rafmacalaba/datause-encoder-data huggingface dataset:
to determine their treatment status. Restricting our sample to the set of individuals who (a) lived in study EAs, (b) were eligible for the treatment (i.e. never-married females aged 13-22), and (c) are either themselves in the study sample or were listed at baseline among the five closest friends gave us a sample of 8,981 individuals in 176 EAs. As described in Section II.C, because there is a positive correlation between the locations of the study participants and their friends, the RS design generates exogenous variation in treatment intensity within each individual’s social network.

In Table 4, we present program effects as a function of the number of treated friends. The covariates included in the analysis of social networks must reflect the fact that we failed to link friends in a non-negligible number of cases and that we only observe treatment status for friends linked to our sample. To account for this endogenous variation in our ability to link friends in the study sample, we include fully flexible controls for the distribution of the number of matched friends. The findings mirror those presented in Table 2: for beneficiaries and non-beneficiaries alike, none of the outcomes is responsive to the number of treated friends. The similarity in the pattern of spillovers in the spatial and social networks provides some support for stratified interference described in Assumption 2.

Returning to clusters defined over spatial units, such as EAs, the purity of a cross-cluster counterfactual will be compromised if the regional intensity of treatment has an effect on outcomes. To test for this, we conclude by following Miguel and Kremer (2004) and Bobba and Gignoux (2013) in using GIS data on the locations of the EA centroids to count the number of treatment and control EAs within distance bands of _<_ 3km and 3-6km from each EA. Since the treated number of EAs is randomized conditional on the total number within each band, we can use this variation to look for cross-cluster spillovers that would violate Assumption 1. Table 5 demonstrates that this cash transfer experiment did not generate strong cross-cluster effects. Coefficient estimates for the number of treated EAs within the two distance bands are always small and statistically insignificant, implying that there are no spillovers for enrollment, test scores, marriage, or fertility across clusters (columns 1, 3, 5, and 7).<sup>39</sup> Exploiting incidental randomization across clusters we confirm Assumption 1 and, as in the within-cluster analysis, find little evidence of spillover effects.

> 39In contrast to Bobba and Gignoux (2013), who find large spillover effects of PROGRESA in Mexico but only on treated individuals, we find no consistent evidence that program beneficiaries experience spillovers from adjacent clusters that are any different from untreated individuals (columns 1, 3, 5, and 7). In other words, the _ST_ and the _SNT_ measured cross-cluster are both zero.

29
# **II. Results**

## **1. The average MFN tariff rate has been increasing.**

In table 1, we show the average MFN tariff rate in Russia calculated both as a simple average and as a trade weighted average. The trade weighted average tariff has increased from 11.4% in 2001 to 14.3% in 2003, but fell slightly to 14.0% in 2005. The simple (unweighted) average increased from 11.7% in 2001 to 12.2% in 2002 to 12.8% in 2003, but fell to 12.4% in 2004 and 12.1% in 2005. This shows that tariffs have increased faster on the products imported more intensively.<sup>7</sup> To be clear, Let Vi be the value of imports of tariff line i and V be the total value of imports. That is, V = ∑ _i_ V Define the share of sector I in total imports as i αi = Vi / V.

Let ti be the MFN tariff rate that we calculate for tariff line i. Then the weighted average MFN tariff that we calculate is: t* = ∑i αi ti . And the simple or unweighted average tariff is t’ = ∑i (ti /n) where n is the number of tariff lines.

## **2. The tariff structure has become more diverse (less uniform).**

The standard deviation of MFN tariffs has been increasing more than the mean of the tariff, especially on a trade weighted basis. On a trade weighted basis, the standard deviation increased from 9.5 in 2001 to 15.2 in 2003, after peaking in 2003 at 18.0; on an unweighted basis the standard deviation of the tariff increased from 10.8 in 2001 to 18.7 in 2003, but the standard deviation fell to 12.7 by 2005.

The increase in the standard deviation implies that the Russian tariff structure has become more diverse, so that there are more highly protected sectors and more sectors with very low tariffs. Russia implemented a tariff simplication reform in 2000-20001, in which a significantly reduced number of ad valorem tariff rates were employed. Our calculations show that tariff simplification should not be confused with tariff uniformity or reduced variance of the tariff structure, as the tariff simplification did not prevent a movement to a less uniform tariff structure, especially in the 2001-2003, the two years immediately following the tariff simplification.

> 7 Another possible explanation is that the structure of imports has shifted toward products with higher tariffs.

5
and/or a broad and effective social safety net may be a better way of dealing with adjustment costs. The point is that whatever the optimal instrument may be, few countries appear to have the institutional capacity to deal with the problem of adjustment to increased competition.

# IV. **The External Environment**

The period under review witnessed mixed developments in the external environment affecting developing countries' trade. On the one hand, GDP in developed countries, an important determinant of overMlI demand for developing country exports, increased at a rate slightly lower than longer term growth trends in these countries - at about 2.0 per cent for the period 1989-1997 compared to 2.2 percent in the 1980's and 3.0 per cent in the 1970's (OECD, 1998). On the other hand, market access conditions for developing countries improved, in part as a result of standstills and subsequent liberalization linked to the iJRA as well as through subsequent reductions in tariffs following the Information Technology Agreement. The actual implementation of the URA had not been completed by the end of the period examined, nor the ITA. But there is evidence that access conditions improved in many respects in the markets of developed countries through most of the period under review. Nevertheless, access continued to be impeded by high trade barriers in certain sectors, such as agriculture and textiles; and, while progress was made on issues such as escalation, the problem persisted in certain sectors, for example textiles and leather products. Moreover, towards the end of 1998, evidence mounted on the resurgence of protectionism in certain sectors e.g. steel, in both the US and the EU markets. Finally, as part of the URA, WTO members committed themselves to take several explicit additional steps in favor of developing countries in areas such as anti-dumping, whose implementation it would be interesting to review.<sup>1</sup> 8 The market access analysis for this study relied to a large extent on analyses of indicators of tariff and non-tariff barriers prepared by the OECD. The focus is on market access conditions in the 44
infringe competition. It is also charged with removing unnecessary regulatory barriers and simplifying administrative procedures through ex post assessment of norms.

The applicable regulations are laws and decrees issued between 2007 and 2017 that specifically target the ICT sector and either facilitate or complicate competition. Facilitating regulations improve the administrative efficiency of entering or operating in an ICT market, improving competition by facilitating both market entrance and resource allocation, and potentially contributing to productivity growth. Conversely, regulations that impose additional administrative requirements or make current ones more complex are likely to generate the opposite effect, hindering market entrance, limiting resource movement, and thus deterring productivity growth. However, it could also be argued that new regulations can also address market failures because an explicit purpose of some is to improve service quality. In this sense, they could raise quality or productivity standards for entering a market or for doing business and remaining in the market, which would translate into higher average market productivity. Possibly, each new regulation could boost or depress productivity—the complexity it adds makes it hard to determine whether it promotes or restricts competition.

To disentangle the layers of complexity, we look at how enforcement of any type of regulation affects productivity and markups: First, we study the behavior of firms that were affected by either an increase or a reduction in regulatory requirements. We then do the analysis separately by isolating observations that were affected only by one type of regulation.

We match changes in regulations and their enforcement over time to the firm-level panel. Regulatory cases are matched to firm data by looking at the market affected, defined by the 2-digit ISIC sector and by the year of implementation. We compare the behavior of these firms to that of firms in the same ICT branch that were not subjected to any new regulation, controlling for firm, sector, and year characteristics through fixed effects—if other sector-specific regulations had been introduced, their effect will not be confounded because it is controlled by the fixed effects. The regulatory variables are coded as dummies where 1s imply a change in regulation as defined above. Appendix A.2 presents a compilation table and a summary of important ICT regulatory changes with reference to dates and justifications of the legal changes. For interpretation purposes, the regulations identified are matched to the OECD competition checklist to assess whether they affect the number of suppliers, the ability of suppliers to compete, the incentives of suppliers to compete, or the choices and information available to customers.

Osiptel and Indecopi enforcement data capture the number of investigations of anticompetitive behavior that resulted in a fine or a corrective measure in each year and sector. Additionally, for telecommunications, the data set contains the fines by company name, which are then mapped to a corresponding ISIC code. Each resolution that found infringe14
- 23 -

much more likely to be grown by the poor in Cote d'Ivoire than by the nonpoor, which implies that higher prices for that crop woull mainly benefit the poor, while lower prices would be especially harmful to them. Finally, sugar is grown by very few poor houceholds, and indeed is more likely to be grown by non-poor households. Thus reductions in producer subsidies to **sug3r** producers in C6te d'Ivoire would have almost no negative effect on the poor.

In Peru producers of yellow maize benefit from very high levels of effective protection. Yet from the viewpoint of the poor, it would be much better to subsidize production of white **maize** since **that crop is** much **more** grown by them than yellow maize. On the other hand, wheat production **also** receives a high level of effective protection, but this is more favorable for the poor; if this protection were reduced in order to "get prices right" a relatively large proportion of the poor would be hurt. Finally, unlike C6te - d'Ivoire, cotton production is very rare among the poor in Peru any changes in the price of cotton would have almost no effect on the poor in their role as producers.

Of course, "getting the prices right" will affect the poor in their role as consumers as well. Table 6 provides some data on the fraction of total expenditures devoted to specific food items among both poor and non-poor households in C6te d'Ivoire and Peru. Turning first to the C6te d'Ivoire data, one sees that although rice is an important component of food consumption among the poor, they are partially protected against any price increases by the fact that they produce more than half of what LhQy consume. - However, this is not true among those poor who are found in urban areas they produce only a small fraction of what they consume. Unlike wheat, sugar does not constitute such a large fraction of the budgets of the poor, so that
would require a certain degree of homogeneity for these countries, since  may vary depending on a host of factors caused by any difference ranging from economic structures to modeling methods and survey designs. Complicating this issue even further, for the same country,  is likely to be different for different household welfare outcomes; thus an appropriate range of correlation term for, say, household food consumption must be estimated separately from that for household non-food consumption. Over time,  might also change.

Second, this method provides at best bound estimates, rather than point estimates. While bound estimates are certainly more useful than no estimates at all in the absence of true panel data, they also leave room for greater precision. There always exists a tradeoff between precision and encompassment with the bound approach: a larger bound interval is more likely to encompass the true rates but will be less precise, and vice versa.

In this section we generalize the method introduced in Dang et al. (2014) by presenting a method to estimate  based only on a country’s own cross sectional surveys. We discuss the asymptotic properties of this new point estimator. We then extend the method to settings with three survey rounds or more. We maintain the Dang et al. (2014) framework described in the previous section.

# **III.1. Theoretical Estimates for ρ**

We offer the following proposition to obtain the simple correlation coefficient between household consumption in two survey rounds  _yi_ 1<sup>_y_</sup> _i_ 2 , which is closely related to  .

**Proposition 1- Approximate estimation of**  _yi_ 1<sup>_y_</sup> _i_ 2

_Assume household consumption follows a simple linear dynamic data-generating process_ ' _given by yi_ 2     _yi_ 1  _i_ 2 _(*), where_<sup></sup> _i_ 2<sup>_is the random error term. Also assume that the_</sup> _sample size of each household survey round is large enough (or N_   ), _the number of cohorts (C) constructed from the survey data is fixed, and the cohort dummy variables satisfy_ 9
# **1 Introduction**

The collapse of the Rana Plaza factory building in Bangladesh in April 2013 killed over a thousand workers and injured about 2,500 others. It is widely considered the worst accident in the history of the global garment industry, and the world’s worst industrial disaster since the 1984 Bhopal gas tragedy in India (Reuters, 2017). In its aftermath, local and international attention on the ready-made garment (RMG) sector intensified, resulting in a series of new policies and changes to the behavior of retailers. Direct reforms included an increase in the minimum wage and highprofile but voluntary audits examining safety. Indirectly, retailers may have pressured factories to improve working conditions or have become hesitant to buy from factories that subcontract or that appear to be skirting labor regulations. While the intended goal was to improve working conditions and raise wages, a concern arose that reforms which raise costs among garment factories could ultimately hurt workers.

In this paper, we evaluate the net effects of these post-Rana Plaza changes on workers in the RMG sector in Bangladesh. We use six rounds of the Bangladesh Labor Force Survey, spanning from 2003 to 2016, and employ a triple-difference estimation strategy that compares workers in the garment sector to workers in other manufacturing industries, in four “treated” districts whose garment factories comprise 99% of garment exports versus the rest of Bangladesh, pre versus – post Rana Plaza. We examine four key outcomes wages, an index of working conditions, hours – of work, and whether the worker receives a written contract and test parallel trends for each outcome. While we generally fail to reject the null hypothesis of divergent trends among garment workers in treated districts (compared to non-treated districts) pre Rana Plaza, we caution that the results for contracts should be viewed as suggestive because the test there is lower-powered and some of the point estimates are large. Each survey wave was collected in all four quarters of the year, allowing us to separately examine short-term effects (in the remainder of 2013) and medium-term effects (in 2015 and 2016) of the reforms that followed the event. While we believe these medium-term results are important, we acknowledge that by several years after Rana Plaza, it is more likely that there has been a shock that differentially affected garment workers in treated districts, and so the medium-term results should be viewed as somewhat more suggestive than the 2
income.<sup>12</sup> We examine whether households that depend on remittances face fewer shocks and whether these households behave differently from other households in coping with shocks.

**Figure 5. Shocks faced by Ethiopian households**

<!-- Start of picture text --> 24%28% 23% 25%<br>5%<br><!-- End of picture text --> <!-- Start of picture text --> 16%<br>11%<br>5% 4%<br><!-- End of picture text --> <!-- Start of picture text --> Non remittance receiving<br>households<br>Domestic remittances<br>International remittances<br><!-- End of picture text --> Source: Authors’ calculations based on Ethiopia Welfare Monitoring Survey 2004.

In Ethiopia, we find that households that depend on international remittances report facing fewer shocks from food shortages, illness and drought compared to other households (figure 5). The remittance-receiving households that are affected by drought tend to mostly in rural areas. While remittance-dependent households report facing fewer shocks in terms of illness of household members—perhaps since better nutrition is usually associated with better health—the difference with the other households is smaller compared to the direct shocks to food security.

**Table 4. Remittance recipient households do not sell productive assets and use own cash to cope with food shortage shocks**

||Households not<br>receiving<br>remittances|Domestic<br>remittances|International<br>remittances|
|---|---|---|---|
|Food Aid|42.3|55.9|0|
|Sale of livestock and livestock products|40.5|3.9|0|
|Sale of other agricultural products|18.2|3.7|0|
|Sale of household assets|4.1|4.6|11.5|
|From own cash|10.3|5.3|31.3|
|Others|15.6|33|48.9|

Source: Authors’ calculations based on Ethiopia Welfare Monitoring Survey 2004.

> 12 However, among the ―urban‖ households that receive remittances, 16 percent report being engaged in agricultural or related activities.

18
# **Annex – Supporting  Data for Charts**

**Table 5 Microfinance Regulation and Supervision by Region**<sup>**38**</sup>

||MENA<br>(%)|Developing<br>Countries (%)|All (%)|
|---|---|---|---|
|**Not regulated**|50.0|37.9|47.7|
|**Supervised only by a bank regulator**|30.0|42.7|35.4|
|**Supervised only by other regulator**|20.0|7.8|7.7|
|**Supervised by both**|0.0|11.7|9.2|
|**Number of countries**|10|103|130|

Source: M Khaled, CGAP, 2010 38 (Pearce 2010) 50
Table A3.6. Sectoral GDP shares and urban population share in 2013 and by simulation in 2030 (%)

||2013|base se|r-eq+fg se|r-eq+db se|r-eq+tx|mdg-eq+fg mdg-eq+tx|
|---|---|---|---|---|---|---|
|Agriculture|23.5|18.8|18.5|18.3|18.5|17.7<br>18.0|
|Industry|25.5|26.8|26.6|26.0|26.5|26.2<br>26.1|
|Services - private|42.1|44.9|44.6|45.1|44.4|44.1<br>43.3|
|Services -government|9.0|9.6|10.4|10.7|10.5|12.0<br>12.6|
|Total|100.0|100.0|100.0|100.0|100.0|100.0<br>100.0|
|Urbanpopulation share|27.8|29.1|29.2|29.3|29.2|29.5<br>29.3|

35
even the best consumption data need not adequately reflect certain “non-market” dimensions of welfare that differ between urban and rural areas, such as access to public services (invariably better in urban areas) and exposure to crime (typically more of a problem in urban areas).

We make two key assumptions about poverty measurement. Firstly, we confine attention to standard additively separable poverty measures for which the aggregate measure is the (population-weighted) sum of individual measures. This includes the two measures reported in this paper, the headcount index and the poverty gap index.<sup>7</sup> Secondly, we also take it as axiomatic that simply moving individuals between urban and rural areas (or countries), with no absolute loss in their real consumption, cannot increase the aggregate measure of poverty. Relocation on its own cannot change aggregate poverty.

These assumptions justify confining our attention to absolute poverty measures, by which we mean the poverty line is intended to have a constant real value both between countries and between urban and rural areas within countries.<sup>8</sup> A key issue is then how to deal with the fact that the cost-of-living (COL) is generally higher in urban areas. Casual observations suggest that relatively weak internal market integration and the existence of geographically non-traded goods can yield substantial cost-of-living differences between urban and rural areas. Any assessment of the urbanization of poverty that ignored these COL differences would simply not be credible. Yet existing Purchasing Power Parity (PPP) exchange rates used to convert the international line into local currencies do not distinguish rural from urban areas.

To address this problem we turn to the World Bank’s country-specific _Poverty Assessments_ (PA’s), which have now been done for most developing countries. These are core reports within the Bank’s program of analytic work at country level; each report describes the

> 7 The poverty gap index is the mean distance below the poverty line as a proportion of the line (where the mean is taken over the whole population, counting the non-poor as having zero poverty gaps.) On the larger set of additively separable measures see Atkinson (1987).

> 8 This does not allow the possibility that a new migrant to urban areas experiences relative

deprivation. One can question how relevant this is for very poor people (Ravallion and Loskshin, 2005).

7