rafmacalaba/data-use-mentions-extended huggingface dataset:
some of the differences between the predicted poverty rates can be explained by particularities of how poverty is measured in a country.

Figure 8 shows the predicted and survey-based poverty rates for other populous, or poor and data deprived countries. In most cases, the two are fairly well aligned, though there are exceptions, such as the cases of China and India just discussed as well as the República Bolivariana de Venezuela, where the predicted poverty rates are much lower than the surveybased ones. Figures A.3-A.12 in the annex show similar plots for 218 economies.

PIP currently assigns countries without any poverty estimate (or without national accounts data to extrapolate/interpolate from an old poverty estimate) the regional poverty rate. This concerns about 3% of the global population. In Appendix B we show how PIP’s regional and global poverty rates would change if we used the estimates from our two tiers instead of applying the regional average, as PIP currently does. Global poverty is projected to be revised upwards in 2019 by 11 million at the $2.15 line and 16 million at the $6.85 line. For certain countries the implications are larger, notably Afghanistan, Somalia, and the Democratic People’s Republic of Korea.

# 7 Conclusion

This paper proposes a method to estimate welfare distributions in contexts where little data is available. Data deprivation is often a bigger concern in poorer and more fragile countries where data collection might be limited due to active conflict, lack of resources, or institutional fragility, but where efforts to monitor changes in living conditions are essential. While comprehensive surveys of household income and consumption remain the best way to measure household welfare, we offer a simple alternative to estimate welfare distributions when surveys are not available.

The method consists of leveraging income or consumption distributions from more than 2,000 household surveys available in the World Bank’s Poverty and Inequality Platform (PIP) for 168 countries covering the period between 1991 and 2000. We combine these distributions with more than 1,
# **<u>Apendix Table 3: Impact of Treatments on Agricultural Outcomes and Household Expenditure</u>**

|<br> <br>**Regressions with stratification cell**|<br>**fixed effects but without a**|<br>**dditional baseline cont**|<br>**rols**|||||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||Land under cultivation|Total value of inputs|Proceeds from crop|Value of crop not sold|Value of crop output|Farm profit (output‐|Total expenditure in|
|**Dependent variable:**|[acres]|[MK]|sales [MK]|[MK]|(sold & not sold) [MK]|intput) [MK]|last 30 days [MK]|
|Ordinary Account|0.11|10,923.27|14,546.26|26.20|14,630.56|6,399.13|831.83|
||(0.22)|(6,841.25)|(12,851.36)|(4,619.93)|(15,526.30)|(11,488.35)|(955.61)|
|Ordinary x Private Raffle|0.25|‐5,296.73|3,216.05|6,607.88|11,626.56|15,588.30|398.97|
||(0.24)|(7,416.33)|(14,
- **Ruiz, Isabel, and Carlos Vargas-Silva.** 2013. “The Economics of Forced Migration.” _The Journal of Development Studies_ , 49(6): 772–784.

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- **Verdier, Thierry, Alan Manning, Alberto Bisin, and Yann Algan
and September-December. The PoI data has information on one’s employment status, time usage, and demographic characteristics like gender, education level, and marital status. The PoI data shows how much time a person spends on household activities, at work, and traveling. Reported time on travel is spent by a person traveling from one place to another for various purposes, including work-related activities. The CP survey does not ask specific questions about time spent commuting to work, searching for a job, or on leisure.

We use six waves of PoI from May-August 2020 to January-April 2022 (or from the 20th to the 25th wave). We also match the households in the PoI data to those in the CP data. Appendix Table D2 lists the study periods for the two sectional data sets. We restrict our sample to women (or households having women) aged between 15 and 65 at their in the data. Table D3 lists the variables used in the first appearance Appendix analysis and their definitions. Appendix Table D4 presents the summary statistics of our study sample. Panel A displays the household characteristics in December 2020. Differences between households in treated vs. control HRs regarding rural residence, number of people, and per-capita income and expenditures are relatively small. In panel B, we compare women in treated HRs to those in control HRs in May-August 2020. The distributions of age, marital status, and education are comparable for the two groups of women. Women in treated areas are less likely to participate in the labor market, but conditional on participation; they are more likely to be employed. They also tend to more time on household activities and work but less time on travel than women spend in control areas.

# **3.2 Delhi Primary Survey**

As explained above in footnote 28, we cannot include Delhi in our baseline analysis. To complement our inquiry, however, we use primary data collected in February 2020 by the Gesellschaft f ̈ur Internationale Zusammenarbeit (GIZ) India (Mahendru, 2022
Power is higher against starker alternatives, such as that rural wages do not respond at all to agricultural prices. More precisely, I can use the bootstrapped _t_ -distribution to answer the question: How likely would I have been to reject _H_ 0 : _γ_ = 1 had the true _γ_ been at or very near zero? Panels (a) and (b) of Figure 2 plot empirical power functions over the range of alternatives _γ ∈_ [0 _,_ 1] for the specifications in column 3 and 5, respectively. (Alongside are the corresponding power functions based on the standard normal test-statistic; consistent with Bester et al., 2011, the asymptotic approximation usually leads to over-rejection.) These bootstrap-based power functions indicate that at a true _γ_ of zero, _H_ 0 : _γ_ = 1 would be rejected with 95% certainty in the column 3 specification, and with closer to 90% certainty in the column 5 specification. In this sense, then, power is reasonably good.

In sum, the evidence does not support the view that rural wages are unresponsive to agricultural price changes over a half-decade period. Moreover, the magnitude of the wage responses are consistent with at least one version of the specific factors model. Next, I assess how this model fares against a particular alternative, that of segmented labor markets.

# **3.7 Test of sectoral labor mobility**

To test perfect intersectoral mobility of labor, I use the same procedure described above to construct log-wage district fixed effects for the 2004-05 and 2009-10 NSS-EUS rounds, except in this case using only wage data for _nonagricultural_ jobs. The dependent variable in panel (b) of Table 3 is thus the time difference of these district fixed effects scaled by _ψd_ . Relative to the previous analysis, 17 districts are missing the dependent variable because of lack of data on nonagricultural wage jobs and are thus dropped.

The estimates in columns 1-5 of panel (b) do not differ much from their counterparts in panel (a),
presence of a single cointegrating vector, Johansen (1992) shows that if the equations of the marginal model have no cointegration, the single-equation estimator is equivalent to the estimator resulting from system estimation of all the equations.<sup>24</sup> To estimate the coefficients in equation (1), we use the pooled mean group ( _PMG_ ) estimator developed by Pesaran, Smith, and Shin (1999).<sup>25</sup> In practical terms, we embed the production function equation (1) into an ARDL( _p,q_ ) model:

> 24 The single-equation analysis could be inefficient under certain circumstances (see Johansen 1992).

> 25 This estimator has been previously implemented in different contexts, for example Cameron and Muellbauer (2001) analyze the relationship between earnings, unemployment and housing in a panel of UK regions, while Égert et al. (2006) consider exchange rates, productivity and net foreign assets in a panel of countries.

12
surveys. This meant that the focus on the top end of the income distribution was mostly neglected for several decades, in favor of index measures of inequality that did not bring into focus the very top end of the distribution, unlike Kuznets’ work (Milanovic, 2014).

The neglect of the top end of the income distribution ended with Piketty’s publication of long-run estimates of top income and wealth shares for France for the 20<sup>th</sup> century (Piketty, 2001, 2003), which reinvigorated research on the top end of the income and wealth distributions. Piketty (2003) used data and methods when analysing inequality in France for most of the 20<sup>th</sup> century that were similar to those used by Kuznets (1953)- national accounts data, published tax tables with aggregated data

> 3 Alvaredo and Atkinson (2022) note that similar research had already been undertaken for South Africa by Frankel and Herzfeld (1943) - a decade before Kuznets and Jenks' work for the United States, although this was limited to whites only.

3
drawback of all firm survey data arises from the exit of firms from the sample. Exit of a firm does not necessarily mean that the firm has truly disappeared from the economy. To mitigate this issue, the article corrects for selection bias in the estimation of the production function. Notwithstanding this limitation, the dataset is well suited to the present study and has been extensively used to investigate issues affecting African manufacturing firms, such as size distribution and productivity (S ̈oderbom and Teal (2004), Van Biesebroeck (2005b)) and trade policy (Van Biesebroeck (2005a), Bigsten et al. (2004)), among others.

The tariffs data were compiled and provided by the _Centre d’Estudes_<sup>_ ́_</sup> _Prospectives et d’Informations Internationales_ . The tariffs data consist of the applied bilateral three-digit sector-level tariff rates between Ghana and its trading partners. Ghana’s export of manufacturing goods is concentrated in a few countries; hence, tariffs weighted by sector-level bilateral trade share is very similar to simple average tariffs. Because of the similarities between the simple average tariffs and trade-weighted average tariffs, using either of the two produces similar results in the estimation of the main econometric specification.<sup>9</sup>

# **3 Theoretical framework**

The key point of the analysis in this article is to evaluate the effect of trade openness on competition. In an institutional environment as described in 2.1, market imperfections and distortions are prevalent and expected. On the other hand, trade liberalization has the potential to increase competition and improve the allocative efficiency of the economy. Indeed, the theoretical model of Melitz (2003) predicts that trade induces competition by raising the minimum productivity survival threshold; consequently, the resources of exiting firms will be reallocated towards more productive firms.

Whether trade liberalization does induce competition becomes an empirical question that needs to be investigated. Previous empirical studies of developing economies have focused on Latin American and Asian countries (Pavcnik, 2002
(DMSP-OLS). DMSP-OLS final output provides composite annual light density measures at a coarse footprint of 5km _×_ 5km pixel resolution (see Donaldson and Storeygard (2016) for a comparative analysis on this literature). Compared to the imaging sensors suite in DMSP-OLS, VIIRS provides higher quality remote sensing imagery in terms of spatial resolution, coverage interval, and the ability to detect weak light sources, which makes it ideal for our analysis. The VIIRS Day/Night Band sensor unit has a 742m _×_ 742m footprint, and thus can detect boat activities within as fine an area as 0.2 mile squares. Couple with the implementation of an automatic boat detection identification system that converts light intensity to actual boat counts, VBD has greatly advanced the usefulness of

> 11Night-time fishing often requires the emission of high-luminous light to attract catch.

> 12 We collect the raw-, raster-formatted VBD’s light intensity and boat detection data at https://ngdc.noaa.gov/eog/viirs/download ~~b~~ oat.html [Accessed July 26, 2018].

8
MENA interaction terms are added to the baseline equation (4) to capture differential regional effects.

Since the estimation of exporter size includes country-controls, some countries are excluded due to lack of a complete data profile. In estimation of equation (4), country coverage ranges from three countries with full country controls, to all countries without any country controls. In regressions with complete controls, only Morocco, Jordan and Egypt are represented. For the analysis of exporter size, data is at the country-sector-year level and additional variables are included in the regression to control for country level factors: the log of GDP per capita (PPP), trade openness and a tariff rate<sup>21</sup> (Table 7 in the Appendix). GDP can benchmark for market size. Country-level determinants included in the regression are the log of GDP per capita (PPP), trade openness, and a tariff rate. As a measure of trade openness, the total export and import value in manufacturing and services is divided by a country’s GDP ( _TradeOpen_ ) using data from the WDI.<sup>22</sup> The share of trade relative to GDP is similar in MENA and non-MENA regions, 0.79 and 0.78 respectively.

Results are examined when financial sector development is proxied as previously by the private credit to GDP ratio. Table 5 shows results where the complementarity between external finance dependence and private credit-GDP ratio is significantly and positively associated with both mean and median exporter size.

Comparing regions, financial impacts on export size are smaller in MENA when associated with _mean_ exporter size ( _Private Credit / GDP * MENA_ ). For a given sector, an identical increase in the private credit-GDP ratio yields a smaller increase in mean exporter size in MENA than in nonMENA countries.

However, large firms in MENA may be benefiting more from financial sector development than smaller firms. The interaction term _Private Credit/GDP*EXTFIN*MENA_ is positive in the case of _mean_ exports but not _median_ exports.
CPS and IPS teachers are similar according to the teacher characteristics score (Column 4), while SPS teachers perform significantly better in this dimension (+1.7 SD column 5). However, CPS outperforms IPS on the classroom setting and equipment scores. In these two dimensions, CPS are equivalent and maybe even superior (for equipment) to SPS, confirming that structural quality has been significantly improved by the construction program. The results are strikingly different for the process quality scores. CPS and IPS are not significantly different in terms of curriculum and pedagogy or teacher-child interaction, whereas on both aggregate measures, SPS outperform IPS and CPS.<sup>26</sup> Overall, these results confirm that CPS are of better quality than IPS but maybe not on the dimensions that are the most important for children’s performance. The upgrading from IPS to CPS mostly affected the learning environment by providing better equipment, while teacher quality was not fundamentally affected. In contrast, SPS teachers are significantly more educated and perform better in terms of instructional quality.

# **_Parental assessments_**

We use questions from the caregiver survey to compare differences in cancelled classes, perceived teacher quality, financial contributions, and travel time between the types of preschools (Table B4). CPS classes get cancelled significantly more often than SPS classes. Parents of children enrolled at CPS assess the kindness of CPS teachers as higher than that of their SPS counterparts (though these differences tend to be relatively small). This could be explained by the greater likelihood of CPS teachers being from the same community as the parents. At endline, parents of children enrolled at SPS assess the reliability of SPS teachers as higher than that of CPS teachers. While financial contributions for teacher salary and construction tend to be almost zero for all types of schools, contributions for school materials are substantial: On average, parents of children enrolled at CPS have paid $35 in the current school year at midline and $47 at endline (with similar amounts for IPS
declarations were deemed fraudulent and the amount of taxes recovered (if any). These variables are unique and crucial for our definition of inspector actions that may potentially respond to the provision of information and the monitoring. Third, we obtained information from the RMU on the qualitative comments made to each import declaration. For the declarations lodged during the period of the RCT whereby improvements to the qualitative comments were the treatment, we use text analysis methods to extract several characteristics of those comments, including their length, the presence of prices, of weights, and the reference to prior transactions, as will be further described in Section 5 and in the Appendix.

# **Risk scores, valuation advices and scanning results**

We obtained from the service provider GasyNet additional information that we merge to the ASYCUDA data on the risk score assigned to each declaration (related to the risk of noncompliance with customs regulations ranging from 1 to 9), the recommendation of an inspection channel (documentary control, physical frontline inspection, or no inspection), and whether a declaration was subject to a scanner exam. Most importantly, and as discussed in Section 2, the service provider also issues valuation advices. Since this service is costly given that it requires additional research, valuation advices are carried out for only a subset of declarations, about 6 percent of the total per year, with the selection made by the service provider and the customs administration based on risk criteria. To our knowledge, ours is the first study with access to this type of risk and third-party valuation information. Further discussion on these valuation advices is provided in Section 4.

Madagascar’s raw customs data cover the universe of Madagascar’s formal import transactions, that is, imports made under five regimes: final imports for consumption (imports for home use), re-imports, temporary admissions, inward processing, warehouse, and other. Our sample for the econometric analysis includes only import declarations that are subject to taxation and to a physical or documentary control by customs frontline inspectors
WEBs.

The literature on the WEBs of corridors typically looks at a single corridor to assess impacts. However, there is much to learn from differences across projects and the resulting variation in the design and implementation of transport investment projects and the practitioners working on them. This paper aims to shed light on the link between project design and implementation and WEBs and help bridge the divide between the academic literature on WEBs and the practical project design and appraisal.

The paper assesses which design and implementation characteristics of transport corridor projects and country characteristics could help maximize the WEBs from transport corridors. To analyze this question, we collected data on transport corridor projects supported by three international development organizations: the Asian Development Bank (ADB), the Japan International Cooperation Agency (JICA), and the World Bank.

The paper contributes to two strands of the literature. One strand focuses on estimating the WEBs of large transport infrastructure, using detailed locational data. Several studies summarize the estimated impacts of transport infrastructure investments and applied estimation approaches using qualitative approaches (Berg et al. 2017; Redding and Turner 2015; Straub 2011) or quantitative methods, such as systematic reviews and meta-analyses (Melo, Graham, and Brage-Ardao 2013; Roberts et al. forthcoming). The literature estimates that, on average, the impacts of corridor investments on economic welfare and equity tend to be beneficial, although they are often detrimental for environmental quality. Roberts et al. (forthcoming) highlight that, around this positive average impact, the local impacts could vary widely depending on the

> 1 For a review of the literature, see Roberts et al. (forthcoming).

2
Policy Research Working Paper 10104

# **Abstract**

Education is a powerful tool for social mobility and cultural integration. However, it is one of the largest hurdles for migrants—particularly for forcefully displaced migrants, given their more vulnerable condition and lack of resources to pay for private education. This paper explores educational gaps between migrants and natives in Chile, a country that provides free public education to newcomers. The paper analyzes an administrative data set that includes all students in the Chilean educational system and follows students from 2017 to 2018. Using a research discontinuity design around the cut-off for financial aid to tertiary education, this paper investigates whether access to financial aid generates incentives for forced migrants to enroll in tertiary education. This research confirms previous findings that show that migrants have lower advancement and enrollment rates than natives at every school level. Moreover, it finds that financial aid applications constitute a major roadblock preventing migrant students from accessing higher education. Furthermore, the paper presents suggestive evidence showing that the interaction between the type of school (vocational vs. technical) and the migrant condition affects applications for financial aid.

This paper is a product of the Social Sustainability and Inclusion 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/prwp. The authors may be contacted at fjmenese@gmail.com.

_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