unskilled workers; however, a negative impact of the 1990 labor flexibilization on the skill premia, which increased the hiring of less expensive temporary and agency workers. Furthermore, the positive sector skill intensity impact on wage inequality is more important for trade with less developed countries than with the developed ones.

Keywords: emerging countries; international trade; labor flexibilization; manufacturing sector; skill premia; skill-biased technical change

JEL Classification: J31; J82; J88; F14; F15; F16; O14; O33; O54

Corresponding author: Yezid Hernández-Luna, Associate Professor, Faculty of Economics and Administrative Sciences, Jorge Tadeo Lozano University, Carrera 4 # 22-61, Bogotá, Colombia, E-mail: yezid.hernandezl@utadeo.edu.co

Acknowledgments

The authors acknowledge the advice of Professors Federico TRIONFETTI, Patrick SEVESTRE, Stephen BAZEN, and Christian SCHLUTER from the Aix-Marseille School of Economics. Likewise, Professors Anne-Célia DISDIER, Pierre-Philippe COMBES, Juan-Carlos GUATAQUI-ROA, Ivan LEDEZMA-RODRIGUEZ, Luis-Ricardo ARGUELLO-CUERVO, and Karolien de BRUYNE. Also, Christian-Manuel POSSO-SUÁREZ from Banco de la República de Colombia and Sara-Patricia RIVERA from the National Department of Planning. The writing assistance of Karol-Marcela CUERVO-CUERVO and the proofreading help of Liam SIMPSON and Louise DUCKLING.

Research funding: This research received no specific grant from public, commercial, or not-for-profit funding agencies.

Appendix A: Variable Definitions

Table A1:

Occupational categories described in the annual manufacturing survey EAM – DANE.

Occupational category	Definition
Professionals, technicians, and technologists	Refers to people directly linked to productive activities or tasks directly related to production. This category includes mechanical, chemical, industrial, electrical, mining, and oil engineers, etc., and technicians and technologists who work directly in the production area
Workers and production operators	Corresponds to workers dedicated to manufacturing, processing, assembly, assembly, maintenance, inspection, storage, packaging, loading and unloading, such as internal messengers, stokers, machinery cleaning personnel, supervisors and forepersons who work manually, drivers of vehicles that transport raw materials, materials or products only within the establishment, workers dedicated to the maintenance and repair (mechanical, electrical, etc.) of industrial machinery and equipment. Administrative heads and supervisors mainly devoted to the surveillance of worker personnel, surveillance and service personnel who work in the production area. Apprentices from reformatories, orphanages, or the like are included; likewise when they produce goods or services that have a commercial value, and apprentices who, for an agreed period, work in the productive part of the establishment where they receive training in a profession, art, or trade and are paid a salary in cash or kind
Administration and sales staff	Includes: the people who direct the economic, financial, and administrative aspects of the establishment and are in charge of preparing and conducting the general policy of the company, such as managers, assistant managers, and paid directors. Administrative heads, supervisors, surveillance personnel, guards, service personnel who do not work in the area of production, vendors, and distributors, if the establishment is in charge of the place of work and remuneration. It does not include the staff of warehouses, administrative offices, management, warehouses, and other auxiliary units that do not depend directly on the establishment or are located in a physical location other than the production plant

Source: DANE (1996).

Appendix B: Comparison of Imputed Variables With the Baseline and the Alternative Methodologies

To fill in the gap for workers variables in the long database during 1995–1999, we implement a baseline methodology summarized in Tables B1 and B2, where we present as examples years 1994 and 1995:

Table B1:

Baseline methodology to estimate missing labor data.

	Variable	1994	1995	Methodology
1	tot_emp_per (total permanent employees)	91,260	MD	5 + 9
2	tot_emp_temp (total temporary employees)	6442	MD	6 + 10
3	tot_emp_agen (total agency employees)	12,350	MD	7 + 11
4	TOT EMP PTA	Sum (1 + 2 + 3)	Sum (1 + 2 + 3)	Rule of three: in 1994 4 ⟶ 5 in 1995 X ⟶ 5
5	per_sk (permanent skilled employees)	35,569	36,822
6	temp_sk (temporary skilled employees)	344	557
7	agen_sk (agency skilled employees)	4042	3749
8	TOT PTA SK	Sum (5 + 6 + 7)	Sum (5 + 6 + 7)
9	per_usk (permanent unskilled employees)	55,691	MD	(1 − (10/12) − (11/12))*12
10	temp_usk (temporary unskilled employees)	6098	MD	(Moving average of the last 2 years (10/12))*12
11	agen_usk (agency unskilled employees)	8308	MD	(Moving average of the last 2 years (11/12))*12
12	TOT PTA USK	Sum (9 + 10 + 11)	Sum (9 + 10 + 11)	4–8

Note: MD, missing data.

Table B2:

Baseline methodology to estimate missing wages data.

	Variable	1994	1995	Methodology
1	Skilledwage (skilled workers’ wages)	Sum (3 + 5)	MD	(3 + 5) Do not include Administrative and Sales staff wages
2	Unskilledwage (unskilled workers’ wages)	1,256,716,307	MD	(Moving average of the last 3 years (2/4))*4
3	Skilledwage_sinAV (skilled workers’ wages not including Administrative and Sales staff)	415,813,189	MD	(1–(2/4))*4
4	Susalpresrem_pnPT (production workers, permanent and temporary, wages, salaries, benefits, and remunerations)	Sum (2 + 3)	2,121,508,200	This variable includes wages of skilled and unskilled workers
5	Susalpresrem_AVPT (Administrative and Sales staff, permanent and temporary, wages, salaries, benefits, and remunerations)	1,051,613,222	1,494,628,819

Note: MD, missing data.

We propose an alternative imputation methodology to evaluate how sensitive the baseline estimation is to other specifications. Since the resulting analysis shows that the estimated variables are virtually the same, checking for sensibility in the article’s econometric estimates of Equations (1–4) and regressions in Appendix C was unnecessary.

The alternative methodology is modeled as follows: we use benchmark variables correlated with the incomplete variables available in the dataset to imitate its shape and trend. Before explaining the estimation process, here are some definitions:

i m i k is the data feature i to be imputed. k is the k -th record of the feature i m i .
s b i k is the feature i of the benchmark variable with a similar trend to i m i k . k is the k -th record of the feature s b i k .
e s i k is the feature i of data estimated in the baseline estimation. k is the k -th record of the feature e s i k .

The imputation process includes the following stages:

According to the context of the database, It is assumed that i m i k and s b i k have a similar trend and shape but may be on different scales. Table B1 shows the feature pairs.
After defining the features i m i and s b i , we proceed to obtain the normalized values ( i m i k → i m ‾ i k and s b i k → s b ‾ i k ) of the different records of the two pairs of features, using the following Equations (B1) and (B2):
(B1) i m ‾ i k = i m i k max ( s b i )
(B2) s b ‾ i k = s b i k max ( s b i )
A fitting algorithm matches the maximum value of the feature i m ‾ i k with the maximum value of the feature s b ‾ i k .
The missing records of the feature i m ‾ i k are replaced by the records of the feature s b ‾ i k .
The normalization i m ‾ i k → i m i k is reversed to obtain the imputed feature.

After obtaining the imputed feature, we present below computation of Cosine Similarity Metrics and Correlations to compare the baseline estimations e s i k to the alternative imputation methodology i m i k :

Table B2 presents the calculations of the Cosine Similarity Index.
Likewise, we present in Table B3 a correlation analysis of the variables using different techniques, such as Pearson, Spearman and Kendall.

Table B3:

Pairs of features to impute with their respective variable that has a similar behavior.

i m i k	s b i k
emp_per (permanent employees)	per_sk (permanent skilled employees)
emp_temp (temporary employees)	temp_sk (temporary skilled employees)
emp_agen (agency employees)	agen_sk (agency skilled employees)
Skilledwage (skilled workers’ wages)	Susalpresrem_AVPT (Administrative and Sales staff, permanent and temporary, wages, salaries, benefits, and remunerations)
Unskilledwage (unskilled workers’ wages)	Susalpresrem_pnPT (production workers, permanent and temporary, wages, salaries, benefits, and remunerations)

Table B4:

Similarity of cosines between the alternative imputation and the baseline methodologies.

Feature	Value
emp_per (permanent employees)	0.999
emp_temp (temporary employees)	0.995
emp_agen (agency employees)	0.988
Skilledwage (skilled workers’ wages)	0.999
Unskilledwage (unskilled workers’ wages)	0.999

Table B5:

Correlation between the alternative imputation and the baseline methodologies.

Feature	Correlation type	Value
emp_per (permanent employees)	Pearson	0.998
emp_temp (temporary employees)	Pearson	0.993
emp_agen (agency employees)	Pearson	0.983
Skilledwage (skilled workers’ wages)	Pearson	0.999
Unskilledwage (unskilled workers’ wages)	Pearson	0.999
emp_per (permanent employees)	Spearman	0.998
emp_temp (temporary employees)	Spearman	0.977
emp_agen (agency employees)	Spearman	0.975
Skilledwage (skilled workers’ wages)	Spearman	0.999
Unskilledwage (unskilled workers’ wages)	Spearman	0.999
emp_per (permanent employees)	Kendall	0.984
emp_temp (temporary employees)	Kendall	0.921
emp_agen (agency employees)	Kendall	0.908
Skilledwage (skilled workers’ wages)	Kendall	0.988
Unskilledwage (unskilled workers’ wages)	Kendall	0.992

According to the results in Tables B2 and B3, there is a high similarity and correlation between the variables imputed with the baseline and the alternative methodologies.

Appendix C: Estimating the Average Wage Gap

Following Boeri and van Ours (2014) methodology, originally used to estimate the so-called union wage gap, we adapt the econometric method to estimate the skill bias wage gap. The model is as follows:

(C1) log ( w ‾ s t ) = α D s t + X s t ′ γ + ε s t

where D s t is a dummy variable equal to 1 when the sector is relatively more skilled-intensive, measured as the ratio of skilled workers over unskilled workers, i.e. if the sector relative skill intensity is higher than the total (annual) average across subsectors of 0.57 (29 subsectors out of 67) in the short database and 0.56 (7 subsectors out of 26) in the long database. X s t ′ is a matrix of specific characteristics affecting a sector’s real average wage. In this case, the matrix includes sector real average assets, sector real average production, sector average permanent, temporary, and agency workers, as well as several measures of trade integration: total trade with Developed and Less Developed Countries, imports and exports with Developed and Less Developed Countries, change in actual tariffs to 1980 levels, and the number of exporters by sector. α ˆ represents the skill intensity sector’s average wage gap such that α ˆ = log ( w ‾ H A ) − log ( w ‾ L A ) ≈ w ‾ H A − w ‾ L A w ‾ L A , where w ‾ H A and w ‾ L A represent the average wage of the skilled intensive sector (H) and the less skilled intensive sector (L). Tables C1 and C2 present the coefficients obtained by OLS.

Table C1:

Skill sector wage gap, 1993–2012. OLS (real variables).

Dep Var: ln (real av wage Pr)	(1)	(2)	(3)	(4)
Dummy skill intensity	0.1379^***	0.0815^***	0.0917^***	0.1296^***
	(7.375)	(3.610)	(5.741)	(4.361)
Ln (real av assets)	0.1611^***	0.1357^***	0.1253^***	0.1369^***
	(8.446)	(6.726)	(9.028)	(6.427)
Ln (real av production)	0.0402	0.0686^**	0.1214^***	0.0985^**
	(1.731)	(2.884)	(6. 952)	(3.243)
Ln (Perm)	0.2061^***	0.2427^***	0.1819^***	0.2766^***
	(7.681)	(8.430)	(6.759)	(9.143)
Ln (Temp)	−0.0956^***	−0.0870^***	−0.0491^***	−0.0400
	(−6.657)	(−5.372)	(−3.517)	(−1.511)
Ln (Agen)	−0.1480^***	−0.1504^***	−0.1479^***	−0.2409^***
	(−10.384)	(−8.747)	(−12.011)	(−7.469)
Ln (trade dc)	−0.0061
	(−0.715)
Ln (trade ldc)	0.0547^***
	(4.630)
Tariff change		−0.0035^***
		(−4.271)
Ln (imports dc)			0.0237^***
			(4.006)
Ln (exports dc)			−0.0573^***
			(−9.540)
Ln (imports ldc)			0.0266^***
			(3.375)
Ln (exports ldc)			0.0207
			(1.948)
Ln (exporters)				−0.0063
				(−0.278)
Constant	5.0636^***	5.6986^***	4.8186^***	5.1732^***
	(26.783)	(33.680)	(32.082)	(17.258)

Obs	360	322	360	132
R ²	0.893	0.891	0.933	0.896
F	203.976	180.364	264.629	473.122

*p < 0.05, **p < 0.01, ***p < 0.001. Source: own calculations based on DANE, DIAN, and World Bank data. Notes: t-statistics in brackets. av, average; Pr, projected variable; Perm, permanent workers; Temp, temporal workers; Agen, agency workers; dc, developed countries; ldc, developing countries, Tariff change to 1980 level. Sector 314 is not included in tables and is discarded from estimations because there are few observations for most variables. The associated coefficients to the independent variable “Dummy skill intensity” (in bold) are interpreted as the skill sector wage gap.

Table C2:

Skill sector wage gap, 2000–2012. OLS (real variables).

Dep Var: ln (real av wage Pr)	(1)	(2)	(3)	(4)
Dummy skill intensity	0.1923^***	0.1756^***	0.2016^***	0.1690^***
	(12.686)	(8.923)	(12.466)	(6.616)
Ln (real av assets)	0.1106^***	0.1040^***	0.1098^***	0.0613^**
	(7.318)	(6.505)	(7.569)	(2.779)
Ln (real av production)	0.0765^***	0.0970^***	0.0775^***	0.1450^***
	(4.524)	(5.585)	(4.746)	(6.865)
Ln (Perm)	0.1343^***	0.1938^***	0.1226^***	0.2497^***
	(8.288)	(10.846)	(7.554)	(9.838)
Ln (Temp)	−0.0247^*	−0.0307^*	−0.0235^*	−0.0257
	(−2.225)	(−2.555)	(−2.136)	(−1.586)
Ln (Agen)	−0.1383^***	−0.1551^***	−0.1399^***	−0.2618^***
	(−11.761)	(−10.406)	(−12.033)	(−11.161)
Ln (trade dc)	0.0347^***
	(5.590)
Ln (trade ldc)	0.0143
	(1.684)
Tariff change		−0.0015
		(−1.872)
Ln (imports dc)			0.0258^***
			(5.057)
Ln (exports dc)			0.0082^*
			(1.994)
Ln (imports ldc)			−0.0071
			(−1.278)
Ln (exports ldc)			0.0269^***
			(3.497)
Ln (exporters)				0.0531^***
				(4.653)
Constant	5.2232^***	5.6328^***	5.2910^***	5.4373^***
	(46.391)	(37.570)	(45.026)	(27.530)

Obs	720	604	708	273
R ²	0.823	0.781	0.829	0.789
F	208.408	154.166	197.023	178.527

*p < 0.05, **p < 0.01, ***p < 0.001. Source: own calculations based on DANE, DIAN, and World Bank data. Notes: t-statistics in brackets. av, average; Pr, projected variable; Perm, permanent workers; Temp, temporal workers; Agen, agency workers; dc, developed countries; ldc, developing countries, Tariff change to 1980 level. Sector 314 is not included in tables and is discarded from estimations because there are few observations for most variables. The associated coefficients to the independent variable “Dummy skill intensity” (in bold) are interpreted as the skill sector wage gap.

Sector 353 is merged with sector 354, and Sector 361 is merged with sectors 362 and 369. Time dummies not reported.

Appendix D: Figures and Tables

Figure D1:

GDP at market prices, imports, and exports of goods and services, 1990–2014 (constant 2005 Million US$). Source: World Bank Data.

Figure D2:

Gini index by geographic area. Colombia, 1991–2014. Notes: “From 2002 on, the income figures are not comparable with those of earlier years, owing to the application of new methodological criteria developed by the National Administrative Department of Statistics (DANE) and the National Planning Department (DNP) in the framework of the mission for the splicing of employment, poverty, and inequality series (MESEP)”. Source: CEPAL CEPALSTAT.

Figure D3:

% of total employment in Colombia, by sector 1990–2014. Source: World Bank Data.

Figure D4:

Equation (3), annual percentage change of skill premia (projected) and trade derivatives (dt) 2000–2012. Notes: trade derivatives obtained from Equations (4) and (7) in the text. Change SP Pr, annual percentage change (during 2000–2012) of projected skill premia; ldc, developing countries; dc, developed countries. SBTC1 and SBTC4 correspond to the SBTC measures obtained from Table 3 in the article. WB Pr is an SBTC measure: Projected wage bill variable.

Figure D5:

Equation (3), annual percentage change of skill premia (wages and social security of permanent, temporal, and agency workers) and trade derivatives (dt) 2000–2012. Notes: trade derivatives obtained from Equations (4) and (7) in the text. Change SP W–SC, annual percentage change (during the period 2000–2012) of wages and social security skill premia; ldc, developing countries; dc, developed countries. SBTC3 and SBTC6 corresponds to the SBTC measures obtained from Table 3 in the article. WB W–SC is an SBTC measure: wages and social security wage bill variable.

Figure D6:

Equation (3), annual percentage change of skill premia (wages and salaries of permanent workers) and trade derivatives (dt) 2000–2012. Notes: trade derivatives obtained from Equations (4) and (7) in the text. Change SP W–S, annual percentage change (during the period 2000–2012) of wages and salaries skill premia; ldc, developing countries; dc, developed countries. SBTC5 corresponds to the SBTC measure obtained from Table 3 in the article. WB W–S is an SBTC measure: wages and salaries wage bill variable.

Figure D7:

Equation (3), annual percentage change of skill premia (projected) and skill intensity derivatives (si) 2000–2012. Notes: skill intensity derivatives obtained from Equations (4) and (6) in the text. Change SP Pr, annual percentage change (during 2000–2012) of projected skill premia; ldc, developing countries; dc, developed countries. SBTC1 and SBTC4 correspond to the SBTC measures obtained from Table 3 in the article. WB Pr is an SBTC measure: Projected wage bill variable.

Figure D8:

Equation (3), annual percentage change of skill premia (wages and social security of permanent, temporal, and agency workers) and skill intensity derivatives (si) 2000–2012. Notes: skill intensity derivatives obtained from Equations (4) and (6) in the text. Change SP W–SC, annual percentage change (during 2000–2012) of wages and social security skill premia; ldc, developing countries; dc, developed countries. SBTC3 and SBTC6 correspond to the SBTC measures obtained from Table 3 in the article. WB W–SC is an SBTC measure: wages and social security wage bill variable.

Figure D9:

Equation (3), annual percentage change of skill premia (wages and salaries of permanent workers) and skill intensity derivatives (si) 2000–2012. Notes: skill intensity derivatives obtained from Equations (4) and (6) in the text. Change SP W–S, annual percentage change (during 2000–2012) of wages and salaries skill premia; ldc, developing countries; dc, developed countries. SBTC5 corresponds to the SBTC measure obtained from Table 3 in the article. WB W–S is an SBTC measure: wages and salaries wage bill variable.

Figure D10:

Equation (4), annual percentage change of skill premia (wages and salaries of permanent workers) and trade (dt) and skill intensity (si) derivatives 2000–2012. Notes: trade and skill intensity derivatives obtained from Equations (5)–(7) in the text. Change SP W–S, annual percentage change (during 2000–2012) of wages and salaries skill premia. SBTC2 and SBTC5 correspond to the SBTC measures obtained from Table 3 in the article. WB W–S is an SBTC measure: wages and salaries wage bill variable.

Figure D11:

Equation (3), annual percentage change of skill premia (projected and W–SC) and trade (dt) and skill intensity (si) derivatives 1993–2012. Notes: trade and skill intensity derivatives obtained from Equations (5)–(7) in the text. Ldc, developing countries; Dc, developed countries. Change SP W–SC, annual percentage change (during 1993–2012) of wages and social security skill premia. Change SP Pr, annual percentage change (during 1993–2012) of projected skill premia. SBTC1 corresponds to the SBTC measure obtained from Table 2 in the article. WB W–SC is an SBTC measure: wages and social security wage bill variable. WB Pr is an SBTC measure: Projected wage bill variable.

Table D1:

Most important Colombian trade partners, GDP per capita (constant 2005 US$).

Country	1991	2000	2010	Average (20 years)
Austria	28,717	35,027	38,803	34,266
Belgium	28,485	34,009	36,742	33,237
Bolivia	857	965	1177	983
Bulgaria	2648	2707	4461	3173
China	499	1122	2869	1368
Colombia	2841	3074	3938	3276
Cyprus	16,321	20,890	23,157	20,591
Czech Republic	8,661	10,379	14,174	11,110
Denmark	36,822	45,340	46,293	43,841
Ecuador	2698	2613	3251	2856
Estonia	–	7,134	10,393	8106
Finland	26,292	33,217	38,065	32,649
France	28,019	32,392	33,898	31,621
Germany	29,113	32,662	36,127	32,369
Greece	15,707	18,041	21,309	18,831
Hungary	7448	8,810	10,926	9188
Ireland	22,484	41,954	44,583	38,400
Italy	25,952	29,872	29,163	28,861
Latvia	4747	4571	7391	5407
Lithuania	6304	5098	8829	6256
Luxembourg	54,251	72,394	80,276	70,035
Malta	10,136	15,002	16,050	13,723
Netherlands	29,762	37,547	41,110	36,282
Peru	2014	2487	3835	2674
Poland	4380	6,790	10,036	6900
Portugal	14,402	17,891	18,648	16,987
Romania	3356	3327	5635	4086
Slovak Republic	7626	8,957	14,263	9967
Slovenia	11,394	15,033	19,054	15,338
Spain	19,242	23,921	25,318	23,217
Sweden	30,267	36,576	42,826	36,265
United Kingdom	25,520	34,059	37,611	33,519
United States	32,504	40,965	43,952	39,794
Venezuela, RB	5661	5256	6010	5567
EU (average)	19,156	23,467	26,487	23,160
CAN1 (average)	1856	2022	2754	2171
CAN2 (average)	2103	2285	3050	2447

Source: World Bank Data. Own calculations Notes: CAN1, Andean Community of Nations without Colombia (Bolivia, Ecuador, and Peru). CAN2, Andean Community of Nations with Colombia. EU, European Union 27 countries. The countries in bold define trade variables with developed and less developed countries.

Table D2:

Trade balance share and trade integration variables 2000–2012.

Relative exports							Trade integration
CIIUR3	Av X/M Ldc	Annual Δ %	Av X/M Dc	Annual Δ %	Av X/M Totc	Annual Δ %	Δ tar 00	Δ tar 12	Av Δ tariffs	Av exporters	Annual Δ %	% Av shtradeldc	Δ %	% Av shtradedc	Δ %
151	1.29	17.61	2.10	−9.13	1.58	−4.86	9.06	9.93	7.71	334.33	−5.54	36.75	2.11	30.79	−22.16
152	0.25	−11.20	1.19	−2.14	0.54	−2.55	9.17	−12.48	3.17	38.50	−4.84	34.87	−43.55	26.61	4.02
153	48.97	−23.85	0.25	20.26	4.41	−18.57	8.30	10.76	6.94	147.17	−1.86	58.77	−42.10	17.12	−5.09
154	0.72	4.73	0.07	3.59	0.34	−0.98	8.58	13.49	9.37	312.17	−1.83	45.57	−32.32	35.50	18.54
155	1.62	NA	2.05	NA	1.75	NA	8.77	13.17	9.50	65.67	−3.79	45.48	−21.14	18.01	17.39
157	40.42	−23.05	150.54	29.86	26.13	−5.49	NA	NA	NA	NA	NA	27.22	−2.41	10.29	−0.08
158	4.32	6.83	0.75	0.61	1.93	2.25	NA	NA	NA	NA	NA	38.61	−3.83	24.54	3.64
159	1.88	−10.32	0.18	−4.51	0.57	−9.42	NA	NA	NA	NA	NA	36.00	−16.25	39.67	12.98
160	3.13	40.80	152.73	−21.28	4.48	25.94	1.20	6.20	1.97	8.50	2.71	27.13	−44.81	29.80	0.89
171	0.92	−1.27	0.05	−24.54	0.64	−2.45	38.99	51.12	40.60	208.67	−3.85	27.32	−17.84	9.59	−8.74
172	0.76	−10.00	0.06	−3.71	0.46	−3.52	38.92	46.88	40.03	658.17	−5.48	43.48	35.34	18.36	−15.78
173	NA	NA	NA	NA	NA	NA	36.93	48.37	38.63	536.67	−8.27	NA	NA	NA	NA
174	1.97	−14.81	1.04	−7.52	1.40	−10.02	NA	NA	NA	NA	NA	33.79	21.22	33.54	−29.29
175	4.25	−18.32	2.36	1.03	3.12	−12.89	NA	NA	NA	NA	NA	51.90	15.12	27.22	−7.46
181	5.18	−22.41	11.80	−4.95	7.29	−16.69	52.11	57.16	52.89	1299.17	−8.44	38.23	30.99	40.92	−40.42
182	5.88	−1.45	27.17	14.11	12.25	4.88	54.54	64.18	58.24	32.75	NA	6.06	4.04	84.66	−8.11
191	41.03	0.07	17.93	3.13	25.16	2.80	21.37	26.51	22.41	681.83	−8.04	32.77	8.50	41.38	−21.55
192	0.45	−10.42	1.67	−15.70	0.51	−12.45	36.92	42.01	37.92	515.67	−7.37	65.44	9.20	7.06	−11.49
193	0.38	−19.68	11.15	−7.61	1.56	−15.68	NA	NA	NA	NA	NA	40.37	31.85	38.71	−17.98
201	8.24	−1.28	0.36	11.59	1.66	3.62	27.59	35.11	28.54	74.33	−11.81	36.69	−15.29	34.82	−17.57
202	0.21	−10.01	1.79	−32.74	0.49	−20.26	22.69	29.71	23.63	355.67	−12.81	47.45	18.45	19.14	−36.92
203	23.73	−35.31	0.13	−27.22	0.78	−21.19	NA	NA	NA	NA	NA	28.19	−12.67	33.61	24.49
204	57.20	−17.28	0.37	16.05	2.50	14.48	NA	NA	NA	NA	NA	34.30	9.62	36.92	−68.69
209	0.96	−27.59	1.08	−6.63	0.92	−23.08	NA	NA	NA	NA	NA	57.89	5.40	21.36	−11.15
210	7.63	0.41	0.06	−7.10	0.81	4.58	13.12	20.18	14.08	680.83	−1.80	30.85	10.66	35.46	−14.98
221	11.49	−11.06	0.38	1.80	1.24	−0.61	18.13	19.82	18.03	772.17	−6.65	26.85	5.37	33.91	0.57
222	1.72	−11.19	0.32	8.66	1.09	−6.05	8.58	13.89	9.37	646.33	−4.21	55.47	20.28	11.35	−4.26
223	12.56	NA	0.01	12.17	0.05	−8.55	NA	NA	NA	NA	NA	7.90	2.31	70.78	−22.22
232	1.68	16.82	5.43	−24.88	2.74	−16.05	1.40	9.10	2.00	NA	NA	8.40	−13.15	59.89	−3.48
241	0.86	−0.11	0.06	3.00	0.20	1.64	12.21	17.59	12.85	533.33	2.22	18.40	−1.34	49.72	−13.14
242	2.78	−0.92	0.11	−19.97	0.53	−7.38	7.62	11.94	8.18	1016.50	−2.24	24.94	−0.08	39.36	−6.15
251	0.36	−13.61	0.03	−3.90	0.20	−11.33	25.32	34.75	26.53	47.67	−1.21	31.44	−4.04	19.72	−7.12
252	1.54	−10.17	0.51	10.93	0.89	−1.40	31.46	42.54	33.04	1323.50	−1.40	36.29	8.70	28.94	−8.78
261	2.66	−9.73	1.06	4.25	1.50	−1.74	27.99	35.96	28.97	291.83	−5.45	30.19	3.39	34.18	−11.74
269	1.55	−5.95	2.69	−13.68	2.14	−9.64	27.54	35.87	28.77	466.33	−9.40	32.00	26.87	38.52	−27.69
271	0.74	10.04	2.12	−5.81	1.26	0.01	9.89	17.14	10.78	236.50	6.87	27.55	3.07	32.59	−13.47
272	0.50	11.60	16.76	36.89	2.77	27.59	10.76	17.06	11.62	209.67	0.00	28.42	−39.02	40.54	36.99
281	2.13	−6.99	1.11	3.11	1.19	−2.44	21.25	31.02	22.52	NA	NA	24.31	1.89	38.74	17.49
289	0.97	−12.53	0.21	−3.49	0.48	−7.51	21.05	29.31	22.20	1105.50	−2.49	33.64	8.26	35.00	−7.92
291	1.42	−14.79	0.02	4.87	0.12	−0.59	10.53	18.43	11.56	886.00	1.85	14.31	11.09	59.72	−14.07
292	0.88	−19.78	0.02	−5.41	0.07	−6.20	13.25	18.73	13.89	792.00	1.22	7.86	8.26	62.95	−16.25
293	0.73	−18.74	0.10	−10.12	0.53	−14.50	3.57	8.82	4.59	134.17	−0.29	57.26	24.66	9.54	−10.87
311	1.60	−15.76	0.10	−12.31	0.34	−3.80	19.73	28.48	20.92	237.60	NA	23.97	15.62	47.12	−28.71
312	2.13	−8.94	0.03	12.44	0.27	2.70	18.13	26.55	19.34	232.17	2.54	21.46	9.34	51.60	−18.80
313	1.27	−6.04	0.11	−8.60	0.44	−4.73	17.13	26.26	18.50	107.50	7.20	28.84	−1.19	33.95	−8.99
314	1.79	−4.95	0.13	29.88	0.77	3.57	22.09	29.35	23.27	47.67	−1.14	39.78	22.21	24.44	−13.18
315	0.31	−14.82	0.05	−2.61	0.18	−9.47	18.56	21.11	18.80	191.17	−4.88	49.26	30.28	30.83	−13.94
319	0.59	−21.47	0.02	11.81	0.11	−7.36	21.71	28.70	22.45	252.17	4.27	21.15	21.56	43.60	−12.27
331	0.91	−22.98	0.03	3.41	0.06	−1.13	16.93	21.47	17.43	104.00	0.74	7.62	7.91	66.72	−9.91
332	0.23	−8.72	0.01	−0.53	0.06	−3.01	17.39	21.02	17.63	75.33	0.56	19.94	15.32	48.77	−5.05
341	0.62	−13.38	0.01	−17.64	0.49	−14.27	9.98	11.82	10.14	55.33	−0.35	49.22	−32.36	9.50	1.58
342	3.74	−41.77	0.05	−8.26	0.21	−17.74	13.74	22.49	15.14	33.83	−2.95	37.23	31.90	39.70	−59.48
343	2.45	−17.51	0.06	7.31	0.38	−1.90	18.47	24.00	19.41	247.50	−1.83	26.49	17.74	39.06	−9.72
351	2.06	0.41	0.03	9.66	0.04	10.25	20.11	29.01	21.28	15.67	−8.78	6.07	8.43	71.19	−33.70
353	6.30	−22.48	0.05	8.44	0.05	−3.66	25.74	30.50	26.24	51.83	−7.35	0.34	−1.73	85.70	−0.82
359	0.31	−12.29	0.03	−20.17	0.27	−12.42	13.87	17.46	14.26	85.83	−2.27	30.75	1.08	3.33	−1.79
361	2.70	−22.83	1.24	−8.91	1.50	−14.67	33.73	38.50	34.47	544.67	−7.00	39.67	21.76	31.20	−23.06
369	0.31	−10.11	1.58	−4.58	0.78	−9.57	20.49	25.58	21.23	870.83	−5.69	35.92	28.81	37.82	−18.62

Source: own calculations based on DANE, DIAN, World Bank Data, and DNP. Notes: NA, Not available information. In the case of the annual percentage change, it is not calculated because data was not available for the whole period. In other cases, information was not available for the specific sector. Ldc, developing countries; Dc, developed countries; totc, all countries; Δ % , annual percentage change; Δ tar, tariffs reduction to the 1980 level; Av, average; shtradeldc, share of trade to developing countries; shtradeldc, share of trade to developed countries. Sectors 156, 231, 243, 273, 300, 321, 322, and 323 are not included in tables and are discarded from estimations because data for most variables is incomplete.

Table D3:

Skill premia, skill intensity, wage bill 2000–2012.

Skill premia							Skill intensity						Wage bill
CIIUR3	Av Pr	Annual Δ %	Av W–S	Annual Δ %	Av W–SC	Annual Δ %	Av Pr	Annual Δ %	Av Perm	Annual Δ %	Av Pe-Te-Ag	Annual Δ %	Av Pr	Annual Δ %	Av W–S	Annual Δ %	Av W–SC	Annual Δ %
151	2.61	−0.12	1.49	0.33	1.33	−0.37	0.49	−0.98	0.68	−0.50	0.48	−1.40	0.56	−0.50	0.60	0.14	0.39	−1.07
152	2.28	1.14	1.56	0.38	1.57	0.56	0.64	−0.31	0.75	0.19	0.62	−0.75	0.59	0.32	0.61	0.14	0.49	−0.09
153	1.47	1.01	1.60	3.89	1.28	1.38	0.91	1.88	0.93	2.17	0.89	1.58	0.57	1.17	0.61	1.40	0.53	1.37
154	2.61	0.37	2.68	3.02	1.53	1.33	0.88	2.77	1.08	1.91	0.86	2.28	0.69	1.00	0.73	0.88	0.56	1.67
155	1.51	0.83	1.26	1.59	1.21	0.43	0.77	−0.32	0.80	0.03	0.75	−0.60	0.54	0.24	0.56	0.73	0.48	−0.09
157	1.87	−1.68	1.09	1.44	1.61	−1.70	0.60	3.04	0.64	2.50	0.57	2.33	0.53	0.64	0.52	0.72	0.48	0.30
158	2.73	1.47	1.91	2.30	1.39	1.38	0.62	−0.15	0.84	0.45	0.61	−0.39	0.63	0.49	0.66	0.78	0.46	0.52
159	1.41	−1.53	2.33	0.87	1.47	−1.44	1.64	3.52	1.46	4.20	1.62	3.38	0.69	0.58	0.70	0.27	0.70	0.53
160	1.44	−0.41	1.11	15.28	1.23	0.91	0.67	17.06	0.66	16.88	0.65	16.92	0.44	7.51	0.48	6.61	0.40	8.61
171	3.10	0.35	0.73	2.66	1.58	−1.06	0.24	1.57	0.30	1.41	0.23	1.33	0.42	1.14	0.42	1.58	0.27	0.18
172	2.30	1.49	0.63	2.23	1.44	4.92	0.29	1.75	0.36	−0.79	0.28	1.43	0.40	2.01	0.39	1.38	0.29	4.28
173	3.16	3.55	1.06	8.28	1.18	−1.33	0.24	2.05	0.47	6.09	0.23	1.55	0.41	3.65	0.50	4.78	0.21	0.15
174	3.16	0.59	1.47	2.80	1.17	1.78	0.37	2.61	0.62	2.79	0.37	2.29	0.54	1.46	0.59	1.10	0.30	2.95
175	2.47	0.39	1.32	3.71	1.48	−0.31	0.43	0.71	0.55	2.35	0.43	0.37	0.51	0.55	0.56	1.67	0.39	0.04
181	2.38	−1.48	1.29	2.07	1.16	−0.22	0.33	2.44	0.58	2.15	0.32	2.17	0.44	0.53	0.56	0.93	0.27	1.40
182	6.39	8.02	1.62	12.44	0.87	1.70	0.23	4.85	0.71	10.33	0.21	4.68	0.57	6.35	0.59	5.64	0.16	5.53
191	3.30	−3.71	2.46	2.46	1.28	−1.31	0.30	0.04	0.87	5.15	0.29	−0.73	0.50	−2.06	0.70	0.91	0.27	−1.59
192	2.54	−0.74	1.07	2.92	1.24	−0.78	0.30	1.14	0.48	3.51	0.29	0.78	0.43	0.23	0.51	1.41	0.26	−0.01
193	2.50	−0.44	1.12	3.98	1.30	−3.55	0.34	1.88	0.49	5.82	0.33	1.63	0.46	0.75	0.53	1.84	0.30	−1.40
201	3.35	−2.37	1.42	−4.63	2.21	1.18	0.39	−2.57	0.48	−3.02	0.38	−2.87	0.56	−2.28	0.58	−2.15	0.45	−0.93
202	2.43	−0.47	1.05	1.57	1.49	−3.14	0.40	1.92	0.53	5.36	0.40	1.75	0.49	0.69	0.51	0.73	0.37	−0.87
203	2.12	−0.50	0.68	−1.37	1.08	−2.32	0.29	−3.11	0.38	−0.36	0.28	−3.46	0.37	−2.20	0.40	−0.79	0.23	−4.20
204	1.64	−0.78	0.59	−3.67	0.98	0.98	0.20	0.34	0.35	−3.47	0.20	−0.15	0.25	−0.32	0.36	−2.37	0.16	0.68
209	2.00	−0.93	1.03	3.33	1.04	−8.35	0.35	−1.25	0.55	3.64	0.35	−1.70	0.41	−1.43	0.50	1.91	0.26	−7.55
210	1.93	0.01	1.05	0.72	1.45	−1.03	0.53	0.41	0.59	0.62	0.51	−0.15	0.50	0.21	0.51	0.36	0.43	−0.65
221	1.79	1.97	5.88	7.00	1.52	3.40	3.25	4.64	3.44	3.34	3.22	4.48	0.84	1.21	0.84	1.25	0.81	1.60
222	2.11	−1.65	1.39	−0.70	1.48	−0.05	0.62	1.70	0.72	1.43	0.61	1.49	0.56	0.01	0.58	−0.29	0.47	0.77
223	1.89	2.53	1.62	2.85	1.22	1.77	0.77	−1.30	0.90	−0.23	0.76	−1.43	0.58	0.58	0.61	1.26	0.47	0.21
232	1.58	1.22	1.56	10.75	1.38	1.47	0.83	9.72	0.95	8.11	0.81	9.11	0.55	5.79	0.60	4.99	0.51	6.15
241	2.46	2.54	2.37	4.29	1.73	2.06	0.96	1.82	1.14	2.79	0.94	1.47	0.70	1.38	0.70	1.34	0.62	1.44
242	3.38	−0.42	4.72	0.98	1.58	−0.26	1.32	0.81	1.76	1.29	1.30	0.55	0.82	0.08	0.82	0.19	0.67	0.09
251	2.32	−1.30	1.32	0.78	1.49	0.04	0.57	2.52	0.64	1.67	0.56	2.27	0.57	0.53	0.57	0.35	0.45	1.24
252	2.89	−0.19	1.47	1.00	1.45	−1.02	0.47	0.62	0.63	1.28	0.46	0.14	0.58	0.18	0.60	0.41	0.40	−0.53
261	1.69	1.46	0.76	7.44	1.11	−0.54	0.39	3.84	0.51	6.32	0.37	3.42	0.39	3.27	0.43	4.26	0.29	2.05
269	2.71	−0.82	0.93	−1.75	1.59	−0.55	0.34	−0.95	0.42	−0.60	0.33	−1.36	0.48	−0.94	0.48	−0.93	0.34	−1.21
271	2.41	5.71	1.59	11.34	1.65	5.30	0.58	5.58	0.70	7.01	0.57	5.20	0.55	4.56	0.57	4.31	0.46	5.29
272	3.22	0.14	1.34	0.83	1.28	−0.07	0.41	0.60	0.69	−1.07	0.40	0.38	0.56	0.30	0.57	0.33	0.33	0.19
281	2.60	0.30	1.37	0.29	1.13	−1.91	0.44	−1.54	0.66	0.73	0.43	−1.73	0.53	−0.60	0.58	0.12	0.33	−2.28
289	2.82	1.02	1.33	2.08	1.38	−0.91	0.41	−0.18	0.57	2.10	0.40	−0.48	0.54	0.39	0.57	0.90	0.36	−0.87
291	2.66	−0.32	1.69	0.01	1.33	0.01	0.57	0.43	0.77	0.57	0.56	0.19	0.60	0.04	0.63	0.00	0.43	0.11
292	1.98	−0.75	1.17	1.80	1.17	−0.27	0.52	1.32	0.68	2.10	0.52	1.00	0.51	0.28	0.54	0.84	0.38	0.45
293	2.57	−1.29	1.57	2.22	1.39	0.05	0.45	1.72	0.60	2.72	0.42	0.80	0.53	0.19	0.61	0.84	0.37	0.54
311	2.78	−5.36	2.75	−5.85	1.73	−5.93	0.99	−1.21	1.18	0.20	0.97	−1.57	0.71	−1.65	0.71	−1.46	0.61	−3.03
312	3.28	3.37	2.47	5.19	1.68	2.42	0.65	1.28	0.90	2.88	0.64	1.12	0.67	1.66	0.70	1.63	0.51	1.69
313	1.98	−1.72	1.46	0.39	1.08	−2.57	0.69	2.38	0.89	1.96	0.68	1.96	0.58	0.27	0.59	0.16	0.42	−0.38
314	3.62	−2.58	2.05	4.57	1.09	−1.93	0.54	6.09	0.87	5.92	0.53	6.15	0.65	1.41	0.66	1.85	0.36	2.79
315	2.55	1.13	1.71	1.41	1.42	1.97	0.54	2.02	0.74	0.54	0.52	1.74	0.57	1.27	0.63	0.50	0.42	1.97
319	3.50	−2.24	2.99	13.11	1.60	−4.82	0.34	4.42	1.02	15.60	0.33	4.11	0.51	1.12	0.67	4.86	0.33	−0.59
331	2.52	−1.17	1.36	5.97	1.52	−2.94	0.51	5.52	0.59	6.07	0.50	5.20	0.56	1.80	0.57	2.36	0.43	1.18
332	2.12	−0.75	1.65	−2.24	2.00	1.29	0.77	−1.71	0.79	−2.31	0.75	−1.96	0.61	−0.96	0.62	−0.90	0.59	−0.26
341	1.97	1.97	2.35	7.77	1.35	3.61	0.59	7.34	0.88	4.62	0.55	7.10	0.52	3.65	0.68	2.49	0.41	5.00
342	2.89	−0.97	1.50	1.26	0.96	−0.49	0.37	0.14	0.72	3.21	0.37	−0.19	0.52	−0.40	0.60	0.56	0.26	−0.50
343	2.67	−0.81	1.41	2.98	1.17	−0.97	0.49	3.34	0.66	3.63	0.48	3.23	0.56	1.10	0.58	1.24	0.36	1.35
351	2.44	7.37	1.92	−1.49	1.64	3.41	0.70	−8.83	0.90	−6.80	0.71	−8.88	0.59	−0.70	0.64	−0.43	0.50	−2.69
353	1.73	6.29	0.88	20.29	1.82	6.14	0.67	20.77	0.63	19.74	0.65	20.45	0.44	9.71	0.42	8.95	0.45	9.98
359	3.14	1.88	3.17	0.90	1.28	−0.32	0.80	−0.68	1.37	0.96	0.79	−0.92	0.71	0.34	0.76	0.23	0.50	−0.59
361	2.46	0.76	1.38	3.33	1.23	2.60	0.43	1.93	0.64	1.75	0.43	1.67	0.52	1.37	0.58	1.50	0.34	2.86
369	2.46	1.26	1.50	3.26	1.38	0.53	0.50	0.70	0.66	2.51	0.49	0.39	0.55	0.91	0.59	1.36	0.40	0.53

Source: own calculations based on DANE, DIAN, World Bank Data, and DNP. Notes: av, average; Pr, projected variables; W–S, wages and salaries of permanent workers; W–SC, wages and social security of permanent, temporal, and agency workers; Perm, permanent workers; Pe-Te-Ag, permanent, temporary, and agency workers. Sectors 156, 231, 243, 273, 300, 321, 322, and 323 are not included in tables and are discarded from estimations because data for most of the variables is very scarce.

Table D4:

Trade balance share and trade integration variables 1993–2012.

Relative exports							Trade integration
	Av X/M Ldc	Annual Δ %	Av X/M Dc	Annual Δ %	Av X/M Totc	Annual Δ %	Δ tar 80–94	Δ tar 80–12	Av Δ tariffs	% Av shtradeldc	Δ %	% Av shtradedc	Δ %
311	1.02	2.20	6.70	−7.11	3.27	−5.31	9.77	6.97	8.00	49.44	−27.61	20.02	7.78
313	1.30	−3.92	0.18	−4.58	0.43	−3.61	NA	NA	NA	38.24	−16.37	35.41	10.04
321	9.91	−19.17	9.97	−4.03	7.42	−12.28	38.17	48.79	39.26	29.08	−28.73	34.05	25.78
322	3.66	−15.12	12.38	−3.95	5.52	−11.84	54.08	60.67	54.86	49.28	−43.47	30.72	29.96
323	1.00	−18.81	2.68	−19.66	1.38	−20.44	21.70	26.51	22.19	43.32	−9.95	31.95	14.10
324	0.74	−7.12	0.59	−9.63	0.67	−7.87	37.07	42.01	37.65	15.00	−35.25	52.85	35.16
331	2.97	−17.50	0.92	−0.56	1.20	−6.80	25.95	32.41	25.87	27.53	−13.63	43.36	−13.27
332	5.47	8.43	0.06	−0.51	0.62	7.81	33.44	38.50	34.15	39.80	−30.23	33.02	19.53
341	3.68	−6.31	0.31	−4.50	0.89	−1.08	13.21	20.18	13.75	38.03	−13.52	26.06	19.03
342	0.69	0.46	0.06	6.90	0.17	4.84	13.42	16.86	13.57	34.12	−14.27	26.56	25.57
351	2.14	6.64	0.14	−3.78	0.45	4.78	8.29	16.21	9.16	50.68	−13.62	16.70	8.16
352	1.14	24.23	4.82	−12.23	2.05	−3.01	6.41	11.94	7.86	41.60	−8.88	21.11	12.27
353^a	0.34	−2.55	0.03	3.22	0.18	1.62	3.17	8.08	3.45	50.94	25.64	14.91	−22.62
355	1.46	−3.33	0.38	4.83	0.71	1.56	25.31	34.75	26.10	23.29	−20.15	29.59	18.60
356	1.40	0.71	1.55	1.01	1.49	1.20	31.70	42.54	32.51	32.25	−14.34	30.81	20.06
361^b	0.52	23.10	1.73	3.19	1.01	6.55	28.12	35.92	28.51	38.87	−15.54	27.59	16.86
371	0.35	19.45	11.64	25.31	2.02	22.62	9.64	17.14	10.42	35.42	−18.22	25.36	2.73
372	0.85	−2.50	0.24	−1.31	0.43	−1.04	10.70	17.06	11.29	39.28	41.09	31.78	−36.71
381	1.02	−13.87	0.02	8.19	0.08	2.83	21.20	30.17	21.96	39.43	−10.60	27.52	14.42
382	0.65	−7.43	0.04	7.05	0.13	7.12	10.62	16.52	11.20	60.44	−23.26	12.08	25.25
383	0.55	3.22	0.03	1.90	0.19	5.00	19.73	27.02	20.70	41.57	−29.23	17.29	29.06
384	0.52	−10.90	0.03	0.18	0.05	0.40	17.13	23.66	18.18	38.32	−13.98	25.83	8.43
385	0.37	−10.84	2.09	−9.10	1.29	−12.97	16.20	20.50	16.54	67.66	−9.83	6.67	10.20
390	1.80	−2.39	2.39	−0.95	1.36	−1.05	20.56	25.58	20.92	40.56	−24.26	26.77	46.14

Source: own calculations based on DANE, DIAN, World Bank Data, and DNP. Notes: NA, Not available information. In the case of the annual percentage change, it is not calculated because data was not available for the whole period. In other cases, information was not available for the specific sector. Ldc, developing countries; Dc, developed countries; totc, all countries; Δ % , annual percentage change; Δ tar, tariffs reduction to the 1980 level; Av, average; shtradeldc, share of trade to developing countries; shtradeldc, share of trade to developed countries. Sector 314 is not included in tables and is discarded from estimations because data for most variables was incomplete. ^aSector 353 is merged with sector 354. ^bSector 361 is merged with sectors 362 and 369.

Table D5:

Skill premia, skill intensity, wage bill 1993–2012.

Skill premia					Skill intensity				Wage bill
CIIUR2	Av Pr	Annual Δ %	Av W–SC	Annual Δ %	Av Pr	Annual Δ %	Av Pe-Te-Ag	Annual Δ %	Av Pr	Annual Δ %	Av W–SC	Annual Δ %
311	2.04	0.80	1.77	−0.04	0.68	−0.04	0.55	−0.26	0.32	0.58	−0.14	0.49
313	1.52	−1.36	1.69	−0.35	1.53	3.32	1.33	3.01	0.60	0.69	0.90	0.69
321	2.47	0.98	1.88	0.04	0.33	1.11	0.23	1.84	1.17	0.44	1.25	0.30
322	2.36	−0.57	1.77	−1.30	0.33	1.30	0.26	1.78	0.42	0.43	0.29	0.31
323	2.57	0.15	2.11	−1.59	0.32	1.07	0.25	1.39	0.69	0.45	−0.15	0.34
324	2.47	−0.86	1.87	−2.10	0.30	1.07	0.23	1.10	0.12	0.42	−0.70	0.30
331	2.48	−0.34	2.45	−0.27	0.36	−1.31	0.26	−1.07	−0.88	0.47	−0.81	0.39
332	2.30	1.32	1.77	0.51	0.42	0.90	0.34	0.51	1.16	0.49	0.61	0.38
341	1.99	−0.34	2.23	2.06	0.52	0.20	0.35	−0.16	−0.07	0.51	1.14	0.44
342	1.93	1.69	2.10	2.01	1.15	1.45	0.90	1.25	1.08	0.68	1.29	0.65
351	2.31	1.30	2.70	1.59	0.88	0.17	0.56	−0.39	0.48	0.67	0.47	0.60
352	3.25	0.66	2.01	−0.50	1.34	−1.03	1.10	−0.83	−0.07	0.81	−0.37	0.68
353^a	1.83	−0.39	3.54	1.31	0.70	2.21	0.38	1.14	0.80	0.54	1.13	0.55
355	2.14	0.38	1.95	−0.35	0.54	2.54	0.41	3.22	1.44	0.53	1.65	0.44
356	2.66	1.14	2.27	0.13	0.47	0.54	0.31	−0.30	0.77	0.55	−0.10	0.41
361^b	2.30	0.79	2.19	0.71	0.34	0.12	0.23	−0.48	0.53	0.44	0.15	0.33
371	2.45	3.05	3.27	5.21	0.52	3.80	0.28	2.58	2.74	0.53	4.00	0.45
372	2.78	3.50	1.79	−1.05	0.42	−0.58	0.30	0.81	1.33	0.53	−0.16	0.34
381	2.54	0.76	1.99	−0.34	0.41	1.05	0.29	0.51	0.94	0.51	0.11	0.36
382	2.26	0.59	2.15	0.27	0.51	1.16	0.35	0.67	0.83	0.53	0.53	0.42
383	2.72	0.99	2.33	−0.13	0.61	0.21	0.42	0.39	0.47	0.62	0.13	0.49
384	2.14	2.54	2.44	0.77	0.51	2.60	0.35	2.00	2.29	0.52	1.57	0.45
385	2.62	−1.50	2.39	−0.25	0.54	1.05	0.38	−0.74	−0.17	0.58	−0.44	0.47
390	2.46	0.43	1.90	0.62	0.51	0.41	0.38	−0.06	0.38	0.55	0.31	0.42

Source: own calculations based on DANE, DIAN, World Bank Data, and DNP. Notes: av, average; Pr, projected variables; W–S, wages and salaries of permanent workers; W–SC, wages and social security of permanent, temporal, and agency workers; Perm, permanent workers; Pe-Te-Ag, permanent, temporal, and agency workers. Sectors 156, 231, 243, 273, 300, 321, 322, and 323 are not included in tables and are discarded from estimations because data for most of the variables is very scarce. Sector 314 is not included in tables and is discarded from estimations because data for most variables was incomplete. ^aSector 353 is merged with sector 354. ^bSector 361 is merged with sectors 362 and 369.

Table D6:

Estimates of Equation (1) with instrumental variables 2000–2012.

Dep Var	(1)	(2)	(3)
	D.wage bill Pr	D.wage bill W–S	D.wage bill W–SC
D.ln (av sk premia Pr)	0.2057^*
	(2.128)
D.ln (capital/Prodbr)	0.0370	−0.0055	0.0359^*
	(1.899)	(−1.170)	(1.989)
D.ln (av sk premia W–S)		0.2214^***
		(14.430)
D.ln (av sk premia W–SC)			0.0905
			(0.340)
Constant	0.0061^***	0.0001	0.0055^**
	(3.337)	(0.238)	(2.605)

Obs	516	516	516
R ²	0.326	0.964	0.273
Overid	1.255	2.787	0.496
Overid_P	0.534	0.248	0.780
Underid	10.744	3.648	1.319
Underid_P	0.013	0.302	0.725
Weakid	4.166	1.387	0.456

*p < 0.05, **p < 0.01, ***p < 0.001. Source: own calculations based on DANE and DIAN data. First difference estimator. Instrumental variables: average experience, experience square and sector share of women. Estimation with robust standard errors. Overid test: H₀ equation is under-identified, H₁ equation is identified. Underid test: H₀ instruments are valid, Weakid test: H₀ instruments are weak, Stock-Yogo critical value at 5 %: 13.91. Notes: t-statistics in brackets. av, average; sk, skill; Prodbr, gross production; Pr, projected variable; W–S, wages and salaries of permanent workers; W–SC, wages and social security of permanent, temporal, and agency workers.

Table D7:

Estimates of Equation (1) with instrumental variables 1993–2012.

Dep Var	(1)	(2)
	D.wage bill Pr	D.wage bill W–SC
D.ln (av sk premia Pr)	0.3696^*
	(2.094)
D.ln (capital/Prodbr)	0.0086	0.0090
	(0.350)	(0.297)
D.ln (av sk premia W–SC)		0.4285^*
		(2.592)
Constant	0.0065^**	0.0004
	(3.067)	(0.122)

Obs	240	240
R ²	−0.080	0.201
Overid	1.789	1.953
Overid_P	0.409	0.377
Underid	4.827	2.776
Underid_P	0.185	0.427
Weakid	1.675	0.948

*p < 0.05, **p < 0.01, ***p < 0.001. Source: own calculations based on DANE and DIAN data. First difference estimator. Instrumental variables: average experience, experience square and sector share of women. Estimation with robust standard errors. Overid test: H₀ equation is under-identified, H₁ equation is identified. Underid test: H₀ instruments are valid, Weakid test: H₀ instruments are weak, Stock-Yogo critical value at 5 %: 13.91. Notes: t-statistics in brackets. av, average; sk, skill; Prodbr, gross production; Pr, projected variable; W–S, wages and salaries of permanent workers; W–SC, wages and social security of permanent, temporal, and agency workers. Regressions based on wages and salaries of permanent workers are discarded from the long database. Only the best specifications of Equation (1) are reported.

Table D8.1:

Estimates of Equation (3) 2000–2012.

Dep. Var:	SBTC(1)	SBTC(3)	SBTC(4)	SBTC(5)	SBTC(6)	D.wage bill Pr	D.wage bill W–S	D.wage bill W–SC	SBTC(1)	SBTC(3)	SBTC(4)	SBTC(5)	SBTC(6)	D.wage bill Pr	D.wage bill W–S	D.wage bill W–SC
Tr Var:	Δ tariffs	Δ tariffs	Δ tariffs	Δ tariffs	Δ tariffs	Δ tariffs	Δ tariffs	Δ tariffs	Trade ldc	Trade ldc	Trade ldc	Trade ldc	Trade ldc	Trade ldc	Trade ldc	Trade ldc
SI-Pr*Tr Var	0.0052^***		0.0051^***			0.0054^***			0.275^**		0.252^*			0.242^*
	(−6.071)		(−6.306)			(−6.875)			(−2.653)		(−2.456)			(−2.312)
Sh-tem	−0.262^***	−0.308^***	−0.177^*	0.039	−0.334^***	−0.129^*	0.068	−0.250^***	−0.152	−0.184^*	−0.095	0.087	−0.225^*	−0.075	0.127	−0.171^*
	(−4.002)	(−4.684)	(−2.487)	(−0.509)	(−4.219)	(−2.461)	(−0.566)	(−3.554)	(−1.503)	(−2.060)	(−0.738)	(−0.711)	(−2.410)	(−0.727)	(−1.104)	(−2.220)
SI-Perm*Tr_Var				0.0043^***			0.0043^***					0.177^**			0.168^**
				(−4.194)			(−5.088)					(−3.136)			(−3.009)
SI-Pe-te-ag*Tr_Var		0.0054^***			0.0048^***			0.0053^***		0.283^**			0.264^*			0.249^*
		(−5.163)			(−4.322)			(−4.102)		(−2.697)			(−2.565)			(−2.406)
Const	0.051	0.067^*	0.012	−0.08^**	0.080^*	−0.003	−0.082^*	0.044	0.011	0.023	−0.012	−0.081	0.038	−0.0125	−0.084	0.022
	(−1.866)	(−2.323)	(−0.455)	(−2.936)	(−2.223)	(−0.158)	(−2.146)	(−1.326)	(−0.276)	(−0.646)	(−0.239)	(−1.749)	(−0.964)	(−0.285)	(−1.941)	(−0.649)

Obs	423	423	423	423	423	471	471	471	504	504	504	504	504	561	561	561
RSS	1.068	1.092	1.608	1.465	2.034	1.805	1.688	2.304	1.311	1.293	1.972	1.961	2.286	2.249	2.223	2.711
Z-statistic	3.911	3.521	4.524	4.250	3.114	4.720	3.567	4.524	−1.075	−1.064	−0.875	−0.731	−1.008	−1.013	−0.143	−0.806

*p < 0.05, **p < 0.01, ***p < 0.001. Source: own calculations based on DANE and DIAN data. Arellano-Bond Estimation with robust standard errors. SBTC(1)–SBTC(6) are estimated from models (1)–(6) in Table 3 in the article. D: first difference. Notes: t-statistics in brackets, SI, skill intensity; Pr, projected variable; W–SC, wages and social security of pe-te-ag workers; W–S, wages and salaries of permanent workers; Perm, permanent workers; Pe-te-ag, permanent, temporal, and agency workers; Δ tariffs, tariff reductions between the actual level and the one in 1980; ldc, developing countries; dc, developed countries; Tr Var, trade integration variable; Sh-term, temporal over Pe-te-ag workers. Z-statistic tests the null hypothesis that b 1 − c 1 = 0 from Equations (2) and (3).

Table D8.2:

Estimates of Equation (3) 2000–2012.

Dep. Var:	SBTC(1)	SBTC(3)	SBTC(4)	SBTC (5)	SBTC (6)	D.wage bill Pr	D.wage bill W–S	D.wage bill W–SC	SBTC(1)	SBTC(3)	SBTC(4)	SBTC(5)	SBTC(6)	D.wage bill Pr	D.wage bill W–S	D.wage bill W–SC
Tr Var:	Trade dc	Trade dc	Trade dc	Trade dc	Trade dc	Trade dc	Trade dc	Trade dc	Exporters	Exporters	Exporters	Exporters	Exporters	Exporters	Exporters	Exporters
SI-Pr*Tr Var	0.188^***		0.204^***			0.216^***			0.034^***		0.037^***			0.04^***
	(−7.824)		(−7.216)			(−7.662)			(−3.658)		(−4.194)			(−4.168)
Sh-tem	−0.253^***	−0.266^***	−0.197^**	0.030	−0.319^***	−0.141^**	0.085	−0.251^***	−0.193^**	−0.274^***	−0.120	0.147	−0.389^***	−0.11	0.153	−0.360^**
	(−4.180)	(−4.518)	(−2.835)	(−0.542)	(−4.815)	(−3.076)	(−1.107)	(−4.557)	(−3.203)	(−4.039)	(−1.276)	(−1.132)	(−3.347)	(−1.039)	(−1.127)	(−2.905)
SI-Perm*Tr_Var				0.198^***			0.205^***					0.023^***			0.024^***
				(−9.359)			(−8.849)					(−3.568)			(−3.57)
SI-Pe-te-ag*Tr_Var		0.180^***			0.164^***			0.185^***		0.036^***			0.032^**			0.034^**
		(−7.237)			(−4.865)			(−4.632)		(−3.629)			(−3.108)			(−3.181)
Constant	0.063^*	0.070^**	0.031	−0.068^**	0.089^**	0.013	−0.083^**	0.059^*	−0.036	−0.005	−0.088^*	−0.175^**	0.049	−0.094^*	−0.173^**	0.036
	(−2.456)	(−2.771)	(−1.114)	(−3.003)	(−3.11)	(−0.687)	(−2.732)	(−2.402)	(−1.405)	(−0.190)	(−2.298)	(−3.232)	(−1.106)	(−2.085)	(−2.946)	(−0.764)

Obs	504	504	504	504	504	561	561	561	230	230	230	230	230	231	231	231
RSS	1.16	1.217	1.737	1.585	2.262	1.938	1.761	2.591	0.588	0.552	0.936	0.845	1.084	1.011	0.894	1.2
Z-statistic	−0.691	−0.360	−1.061	−1.847	−0.249	−2.135	−0.956	−0.458	3.063	2.762	3.443	3.518	2.470	3.221	0.039	3.641

*p < 0.05, **p < 0.01, ***p < 0.001. Source: own calculations based on DANE and DIAN data. Arellano-Bond Estimation with robust standard errors. SBTC(1)–SBTC(6) are estimated from models (1)–(6) in Table 3 in the article. D: first difference. Notes: t-statistics in brackets, SI, skill intensity; Pr, projected variable; W–SC, wages and social security of pe-te-ag workers; W–S, wages and salaries of permanent workers; Perm, permanent workers; Pe-te-ag, permanent, temporal, and agency workers; Δ tariffs, tariff reductions between the actual level and the one in 1980; ldc, developing countries; dc, developed countries; Tr Var, trade integration variable; Sh-term, temporal over Pe-te-ag workers. Z-statistic tests the null hypothesis that b 1 − c 1 = 0 , from Equations (2) and (3).

Table D9:

Estimates of Equation (4) 2000–2012.

Dep. Var:	SBTC(2)	SBTC(5)	D.wage bill W–S
Tr Var:	Exporters	Exporters	Exporters
Tr Var	0.0202^**	0.0273	0.0365
	(3.280)	(1.185)	(1.517)
Sh-tem	0.0303	0.0927	0.1005
	(1.114)	(0.869)	(0.910)
SI-Perm*Tr Var	−0.0124^***	−0.0226^*	−0.0232^*
	(−3.302)	(−2.036)	(−2.027)
SI-Perm	0.0478^***	0.2425^***	0.2479^***
	(3.658)	(4.580)	(4.528)
Constant	−0.1040^**	−0.2928^*	−0.3389^**
	(−3.073)	(−2.365)	(−2.610)

Obs	230	230	231
RSS	0.033	0.668	0.695

*p < 0.05, **p < 0.01, ***p < 0.001. Source: own calculations based on DANE and DIAN data. Arellano–Bond Estimation with robust standard errors. SBTC(1)–SBTC(6) are estimated from models (1)–(6) in Table 3 in the article. D: first difference. Notes: t-statistics in brackets, SI, skill intensity; W–S, wages and salaries of permanent workers; Perm, permanent workers; Sh-term, temporal over Pe-te-ag (permanent, temporal, and agency) workers; Tr Var, trade integration variable. Only the best specifications of Equation (4) are reported.

Table D10:

Estimates of Equation (3) 1993–2012.

Dep. Var:	SBTC(1)	SBTC(2)	D2.wage bill W–SC	SBTC(2)	D2.wage bill Pr
Tr Var:	Δ tariffs	Δ tariffs	Trade ldc	Trade dc	Trade dc
SI-Pr*Tr_Var	0.0019^**				0.0369^*
	(−3.287)				(−2.65)
Share temporal	−0.0021	0.0399	0.0037	0.0625^*	0.0046
	(−0.846)	(−1.385)	(−0.073)	(−2.212)	(−1.029)
SI-Pe-te-ag *Tr Var			0.15006^*
			−2.43
Constant	−0.0184^**	−0.0333^*	−0.0197	−0.0406^*	−0.0100^*
	(−3.315)	(−2.575)	(−1.029)	(−2.484)	(−2.263)

Obs	414	253	264	264	432
R ²	0.009	0.013	0.004	0.012	0.003
F	5.518	4.747	3.517	3.187	4.503
Z-statistic	3.439	2.920	−2.029	0.157	0.043

*p < 0.05, **p < 0.01, ***p < 0.001. Source: own calculations based on DANE and DIAN data. Fixed Effects Estimation with robust standard errors. SBTC(1) and SBTC(2) are estimated from models (1) and (2) in Table 2 in the article. D2: second difference. Notes: t-statistics in brackets, SI, skill intensity; Pr, projected variable; W–SC, wages and social security of Pe-te-ag workers; Pe-te-ag, permanent, temporal, and agency workers; Δ tariffs, tariff reductions between the actual level and the one in 1980; ldc, developing countries; dc, developed countries; Tr Var, trade integration variable, share temporal: temporal over Pe-te-ag workers. Z-statistic tests the null hypothesis that b 1 − c 1 = 0 , from Equations (2) and (3). Regressions based on wages and salaries of permanent workers are discarded from the long database.

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Received: 2022-03-27

Accepted: 2023-07-13

Published Online: 2023-08-10

International Trade, Labor Flexibilization, and Wage Inequality: The Colombian Manufacturing Case

Abstract

Acknowledgments

Appendix A: Variable Definitions

Appendix B: Comparison of Imputed Variables With the Baseline and the Alternative Methodologies

Appendix C: Estimating the Average Wage Gap

Appendix D: Figures and Tables

Bibliography

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