THE SHORT AND LONG EFFECTS OF EXPORTS ON LABOR MARKET OUTCOMES: EVIDENCE FROM BANGLADESH Raymond Robertson (Texas A&M) Deeksha Kokas (World Bank) Diego Cardoza (University of Chicago) Gladys Lopez-Acevedo (World Bank)
WORLD BANK-IMF-OECD CONFERENCE September 2020
OUTLINE OF THE PRESENTATION ▪Motivation ▪Data and Empirical Approaches ▪Empirical results ▪Conclusion
MOTIVATION ▪ Covid-19 is impacting demand for developing country exports ➢U.S. retail sales fell 8.7 percent in March 2020 due to the coronavirus
(COVID-19), the largest decline since 1992.
▪ Academic literature focuses on adverse effects on developed
country labor markets (ADH (2013); Hakobyan and McLaren (2016) and several others)
OUR CONTRIBUTION ▪ Estimate local labor market effects of exports from
Bangladesh
▪ Estimate heterogeneity of these local effects of exports for
different groups
▪ Estimate implications of long-run general equilibrium
predictions from neoclassical trade theory
THEORETIC INTUITION ▪
Combine the local labor market analysis (short-run) with the neoclassical trade theory general equilibrium predictions (longrun)
Short-run: Ricardo Viner
▪ • • •
Localized effects will show up on wages for those fully employed Those employed on the margin would have employment effects (e.g. lower informality) Local effects dissipate over time
▪ Long-run: Stolper-Samuelson • •
In the long run, the wage effects will be determined by factor intensity In the long run, wage effects will NOT be localize: they will affect the two factors regardless of industry
Draft of the paper includes simulation results
REASONS BANGLADESH IS AN EXCELLENT CASE STUDY ➢Bangladesh’s exports and imports up sharply
since 1970s
Bangladesh’s exports and imports as a share of GDP, 1970–2016, % 30 25 20 15
➢Bangladeshi
export growth highly concentrated within the “ready-made” garments (RMG) sector
10
5 -
Exports
➢Relative to other countries, such as India,
Bangladesh may be more likely to experience the kind of long-run general equilibrium effects predicted by neoclassical theory, as labor markets seem more integrated.
Textiles and apparel Fabricated metals Chemicals Food, beverages and tobacco Agriculture and mining Fishing, forestry and logging Basic metal industries Other manuf. Industries Wood Paper Non-metallic minerals
Imports
92.10% 2.08% 1.77% 1.56% 0.76% 0.46% 0.38% 0.31% 0.23% 0.20% 0.16%
DATA AND EMPIRICAL APPROACH LONG RUN: CAPTURING SS EFFECTS USING MINCERIAN REGRESSIONS SHORT RUN: CAPTURING LOCAL LABOR MARKET EFFECTS USING BARTIK ANALYSIS
We begin by rigorously estimating the relationship between the change in national exports on local labor markets using the share of local employment in each industry as weights.
We provide an alternative approach by analyzing how the gender wage gap, skill premium, as well as the textiles and apparel wage premium, have evolved since 2005. We rely on the Stolper-Samuelson theorem, expecting to observe a relationship between output price changes and relative factor earnings.
Analysis in this paper is based on the LFS for 2005,2010, 2013 and 2016. Once we cleaned the labor force survey data, we subsequently matched the microdata with annual commodity trade data from COMTRADE using the ISIC Rev. 3.1. The trade data contains information about US imports from Bangladesh.
ECONOMETRIC MODELLING We use a two-stage econometric analysis First stage: We estimate the contribution of US import demand to the increase in Bangladesh exports
Second stage: We estimate the effect of an increase in exports on local economic outcomes. These economic outcomes would include informality rates and wages for different worker types (male, female, rural, skilled, unskilled, young, and old) đ?’šđ?’…đ?’•+đ?’? − đ?’šđ?’…đ?’• = đ?œˇđ?&#x;Ž + đ?œˇđ?&#x;? đ?’™đ?’…đ?’•,đ?’•+đ?’? + đ?‘żđ?’„ đ?œˇđ?’„ + đ???đ?’… , đ?‘‘ • đ?‘Śđ?‘Ą+đ?‘› − đ?‘Śđ?‘Ąđ?‘‘ : change in outcomes (average wage, employment, etc.)
� • ��,�+� : trade exposure index (change in exports weighted by sectoral employment).
• đ?‘‹đ?‘? : control variables (for example đ?‘Śđ?‘Ąđ?‘‘ to control for trends)
BARTIK-TYPE INSTRUMENT ▪ To ensure truly exogeneity of our instrument, we need a variable that predicts imports
from Bangladesh based solely on the US internal demand growth, rather than supply-side determinants like changes in the production volumes from other garments exportcompeting countries.
▪ Hence, we construct our instrument using time-series regressions of Bangladesh
exports to the US on the US GDP by industry at the four-digit level, from 1991 to 2018 annually. Predicted values from these regressions would serve as a proxy for Bangladesh exports to the US explained exclusively by the latter's domestic aggregate demand.
▪ This variable is, by construction, orthogonal to every supply-side factor in the
international garments market, and to every Bangladeshi local market condition
CHANGE IN EXPORTS PER WORKER BETWEEN 2005-10 AND 2005-13 (USD PRICES OF 2005) 2005-2010
Source: Prepared by the authors using data from the Bangladesh LFS. These heat maps show regional variation in trade exposure i.e. change in exports per worker between 2005-2010 and 2005-2013 (USD, prices of 2005).
2005-2013
RESULTS (1) : SHORT RUN EFFECTS MATCH EARLIER STUDIES BUT DISSIPATE OVER TIME Change in the informality rate after a US$100 increase in exports per worker (percentage) 1.00%
16,000
Coefficient
Effect on informality (percent)
21,000 Confidence interval (90%)
11,000
6,000 1,000
Coefficient
0.50%
Confidence interval (90%)
0.00% -0.50% -1.00% -1.50% -2.00%
All workers
Males
Females
Low-skilled High-skilled
Notes: Author's estimates using data from Bangladesh LFS and UN COMTRADE. The confidence intervals are set at the 90% level. These graphs are based on 2SLS regression computed to estimate effect of an increase in exports on real wages and informality. This relationship is estimated for different worker types (male, female, rural, skilled, unskilled, young, and old).
All workers
Males
Females Low-skilled
2005-2016
2005-2013
2005-2010
2005-2016
2005-2013
2005-2010
2005-2016
2005-2013
2005-2010
2005-2016
2005-2013
2005-2010
2005-2016
2005-2013
2005-2016
2005-2013
2005-2010
2005-2016
2005-2013
2005-2010
2005-2016
2005-2013
2005-2010
2005-2016
2005-2013
2005-2010
2005-2016
2005-2013
-4,000
2005-2010
-2.50%
2005-2010
Effect on real wages (Bangladeshi takas)
Change in the average annual real wage after a US$100 increase in exports per worker (Real Bangladeshi Takas of 2017)
Highskilled
RESULTS (2): RESULTS CONSISTENT WITH LONG-RUN GENERAL EQUILIBRIUM NEOCLASSICAL MODELS
CONCLUSION Our results are reflective of a pattern. Labor market responses to export shocks differs across regional labor markets in the short-run (i.e. between 2005 and 2010) but dissipate gradually. ▪ Rising apparel exports are a female-specific industry shock
▪ Differentiated regional effects on labor outcomes is only a temporary phenomenon ▪ This could be because poorer countries like Bangladesh may have less adjustment
costs enforced on factories, and workers may be more motivated to be mobile.
▪ In contrast, these effects seem to become more economy-wide and not region or
industry specific as predicted by Stolper Samuelson theorem in the long run.
▪ Several studies highlight local labor market impacts of imports (either
directly or through tariff reductions) in developed and developing countries ▪ ▪ ▪ ▪ ▪
Autor, Dorn and Hanson, 2013 (US-China trade) Topalova, 2010 (India) Dix-Carneiro and Kovak, 2017 (Brazil); Kovak, 2013 (Brazil) Hakobyan and McLaren, 2016, (Mexico) McCaig and Pavnick (2018)
▪ Very few focus on ▪ Exports ▪ Estimating dissipation ▪ Long-Run GE Effects linked to neoclassical trade theory
SHORT RUN: RICARDO VINER
WA
Wa1 Wa2
Wa0 La1 La0
Ls0 LA1
LA2
• Localized effects will show up on wages for those fully employed • Those employed on WS the margin would have employment effects (e.g. lower informality) Ws2 • Local effects dissipate over time
LONG RUN: STOLPER-SAMUELSON
Wf CS(Wf,Wm)=PS0
CA(Wf,Wm)=PA1 CA(Wf,Wm)=PA0
Wm
• In the long run, the wage effects will be determined by factor intensity • In the long run, wage effects will NOT be localize: they will affect the two factors regardless of industry • Mincerian wage equations capture the economy-wide returns to different demographic groups independent of industry
(1) đ??śđ?‘–đ?‘Ą = đ?‘“(đ?‘?đ?‘–đ?‘Ą đ?‘Śđ?‘–đ?‘Ą , đ?‘¤đ?‘–đ?‘Ąđ?‘— ) Shephard’s lemma allows us to derive factor demand curves from the cost function. Holding capital fixed, the demand for males and females are derived as a function of output prices and factor prices. Using ď Şitj to represent the demand for factor j at time t in industry i: (2)
đ?œ•đ??śđ?‘–đ?‘Ą đ?œ•đ?‘¤đ?‘–đ?‘Ąđ?‘—
= đ?œ‘đ?‘–đ?‘Ąđ?‘— (đ?‘?đ?‘–đ?‘Ą , đ?‘¤đ?‘–đ?‘Ąđ?‘— )
Note that equation (2) represents the marginal revenue product of each factor, which, of course, is the factor demand curve. Under normal assumptions:
(3)
đ?œ•đ?œ‘đ?‘–đ?‘Ąđ?‘— đ?œ•đ?‘¤đ?‘—đ?‘Ą
< 0 đ?&#x2018;&#x17D;đ?&#x2018;&#x203A;đ?&#x2018;&#x2018;
đ?&#x153;&#x2022;đ?&#x153;&#x2018;đ?&#x2018;&#x2013;đ?&#x2018;Ąđ?&#x2018;&#x2014; đ?&#x153;&#x2022;đ?&#x2018;?đ?&#x2018;&#x2013;đ?&#x2018;Ą
>0
Assuming full employment in every period t: (4) ÎŚđ?&#x2018;Ąđ?&#x2018;&#x2014; = Ď&#x192;đ?&#x2018;&#x2013; đ?&#x153;&#x2018;đ?&#x2018;&#x2013;đ?&#x2018;Ąđ?&#x2018;&#x2014;
MALES
FEMALES
25
24
24
23
23
22
22
21
21
20
20
19
19
18
18
17
17
16
16
15 1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 Male Wage 1
Male Wage 2
14 1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 Female Wage 1 Female Wage 2
RELATIVE WAGES 1.18 1.16 1.14
1.12 1.1 1.08 1.06
1.04 1.02 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 Ind1
Ind2