rafmacalaba/data-use-mentions-extended huggingface dataset:
for participants with higher levels of income, loss aversion is likely to be an issue. Therefore, we limited the levels for monthly income to current income or increases from current income. In future work, it would be worth exploring what levels of nonwage benefits would induce workers to take even small wage cuts.

> xv Individual characteristics cannot be included directly in the conditional logit model, because these characteristics do not vary across alternatives; they can, however, be included when interacted with regressors that vary across alternatives.

xvi We attempted to include all men from several small employment categories, as discussed in Section 2; however, since these categories were so small, they do not affect the overall distribution of characteristics among men, or create substantial differences between the weighted and unweighted results.

xvii The level of female labor force participation implied by our roster is lower than the national average for Bangladesh (33 percent, according to the 2013 Labour Force Survey). However, our sample was focused on two urban and peri-urban cities, and is not meant to be representative of Bangladesh as a whole. xviii We asked workers who had multiple jobs to focus on the job in which they spent the most hours. xix As noted above, the weighting makes little difference when examining the distribution of employment types by gender, since all women received a sampling weight of one, and nearly all men received a sampling weight of 2.52.

xx Individuals either provided us with an estimate of their monthly income, or a range into which their income fell. For those who provided a range, we used the midpoint of the range. We were unable to collect information about income (an estimate or a range) for 36 individuals. Another 21 reported zero income. xxi As a comparison, the World Bank estimates that annual per-capita income in Bangladesh was about $1,359 USD in 2016, or about $113 USD per month.

xxii Among the 65 respondents in our sample who indicated that they were household members working in
existing databases. Figure A.1 shows a comparison between the number of firms captured by the World Bank’s Global BOS database and the OECD database. We found that in almost all cases, the BOS database captures significantly more firms with state participation and in some cases, as many as 30 times more than comparable exercises, as in the case of Poland. Figure A.2 shows a similar finding when comparing the BOS database to the IADB database of SOEs.

_Figure A.1 Comparison of Country-level SOE figures. World Bank’s Global BOS database vs. OECD database_ <!-- Start of picture text --> Comparison OECD Total WB SOE<br><!-- End of picture text --> <!-- Start of picture text --> Comparison OECD Total WB SOE<br>4,500 4051<br>4,000<br>3,500<br>3,000<br>2,500<br>2,000<br>1,500 1226<br>841<br>1,000 593<br>370 360 406 431<br>500 32 28 66 209 71 59 25 184 39 37 39 126<br>Costa Rica Estonia Hungary Latvia Argentina Chile Turkey Slovenia Colombia Poland<br>Number of firms<br><!-- End of picture text --> <!-- Start of picture text --> Costa Rica Estonia Hungary Latvia Argentina Chile Turkey Slovenia Colombia Poland<br><!-- End of picture text --> Source: Authors’ compilation based on Global BOS database and OECD (2017) data 37
firms.

To summarize, particularly important BE elements for relatively small firms are corruption control, both basic and modern infrastructure, the human capital environment, and land access. Access to finance is also important for relatively smaller firms in terms of firm growth. For relatively large firms, physical safety appears to be relatively important, and access to finance is important for improving productivity.

# **7. Conclusions**

This paper expands earlier literature on the relationship between firm performance and the business environment using multiple popular data sources and measures, including both the de jure _Doing Business_ indicators at the country level, and the de facto _Enterprise Survey_ 23
existence<sup>4</sup> . For this paper, we obtained raw survey data from the Doing Business team, for years 2005 through 2010, covering more than 180 countries.

Based on this raw data, we constructed two variables indicating the existence of credit reporting institutions in 2010. The first variable is a dummy that is equal to one if the country has a credit bureau and zero otherwise. The second variable is a dummy that is equal to one if the country has a credit registry and zero otherwise<sup>5</sup> . The Doing Business team defines a credit registry as a publically owned entity that collects information on borrowers and shares it with regulated financial institutions. A credit bureau is a privately owned entity which collects information on borrowers in the financial system and facilitates the exchange of credit among lenders. For the purpose of this paper and the hypotheses stated in section 2, the key distinction is that the credit registry makes reporting of loan information mandatory for banks (in many cases for loans above a certain threshold only), whereas participation in a credit bureau is voluntary. From a broader perspective, credit registries contain primarily information on loans made by regulated lenders and capture a loan at the time of origination. This information is often used for regulatory and supervisory purposes. Credit bureaus tend to contain more repayment information that allows for the tracking of loans and credit risks over time and they also provide additional data processing services, such as calculating credit scores. Panel A of table 1 shows that 54.5 percent of the countries in our sample have a credit bureau and 45.1 percent have a credit registry. Figure 1 further shows that about 74 percent of countries have either institution (26 percent have no credit reporting institution). Low and middle income countries have a relatively higher presence of credit registries while credit bureaus are more common in high income countries (Figure 1).

For countries with a credit bureau, we constructed an indicator measuring the coverage of the information
The Asset volatility (book) variable reflects investment variability over time. We exclude firms with fewer than five asset growth observations, and trim this and other firm-level variables at the 5<sup>th</sup> and 95<sup>th</sup> percentiles. This yields 27,093 asset volatility observations with a mean of 0.344, as seen in Table 1.

An alternative volatility variable, Asset volatility (stock), is calculated as the annual average of market-based annualized asset value volatility measures based on Merton’s model following Anginer et al. (2014).<sup>5</sup> We use data from Datastream for the market value of equity and to estimate equity volatility. To ensure sufficient variability, we exclude firm-year observations with less than 90 days of nonzero stock returns. We further assume that the maturity of a firm’s debt is one year, noting that the results are insensitive to this particular assumption.<sup>6</sup> Finally, the dividend yield is taken from Worldscope, while the risk-free return is proxied by the yield on one-year US Treasury bills. Altogether, we have 24,615 Asset volatility (stock) observations with a mean of 0.024, as reflected in Table 1.

Looking at the association between firm volatility and the maturity structure of debt can be problematic since the direction of causality can go either way, with less volatile firms being able to attract more long-term debt. We deal with this identification problem by using the approach of Rajan and Zingales (1998) to construct a measure of firms’ “desired” debt maturity structure. This index, Maturity, is given by the use of long-term finance for sectors in the US on the assumption that firms in the US are not as constrained in their choice of short-term vs. long-term external finance. Specifically, Maturity is computed as the sectoral

> 5 See Appendix A1 of Anginer et al. (2014) for a description of Merton’s
individuals over 14 semesters and examine their transitions into and out of the formal labor market – and at the family level – to examine family-level movements in and out of the BF program. Figure A.1 in the Annex provides an overview of the data merging process.

**Exiting Bolsa Familia** Exiting BF is defined by using the program payment records ( _folha de pagamento_ ). We define exiting BF as not being in the program for at least four consecutive months. This threshold has been chosen since beneficiaries who do not comply with program rules might get a grace period of three months in which the benefit is suspended, though people are not expelled from the program immediately. Additionally, we consider beneficiary families that enter the permanence rule as if they have left. The reasoning is that we want to identify the impact of employment changes at the municipality level on exit probabilities. The permanence

9
for countries with a fixed exchange rate regime and financial openness index. _Ѳ i_ captures country-fixed effects and _φ t_ refers to time fixed effects.

Third, the existence of cointegration between the residuals from the panel regression in model (5) and the gross public debt-to-GDP ratio and trade openness is tested by employing Pedroni’s (1999) cointegration test (Table A.4.3). The results indicate that the residuals are cointegrated with the two variables. Fourth, following Pedroni (2000, 2001), a grouped mean fully modified ordinary least squares (FMOLS) regression model and a grouped mean dynamic OLS (DOLS) regression model are estimated to correct for endogeneity bias and serial correlation. The dependent variable is the estimated residual from the panel regression in model (5). The independent variables are trade openness (measured by the import penetration ratio) and the gross public debt-to-GDP ratio.

> 5 This test follows Im, Pesaran, and Shin (2003); Maddala and Wu (1999); and Choi (2001). Although the time-varying parameters are constructed under the assumption of a random walk, most results of panel unit root tests reject the null hypothesis of nonstationarity.

36
# 3.3. Welfare data

The main welfare measures examined are constructed from household consumption surveys. To provide a comparable measure of welfare across countries, the aggregate that is used for national poverty measures and that forms the basis for global poverty measurement is used. Consumption aggregates are converted into 2011 purchasing power parities (PPPs) for this analysis.<sup>11</sup> We selected nine countries representing geographic variation in SSA for the analysis of the relationship between droughts and monetary welfare (consumption). They include Ethiopia in Eastern Africa; Mauritania, Niger, and Nigeria in Western Africa; and Lesotho, Malawi, Mozambique, Zambia, and Zimbabwe in Southern Africa. For five of these countries data are available for multiple years. The countries, years, and outcome measures used in analysis are listed in Table 3.3. To allow for ease of matching rainfall patterns to consumption in the first analysis, the selected countries are all from parts of the continent where there is a clear main season of production. Selection was also designed to include countries that had multiple survey rounds that could be pooled for analysis and that would provide sufficient numbers for analysis across livelihood zones.

The empirical analysis is carried out by livelihood zone, which entails pooling these countries and estimating like areas of production within them. The zones used are those defined in Dixon et al. (2019). They allow for the required level of analysis. The authors provide gridded data for the livelihood zones estimated for 2015, which is within our analysis period. We further aggregated the zones estimated for 2015 into five zones. Table 3.4 indicates how the countries included match to the livelihood zones used in the analysis. Table 3.5 reports descriptive statistics for the variables used in the analysis by country and survey year.

**Table 3.3. Household surveys and welfare measures included in the analysis**

|**Country (years)**|**Welfare measure**|
|---
# **<u>Table 1.7. Medium-Term and Long-Term Income Mobility, RLMS 1994-2015 (percentage)</u>**

||<br>**Unconditional**|**Conditional**|
|---|---|---|
|**Panel A: 1994-2004**|||
|Upward mobility|30.7|42.8|
|Immobility|47.3|47.3|
|Downwardmobility|22.1|36.7|
|**Panel B: 2004-2015**|||
|Upward mobility|28.7|42.0|
|Immobility|46.4|46.4|
|Downwardmobility|24.9|37.8|
|**Panel C: 1994-2015**|||
|Upward mobility|37.2|50.5|
|Immobility|40.5|40.5|
|Downwardmobility|22.3|39.2|

**Note** : Estimation results are obtained based on total household income per capita. The terciles are defined using the cross-sectional sample for each year. All numbers are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year. Real incomes are adjusted for regional differences in the cost-of-living by using the regional value of fixed basket of goods and services. Estimation sample size is 4939 panel individuals from the 5th and 13th rounds of the RLMS, 3715panel individuals from the 13th and 24th rounds of the RLMS and 2476 panel individuals from the 5th and 24th round of the RLMS.

55
0.4|
|4|0.4|0.9|-0.5|
|5|0.3|0.9|-0.7|
|6|0.3|1.0|-0.9|
|7|0.2|1.0|-1.1|
|8|0.1|1.0|-1.3|
|9|0.0|1.0|-1.7|
|10|-0.1|1.1|-2.0|

Note: the table shows the annualize change in average annual consumption at the decile level between different waves of imputed GHS data. Annualized changes are calculated using the compound annual growth rate. Household consumption data for different GHS waves is imputed using the main specification presented in this paper (also referred to as “Original Model”).

37
_CHAPTER 8. CHALLENGES AND POTENTIAL RESEARCH TOPICS_ 66 estimating small area means, Molina and Strzalkowska-Kominiak (2020) proposed using the same idea of “borrowing strength” behind SAE, to estimate the design MSE of small area estimators. Reliable design MSE estimators for general non-linear indicators are still a challenge.

7. _Correction for non ignorable non-response:_ As already discussed, under sample selection bias (or non-ignorable informative sampling), the selection of units depends on the values of the target variable. In that case, the model for the sample units differs from the model assumed for the population units and hence the sample model should be fitted to the sample data. Pfeffermann and Sverchkov (2007) obtained adjusted EBLUPs of small area means under non-ignorable informative sampling, and Cho et al. (2024) have extended the procedure to general non-linear parameters and more general models for the sampling weights. Similarly, in the case of non-ignorable non-response, the indicators of responding depend on the values of the target variable, causing the model followed by the respondents (which are a subset of the sample units) to differ from the model for the sample units. Sverchkov and Pfeffermann (2018) studied SAE of small area means, accounting for non-ignorable informative sampling and non-ignorable non-response. Extension to non-linear indicators is currently under study.

8. _Estimation of multidimensional poverty indicators:_ Multidimensional poverty indicators assess poverty from a wider perspective, but there are multidimensional indicators of different natures. Some of them are based on constructing a single index by applying principal components or other dimension reduction procedure to a set of variables measuring different deprivations. Since the resulting index is quantitative, unit-level SAE procedures such as Census EB might be applied similarly to estimate linear or non-linear functions of that index in the area units. Other multidimensional poverty indicators
impact of changes in the firm’s operating fixed costs. The multisector model that we study combines elements of the economic growth and heterogeneous firms literatures. This framework considers the standard role of power infrastructure as an amplifier of the productivity of private inputs, but also its capacity to influence the fixed costs faced by firms.

Using Brazilian data on electricity access and generation capacity, sectoral GVA shares, investment expenditure shares, and differences in firm size across industries, our main result confirms that electricity infrastructure and changes in relative firm size are important driving mechanisms of the structural transformation. Our econometric estimates suggest that advances in electricity access mainly favor manufacturing in terms of productivity gains and entry-barriers reductions, followed by services and agriculture. Our model simulations, in turn, imply that the advances in grid access and capacity accelerate the process of the structural transformation (i.e., increase the share of services) through effects channeled by cross-sector differences in electricity intensity and entry costs, but help increase the share of the manufacturing sector through the increase in the required investment for capital formation.

Quantitatively, the model predicts that electricity infrastructure formation explains 17 percent of the observed structural transformation process in Brazil from 1970 to 2006. Fixed costs variations attributed to changes in electricity capital seem to have a small effect. However, changes in the total relative sectoral operating fixed-costs account for 8.8 percent of this process. The extreme case is manufacturing, where electricity formation and fixed-costs variations account together for 42.2 percent of the evolution of the GVA share. The model attributes the explanation of the remaining reallocations of resources across industries to the evolution of the relative sectoral TFP. In terms of GDP per capita, the electrification process is responsible for 32.3 percent of the observed increase, and the positive growth of the sector-specific fixed costs for a loss of 15.6 percent.

We also perform policy exercises to better understand the effectiveness of different electrification strategies. The first two relate to the Brazilian experience with partisan incentives 56
” (Eichengreen and Hausmann, 1999; Hausmann and Panizza 2011; Hale et al., 2016). During crises, currency risks tend to increase, as local currencies tend to depreciate. Borrowers might want to reduce

> 2 Other papers document a shortening of the maturity structure in firms’ balance sheets, which consider short-term debt (under one year in maturity) and long-term debt (over one year) (Gonzalez, 2015; Demirgüç-Kunt et al., 2020). However, these analyses cannot distinguish between firms not issuing during crises (hence, shortening the maturity structure of the existing debt) or the new debt issued during crises being shorter term. In contrast, issuance data allow us to study the behavior of the maturity of new debt.

7
# <u>Box 1:</u>

<u>East Asia and Pacific (18): China; Fiji; Hong Kong SAR, China; Indonesia; Macao SAR, China; the</u> Federated States of Micronesia; Myanmar; Palau; Papua New Guinea; Philippines; Samoa; Solomon Islands; Taiwan, China; Thailand; Timor‐Leste; Tonga; Vanuatu; Vietnam.

<u>Latin America (20): Antigua and Barbuda, Belize, Colombia, Costa Rica, Cuba, Dominica,</u> Dominican Republic, Grenada, Guatemala, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Trinidad and Tobago, República Bolivariana de Venezuela.

<u>South Asia (4): Bangladesh, India, Pakistan, Sri Lanka.</u> <u>Sub‐Saharan Africa (4): Comoros Islands, Madagascar, Mozambique, Seychelles.</u> Our estimates indicate mangroves in developing regions with previous exposure to tropical cyclones covered an area of 79,756 sq. km during 1997‐2000. (See Annex 1 for mangrove area by country.) The largest area of mangroves was in East Asia & Pacific (57 percent), followed by the Latin America & Caribbean (26 percent), South Asia (11 percent), and Sub‐Saharan Africa (6 percent) (Figure 1). The top 10 of the 46 countries account for a total of 80% of mangrove area (Table 1). Indonesia has by far the single largest mangrove area (33% of the total); the remaining top‐10 countries account for less than 10% each.

We compared our country‐level estimates (which are aggregated from 30m to 90m) with the country‐level mangrove estimates of the Mangrove Atlas (Spalding et al. 2010). All of our estimates were within the 95 percent range.

6
the following question: ―Do you feel it is wrong or not wrong if a taxpayer does not report all of his or her income in order to pay less income taxes? (1= not wrong, 2= a bit wrong, 3= wrong, 4=seriously wrong)‖.

In general, these three data sets have the advantage that they are designed as a wideranging survey, which reduces the probability of being suspicious and of creating framing effects by other tax context questions.

8
**Costa, Dora L.** “From mill town to board room: The rise of women's paid labor.” _Journal of Economic Perspectives_ 14, no. 4 (2000): 101-122.

**Dahbura, Juan Nelson Martínez.** “The Short-Term Impact of Crime on School Enrollment and School Choice: Evidence from El Salvador.” _Economía_ 18.2 (2018): 121-145.

**Dávila, Alberto, and José A. Pagán.** “Gender Pay and Occupational-Attainment Gaps in Costa Rica and El Salvador: A Relative Comparison of the Late 1980s.” _Review of Development Economics_ 3, no. 2 (1999): 215-230.

**Dollar, David, and Aart Kraay.** “Trade, growth, and poverty.” _Economic Journal_ 114, no. 493 (2004): F22F49.

**Easterly, William, and Ross Levine.** "Tropics, germs, and crops: How endowments influence economic development." _Journal of Monetary Economics_ 50.1 (2003): 3-39.

**Eaton, Jonathan, and Samuel Kortum.** “Technology, geography, and trade.” _Econometrica_ 70, no. 5 (2002): 1741-1779.

**Feenstra, Robert C., Robert Inklaar, and Marcel P. Timmer.** “The next generation of the Penn World Table.” _American Economic Review_ , no. 105(10) (2015): 3150-3182, available for download at <u>www.ggdc.net/pwt.</u>

**Ferreira, Gustavo FC, and R. Wes Harrison.** “From coffee beans to microchips: Export diversification and economic growth in Costa Rica.” _Journal of Agricultural and Applied Economics_ 44, no. 4 (2012): 517-531.

**Funkhouser, Edward.** “Mobility and labor market segmentation: The urban labor market in El Salvador.” _Economic Development and Cultural Change_ 46,
real effective exchange rate, adjusted so that the average for 1976-85 equals Dollar’s (1992) index of overvaluation<sup>19</sup> (Source: Easterly 2001). The number of years in which a country underwent a systemic banking crisis as a fraction of the number of years in the corresponding period is computed based on data from Caprio and Klingebiel (1999) and Kaminsky and Reinhart (1998).

The terms of trade shock in the matrix of growth determinants _X_ ’ in model (4) is computed as the log difference of the terms of trade (Source: World Bank 2002). The periodspecific shifts _μ_ in model (4) are time dummy variables.

# _3.2 The forecasting model_

The forecasting model used in this study is derived by differencing model (4): where _s=10_ since we work with 10 year periods, matrix _G_ contains the initial output gap, matrix _X_ , and the time specific shift; and φ = ( β , λ ,' μ ) .<sup>20</sup> In general, the forecasting model (5) can be used to forecasts growth for any number of periods of length _s_ , where _s_ could be any positive integer number. However, using this model to produce growth forecasts for the next 1 to 3 years is not recommended since many of the factors affecting the short term outlook are not included in the model.

Coefficients ( α ,ˆ φ<sup>ˆ</sup> ) were estimated by Loayza _et al._ (2005) using the GMM systems estimator on a 5-year average panel dataset<sup>21</sup> (see last column, Appendix Table 1 and the fourth column, Appendix Table 2). The GMM systems estimator, developed by Arellano and

> 19 The Dollar’s index of overvaluation gives an idea to what extent the real exchange rate is distorted away from its free-trade level by the trade regime.

> 20 As a result of the