rafmacalaba/data-use-mentions-extended huggingface dataset:
- 6) derive attrition-adjusted weights for all individuals, including split-off<sup>4</sup> households, then aggregate these weights to the level of the round 2 household;

- 7) trim these weights;

- 8) post-stratify the pooled weights to known population totals.

# _1) Base weights from round 1_

The panel weight calculations are based on the household weights from round 1 of the survey (W1). The round 1 weights incorporate a selection weight factor equal to the reciprocal of the probability of selection of each unit into the round 1 sample, as well as any non-response correction or post-stratification adjustments. These weights are nonzero for all round 1 respondents and form the base of the round 2 calculations.

# _2) Probability of EA selection into round 2_

In many panel surveys, it is often not possible, either for reasons of cost or logistics, to re-survey all responding round 1 households in round 2. It is therefore necessary to choose a sub-sample. The subsample can be chosen as a probability sample of all eligible round 1 households or enumeration areas, or households in certain regions can be disproportionately subsampled compared to their representation in round 1. If, for example, the capital is determined to be the area of highest variation or the most interest analytically, all responding households in round 1 can be selected into round 2 in the capital, and then only a sub-sample of enumeration areas in other regions.

The probability of selection for each household in stratum _h_ is: where _m_ is the number of households sub-sampled into round 2 in stratum _h_ and _M_ is the total number of households in the round 1 sample in stratum _h_ .<sup>5</sup> For the round 1 households in the hypothetical capital, which were retained for round 2 with certainty, p1 would equal one. (Note that if whole EAs are selected lh for round 2, then p1 = ~~,~~ where _lh_ is the number of EAs sub-sampled in stratum _h_ and _Lh_ is the
and quintupling the effective sample size. The results are robust to alternative implementation options, adding additional noise to the welfare measure, and the implementation of both a model-based Monte Carlo simulation, and a design-based simulation from three regions in Northeast Tanzania. When rescaling mean squared errors to equalize coverage rates, the household-level model remained more efficient than a Fay-Herriot model, especially in Sri Lanka. The strong performance of the householdlevel EBP model, in comparison with the area-level Fay-Herriot model, is consistent with simulation results reported in Hidiroglou and You (2016). That study concluded, on the basis of a model-based simulation study with a linear model and unit-level auxiliary data, that unit-level models achieve lower bias and higher coverage rates than area-level models.

The household-level EBP model moderately underestimates uncertainty, leading to sizeable drops in coverage rates compared with estimates from the area-level Fay-Herriot model, especially in Tanzania. This occurs because the model does not incorporate sample weights, and assumes that survey data are independent within area when conditioning the random effect on the sample residuals. In addition, both the household and area-level models do not account for uncertainty in the estimated variance components in the model. Nonetheless, the coverage rate of the household model in both cases is comparable to standard survey estimates that cluster on enumeration areas. Furthermore, the household model performs significantly better than the Fay-Herriot model in terms of accuracy and efficiency when additional noise is added to the welfare measure, especially in Tanzania.

The financial cost of this type of small area estimation procedure is generally low and falling rapidly. Much of the auxiliary data used for the small area estimation, such as estimates of built-up area, nighttime lights, and vegetation, are freely available. There are two notable exceptions. First, the calculation of spatial features at a national scale requires constructing a cloud-free mosaic of Sentinel imagery, considerable computing power, and the expertise to implement software to calculate spatial features. Second, the data on Tanzanian building footprints are proprietary. However, data on building footprints are increasingly being released
# **4 Regression Analysis and Results**

In this section, by using event study and differences in difference designs, we empirically investigate how Covid-19 affected export outcomes for a) all importers and exporters, b) firms that participate in GVCs compared to firms that do not participate in GVCs and c) firms that face NTMs in the pre-period compared to firms that did not face NTMs in the pre-period.

## **4.1 All Importers and Exporters**

We do the following event study to analyze the impact of the Covid-19 shock on firm-level imports and exports. We also investigate whether the effects differ depending on whether firms participate in GVCs or not, that is, run equation 1 separately for GVC versus non-GVC firms in section 4.2.

_Yit,pc_ : outcome variable for firm _i_ selling HS-8 product _p_ to country _c_ at year-month _t_ . We use the inverse hyperbolic sign transformation of the dependent variable so that zero observations are not dropped out. We control for firm, HS-8 product, and partner country fixed effects. Standard errors are clustered at the 8-digit product level. The period of our analysis is February 2019-June 2021 and the frequency is monthly. The time period in our analysis is limited by the availability of high-frequency firm-level data from Panjiva. The omitted month is March 2020, when the WHO characterized Covid-19 as a pandemic. We plot the _βt_ coefficients associated with the outcome variables for each month.

### **Fact 1: Exports were more resilient to the Covid-19 shock than imports (Figures 19, 20,**

### **21).**

This is both on the intensive margin (export volumes Figure 19) and extensive margin (export transactions Figure 20 as well as the number of products exported Figure 21). Even though we observe a decline in imports and exports in our results, the drop in imports for all three measures (Figures 19b, 20b, 21b) is more significant - both in terms of magnitude and duration -
bias the estimates of program effect.<sup>8</sup> We use a more rigorous methodology to ensure that the children in the “treatment” and “control” villages are matched along a wide range of dimensions, but conclusive impact evaluation depends on collecting panel data.

We begin by summarizing findings on the success of nutrition supplementation programs elsewhere, and information from studies monitoring the implementation of the ICDS program. We then (1) examine trends in child malnutrition in different socio-economic groups, (2) analyze whether the ICDS program placement is consistent with its goals of reducing child malnutrition, and (3) evaluate its impact on child anthropometric outcomes as best possible given the available data. The analysis is based on the National Family Health Surveys (NFHS) 1992-93 and 1998-99, which have information on child anthropometry as well as much information on the child, the mother, the household, and the village: including whether an ICDS program was in place in the village.

We find that a major reason why the ICDS program has had little apparent impact on aggregate child nutrition levels is that it is regressively distributed across states: states

> 7 Government of India, 2000. Estimates vary: according to Greiner and Pyle (2000:19) in 1998-99, the Central Government expenditure on ICDS was about $230 million.

> 8 Amongst the national level studies, see NIPCCD 1992, and Deolalikar 2004.

2
# **6 Summary of Findings and Policy Implications**

**Partial credit guarantee schemes can play an important role in MENA countries.** Improving financial infrastructure should remain the policy priority in MENA, but guarantee schemes can also make an important contribution to SME finance, especially in the period when governments are making an effort to remedy the deficiencies in credit information and creditor rights. There is some empirical evidence suggesting that guarantee schemes have contributed to SME lending in some MENA countries or, equivalently, that countries with larger schemes have larger shares of SME lending in total lending.<sup>15</sup> However, this result does not necessary imply that MENA guarantee schemes are well designed and costeffective.  Improvements in design could arguably allow these schemes to reach a larger number of constrained SMEs with the same volume of resources.

**This paper reported the results of a survey of guarantee schemes in the region and represents the first effort to assess their effectiveness** .  Ten guarantee schemes were covered by the survey, varying significantly in size and maturity.   The paper reviews the design of the schemes against a diversified benchmark group comprising developing and developed countries, and makes a preliminary assessment of outcomes.

**Regarding the main outcomes, outreach indicators portray a mixed picture** . The average size of guarantee schemes in MENA is in line with the international average (outstanding guarantees amount to about 0.3% of GDP).  However, there are wide differences across countries, and some schemes seem too small to make any significant impact on SME lending. More importantly, the number of guarantees issued per year (scaled by the population) looks very low by international comparison, while the average value of guarantees seems relatively large.  This suggests that guarantees are still concentrated in a relatively limited segment of firms, and do not yet reach a significant number of smaller firms.

**The survey responses do not allow for a meaningful assessment of additionality.** MENA schemes are not yet conducting systematic
# **3. Descriptive and Experimental Findings**

We divide this section into three parts. First, we document differences between perceived decile (where individuals believe their household stands on the income distribution), and objective decile (the household’s position on the income distribution based on income data from 2019-20). Next, we explore descriptive trends in support for reducing inequality in India, and explore the extent to which perceived and objective income deciles are correlated with support for reducing inequality. Finally, we test if information on the household’s objective decile has any causal effect on support for reducing inequality.

# **3.1. Perceptions of household position in the income distribution**

In Table 1, we list the average difference between perceived decile and objective decile in Column (4), the proportion of households whose perceived decile exceeded their objective decile in Column (5), and the proportion whose perceived decile was lower their objective decile in Column (7). In other words, Column (5) refers to people who perceived their households as richer than they were based on income, whereas Column (7) refers to people who perceived their households as poorer.

Strikingly, over 70% of the sample perceived themselves to be poorer than their incomes would suggest. This is much higher compared to what other studies have found. For instance, Hoy and Mager (2020) found that less than 10% of their online sample from India underestimated their position. A household survey in Buenos Aires found that 55% of the sample underestimated their position (Cruces, Perez-Truglia, and Tetaz 2013). Surveys from highincome countries have found that most people think they are around the middle of the national income distribution, with households below the median typically overestimating their position (Gimpelson and Treisman 2018).

8
# **1.  Introduction**

This paper examines the relationship between firm performance and growth and the business environment in the countries of the South Asia Region (SAR): Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka. The analysis uses data collected for the Enterprise Surveys conducted by the World Bank of formal sector firms in the region, along with surveys of informal sector and rural firms in several countries. The paper serves as an input to the World Bank South Asia Region’s flagship report, “More and Better Jobs in South Asia”.

Surveys of firms across countries can be used to provide a rich description of how managers perceive the costs to them of the business environment in which they operate. Interpreting these surveys requires a conceptual framework, and the analysis in this paper uses the approach set out in Carlin et al. (2006, 2010, 2012) and Carlin and Schaffer (2012). In this framework, the responses of firms to questions about the quality of the business environment can be interpreted as _shadow prices_ : estimations by managers of the cost imposed on the firm by inadequacies of an aspect of the business environment – regulation, physical infrastructure, availability of skilled labor, macroeconomic conditions, rule of law, etc. – for the growth of their firm. Expenditures on mitigation of these costs as reported by firms also fit naturally into this framework. When taken to the data, these predictions inform the policy maker whether, for example, it is the case that improvement in a particular element of the business environment is likely to benefit well- or poorly performing firms; whether public input bottlenecks are more pressing in urban or rural areas; and whether there are important differences across industries in the burden of weaknesses in the business environment. Since the focus of the flagship report is on jobs, we examine in particular how different elements of the business environment affect firms that are expanding employment.

Our modeling framework allows us to interpret clearly the different businessenvironment
)|**397.2**<br>(1630.5)|**132.4**<br>(685.4)|0.00|**472.6**<br>(1532.5)|**148.2**<br>(760.4)|0.00|
|**Number of enterprises**|**778**|**745**||**711**|**663**||
|% of enterprises in presumptive tax net|**44.1**|**29.9**|0.01|**61.0**|**16.8**|0.00|
|Average presumptive tax (birr, imputed)|**641.9**<br>(1825.3)|**224.7**<br>(724.7)|0.00|**1222.0**<br>(472.6)|**262.5**<br>(148.2)|0.00|
|% in sectors with tax-free threshold|68.6|74.6|0.26|**63.2**|**91.9**|0.00|
|**Number of enterprises**|**478**|**458**||**498**|**449**||

Source: Authors’ calculations using ESS 2018/2019 (period 1) and ESS2021/2022 (period 2). Notes: Numbers in parentheses indicate the standard deviation. Significant differences at p _<_ 0.05 are shown in bold. Firms liable for the presumptive tax are defined as those that are estimated to be in the presumptive tax net based
top three-ranked providers increasing by 0.2 to 0.4 stars. Firms that receive the quality ratings treatment are less likely to choose low-quality (3 star or lower) providers and more likely to choose the top-ranked (5 star) providers. Providing quality ratings services therefore does change firm preferences over providers.

Finally, we conducted a six-month follow-up survey, which tests whether providing information about business service providers, and quality ratings, is enough to get more firms to use this market for business services. We find use of the market for business services is low in the control group, with only 10 percent of firms using a service provider, and that the information and ratings treatments have no significant impact on use of these services. Our estimated treatment effects are 3
Global Trade Alert (GTA), and the data vary by country _j_ , product _k_ , and month _t_ . We use them at monthly frequency between January 2018 and December 2021, and we employ export measures in a way so that they are on the exports of a product to country _j_ . Note that the policy measures do not vary much across partner countries so that we make them importer-country-product-time-specific for both import and export (towards the importer) policies upfront. We call them _d_<sup>_m_</sup> _jk,t_<sup>forimport</sup> policies, a scalar for the _m_ th (of two: restraining and facilitating) import measures. Similarly, we refer to export measures importer _j_ is faced with in _k_ by _d_<sup>_x_</sup> _jk,t_<sup>,ascalarforthe</sup><sup>_m_th(oftwo:</sup> restraining and facilitating) export measures. Stacking these measures for all importers _J_ and products _K_ in year _t_ obtains the _JK ×_ 4 matrix ∆ _t_ in month _t_ . A typical row of ∆ _t_ may be referred to by ∆ _jk,t_ .

Second, we use bilateral export data from country _i_ to _j_ and product _k_ , _xijk,t_ for 180 products indexed by _k_ on a monthly basis between January 2018 and December 2021. As the monthly trade data are available for the European Union, the United States, and China, we treat those three as partner countries (exporters or importers) of _J_ = 247 countries and consider all possible bilateral relations between the mentioned three and all other countries in the exporting and importing directions. These data will be used to estimate parameters revealing the ad-valorem-equivalent trade costs associated with the policy variables in ∆ _t_ .

Third, we use cross-sectional bilateral export data for 180 + 1 products and _J_ ( _J −_ 1) country pairs from UNCTAD. These cross-sectional data are obtained from averaging annual export data for the period 2017-2019. Then,
, or to both parents as granted under each country’s maternity leave, paternity leave or parental leave policies.

Under the WBL methodology, maternity leave refers to leave available to the mother for the birth of a child, consisting of days before, during, and immediately after childbirth. Similarly, paternity leave refers to leave available only to the father to be taken immediately for the birth of a child. While maternity and paternity leave refer to leave explicitly granted to mothers and fathers respectively, parental leave is designed for childcare that follows or replaces maternity or paternity leave and can be shared or specifically allocated to mothers or fathers. Some countries reserve a specific portion of parental leave for either parent. For example, according to WBL data for 2021, in Germany, there are 300 parental leave days available for parents to share, in addition to which there are 60 days reserved for the specific use of each parent.<sup>4</sup> Standardized assumptions are used by WBL to ensure data comparability. For example, the WBL methodology assumes women are lawful citizens living in the main business city of the country in question. They are healthy adults without any criminal records and are assumed to be employed as

> 3 The full data set can be accessed at https://wbl.worldbank.org/en/wbl-data.

> 4 Parental Benefits and Leave Act; Sections 4, 15, and 16.
the name of women grew faster post-reform than those registered to males. While increases in the number of registered mortgages (figure 8) were less pronounced, levelling off at 0.18 per block, the number of mortgages on jointly owned plots displayed a rapid increase.

Beyond exploring shifts in registration or changes in the properties of registered plots due to policy reform, administrative data also allow assessing the extent to which concerns about the evaluation design mentioned earlier may be relevant. Table 2 tabulates such data for the 2000-2019 period together with number and area of buildings in 2016 at block level for all of Maseru (column 1), and separately for areas identified as treatment (col. 3) or control (col. 4) and the rest of the city (col. 2). With a size of 62,500 m<sup>2</sup> of which 44% was privately held and registered in 2019, the average block includes about 28 registered plots of some 1,000 m<sup>2</sup> each titled using a sporadic (11) or systematic (17) process.

Beyond confirming trends noted earlier such as increases in the number of plots registered both sporadically and systematically and registered transfers and mortgages, table 2 shows pre-program differences between treatment (MMC 1-3) and control (MMC 27) that are relevant in two respects. First, a higher number of plots registered sporadically before the program in treatment than in control areas (6.23 vs. 0.33 in 2000 and 9.23 vs. 1.22 in 2010) suggests limited comparability between the two. Also, the number of sporadic registrations pre-program in wards MMC 1-3 (6.23 in 2000 and 9.23 in 2010) exceeds that in the remainder of Maseru (2.98 in 2000 and 5.73 in 2010), pointing towards limited representativeness of these areas for the intervention.<sup>12</sup> While interpreted imagery is available only for 2016
ii) measure stakeholders‟ awareness of the schools‟ institutional framework in general, and participatory accountability institutions in particular; and (iii) gage actual parental involvement with the school. In what follows, we present descriptive statistics drawn from the survey to measure -or proxy- school inputs, school and teacher characteristics and effort, “top-down” accountability and transparency, participatory “bottom up” accountability and parental involvement, and parents‟ and community characteristics. The data reveal factors that are significantly correlated with school performance, as measured by student repetition rates, student drop-out rates, and students‟ performance on Albanian language and math exams.

13. Since the new school stakeholder data are drawn from a baseline survey, the analysis discussed in Part II is presented as a series of correlations rather than causal relationships. The results have to be interpreted cautiously as they are drawn from retrospective data collected with a cross-section survey, and are therefore vulnerable to biases due to omissions and endogeneity. Indeed the Albania School Stakeholder Survey was designed first and foremost to collect a baseline so that the impact of interventions the government is still considering can be measured. Nevertheless, the richness of the new data is unique, and allows researchers to investigate the relationship between school outcomes and: (1) school physical and human resources; (2) “topdown” accountability and transparency; (3) “bottom up” participatory accountability institutions and parental involvement; and (4) parents‟ and community characteristics. By employing statistical techniques at the level of RED and district, the problems of endogeneity can be attenuated. And while there is potential endogeneity of parental involvement, this is a greater risk where parents have a choice in determining in which school to enroll their children. In Albania parents appear to have this choice only in large urban areas.<sup>5</sup> In order to limit the problem of possible multicollinearity among variables, principal factor analysis is used on sets of variables that are

> 5 When asked why they chose that
Yes|Yes|Yes|
|**2.**<br>**Limit the age of child to 0–12 years**<sup>b</sup>||||
|_Ref: A. Youngest child is 6–12 years_||||
|B. Youngest child is 0–5 years and childcare available|0.423***|1.907***|0.954***|
||(0.056)|(0.349)|(0.175)|
|C. Youngest child is 0-5 years and no childcare available|0.446***|2.169***|1.085***|
||(0.054)|(0.326)|(0.163)|
|Observations|758|758|758|
|Individual controls|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|
|Regional controls|Yes|Yes|Yes|
|**3.**<br>**Limit the age of child to 0–8 years**<sup>**c**</sup>||||
|_Ref: A. Youngest child is 6–8 years_||||
|B. Youngest child is 0–5 years and childcare available|0.249***|1.294***|0.647***|
||(0.063)|(0.412)|(0.206)|
|C. Youngest child is 0–5 years and no childcare available|0.272***|1.584***|0.792***|
||(0.064)|(0.411)|(0.205)|
|Observations|599|599|599|
|Individual