rafmacalaba/data-use-mentions-extended huggingface dataset:
of seasonality specific to each of the 24 coastal provinces. _εcpmy_ represents idiosyncratic standard errors clustered at the province-level.<sup>22</sup> The estimated coefficients of interest in Equations (1) and (2) are _α_ 1 and _β_ 1, which measure the differential changes in fishery earning (Equation (1)) and fishing intensity (Equation (2)) after _Formosa_ , in the central coastal provinces (i.e. the treatment zone) relative to the unaffected provinces.

# **4 Overall Impacts of** **_Formosa_**

In this section, we present the empirical results from estimating the impact of _Formosa_ on the affected fishery communities. We separately investigate the damages caused to fishery earnings and employment, as well as to fishing intensity in the region. Using LFS, we estimate a massive and significant reduction to fishery income. This evidence is corroborated with a clear decline in both fishing prevalence and intensity, as measured by VIIRS satellite data of night-time boat detection. Finally, we probe our findings with a series of validity and falsification tests, from which we observe no “hypothetical” effects of _Formosa_ on the unaffected provinces and/or industries, or before the accident actually took place.

## **4.1 Impact on labor outcomes in saltwater fisheries**

Table 2 presents the DiD estimates for our primary indicator of economic well-being – fishery monthly incomes. We consider two earning measures: (1) monthly income from the primary occupation (columns 1 and 3) and (2) total monthly income from all sources (columns 2 and 4). In all regressions, the treatment group consists of pre-event saltwater fishers located in the affected provinces of Ha Tinh, Quang Binh, Quang Tri, and Hue. As mentioned, we employ simultaneously two control groups in all subsequent regressions. In Panel A, the counterfactuals are pre-event fishers located in the unaffected coastal provinces. To avoid concerns related to potential spillover effects of _Formosa_ to nearby regions, we restrict
urban households or other regionally disparate populations facing different prices (e.g., Ravallion and Lokshin, 2006; Boom, Halsema and Molini, 2015). Deflators can be used to adjust for such price differences. However, they are sensitive to methodological choices (Ravallion and Bidani, 1994) and, usually, require large amounts of high-quality data on quantities and prices.

If income or consumption data for FDPs are missing from the data at hand but available in other data representative of the same population, one can also use cross-survey imputation techniques to estimate consumption using proxies of well-being. This method uses a baseline survey inclusive of consumption data to model consumption using a regression model inclusive of easily measurable household characteristics as independent variables. The estimated coefficients from the model are then used to predict consumption for FDPs using census data (usually FDPs registration data) that lack consumption but include the same household characteristics used with the baseline survey model. This method has been tested with refugees in Jordan and Chad (Dang and Verme, 2021, Beltramo et al., 2021) providing encouraging preliminary results. These works showed that accurate poverty estimations for refugees can be obtained with a relatively small number of proxies of well-being which are usually already available in the UNHCR proGres registration system. The quality of the imputed poverty estimates depends on the similarity of the population surveyed at baseline and the population used for imputations. Both populations might differ because of the time passed between baseline and imputation survey, and because of different characteristics if both surveys are not representative for the same populations. It is therefore essential for these types of exercises to use survey and census data from the same time period and with very similar predictors.

The treatment of aid is also particularly challenging. By definition, aid is included in consumption aggregates and, hence, will reduce estimated poverty among FDPs. This provides a fair assessment of the current situation and is particularly helpful in the context of comparison with host communities to ensure that they are not receiving less assistance while being more
POLICY RESEARCH WORKING PAPER 2307

# Summary findings

The World Bank has increasingly focused on firm-level surveys to build the data foundation needed for accurate policy analysis in developing and transition economies. Recanatini, Wallsten, and Xu take stock of some recent Bank surveys and discuss how to improve their results. Lessons on data issues and hypothesis testing:

* Use panel data, if possible.

* Have enough information about productivity to estimate a production function.

* Avoid the paradigm of "list the severity of the obstacle/problem on a scale of 1 to S." Instead, ask for data on specific dimensions of the problem that will shed light on alternative hypotheses and policy recommendations.

* Pick particular disaggregated industries and sample those industries in each survey.

- Identify the most important policy interventions of interest and consider how you will empirically identify specific changes by picking instruments useful for doing so.

Lessons on questionnaire design:

* Incorporate only one idea or dimension in each question. Do not ask, in one question, about the "quality, integrity, and efficiency" of services, for example.

* Consider the costs and benefits of numeric scales compared with adjectival scales. Scales in which each point is labeled may be more precise than numeric scales in which only the endpoints are labeled. But responses are very sensitive to the exact adjective chosen and it may be impossible to translate adjectives precisely across languages, making it impossible to compare responses across countries.

* Recognize that the share of respondents expressing opinions will be biased upward if the survey does not include a middle ("indifferent" or "don't know") category and downward if it does include the middle category.

* When asking degree-of-concern and how-great-anobstacle questions, consider first asking a filter question (such as "Do you believe this regulation is an obstacle or not?"). If the answer is yes, then ask how severe the obstacle is.

* Be aware of the effects of context. The act of asking questions can affect the answers given on subsequent
# **Figure 14.  Gross Operating Margin and Net Income over Time**

<!-- Start of picture text --> GOM Net Income to Assets<br>0.5 0.15<br>0.4<br>0.3 0.1<br>0.2<br>0.05<br>0.1<br>0<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008<br>2001 2003 2005 2007<br>Net Income to Assets (Mean)<br>GOM(Mean) GOM (Median)<br>Net Income to Assets (Median)<br><!-- End of picture text --> Source: EGX data, staff calculations.

# **7. Regression Analysis of ICA Data**

Here we study how our five savings-related measures are determined by firm characteristics and access to finance using basic regression analysis. Because all the control variables are only available for the actual years of the survey, we limit our regressions to these years (i.e. 2003, 2005 and 2007). To control for macroeconomic differences across years we include time dummy variables for 2005 and 2007.

An important caveat of this analysis is that we will not be able to establish causality between our savings-related measures of interest and the factors we are using as controls, such as size, industry, and perceptions of business environment. This caveat especially applies to our investigation of the relationship between financial product usage and investment and operating income. It is plausible that firms that have more difficulties accessing external finance will not be able to make investment into productive assets. But the reverse is also possible – firms that are not planning to make investment into productive assets might be the firms with growth potential and hence unable to obtain external finance. Thus, we treat all results as suggestive of correlations between these factors and not as causal factors.

# **7.1 Determinants
Consistent with the objectives of the FAS program, we ask two questions in this study. (1) What is the causal effect of the program on the number of students in program schools? (2) What is the causal effect of the program on inputs, namely the number of teachers, classrooms, blackboards, and toilets, and on student-teacher and student-classroom ratios in program schools? To answer these questions, using school-level baseline and follow-up data on school characteristics and outcomes obtained from program administrative records and telephone interviews of school administrators, we fit appropriate regression-discontinuity (RD) designs to the treatment assignment mechanism in order to obtain reliable nonexperimental estimates of program impacts.

In the last two entry phases preceding this study, phases 3 and 4, a standardized academic test, called the Short Listing Quality Assurance Test (SLQAT), was administered by PEF as the final step in the program entry screening process. In order for schools to enter the program in these two phases, they had to apply to the program, pass a qualitative physical inspection, and then pass the SLQAT. If the school achieves the stipulated minimum student pass rate (the cutoff) in the SLQAT, then the school becomes eligible for the program, and not otherwise. Furthermore, in practice, virtually all schools that become eligible elect to participate in the program.

At the time of the follow-up data collection in October 2008, as phase 4 was the last entry phase, schools that took the phase-4 SLQAT were either untreated or treated based on their SLQAT pass rate relative to the cutoff (i.e., the probability of treatment jumps from zero to one at the cutoff). This structure allows us to apply a sharp RD design to these data. On the other hand, schools that took the phase-3 SLQAT and failed found they had another opportunity to seek entry when phase 4 was announced—some phase-3 SLQAT ―failers‖ reapplied to phase 4, 4
ort elasticity<br>Average for each country of the im p ort dem and elasticities estim ated by K ee, Nicita, and O larreaga (2008)<br>for all its m anufacturing products de...ned as those with HS 6-digit codes higher than HS 290000.<br>Total p opulation (log)<br>Logarithm of the total numb er of inhabitants.<br>Corp orate incom e taxes (% )<br>Statutory corp orate incom e tax rate in p ercentage p oints.<br>Transitional economy status<br>Indicator variable for countries that transitioned from com munism to a capitalist regim e.<br>High-skilled lab or share (% )<br>Share of em ploym ent of skill levels 3 and 4 (high) according to the<br>International Standard Classi...cation of O ccupations in total em ploym ent<br>https://www.ilo.org/public/english/bureau/stat/isco/isco08/index.htm<br>Capital / lab or (log)<br>Real capital stock (in constant 2011 national prices in m il. 2011 US$) divided by total em ploym ent<br>Rule of law index<br>Rule of Law: Estim ate<br>Skill intensity<br>Ratio of skilled workers to unskilled workers at the 2-digit level of SIC revision 1987 concorded to the eight<br>sectors in the EO RA database based on industries’verbal descriptions.<br>Capital intensity<br>Ratio of capital to em ploym ent at the 2-digit level of SIC revision 1987 concorded to the eight sectors in the<br>EO RA database based on industries’verbal descriptions.<br>Natural resource intensity<br>Indicator variable for natural resource intensive sectors in the EO RA database (wood and pap er;<br>p etroleum , chem icals and non-m etallic m inerals; and
.6|11.0|11.3|
|Sargan|0.62|0.60|0.84|0.93|0.93|0.91|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Robust standard errors in parentheses * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 All specifications include firm’s fixed effects. Source: Authors’ calculations using data from ENTIC, INEGI 25
Second, we account for potential differences in survey years. While multi-purpose HH surveys are conducted every 3-5 years, some countries have dated surveys, including Iraq (2012), Madagascar (2012), or Senegal (2011). Since then, individual HHs might have improved on one or more of the seven adequacy dimensions. Yet, literature suggests that housing conditions are “sticky”. For instance, according to the WEO, only seven million people in Sub-Saharan Africa have gained access to clean cooking since 2015 (IEA 2019). In order to test the “stickiness” for all housing conditions, we compare the _AHI_ of the most recent survey year available in HCES to an adjusted _AHI_ where we project every country’s _AHI_ to 2019 making use of available macro data discussed in the previous paragraphs. The results of this comparison are presented in Annex 6 along with the individual correlations of the four adequacy dimensions for which current macro-level data was available. Since the results exhibit great consistency of the housing conditions of the survey year and the most recently available macro-level year, we present the results of the _AHI_ that uses the original survey year without any adjustments to current date.

# **4.Results: The** **_Adequate Housing Index_ across and within Countries**

The _AHI_ provides the first micro-founded, comparative assessment of housing conditions. In the following sections we present the results for 64 emerging economies. The _AHI_ can be disaggregated along several dimensions, allowing to draw a nuanced picture of housing adequacy within and across countries. The aggregate _AHI_ represents the adequate housing deficit for each of these emerging economies. Since we are applying a global framework in denoting housing adequacy, the respective adequacy deficits – also if disaggregated – are comparable in magnitude within and between countries.

## **_4.1 AHI – Housing Adequacy across Countries_**

Across the countries covered, there is a large variance in terms of adequate housing deficits (Figure 1). On average, about 68 percent of houses are
<!-- Start of picture text --> Stunting gap<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>1995 2000 2005 2010<br>Low income Lower middle income Upper middle income<br><!-- End of picture text --> _<mark>Source: Authors’ calculations using DHS and MICS. Results need to be interpreted with caution as the population coverage for some regions and years are below 50%.</mark>_

# _4.5 Population Coverage_

Next, we examine the population coverage in our analysis, i.e. the percentage of population in low- and middle-income countries that we cover through the nationally representative surveys in our analysis in each of the time ranges. We use data from WHO on the number of children below the age of 5 for all low- and middle-income countries in five-year intervals: 1995, 2000, 2005, and 2010. Thus, for each of survey year for a particular country, we use the closest population data available from WHO. Hence, we use population data from: 1995 for surveys conducted in years 1993-1997; 2000 for surveys in years 1998-2002; 2005 for surveys in years 2003-2007; and 2010 for surveys in years 2008-2012.

Table 3: Percentage of low and middle income country population covered in the malnutrition aggregations

|Years|Percentage of population|
|---|---|
|1993-1997|18%|

21
We complement the data from the BRMS with nationally representative household and firm surveys. Our modeling of both emigration and return choices accounts for selection into the sample of return migrants. The Bangladeshi Household Income and Expenditure Survey (HIES), which provides information on both non-migrants and current migrants, allows us to model the initial emigration decision. We make labor demand an explicit part of our model to account for the fact that emigration is restricted by the requirement to obtain a work visa. We document and utilize the fact that the volume of Bangladeshi emigration strongly depends on oil prices.<sup>4</sup> All major destination countries for Bangladeshi migrants are oil exporters, and we show that migrant numbers track fuel revenues very closely. Subject to the probability of obtaining a work visa and to not being dismissed in the destination country, migrant supply depends on individuals’ unobserved location preferences, which govern emigration and migration duration choices. We identify these preference parameters by fitting our structural model to data on both emigration rates and the time migrants spend abroad.

Our original data and analysis provide novel and direct evidence on the linkages between self-employment and migration. Comparing (within individual) activities before and after migration, we document a strong shift into self-employment upon return. Our survey directly asks how self-employment has been financed. Around half of the return migrants in our sample report that savings from abroad were their main source of financing. Migration-related expenses in Bangladesh, while typically lower than the capital needed for self-employment, are nevertheless substantial. We find that while low-skilled workers can often borrow to pay for migration expenses, they can rarely borrow to finance self-employment activities. One explanation is that migrant workers hold a formal job contract that makes loan repayment more secure and shirking more difficult compared to a loan repayment that depends on entrepreneurial profits. The credit market can be thought of as segmented, with a clear distinction between migration loans and entrepreneurship loans, with higher or even prohibitive interest rates for the loans supporting entrepreneurship. We document a causal,
Behrman, J.R., Rosenzweig, M.R., and Taubman, P. (1994). Endowments and the allocation of schooling in the family and in the marriage market: the twins experiment. _Journal of Political Economy_ , 102(6): 1131-1174.

- Behrman, J.R. and Rosenzweig, M.R. (2004). Returns to birthweight. _Review of Economics and Statistics_ , 86(2):586–601.

- Björklund, A. and Salvanes, K.G. (2011). Education and family background: Mechanisms and policies. In E. Hanushek, S. Machin and L. Wößmann (Eds.), _Handbook of the Economics of Education_ , volume 3, chapter 3, pp. 201–247. Elsevier.

- Carneiro, P. (2008).  Equality of opportunity and educational achievement in Portugal. _Portuguese Economic Journal_ , 7(1):17–41.

- Chirwa, E.D., Griffiths, P.L., Maleta, K., Norris, S.A. and Cameron, N. (2014). Multilevel modelling of longitudinal child growth data from the Birth-to-Twenty Cohort: a comparison of growth models. _Annals of Human Biology_ , 41(2):168–179.

- Combes, P.P., Duranton, G. and Gobillon, L. (2008). Spatial wage disparities: Sorting matters! _Journal of Urban Economics_ , 63(2):723–742.

- Corak, M. (2013). Income inequality, equality of opportunity, and intergenerational mobility. _Journal of Economic Perspectives_ , 27(3): 79-102.

Correia, S. (2017). reghdfe: Stata module for linear and instrumental-variable/GMM 26
pre- and post-intervention credit history of all borrowers in our sample that are covered by the Indian credit bureau.<sup>15</sup>

# **3.4.1 Administrative data**

In preparation for the experiment, the lender shared administrative data on customer demographics, loan characteristics, as well as data on prior borrowing and repayment from the lender’s data and credit bureau information at the time of loan application, for all customers in the sample frame.

We first obtained information on borrower and loan characteristics as well as the full repayment records of all borrowers in the sample from the lender. We aggregate these data to monthly frequency, corresponding to the monthly repayment cycles and construct a dummy variable indicating whether a borrower has made their loan payment by the required due date. The lender additionally shared data on the origination and end date of each loan, from which we construct an indicator variable for loans that are repaid in full and on time.

In a second step, we merged the loan and repayment data with the complete credit history of all borrowers that had a record in the Indian credit bureau. The credit bureau data was accessed after the experiment had concluded and includes the borrower’s credit score at the time of loan origination as well as a monthly record of all loans and loan payments for each borrower. Each monthly record includes the amount of the loan, the type of the loan, the date the loan was disbursed, and the dayspast-due of payments on each loan for the last 36 months. We use the credit bureau data to construct indicators of monthly loan repayment and delinquency across the credit market.

Table 1 reports summary statistics at baseline and tests of randomization balance for all contacted customers that are part of our estimation sample. Table 1, column (1), shows summary statistics for all contacted customers. The average customer in our sample is male, 33 years old, has an income of Rs 40,000 (US$ 528), and a loan
Other interventions have provided consulting instead of or in addition to training. The evidence is more positive for this type of interventions, especially for larger firms. For example, Bloom et al. (2013) provided consultancy services to large indian textile firms (with an average size of 273 employees) to improve management practices. Doing so increased productivity by 17 percent in the first year and many of these improvements lasted even after nine years (Bloom et al. 2018). Bruhn, Karlan & Schoar (2018) show a similar result of improving management practices as the result of consulting with 432 small and medium enterprises in Mexico (with an average size of 13.7 employees). There have been few consulting interventions with smaller enterprises. Karlan, Knight & Udry (2015) provided consulting services to microenterprises, which improved management practices, but did not lead to higher profits. Additional sets of interventions targeting improvements in management practices and skills are promising but yet to be rigorously evaluated in developing economies. These include outsourcing and insourcing of talent<sup>3</sup> , improvements in mid-level operational management, improving mechanisms of identifying talent, among others.

# _Mozambique_

Earlier evidence suggests that management practices are poor in Mozambique. Using 2014 data from manufacturing firms, the World Management Survey ranks Mozambique as the country with the lowest average score in the adoption of good management practices, lower than other African countries including Ethiopia, Tanzania, Ghana and Zambia (Lemos & Scur 2014: Figure 1).

In comparison with countries with high management score - United States, Sweden, Japan, and Germany – we see that more than 75 percent of Mozambique’s firms scored below the worst 10 percent firms from these countries (Figure 2). Less than 1 percent of Mozambique’s firms reach the score of the top quartile of firms in these developed countries.

Manufacturing firms in Mozambique score particularly low when it comes to improving production processes (operations) (Figure 3), and score at similar levels as
.

All but one of the controls were constructed from the employment surveys (ENE and ENOE). The income measure used is the log of the hourly labor remunerations; inequality is captured by the Gini coefficient of the per capita labor remunerations; the identification of informal

> 10 Even though the rate of car stolen that are reported is relatively high, a biased explained by unreported events is still possible. However, since our empirical strategy relies on the correlation between changes in crime and changes in the rate of ninis and the rate of unreported stolen cars have changed little over time, the expected bias is small.

14