rafmacalaba/datause-encoder-data huggingface dataset:
# _Interpretation of the results, policy recommendations_

The results of any economic inquiry should be reviewed and debated among the professionals and understood by the policymakers in order to be effectively applied in practice. New scientific knowledge is refined through the challenges and criticisms by peer scientists. A critical synthesis of the results of several studies, meta-analysis, becomes a valuable tool for reviewing and evaluating the empirical research and often for reconciling conflicting findings (Stanley 2001). Ultimately, the reliability of scientific studies is determined by the ability to replicate the study results with new data or in different settings. The important question of any scientific investigation is how the findings of a particular study could be extended and generalized over broader population or in different circumstances.

The applied nature of poverty studies implies that its findings must be presented in such a way that it could be disseminated and evaluated by poverty practitioners and the public. When repeated experiments are not feasible – a situation typical of any social science<sup>9</sup> and of economics in particular – critical reviews and an open professional debate over the new and often contradicting results become necessary to validate the results and to broaden the scientific knowledge.

We find no critical reviews of the poverty studies published in Russia over the last 15 years. As we mentioned in the introduction, no meta-analysis or synthesis of the poverty results has been conducted either. The limited or restricted access to the majority of micro-level datasets could partially explain why no paper in our sample attempts to replicate or verify the results of other studies.

Only a third of the papers offer explicit policy recommendations. While the analysis in a significant proportion of papers in our sample was based on data from regional or local surveys, very few papers attempt to extend the findings to the national level or to show the relevance of the results for the country as a whole.

> 9 Natural sciences such as Astronomy and Biology (when studying the evolution of species) are also nonexperimental and this also gives rise to discussions, disagreements, disputes and incongruities (Mankiw 2002)

16
Data Gaps in Microdata in the Context of Forced Displacement<sup>*</sup> Takaaki Masaki<sup>a</sup> B. Madson<sup>b</sup> Keywords: Refugees, Internally Displaced Persons (IDPs), Forced Migration, Forced Displacement, Microdata JEL: F22; C8

* The authors wish to thank Donatien Beguy, Patrick Brock, Craig Loschmann, Utz Johann Pape, Felix Schmieding, Nistha Sinha, Jeffery Tanner, and Domenico Tobasso for their comments and advice. We are also grateful to the participants at the workshop on “Filling the Remaining Data Gaps in the Forced Displacement Context” organized by the World Bank – UNHCR Joint Data Center on Forced Displacement (JDC). We appreciate technical support from Aivin Solatorio and Olivier Dupriez on the use of the nlp4dev tool. Finally, we express our gratitude to Benu Bidani, Aissatou Dicko, Björn Gillsäter, and Maja Lazic for their guidance and encouragement. This work is funded by the JDC and its partners: the Government of Denmark represented by the Danish Ministry of Foreign Affairs; the European Union represented by the Directorate-General for International Partnerships (INTPA) at the European Commission; and the United States Government represented by the U.S. Bureau of Population, Refugees, and Migration (PRM). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, nor do they represent the views of the Executive Directors of the World Bank and the governments they represent, or the views of UNHCR.

a Senior Economist, World Bank / UNHCR Joint Data Center on Forced Displacement; the World Bank Poverty and Equity Global Practice, tmasaki@worldbank.org.

b Ph.D. candidate in Public Policy, Duke University, braydon.madson@duke.edu.
Table 7: Cobb-Douglas Production Function Estimations Sri Lanka Invesment Climate / Business Environment Surveys 2003 & 2010 Own Response

|**_Dependent variable is natural log of total annual sale_**|**_s_**<br>(1)|(2)|(3)|(|4)|
|---|---|---|---|---|---|
|**_Sample_**|Pooled|2003|2010|Pooled||
|**_Specification_**||(1),2003 only|(1),2010 only|Base effect|2010 Interactions|
|Ln(labor)|0.382+++|0.348+++|0.419++|0.367+++||
||(0.078)|(0.078)|(0.178)|(0.078)||
|ln(capital)|0.084++<br>(0.039)|0.164+++<br>(0.048)|0.007<br>(0.060)|0.080++<br>(0.040)||
|ln(materials)|0.466+++<br>(0.029)|0.447+++<br>(0.038)|0.494+++<br>(0.044)|0.470+++<br>(0.029)||
|>10% government ownership|-0.153|-0.167|-0.781++|-0.173|-0.481|
||(0.183)|(0.192)|(0.385)|(0.187)|(0.400)|
|Losses from crime as a percent of sales|0.183+|0.087|0.325++|0.106|0.180|
||(0.103)|(0.136)|(0.158)|(0.131)|(0.190)|
|Has formal training program|0.070|0.138|-0.158|0.151|-0.216|
||(0.078)|(0.096)|(0.143)|(0.095)|(0.170)|
|Firm exports|-0.007|0.144|-0.304|0.114|-0.350|
||(0.096)|(0.109)|(0.175)|(0.110)|(0.193)|
|Firm is direct importer|0.263++<br>(0.126)|0.457+++<br>(0.147)|0.076<br>(0.228)|0.400+++<br>(0.143)|-0.351<br>(0.250)|
|Has >10% foreign ownership|0.383+++|0.342+++|-0.068|0.398+++|-0.638|
||(0.112)|(0.112)|(0.992)|(0.112)|(0.909)|
|Has internet access (web and/or e-mail)|0.295+++|0.100|0.523+++|0.132|0.402++|
||(0.101)|(0.133)|(0.153)|(0.128)|(0.187)|
|Firm introduced new product/process|0.000|-0.000|0.000|-0.000|0.001|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.002)|
|Share of sales on credit|-0.001|-0.001|-0.004|-0.001|-0.004|
||(0.001)|(0.001)|(0.003)|(0.001)|(0.003)|
|Share of investment financed externally|-0.000|0.018|-0.000|0.021|-0.021|
||(0.001)|(0.021)|(0.000)|(0.021)|(0.021)|
|Percent of electricity from generators|0.012|-0.180|0.214+|-0.164|0.393++|
||(0.085)|(0.114)|(0.120)|(0.119)|(0.167)|
|Constant|3.301+++|2.753+++|6.129+++|3.38|7+++|
||(0.384)|(0.468)|(1.202)|(0.|383)|
|Observations|738|408|330|7|38|
|R-squared|0.834|0.844|0.821|0.|839|
|Adjusted R-squared|0.824|0.833|0.800|0.|827|
|Year fixed effects|Y|N|N||Y|
|Size fixed effects|Y|Y|Y||Y|
|Industryfixed effects|Y|Y|Y||Y|

Standard errors in parentheses + p<0.1 ++ p<.05 +++ p<.01
PISA international test. This scale has a mean of 500 and a standard deviation of 100 among the OECD countries in PISA.<sup>38</sup> As is obvious from the figure, the developing countries that ever participated in one of the tests perform dramatically lower than any country in the group of OECD countries. The variation in the quality of education that exists among OECD countries is already substantial, but the magnitude of the difference to developing countries in the average amount of learning that has taken place after a given number of years of schooling dwarfs any within-OECD difference.<sup>39</sup> Over the past ten years, empirical growth research demonstrates that consideration of the quality of education, measured by the cognitive skills learned, alters the assessment of the role of education in the process of economic development dramatically. When using the data from the international student achievement tests through 1991 to build a measure of educational quality, Hanushek and Kimko (2000) – first released as Hanushek and Kim (1995) – find a statistically and economically significant positive effect of the quality of education on economic growth in 1960-1990 that dwarfs the association between quantity of education and growth. Thus, even more than in the case of education and individual earnings, ignoring quality differences very significantly misses the true importance of education for economic growth. Their estimates suggest that one country-level standard deviation (equivalent to 47 test-score points in PISA 2000 mathematics, on whose scale Figure 4.1 is based) higher test performance would yield around one percentage point higher annual growth rates.

38 To facilitate comparisons, the within-country standard deviation of PISA test scores ranges between 80 and 108 in mathematics in the OECD countries; the U.S. value is 98.

39 The precise scaling on the transformed metric is of course subject to considerable noise, in particular for the early tests and for countries performing far below the international mean. The tests are usually not developed to provide reliable estimates of performance in the tails of the achievement distribution, which would be relevant for very poorly performing countries. However, the rough pattern of overall performance should not be severely affected by the re-scaling. For example, the average performance level of Peruvian students on PISA 2002 where no re-scaling is involved is 292 in math, 333 in science and 327 in reading.

26
Policy Research Working Paper 8167

# **Abstract**

Mozambique is among the African countries most exposed to weather-related hazards. Using detailed gridded precipitation data for individuals’ birth-year and birth-district, this study investigates the effects of extreme rainfall anomalies around the time of birth on long-run well-being. The results show that the socioeconomic outcomes of adults are influenced by weather shocks that occur early in life. Individuals exposed to floods while in utero or during the first year of life are less likely to participate in the labor market. Consequently, the households that they are heading exhibit lower consumption and are more prone to be poor. In disentangling the mechanisms at play, this paper presents suggestive evidence of variation in agricultural output, food security, and subsequent detrimental effects on human capital accumulation as important drivers behind the impacts. The study concludes that policy efforts aimed at accelerating poverty reduction in Mozambique will have to consider the inability of rural households to shield the well-being of children from the consequences of extreme weather shocks.

This paper is a product of the Poverty and Equity Global Practice 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 jbaez@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
|||_Table_<br>|_7 First stage results fr_<br>|_om LATE estimation of_<br>|_Table 8 and Table 9_<br>|||
|---|---|---|---|---|---|---|---|
|||(1)|(2)|(3)|(4)|(5)|(6)|
|||„Training, no grant“|„Training and grant“|„Training, no grant“|„Training and grant“|„Training, no grant“|„Training and grant“|
|Instrument 1|Treatment|0.4226***|0.3860***|0.4196***|0.3875***|0.4414***|0.4254***|
|||(0.000)|(0.000)|(0.000)|(0.000)|(0.002)|(0.000)|
|Instrument 2|Treatment x<br>(log)<br>distance<br>to|0.0517***|‐0.0450***|0.0523***|‐0.0442***|0.0716***|‐0.0620***|
||KCB branch|(0.002)|(0.001)|(0.003)|(0.001)|(0.000)|(0.000)|
||(log)|‐0.0032|0.0050|‐0.0050|0.0081|‐0.0093|0.0143|
||Distance to<br>KCB branch|(0.661)|(0.454)|(0.549)|(0.261)|(0.418)|(0.107)|
||Strata FE|Yes|Yes|Yes|Yes|Yes|Yes|
||Individual<br>controls|No|No|Yes|Yes|Yes|Yes|
||Geography<br>controls|No|No|No|No|Yes|Yes|
||Observations|1,500|1,500|1,474|1,474|1,474|1,474|

_Note: This table displays the first stage results for LATE estimates of Table 11. Columns (1) and (2) correspond to LATE estimates of column (3) in Table 11. Column (3) and (4) correspond to LATE estimates in column (4) in Table 11 and columns (5) and (6) to column (5) respectively. We report the effect of our two instrumental variables – original assignment to the treatment group and its interaction with distance to the closest KCB bank branch – on our two main regressors of interest. All regression control for gender‐ state fixed effects and for the level effect of distance to the closest KCB bank branch. Control variables of column (3)‐(6) include all baseline controls that were significant determinants of attrition and of selection between receiving “training and grant” vs “training, but no grant”. In particular, these are age, marital status, employment status, business ownership, food consumption, non‐food consumption, formal bank account, formal loans, informal loans, education level, literacy level, numeracy level, household size, number of children, number of rooms, number of buildings at baseline, and exposure to conflict events between endline and baseline. Column (5) and (6) also controls for geographic features since the estimation strategy relies on the distance to the closest KCB bank branch, which might correlate with other geographic characteristics. Geography controls include distance to the closest city, distance to the closest road, average land gradient and their interactions with selection to the original treatment group, and the interaction of conflict exposure with the original treatment group. P‐values are in parenthesis displayed below the estimated coefficients. * (**, ***) indicates statistical significance at the ten‐percent (five‐percent, one‐percent) level_ 30
Table 6. Implication 2: Price/Catch Differences by Scale

||Log Price|||Log Catch|||
|---|---|---|---|---|---|---|
|Variable|(1)|(2)|(3)|(4)|(5)|(6)|
|Abreojos|-1.174***|-1.188***|-1.187***|1.347***|1.354***|1.240***|
||(0.029)|(0.020)|(0.014)|(0.293)|(0.217)|(0.173)|
|Abreojos X large|0.168***|0.167***|0.149***|-0.778**|-0.705**|-0.561**|
|scale|(0.058)|(0.046)|(0.035)|(0.351)|(0.289)|(0.246)|
|Constant|3.178***|3.183***|3.185***|4.060***|4.044***|4.062***|
||(0.009)|(0.007)|(0.005)|(0.069)|(0.056)|(0.047)|
|Obs|4309|4309|4309|5014|5014|5014|
|Num. groups|625|1378|3504|702|1571|4080|
|Within-R2|0.795|0.845|0.920|0.041|0.053|0.088|
|Fixed effects|Sp-qtr|Sp-month|Sp-week|Sp-qtr|Sp-month|Sp-week|

Note: Sample includes observations at the weekly level from Abreojos, Pichilingue, and Sargento. The omitted category is Pichilingue/Sargento. All specifications use linear fixed effects at the species-quarter (sp-qtr), species-month (sp-month), or species-week (sp-week) level. Standard errors are clustered at the level of the fixed effect.

5
estimates using household survey data only. Czajka (2017) used tax and survey data from Côte d’Ivoire to implement a similar correction- he adjusted the survey data incomes in the formal private sector in each percentile by the ratio of the tax to survey ratio of mean incomes in each percentile. This adjustment increased the share of the top 1 percent by nearly 50 percent.

Van der Weide et al. (2018) examine inequality in urban Egypt and are concerned with the under capturing of top incomes in household surveys. They note that in most developing countries tax data, even in tabulated form, are not available. The authors also highlight that tax evasion is common in LMICs, as is a large informal sector, which limits the usefulness of tax data even when it does exist. To solve the lack of administrative data in Egypt, Van der Weide et al. (2018) use data on urban house prices, obtained from a private company, to improve estimates of top incomes. There are several steps undertaken by Van der Weide et al. (2018) to use house prices to replace top incomes, not all of which seem reliable. One obvious issue is the lack of house prices in the surveys to estimate a relationship between household income and house prices, which would then be applied to the admin data on house prices. The lack of house prices means the authors estimate the relationship between household incomes and housing rents, which are imputed for owner occupied housing. There is also no detail provided on how rents are imputed. Despite these issues, this is an example of a method that can be used when tax data are not available. The impact of the adjustments using house price data to impute incomes is very substantial- the Gini rises by around 30% after the replacement of top incomes.

# Reweighting and replacement

The third solution to missing top incomes that uses household and administrative data is a combination of the previous two methods. Blanchet et al. (2022) focus on reweighting to solve the under capturing of the top tail in household surveys, but they also use replacement. The authors highlight that in previous work using replacement of household survey top incomes with admin data (including Alvaredo (2011) for the US, Burkhauser et al. (2018) and Alvaredo and Londono Velez (2013), all discussed in the previous section), the point in the distribution where survey data information is replaced with administrative data is chosen arbitrarily (e.g. the top 1%). The novel contribution of the authors is to endogenously determine the optimal merging point between household surveys and tax data. Having done so, the authors use survey calibration methods to ensure that the numbers of individuals in each tax bracket in the surveys and the tax data match.

Whilst the calibration solves non-sampling error problems, Blanchet et al. (2022) note that the method still leaves small samples at the top of the distribution, which can, as discussed above, result in large 19
# **Box 2. Chronology of the ‘Coton Initiative’**

- From February 2002: cotton producer associations in the C-4 begin raising the issue

- of US subsidies in public forums

- June 2002: agriculture ministers recommend the establishment of a regional strategy

- to defend their cotton interests during the conference of West and Central African Ministers of Agriculture in Abidjan

- April 2003: the Sectoral Initiative on cotton is launched in a Special Session of the

- WTO Committee on Agriculture

- June 2003: President Compaoré of Burkina Faso intervenes at the WTO Trade

- Negotiation Committee

- August 2003: the “Draft Decision Concerning Specific Measures in Favour of Cotton

- with a View to Poverty Alleviation” is submitted to the Chairman of the General Council

- July 2003: President Tourné of Mali and president Compaoré publish an opinion

- piece in the NYT (first among three pieces on the topic in less than a year’s time)

- June 2004: President Touré testifies before the US House of Representatives

- International Relations Subcommittee on Africa

- July 2004: President Kérékou of Benin delivers a Memorandum on cotton to

- President Bush of the US on a visit to Senegal

- July 2004: the WTO director-general recognizes the importance of the cotton issue

- and pledges that it would be addressed “ambitiously, expeditiously and specifically, within the agriculture negotiations” (WTO 2004)

- November 2004: the WTO establishes a sub-committee on cotton in the agricultural

- negotiations

- January 2011: ACP countries pledge against the issue of the ‘US-Upland Cotton’

- dispute and re-state the demand of the C-4.

Sources: compilation by the author based on Eagleton-Pierce (forthcoming), Heinisch (2006), ICTSD (nov, 2010)

**Figure 4: Nominal rates of assistance to the cotton sector in C-4 and ESA-4 countries (1970-2005)**

<!-- Start of picture text --> 40<br>20<br>0<br>-20<br>-40<br>-60<br>-80<br>-100<br>Year<br>Benin Burkina Faso Chad Mali<br>Tanzania Uganda Zambia Zimbabwe<br>NRA<br>1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003<br><!-- End of picture text --> Source: Anderson and Valenzuela (2007). The data as been smoothed with a lowess factor of 0.4 for greater readability.

22
paper is different to the literature in that we examine the labor market effects from the perspective of an exporting country.

Others have studied various impacts of exports, but not so much on the labor market dynamics. Much of the literature uses the U.S. -Vietnam BTA on Vietnam as a case study. McCaig (2011), in a seminal paper, uses 2002 and 2004 VHLSSs to examine poverty implications of U.S. market access in Vietnamese provinces. McCaig and Pavcnik (2014) show that thanks to the BTA, workers reallocate from household business to formal enterprises. Fukase (2013) finds provinces more exposed to the BTA experienced a relatively larger wage growth for unskilled workers. <mark>Costa et al (2016), in the case of Brazil, find that rising commodity demand in China is associated with wage growth in exposed Brazilian regions and an increase of workers in formal sector jobs. Brambilla et al (2012) use an Argentina manufacturing firm data set to examine the effect of export destinations on skills.</mark> Our paper is structured as follows: section 2 discusses the background of the BTA. Section 3 presents our data sources. Section 4 discusses the identification strategy, model specifications and expected outcomes. Section 5 presents our baseline empirical results. Section 6 examines the three spillover mechanisms. Section 7 analyzes heterogeneous effects of the BTA across different sub-groups. Finally, section 8 concludes.

# **2. Background of Vietnam and the BTA**

# **2.1 U.S.-Vietnam Bilateral Trade Agreement in 2001**

The U.S.-Vietnam BTA was signed on 13 July 2000 and became effective on 10 December 2001. The United States granted Vietnam Normal Trade Relations (NTR) or Most Favored Nation (MFN) access to the U.S. market immediately upon entry into force of the BTA. What this means is that Vietnamese goods immediately benefit from large, sudden and across the board U.S. tariff 5