rafmacalaba/datause-encoder-data huggingface dataset:
The matching analysis draws only from the baseline survey which interviewed 6,722 households in 57 treatment municipalities and 4,562 households in 9 control municipalities.<sup>11</sup> The subsample for this analysis includes only children who were born during 1975-1994, and who may have graduated from high school between 2003 and 2009.<sup>12</sup> For instance, a child that had completed grade 6 at baseline (either in a treated or control area) was expected to finish high school (grade 11) by 2007 if the child progressed on schedule. In contrast, a child starting primary school (grade 1) in 2002 (baseline) will not be able to finish high school at least before 2013. Therefore, the relevant cohorts of children to estimate the average impacts of the program are those who at baseline had 4 to 10 years of schooling, and who were 18 years old or younger (called ―PSM data‖). The baseline survey is also used to construct most of the pre-program covariates for the matching procedures.

The samples of analysis for the RDD approach are constructed with two different administrative sources of data. The first is the monitoring and evaluation system, SIFA, created for administrative and monitoring purposes at the onset of the FA program. The system is a longitudinal census of program beneficiaries from 2001 to present. To date, there is information on approximately 2.8 million families currently participating in the program. The second source of information is the data from a census of the poor ( _Sisben_ ) carried out between 1994 and 2003 to construct the poverty index score for the proxymeans test. Questions were asked regarding households‘ demographics, structure, durable goods, housing characteristics, human capital, labor force participation, income, and access to basic services. By 2003, the surveys covered over 25 million individuals.<sup>13</sup> Data from SIFA and _Sisben_ were carefully merged using confidential information on date of birth, full name, and national identification numbers. The resulting dataset (―SIFA +

> 11 Nearly 12 percent of the households interviewed in treatment municipalities were not registered with the program (Attanasio et al., 2005).

> 12 We merged the household survey data with administrative data from the standardized test score _Icfes_ . Using this merged dataset, we identified that 95 percent of the population that presented the test in the 2000-2009 period were born during the period1975-1994. In order to study the indirect effects of participant children on non-eligible young adults that were still in school or could rejoin, this sample also includes individuals 19 to 23 years old that were listed as dependents, but are not eligible to the program.

> 13 A new _Sisben_ survey was fielded between 2003 and 2007 to update the information, improve the effectiveness of the targeting scheme, and change the algorithm due to concerns regarding manipulation by local authorities (Barrera, Linden, and Urquiola, 2007; Camacho and Conover, 2011).

12
- Niehues, J. 2014. Subjective Perceptions of Inequality and Redistributive Preferences: An International Comparison. IW-TRENDS discussion papers.

- Norton, M.I., and D. Ariely (2011). Building a Better America – One Wealth Quintile at a Time. Perspectives on Psychological Science, 6 (1): 9-12.

- Page, L. and D. G. Goldstein (2016). Subjective Beliefs about the Income Distribution and Preferences for Redistribution. Social Choice and Welfare, 45: 25-61.

- Page, Benjamin I., Shapiro, Robert Y., 1983. Effects of public opinion on policy. Am. Pol. Sci. Rev. 77 (1), 175–190.

Piketty, T. and W. Saez, 2014. Inequality in the long run. Science 344: 838-843.

- Rainer, H. and T. Siedler (2008). Subjective Income and Employment Expectations and Preferences for Redistribution. Economics Letters, 99 (3): 449-453.

- Ravallion, M. and M. Lokshin (2000). Who Wants to Redistribute? The Tunnel Effect in 1990s Russia. Journal of Public Economics, 76: 87-104.

- Roemer, J. E., & Trannoy, A. (2016). Equality of Opportunity. Journal of Economic Literature, 54(4), 1288– 1332.

- Reyes, G. and L. Gasparini (2017). Perceptions of Distributive Justice in Latin America during a Period of Falling Inequality. World Bank Policy Research Working Paper 8072.

- Slemrod, J. (2006). The Role of Misconceptions in Support for Regressive Tax Reform. National Tax Journal, 59(1), 57–75.

- World Bank. 2016. Polarization and Populism. ECA Economic Update (November), World Bank, Washington, DC. Doi: 10.1596/978-1-4648-1009-1.

Zaller, John R. 1992. The Nature and Origins of Mass Opinion. New York: Cambridge University Press

- Zilinsky, J., 2014. Learning about Income Inequality: What is he Impact of Information on Perceptions of Fairness and Preferences for Redistribution?

41
9 these costs could amount to as much as US$1,500, although they could be significantly reduced if the household contributed its own labor.

As indicated in Table 3.3 below, access to potable water supply in the urban areas of Panama is close to universal, even among the poor. However, sewerage coverage is much lower, reaching only about 30% of poor households. Since increasing sewerage coverage was an important objective of the reform, it was important to establish the affordability of sewerage connections.

> **Table 3.3: Coverage of Water and Sewerage in Urban Areas in Panama** (%)

||**Non-poor**|**Poor**|**Total**|
|---|---|---|---|
|**Water**||||
|public utility service|96.8|87.2|100.0|
|community service|2.1|8.3|100.0|
|Sewerage|63.3|31.2|100.0|

_Source:_ Encuesta de Nivel de Vida, Panama, 1997.

Accordingly, the household survey conducted as part of the subsidy study also questioned households as to their willingness to pay for sewerage connections. The survey distinguished between the cost of the connection charge, which was set at an indicative level of US$850 per household, and that of the associated plumbing and sanitary installations, estimated at around US$1,500 per household.

Table 3.4 presents the results obtained from the poor households in the sample. It is striking that only 65% of households were willing to pay for a sewerage connection even when this was provided free of all cost. This suggests that households were concerned about the bills they would have to pay to use the sewerage service once connected. When households face positive connection costs, the proportion which are willing to pay declines dramatically.

The average willingness to pay among the poor was found to be US$425 per connection, or about 13.5% of average income for households in this group, which is less than a third of the estimated cost of the complementary sanitation facilities required within the household to be able to benefit from a sewerage connection.

These results indicate that the case for a sewerage connection subsidy is considerably stronger than the case for a water consumption subsidy in the specific case of Panama. The reason is that-according to the willingness to pay survey-average levels of water consumption proved to be affordable for households living in poverty even at tariff significantly above current levels. On the other hand, the full costs of connecting to the sewerage network were shown to be well beyond the means of poor households. Hence, given the limited availability of subsidy funds for the water and sewerage sector it would make more sense to allocate such funds towards sewerage connections than towards water consumption.

Notwithstanding, it is important to stress that the previous comments concern only the optimal allocation of subsidy funds within the water and sewerage sector, and as such reflect
the wet season. During the dr.y season, rice is typically replaced by other cereals because they are less dependent on water. Vfegetables are the second most important crop, foLLowed by pulses and oiLseeds. Data on fertilizer use is meant to capture the relationship between location and technology adoption.

Market participation for agricultural products is presented in the second half of the table. We measure the extent to which surveyed households rely on the market to dispose of their excess agricultural output or to meet their food needs. We see that crop commercialization remains low and that most surveyed households consume most if not aLL of their output. Sales are somewhat higher for crop;3 other than paddy and cereals. The situation is reversed in terms of purchases: the average survey household purchases half of the rice and one third of cereals it consumes. The bottom of the table further confirms that the median Nepalese household has a large food deficit, particularly in rice and vegetables. Three quarters of them (two third of all farmers) spend more on rice than the value of their paddy output; two third of them (55% of aLL farmers) spend more on agricultural products than the value of their agricultural output. Marketed surplus is producecL by a small group of farmers. Barrett and Dorosh (1996) describes a similar situation in Madagascar. The geographical location of this marketed surplus is of obvious interest to policy makers.

We complement these L'SMS data with information about urban population in Nepal (34 towns and cities).4 For our purpose, a town is defined as a settlement of more than 10,000 inhabitants. We first compate the distance between each surveyed ward and each of these towns. Distances are normaLly taken along existing roads, except when roads do not exist, in which case we calculate the shortest arc distance to the nearest road, and then the distance

> 41n earlier versions of this paper, we also included population in Indian towns located within 100 km of the Nepalese border. Including them does not affect qualitative results, but their effect on spatial specialization in Nepal is small. They are omitted Irom the paper because instrumenting urban population and distance for Indian towns raises data and computation requirements beyond what we could haridle. This issue is left for further research.

14
20

# Figure 1. Debt-Equity **Swaps** and inflation<sup>in Latin America.</sup>

<!-- Start of picture text --> Argentina DES ond Inflation<br>MC<br>100<br>g .o<br>50<br>_, 17-2 *_- 17-4 *,1 U-2 U-3 4<br><!-- End of picture text --> <!-- Start of picture text --> Brozil DES and Inflation<br>0 *<br>0 1<br>, . - 0<br>-400 0 0.. 0*C_4 . t-<br>300<br>Me LS er ,rthat,an<br>'0<br>~o - _ ._ ._ _._.__ _ _ _I_ I<br>400 2 | -3 6 , @- -<br>*70 .-<br><!-- End of picture text -->
demand growth, rather than supply-side determinants. Hence, we construct our instrument using time-series regressions of Viet Nam exports to its trading partners on the trading partner’s GDP by industry at the four-digit level as follows: where,<sup>_<u>Qj,i,t</u>_</sup> _<u>Q j,i,t</u>_<sup>_j_’s share of industry</sup><sup>_i_’ export;∆</sup><sup>_Yj,t_+</sup><sup>_h_is the change in real</sup> _Qi,t_<sup>=</sup> ∑ _j Q j,i,t_<sup>denotes country</sup> GDP in destination country _j_ .

Predicted values or exports from these regressions serve as a proxy for Viet Nam’s exports to its trading partners explained exclusively by the export market’s domestic aggregate demand. These predicted exports combine with I-O coefficients to generate total exports accounting for supply chain linkages. Subsequently, we use these total exports to generate provincial export exposure in Viet Nam.

Then, estimation will take the form of two-stage least squares, with the first stage being: and the second stage: where ∆ _X_<sup>ˆ</sup> _r,t_ + _h_ is the predicted value obtained from the first stage regression: 12
Policy Research Working Paper 6542

# **Abstract**

This paper presents empirical evidence from household and firm survey data collected during 2009–2010 on the implementation of the 2008 Labor Contract Law and its effects on China’s workers. The government and local labor bureaus have made substantial efforts to enforce the provisions of the new law, which has likely contributed to reversing a trend toward increasing informalization of the urban labor market. Enforcement of the law, however, varies substantially across cities. The paper analyzes the determinants of worker satisfaction with the enforcement of the law, the propensity of workers to have a labor contract, workers’ awareness of the content of the law, and their likelihood of initiating disputes. The paper finds that all of these factors are highly correlated with the level of education, especially for migrants. Although higher labor costs may have had a negative impact on manufacturing employment growth, this has not led to an overall increase in aggregate unemployment or prevented the rapid growth of real wages. Less progress has been made in increasing social insurance coverage, although signing a labor contract is more likely to be associated with participation in social insurance programs than in the past, particularly for migrant workers.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at jgiles@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
analysis; and panel estimates in which the estimated coefficient of distance is allowed to vary over time. The panel formulation is more suitable to incorporate time-dependent trade costs identified in (5) which we do when we build a time-series index of the quality of infrastructure. The panel estimates also allow for a better control for omitted variables by using country-pair specific effects. Hence, under panel estimation, omitted bilateral effects are no longer captured by the distance coefficient. On the other hand, because the number of zero trade flows is important for most of the sample period (especially for the poorest tercile), it is useful to explore several methods for controlling for zero values. This is better done in cross-section than in panel. We start with cross-section estimates, then turn to the panel estimates.

# **4.1 Cross-section Estimates**

We follow the by-now standard approach and estimate: where α _i_ and α _j_ are the importer and exporter fixed effects that capture the multilateral resistance variables, and all other variables that are country specific and that will appear in the panel estimates: GDPs’, multilateral term indexes and indices of the quality of infrastructure.<sup>21</sup> Dij is the distance between i and j and<sup>_z_</sup> _ijm_ includes dummy variables indicating whether the two countries are contiguous, share a common language, or have had a common colonizer.<sup>22</sup> In this first stage, we do not include the usual preferential trade agreement (PTA) dummy as this is not a structural but a policy-based variable that can be both an explanatory factor and a result of the “distance puzzle” (see section 4.3).

Results are reported in table 1 for the first and last periods under different estimation methods to account for the zero trade flows in the data with the evolution of the estimated distance elasticity reported in figures 6 and 7.<sup>23</sup> Column (1) serves as a reference and reports OLS estimates in which the zero trade flows are considered as missing variables (this corresponds to the majority of estimates reported in the meta-analysis). Column (2) reports the results from the standard solution in the literature using

> 21 In this sample with low-income countries it is preferable to use OLS rather than the systems approach used by AvW to avoid the measurement error associated with the multilateral resistance variables. The log-linear approximation proposed by Baier and Bergstrand (2009) will be used in the panel estimates reported below.

> 22 from the Centre d’Etudes et de Prospectives et d’Informations Internationales (CEPII).

> 23 All results are reported in appendix A3, table A3.1.

20
uncertainty related to inland transport times makes costs related with documents, customs and ports become a secondary priority relative to travel costs for existing exporters.

We estimate the effects of time uncertainty in each component of export times for a sub-sample of 24 Sub-Saharan countries for which there is information on the maximum and the average number of days it takes for an exporter to complete each of the exporting procedures<sup>14</sup> : We define time uncertainty as the difference between the maximum time and the average time it takes to conclude each of the different phases representing the total time to export.

Results form Table 6 show a negative and significant impact of inland transit time uncertainty on trade values, with a one day increase in this variable leading to a reduction of exports of 13 percent (column (1)). Or in logs (Column (2)), a one percent increase in uncertainty leading to about a .7 percent reduction in exports. In contrast, uncertainty in the other variables is not significant in reducing exports. These results imply that high uncertainty in road transport times jeopardizes delivery targets. In addition, even if documentation requirements take more time than inland transit, they can either be done in advance or there may be learning by doing, such that exporters become more familiar with the procedures and uncertainty is limited. Finally, while exporters may be able to pay in the port to get things out more quickly, nothing can be done on the road.

In columns (3) and (4) we include both inland travel times and inland travel uncertainty. The coefficients reflecting both variables are significant. When we include only inland transit (see columns (5) and (6)) in the same sample the coefficient is larger, implying that part of the effect of transit time on exports stems from uncertainty.

Given the dominance of transit time over the other time cost variables, in determining trade, we next investigate whether this is a pure geography effect. Specifically, we control for domestic geography by using the GPS estimated distance and time based solely on geography and type of road. In our regressions we include road distance in km

> 14 Benin, Botswana, Burkina Faso , Burundi, Cameroon, Congo, Rep., Côte d'Ivoire, Ghana, Kenya Madagascar, Malawi, Mali, Mauritania, Mozambique, Namibia, Nigeria, Rwanda, Sierra Leone, South Africa, Sudan, Tanzania, Uganda,  Zambia and  Zimbabwe.

12
**Figure 2: Distribution of Learning Studies over Time**

<!-- Start of picture text --> 250 35<br>30<br>200<br>25<br>150<br>20<br>15<br>100<br>10<br>50<br>5<br>0 0<br>Cumulative studies New Studies<br>Number of cumulative studies Number of new studies<br>1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Note: This includes the 227 studies with learning outcomes reviewed across the 6 reviews, ordered by publication date of the latest version.

**Figure 3: Distribution of Learning Studies across Systematic Reviews**

<!-- Start of picture text --> 180<br>159<br>160<br>140<br>120<br>100<br>80<br>60<br>40 32<br>23<br>20<br>6 4 3<br>0<br>1 2 3 4 5 6<br>Number of reviews in which a study is included<br>Number of studies<br><!-- End of picture text --> Note: The total number of learning studies is 227.

40
# **1 Introduction**

In a world of continued cuts in tariff rates of protection, the trade effects of non-tariff measures—including product standards—assume greater importance in research and policymaking. This is particularly true for African exporting countries, many of which now enjoy, at least in principle, substantially duty-free access to major developed country markets, in particular the European Union (EU).

Many product standards are not protectionist in intent. They may not be developed in regard to trade at all. Instead, they may respond to legitimate concerns of consumers or producers relating to, for example, product quality or fitness for purpose. There is evidence, moreover, that certain standards increase and expand trade opportunities in certain sectors (Moenius, 2004). However, product standards can also impact the marginal and/or fixed costs of foreign exporters, and can thereby advantage domestic industries.

There is reason to believe that this problem is particularly relevant to developing country exporters, in particular in Africa. On the one hand, developing countries have largely not been involved in talks on Mutual Recognition Agreements (MRAs) and other agreements designed to mitigate compliance costs (Baldwin, 2000). In the absence of such measures, compliance costs can be substantial.

Table 1 provides summary data on the investment costs required to comply with product standards, as a percentage of firm sales, taken from the World Bank’s Technical Barriers to

- 1 -
**Table 1: Postneonatal mortality rate (deaths at ages 1-11 months per 1,000 livebirths) by gender**

||Children born|Male|Female|Female/Male|
|---|---|---|---|---|
|Armenia|1991-2000*|16.7|19.3|1.16|
||1996-2005|8|10|1.25|
||2001-2010|8|11|1.38|
|Azerbaijan|1991-2000*|38|47.9|1.26|
||2001-2006|15|16|1.07|
|Georgia|1990-99*|16.8|14.5|0.86|
||1995-2004|9.4|10.5|1.12|
||2000-2009|8.1|4.3|0.53|

**<u>Sources</u>** <u>: Armenia: Demographic and Health Surveys 2000, 2005, and 2010: Table 9.4</u> Georgia: Reproductive Health Survey 1999/2000: Table 6.8; Reproductive Health Surveys 2005 and 2010: Table 6.9.2 Azerbaijan: Reproductive and Health Survey 2001: Table 6.8.3; Demographic and Health Survey 2006: Table 9.3

- It is not clear why the first estimates for each country are so much higher than subsequent estimates. However, this is true for both males and females, so it has less effect on the sex ratio of mortality.

24