rafmacalaba/datause-encoder-data huggingface dataset:
shows that 33 out of 35 baseline covariates are statistically similar across the boundary.

The only exceptions are altitude and sugarcane yields. The statistical significance of the difference in sugarcane yields between controlled and other areas could be driven by the difference in altitude because that variable is included in the mechanical estimation of past potential sugarcane yields.<sup>21</sup> A potential concern with the difference in sugarcane yields is that these can come from land concentration. However, as we show in Table 1, there are no differences in the probability of being part of the 1980 Land Reform across the boundary, which is a good proxy for land concentration in the 1980s and the strength of the elites.

Moreover, the difference in mean altitude is very small (17.13 m from a dependent mean of 502.7) and aligns with the observation that the guerrillas occupied higher territories as a military strategy. Yet, we show that the difference in altitude is not correlated with variables that proxy state capacity or economic development. As a robustness exercise, we include altitude as a control variable and find that all variables are statistically similar across the boundary (see Figures D.1 - D.2), confirming the validity of the local continuity assumption for all outcomes.<sup>22</sup> Importantly, results also show that before the guerrillas controlled these territories, the areas were similarly likely to be subject to the national land reform. This initiative redistributed large haciendas to peasants in 1980 in an attempt to palliate increasing levels of distrust of the state and mobilization by the peasantry. Therefore, this result of no differences in land reform across the boundaries shows that the FMLN did not establish its control in places with higher land inequality or differentials in elite strength. Moreover, we find no differences in the number of violent events or massacres across the boundary, yielding further evidence that guerrillas did not establish territorial control according to levels of distrust of the state that are generally associated with historical state and elite repression.

Furthermore, we tested the robustness of the validity of the local continuity assumption to different choices of bandwidth distance around the cutoff. The results confirm the robustness of the local continuity assumption to the bandwidth choice (see Figures D.5 - D.7).<sup>23</sup>

> 21According to the model documentation of GAEZ v.3 project, altitude and terrain variables are used in the first stage of the agro-climatic analysis.

> 22The specification that controls for altitude is not used to report main estimates as it may result in biased coefficients. The estimate that can be identified when adjusting for imbalanced covariates in RD designs is a weighted average of the treatment effects where the weights depend on the conditional distribution of the imbalanced covariate on the treatment, which is not our estimate of interest. See Calonico et al. (2019) for a discussion.

> 23An additional assumption is that there should be no selective sorting across the boundary. We discuss this assumption in depth later.

20
# <u>Table 9:  The Financing of  Trade Credit Supply: Is Trade Credit Demand More Important in Competitive Markets?</u>

The reported estimates are from logit (Colums 1-2) and OLS (Columns 3-4) regressions. AR_d is a dummy indicating the use of accounts receivable and AR_per is the percent of monthly sales sold on credit. We transform AR_per in a continuous variable by using the inverse function of the logistic model. See Table 1 for variable definitions. All regressions include 12 sector dummies and 17 city dummies. Robust p- values are shown in parentheses, ***, **, * indicate significance at the 1%, 5%, and 10% level, respectively.

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||_AR_d_|_AR_d_|_AR_per _|_AR_per _|
|L_Age<br>L_emp|-0.23<br>[0.02]**<br>0.09|-0.3<br>[0.01]***<br>0.09|-0.86<br>[0.00]***<br>0.38|-0.72<br>[0.00]***<br>0.30|
||[0.06]*|[0.14]*|[0.00]***|[0.01]***|
|D_Foreign|0.54|0.6|0.90|0.28|
||[0.04]**|[0.03]**|[0.26]|[0.68]|
|D_State|-0.04|0.13|0.12|0.22|
||[0.82]|[0.53]|[0.77]|[0.58]|
|D_Export|-0.23|-0.34|0.25|0.16|
||[0.32]|[0.19]|[0.73]|[0.81]|
|AP_d|1.22<br>[0.00]***|1.34<br>[0.00]***|||
|AP_per|||0.06<br>[0.00]***|0.08<br>[0.00]***|
|Saleslargest_5|0.03||0.15||
||[0.85]||[0.66]||
|Sales*AP_d|0.47<br>[0.05]*||||
|Sales*AP_per|||0.03<br>[0.18]||
|Bi_mktpower||-0.43<br>[0.07]*||-0.26<br>[0.59]|
|Bi*AP_d||0.75<br>[0.02]**|||
|Bi*AP_per||||0.04<br>[0.03]**|
|Constant|-2.77<br>[0.00]***|-2.78<br>[0.00]***|-11.58<br>[0.00]***|<br>-11.42<br>[0.00]***|
|Observations|1,547|1,080|1,529|1,803|
|PseudoR-squared|0.16|0.15|0.19|0.19|

46
The world distribution of the brain drain. It is interesting to compute average migration rates by region, income group (the World Bank classi...cation distinguishes four categories) and size groups (above 25 million, between 10 and 25 million, between 4 and 10 million and below 4 million). It comes out that the distribution of emigration rates is strongly heterogenous within groups. For example, the disparities between the Caribbean countries and the US are tremendously high in America; large di¤erences are observed between high-income countries such as Malta, Ireland, Hong-Kong and, on the other hand, Australia, Japan or the USA. However, as shown in ...gure 5, disparities in the average rates reveal interesting results.

Regarding the regional distribution (see ...gure 5.A), the most a¤ected continents are Africa and Europe. Oceania and Asia exhibit intermediate rates whilst the average American brain drain is small. The regional disparities are extremely stable between 1990 and 2000. As for income groups (see ...gure 5.B), it is worth noticing that the highest rates are observed in middle income countries. High income countries (less incentives to emigrate) and low-income countries (where liquidity constraint are likely to be more binding) exhibit the lowest rates. As reported in Schi¤ (1996), liquidity constraints in poor and unequal societies explain the increasing relationship between income and migration at low income levels. Papers by Freeman (1993), Faini and Venturini (1993), Funkhouser (1995), World Bank (1994) have shown that emigrants essentially do not come from low income group. It is worth noticing that between 1990 and 2000, the situation clearly improved in lower-middle income countries and deteriorated in low-income countries.

Finally, regarding size groups, we obtain a decreasing relationship between emigration rates and country population sizes. Disparities are also extremely stable over time. Our key indicators suggest that country size, GDP per capita, inequality and poverty rates are important determinants of emigration. Obviously, formal tests would be required to assess the relative e¤ect of selection, network e¤ects, economic, cultural, historical or political determinants of emigration. Whether these factors play di¤erently across skill groups is a crucial issue.

31
Sudanese Government’s Joint Mechanism for Durable Solutions. (2019). Progress Towards Durable Solutions: In Abu Shouk And El Salam IDP Camps, North Darfur, Sudan. United Nations – Sudan. Retrieved from https://www.jips.org/uploads/2019/12/JIPS-Sudanprofilingreport-2019.pdf.

Temgoua Noumedem, C., Sharma, D., and Wai-Poi, M. (2020). Multidimensional Poverty Assessment of Internally Displaced Persons in Iraq. _Policy Research Working Paper_ 9203. Washington DC: World Bank Group.

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<mark>World Bank. (2011).</mark> _<mark>World development report 2012: Gender equality and development</mark>_ <mark>. The World Bank.</mark> World Bank (2020a) World Development Indicators: Population dynamics. Last updated: 16 December 2020. Retrieved from http://wdi.worldbank.org/table/2.1# World Bank (2020b) World Development Indicators: Poverty rates at international poverty lines. Last updated: 16 December 2020. Retrieved from http://wdi.worldbank.org/table/1.2# Zenn, J., and Pearson, E. (2014). Women, Gender, and the evolving tactics of Boko Haram. _Journal of Terrorism Resarch_ , 5(1): https://doi.org/10.15664/jtr.828.

Zetter, R., and Ruaudel, H. (2014). Development and protection challenges of the Syrian Refugee Crisis. _Forced Migration Review,_ 47:6-10 33
Table 1: Overview of design

||**Wor**|**ker Production**|
|---|---|---|
||Independent|Cooperation within group|
|**SectatorRedistribution**<br>Within group|T1|T2|
|**p **<br>Between groups|T3|T4|

_Note:_ The table provides an overview of the four treatments in the experiment, which differ in terms of production and redistribution. **Worker Production** : “Individual” implies that a worker’s earnings was only determined by whether he or she completed the assignment; ”Cooperation within group” implies that a worker’s earnings also depended on whether the two other workers in the group completed the assignment. **Spectator Redistribution** : “Within group” implies that the spectator decision was about redistributing earnings between two workers from the same group; “Between group” implies that the spectator decision was about redistributing earnings between two workers from different groups.

12
# **Tentative First Steps: An Assessment of the Uruguay Round Agreement on Services**

# Bernard Hoekman

Finance and Private Sector Development Team Technical Department, ECA/MNA Regions, The World Bank Tel: 202 473 1185; Fax: 202 477 8772

# Research Fellow

Center for Economic Policy Research 25-28 Old Burlington Street, London WlX 1LB Keywords: Trade in services, GATS, trade policy, multilateral trade negotiations. JEL Classification: F13.

Presented at a World Bank Conference, The Uruguay Round and the Developing Economies, January 26-27, 1995. The views expressed in this paper are personal and should not be attributed to the World Bank. I am grateful to Julian Arkell, Carlos Braga, Alan Deardorff, Brian Hindley, Guy Karsenty, Patrick Low, Will Martin, Petros Mavroidis, Pierre Sauv6, Richard Snape and Alan Winters for helpful comments and discussions. My thanks as well to Ying Lin for research assistance, to Faten Hatab for converting and printing the GATS schedules and to David Hartridge for providing them.
precautionary measures. Ghanaian households that receive international remittances tend to have electricity. Without controlling for endogeneity of the remittance-receiving decision, 45 percent of households that do not receive remittances have electricity. 52 percent of households that receive remittances from other African countries have electricity and 80 percent of households that receive remittances from OECD countries have electricity. After controlling for endogeneity of remittance-receiving status, 80 percent of households that receive remittances from OECD countries have electricity, versus 69 percent of comparable households that do not receive remittances. Of households that receive remittances from other African countries, 51 percent have electricity, versus 46 percent of comparable households that do not receive remittances.

Similarly, after controlling for endogeneity of remittance-receiving status, 28 percent of Ghanaian households that receive remittances from OECD countries have a fixed telephone, versus 24 percent of comparable households that do not receive remittances. Of households that receive remittances from other African countries, 30 percent have a fixed telephone, versus 16 percent of comparable households that do not receive remittances. In the case of mobile phones, after controlling for endogeneity of remittance-receiving status, 69 percent of households that receive remittances from OECD countries have a mobile telephone, versus 55 percent of comparable households that do not receive remittances. Of households that receive remittances from other African countries, 39 percent have a mobile telephone, versus 32 percent of comparable households that do not receive remittances.

As shown in annex table 4a, regression estimates on the matched Ghanaian households further reveal that receiving remittances from OECD countries have a statistically significant and positive impact on the ownership of better houses and communication amenities. Similarly annex table 4b shows that remittances from OECD have a negative and significant impact on having low quality houses and communication amenities. Remittances from Africa enable households to have amenities such as electricity and fixed and mobile phones as evident from the statistically significant coefficients of these variables in annex table 5a. A smaller amount of remittances received by households from migrants in Africa partly explains why these households may not be able to make long term investments in housing (see annex tables 5a and 5b).

We use a nationally-representative household survey for Burkina Faso, the Core Welfare Indicators Questionnaire Survey, conducted in 2003 to examine the resilience of houses to future disasters. This survey provides information on the sources of migrant remittances. Of the 7,339 households in the sample, 13.7 percent receive remittances from Cote d’Ivoire, the largest intra-African destination, while 2.2 percent of households receive remittances from France, which is the most important destination of migrants outside Africa (see annex table 6). We used propensity score matching methods to 16
2016 are over-represented in the top quintiles of the income distribution in their countries of origin (World Bank Group, 2018). As for voluntary migrants, the costs of the journey, admission criteria, and networks at the destination are factors shaping these selection effects, in addition to opportunities to earn income at the destination versus the origin.

# **3.2 Shocks to physical and human capital and mental health**

Refugees or IDPs migrating to seek protection most likely differ from other migrant populations because their decision to migrate is also associated with shocks to their physical and human capital and/or mental health. Such shocks either triggered their moves in the first place or affected them during transit.

**Loss of physical capital** Those forcibly displaced tend to have lower levels of assets compared to hosts and compared to their situation before displacement, cross-sectional data for IDPs and refugees from different low and middle-income countries show (Fiala, 2009; Krishnan et al., 2020; Ib ́a ̃nez and Moya, 2010; Mejia-Mantilla et al., 2019; Pape et al., 2018; World Bank, 2019b). They usually cannot take their assets and seldom have enough time to sell them before their move; their assets are seized or they are not able to access assets left behind anymore.

**Loss of human capital and skills** Education and skills acquisition are likely to be disrupted through conflict and forced displacement. Children in Colombia recently internally displaced, for example, have significant gaps in educational accumulation and enrollment compared to other migrants and non-migrants. These gaps are larger than those of children who live in Colombian municipalities with high conflict (Wharton and Oyelere, 2011). Disruptions of work experience and longer periods of forced inactivity further deteriorate human capital. Refugees often face legal constraints to access the labor market (see later discussion). They also face more difficulties to access the labor market because they do not choose their destination primarily based on labor demand, usually have little time to prepare for the labor market at their destination, and often arrive as part of a larger group, that is, they are part of a large labor supply shock. At the level of the household, the separation from or

6
sector for each country. Complementary data sources were required to compute these parameters. The labor shares were defined as the ratio of the wage bill over total value added for each sector, and the consumption shares as the share of each sector in total consumption. These parameters were calculated from input-output (IO) tables from Mexico and Brazil. We used the IO table of 2003 for Mexico, and 2005 for Brazil. Average formal and informal wages in Mexico and Brazil were calculated with data from the Labor Force Surveys (LFS). The parameters for the simulations are shown in Table 4. The wages in each country-sector are expressed as a ratio of each country’s average wage.

# **5 Results**

The proposed estimations and simulations provide two sets of results. The first concerns the estimates of labor mobility costs. To be more precise, the PPML estimations provide estimates of workers’ perceived entry costs associated with movements across industries of employment as well as with movements in and out of informality. In turn, the simulations utilized the estimated entry costs to analyze the effect of a price reduction affecting the manufacturing industry. As will become apparent, this industry might play a key role in the formalization of labor markets in developing countries.

## **5.1 Estimates of Labor Mobility Costs: Entry Costs and Formalization**

Table 5 shows the estimates of labor mobility costs. To be more precise, it reports normalized entry costs<sup>6</sup> . As explained above in Section 3, Table 5 presents estimates of _C/ν_ . Assuming that _ν_ =1, and given that the literature normalizes wages by the average real wage,

> 6The z-statistics are reported inside parentheses. All coefficients in Table 5 are statistically significant. For the simulations we assume that this moving cost is zero

15
Policy Research Working Paper 8470

# **Abstract**

Recent literature has shown evidence of positive contributions of export promotion agencies around the world in raising exports, through the intensive and extensive margins of trade. The number of export promotion agencies has increased substantially over the past two decades, and most of them focus on assisting exporters in understanding and finding markets for their products. This paper describes the characteristics of export promotion agencies around the world, using a novel database from the World Bank, in collaboration with the International Trade Center in Geneva, covering 2005–10. In addition, it presents a short summary of the literature on the impacts of export promotion agencies.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region and the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at marciocruz@ worldbank.org; dlederman@worldbank.org, and lzoratto@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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