rafmacalaba/data-use-mentions-extended huggingface dataset:
Financial Regulation and Development purpose, particularly the data collection work on the bank balance sheets of Warren E. Weber at the Federal Reserve Bank of Minneapolis (Rolnick and Weber 1983; Weber 2003, 2006), which were complemented with additional data on tax introduction years and policy characteristics. In this case, while we can follow the balance sheet composition and profitability, there is no information on branching. However, this is only partially a problem, because branch-opening may only be one instrument through which financial development occurs, and as long as we register an increase in deposits, which is observed, this points to the mechanism presented in this model.

We contribute to three established bodies of literature, building a simplified model of banking (Freixas and Rochet 1997) with reduced-form maturity mismatch (Diamond and Dybvig 1983) and bank liquidity choice (Allen and Gale 2000). First, this paper contributes to the literature on financial inclusion, showing that policies that promote safe assets can drive branch expansion. From a theoretical perspective, Townsend and Zhorin (2014) present an innovative framework to evaluate the trade-offs that banks face in branch installation in emerging market countries, with a special focus on geography and industrial organization. A series of applied papers have highlighted the political and economic reasons for branch expansion (Assuncao, Mityakov, and Townsend 2012; Keniston et al. 2012) and its effects on poverty, firm growth, and competition (Beck, Demirguc-Kunt, and Maksimovic 2004, 2005; Burgess and Pande 2005; Burgess, Pande, and Wong 2005). Second, we join the literature on the incentive perspective of banking regulation. Besley and Ghatak (2013), Dewatripont and Tirole (2012), and Fahri and Tirole (2012) all discuss financial regulation and/or special bank taxation as a device to counterbalance implicit bailouts, to achieve optimal managerial discipline, and to balance large-scale maturity mismatch. These conclusions are in line with the work of Kashyap, Rajan, and Stein (2008), who propose the replacement
survival that we do not observe. Additionally, since our analysis of reform episodes relies solely on time series variation, it results in weak identification. However, the findings can still provide a correlation between reform episodes and firm survival.

# <u>5.2.1 Firm dynamics and growth</u>

We begin by summarizing firm and job dynamics, relative to annual GDP growth. Figure 6, Panels A and B, shows the absolute number and one-year growth rates of entering and exiting firms, by year, relative to one-year GDP growth. For most of the period, we find that the formal private sector both gains and loses about 200 firms per year, which corresponds to average entry and exit rates of around 10%.

Next, in Figure 7, Panels A and B, we present changes in new employment at new and exited firms (Panel A) and jobs created and lost at incumbent firms (Panel B), relative to annual GDP growth. Over time, net employment is always positive, that is more jobs are created at new firms than lost at exiting firms. However, the absolute number of new jobs added to the private sector by new firms is remarkably small – on average, fewer than 3,000 formal, private sector jobs are added on an annual basis. In comparison, the net number of new jobs generated at incumbent firms is negative for all years from 1979 through 1995. The largest net job losses occurred during the 1980 macroeconomic downturn – over 10,000 jobs were shed in 1979 – although net jobs at incumbent firms remained negative even during periods of GDP growth.

# <u>5.2.2 Empirical model</u>

Our comprehensive panel data allows us to study the firm-level determinants of firm survival. Unlike previous studies, with the exception of Shiferaw (2007, 2009) and Bedi and Shiferaw (2009), we rely on census-based panel data, which allows for better tracking of firm entry and exit. However,
, “Economic Prospects and Policy Challenges for the GCC countries”, GCC Surveillance Note.

<mark>Islam, A and D Lederman (2020) Data Transparency and Long-Run Growth.</mark> _<mark>Policy Research Working Paper 9493</mark>_ <mark>. World Bank, Washington, DC.</mark> <mark>Khemani, S. (2020). Political Economy of Reform.</mark> _<mark>Oxford Research Encyclopedia of Economics and Finance.</mark>_ <mark>Li, C (2009), “China’s new think tanks: Where officials, entrepreneurs, and scholars interact”,</mark> _<mark>China Leadership Monitor</mark>_ <mark>29: 1-21.</mark> Mansuri, G and V Rao (2012), _Localizing Development: Does Participation Work?_ , Washington, DC: World Bank.

<mark>Mikolov, T, K Chen, G Corrado and J Dean (2013), “Efficient estimation of word representations in vector space”, arXiv preprint arXiv:1301.3781.</mark> 18
are in total manufacturing. There is an uptick in apparel manufacturing’s capital intensity in Vietnam between 2015 and 2017 but the overall level remains at less than one-third of total manufacturing’s capital intensity. In the US economy, although capital intensity in apparel far exceeds that in Ethiopia or Vietnam (as shown by the difference in the vertical axis), capital-labor ratios increased substantially in the apparel industry between 2000 and 2010 but then leveled off. While the apparel products produced in the United States are likely not comparable to those produced in Ethiopia and Vietnam, the leveling of capital intensity in the apparel industry is indicative of a stall in technological change in this industry. One reason for this appears to stem from the difficulty associated

> 7 Authors’ calculations based on firm-level data for Ethiopia, Tanzania and Vietnam, the NBER productivity database and KLEMS.

23
More generally, sectors employ occupations in different proportions, creating a direct link between sectoral and occupational employment transitions. Figure OA.3 in the Online Appendix shows this link by plotting occupational employment shares separately _within_ the manufacturing and service sectors against countries’ GDP per capita. Overall, the service sector uses professional and clerical occupations more intensely at all development levels. The manufacturing sector is highly concentrated in trade and service occupations in poorer countries but diversifies across occupations in richer ones. Gender differences across occupations within these sectors are relatively small in poorer countries but increase in richer ones. The largest gender gap emerges in clerical occupations, where women account for the entire employment increase with development. Men and women both contribute to the rise in professional and managerial jobs in rich countries, but men remain 5-10 p.p. more likely to work in these occupations than women.

# **2.4 Gender Segregation across Sectors and Occupations**

Having documented gender differences in employment choices across sectors and occupations, we now use the Theil Information H-Index to systematically measure gender segregation across occupation-sector pairs in each country-year. Similar to the Gini coefficient, the H-Index measures segregation on a normalized scale between 0 and 1 by using the entropic “distance” between the observed empirical distribution and an ideal gender-equal distribution. A higher value indicates greater segregation, while zero indicates perfect gender equality. The index allows decomposing total gender segregation across occupation-sector pairs into two additive components, which separately measure gender differences _across_ sectors and across occupations _within_ sectors. Appendix A.1 formally defines the index and decomposition.

Figure 3a plots the segregation across occupation-sector pairs against countries’ real GDP per capita, pooling data across all countries and years. The figure shows an inverted U-shape relationship, indicating that gender segregation is lower in poor and rich countries but high in middle-income countries. Figure 3b plots the share of total gender segregation that is explained by differences across occupations _within_ sectors. This component is significantly higher in richer countries, where it accounts for up to 50% of
the Household survey for year 2012-2014, consumption per capita is the ratio of consumption and population. Source: Authors’ calculations based on FAO/WFP. 2012-2014

# **3.6. City Characteristics and Price Differentials**

When markets are integrated and well-functioning, the price difference of a homogeneous product across regions converges to the cost of trading that product across the regions through the forces of arbitrage. The costs of trading include, for example, transportation and intermediation costs, and other costs related to, for example, overcoming regulatory obstacles that restrict trade. However, high transport costs, imperfect markets and barriers to trade may prevent arbitrage, resulting in market segmentation and high price differences between geographically separated locations, with associated welfare costs.

When markets are segmented, price dynamics in each region matter for price determination. In that case price differences may be associated with supply or demand conditions (differences in production costs, resource endowments, or with consumer preferences and market size).

To what extent do the heterogeneous characteristics of state capitals related to supply and demand conditions, and to trade costs can explain the observed patterns of price differences in South Sudan?

Methodologically, to identify the effects of state-capital characteristics on the price difference of a variety of products in each of these state-capitals relative to the price in Juba we estimate the following model: 18
of exposure. The average effect of three years of exposure on the probability of enrolling in seventh grade is very large and precisely estimated ( _p_ -value _<_ 0.001). The magnitude of this effect implies that the enrollment rates in these disadvantaged and rural areas achieve the secondary school enrollment rates in urban Mexico (95 percent). The average marginal effect of an extra year of the program is +10 percentage points ( _p_ -value = 0.006) in the probability of enrolling in seventh grade. The effect of two years of exposure, although not statistically different, is larger than the experimental estimate displayed in Table 2 (fifth column) suggesting that the impact does not fade out after one year.

# **6.2 The Effect of the Program Beyond the Evaluation Sample**

We now broaden our analysis to the entire population of schools in the state of Chiapas. We examine whether the _Plus_ modality of the program at scale has promoted educational opportunities for children in these disadvantaged communities, possibly resembling the results from the second field experiment (see Section 3). To do so, we match administrative records on the government rollout of the program during the fall of 2017 with village-level educational outcomes from the population census data (data collection in the fall of 2019) for the majority of the schools and communities, which include those that were part of the second experiment.<sup>20</sup> Our first outcome is the village-level lower-secondary enrollment rates among children between 12 and 14 years old. This variable is available in the census for 1,417 communities in Chiapas. It is not immediately comparable with our previous measure of enrollment in seventh grade for two reasons. First, the census-based information represents the stock (rates) of children enrolled in secondary school in a given year, while our previous measure represents the flow of new students enrolling in secondary schools. Second, the census-based variable includes children in the village who are enrolled in primary schools that are not
p<0.05, * p<0.1.<br>Source: Estimation from VHLS|<br>egressions of t<br>ument and oth<br>estimate a pr<br>s, and other ex<br>corrected for s<br> <br>S 2010, 2012, 2|<br>he probability o<br>er explanatory v<br>obit model of<br>planatory varia<br>ampling weigh<br>014 and 2016.|<br>f women work<br>ariables using<br>maternal emp<br>bles. Standard<br>ts and cluster c<br>|<br>ing in wage-pa<br>probit, and esti<br>loyment using<br>errors are estim<br>orrelation at th|<br>ying jobs. We<br>mate the genera<br>the child car<br>ated using boo<br>e commune lev|<br>first model the<br>lized residuals<br>e variable, the<br>tstrap with 200<br>el.|

38
Figure 13. Net cash position after tax reforms Panel a. Fiscal incidence as of 2021 (after PIT reform) <!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>-10%<br>-20%<br>-30%<br>-40%<br>Direct Taxes Indirect Taxes Pension Contributions<br>Non-Pension Contributions Education Health<br>Net Benefit Position Net Cash Position<br>Panel b. Fiscal incidence as of 2022 (after PIT + GST reform)<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>-20%<br>-40%<br>Direct Taxes Indirect Taxes Pension Contributions<br>Non-Pension Contributions Education Health<br>Net Benefit Position Net Cash Position<br>income plus pension<br>Taexes/transfers as share of gross market<br>plus pension<br>Taxes'transfers as share of gross market income<br><!-- End of picture text --> _Source_ : Staff calculations using BLSS 2017.

Note: x-axis shows deciles based on gross market incomes plus pension. Y-axis shows taxes/transfers as share of gross market income plus pension.

25
36.5|23.2|13.0|
|**ELMPS 2018 in est.**|30.1|37.8|23.3|8.9|

Source: predictions based on models in Table 2, Table 3, and Table 4

# **4.4. Profiles: Grouping and Clustering Analysis**

In what follows we attempt to characterize which informal firms operating inside establishments are least likely to be formalizable and which have a higher potential for formalization.<sup>22</sup> We do this first by grouping informal firms into different profiles (collections of characteristics that explain formality) based on Model 2 for the EcC data and Model 3 for the pooled ELMPS data. We also use cluster analysis to classify informal firms into 10 clusters based on the variables in EcC Model 2 and ELMPS Model 3. After examining how the mean characteristics of these clusters compare to those of all informal firms within establishments (shown in Table 13 and Table 14 in the appendix), we attempt to assign these clusters names that best describe the firms that fall within them. Finally, we identify, separately for each data set, which groups or clusters of informal firms are least likely and most likely to be formalizable based on the proportion that falls into each quartile of the probability of being formal.

As shown in Table 6, according to the ELMPS, the firms most represented in the bottom quartile of the probability of formality are one or two-person firms that are relatively young (<5 years of age) and that

> 22 The very high informality rates for firms operating outside fixed establishments precludes this kind of analysis for these firms since few of them are likely to be appropriate candidates for formalization.

38
a large informal sector is a distinctive characteristic of many developing countries (Perry and Maloney, 2007; La Porta and Shleifer, 2014; Ulyssea, 2020), countries in South Asia exhibit even higher rates of informality (Bussolo et al., 2020) such that more than 90% of the businesses in South Asia are informal. Thus, South Asia provides a unique setting for this type of analysis. In addition, South Asia was also among the regions that were disproportionately affected by the COVID-19 shock (Brucal et al., 2021). The combination of these two factors reinforces the importance of analyzing the role of informality in explaining firm-level outcomes.

Our study makes four main contributions to the literature. _First_ , our analysis takes advantage of several rounds of novel firm-level data specifically collected to analyze the 3
A of table 6 uses log night lights derived from annual composites. Panel B of the same table uses log night lights derived from pixel averaged monthly composites. The benchmark specification regresses log GDP on log night lights, state fixed effects and year fixed effects. Standard errors are clustered at the state level in all cases. Column 1 presents the result of the benchmark specification. We sharply estimate an elasticity of roughly 0.24.

Next, in column 2 we test whether night lights are capable of predicting annual fluctuations by extending the previous specification to include state trends. Since we are primarily interested in short term deviations from the state growth path, this is the key specification for our analysis. As in the previous case we sharply estimate an elasticity in the order of 0.11 (t=2.6). This result is important because it suggests that night lights do a reasonably good job of predicting annual fluctuations in GDP.

In column 3 we test for ratchet effects, that is whether, relative to the state mean over time, increases and decreases in night lights are symmetrically related to increases and decreases in GDP. This calculation is preformed in two steps: (i) We demean the data by regressing GDP and lights on year and state fixed effects. (ii) We regress the GDP residuals on absolute value positive lights residuals, and absolute value negative lights residuals. We find that the coefficients are

> 12The source of state level GDP data is INEGI. GDP is measured in constant 2008 pesos.

16
500 firms.

The FactSet database offers unique advantages as it provides information on firms’ fundamentals matched with detailed information on firms’ suppliers and customers, as well as ownership ties. This information is crucial for assessing a company’s dependencies and market position. Given its global coverage, the dataset allows for a comprehensive understanding of the complexity and geographic dispersion between companies. Finally, since the dataset tracks variables over time in a precise manner, it enables the analysis of trends and changes within these relationships over the years.<sup>1</sup> To maximize the utility of the data while minimizing the potential for error, we undertake three primary analyses. Initially, we perform a descriptive exercise using network analysis metrics to examine the role of US Fortune 500 firms, specifically the 48 influential American lead firms in GVCs identified from this list, within the agri-food value chains. This focus is chosen based on the ability of these firms to exemplify significant economic influence within the national agri-food sector.<sup>2</sup> Our findings reveal that the Fortune 500 lead firms are notably more interconnected and increasingly central to the agro-food GVCs. However, since 2018, they seem to be concentrating their activities within their corporate boundaries, in contrast to non-US-based firms.

This observation leads us to conduct a broader and systematic firm-to-firm connectivity analysis. We expand our inquiry to include all firms listed in the FactSet database that operate in agri-food-related sectors. Our findings indicate that the most connected firms are also those that have significantly increased their number of customers and suppliers over time. Furthermore, a relatively small group of highly interconnected firms consistently occupies central positions within the network. Despite their central roles, these firms typically do not diversify their core relationships within narrowly defined indus-

> 1FactSet’s coverage is particularly strong in the United States, where the firms in our sample account for around 80% of aggregate industry output.

> 2The substantial coverage of both Fortune 500 and non-Fortune 500 US agribusinesses in our sample justifies the initial
**Table A.2:** Balance Checks on Village Characteristics: Administrative Data

||Latitude|Longitude|Altitude|Coastal|Forest|Agric.|Rice|Corn|Rubber|Palm oil|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|New village head|0.659|0.146|106.152|-0.009|0.013|-0.049|0.144|-0.190<sup>_∗∗_</sup>|-0.026|-0.003|
||(0.725)|(3.019)|(161.475)|(0.073)|(0.089)|(0.085)|(0.127)|(0.083)|(0.060)|(0.008)|
|Observations|512|512|512|512|512|512|512|512|512|512|
|Control mean|4.76|110.6|179.6|0.093|0.14|0.92|0.56|0.17|0.034|0.012|
|Robust p-value|0.327|0.994|0.377|0.991|0.930|0.453|0.228|0.015|0.733|0.540|
|MSE-opt. bandwidth|23.1|19.4|17.0|22.6|20
on this knowledge.

To better understand the role played by intangible capital in production, we also estimated a Cobb-Douglas production function where produced, natural and intangible capital are the factors of production. Using fixed effects estimation for all countries in our sample (115 countries over the years 1995, 2000 and 2005), we find that the shares of produced, natural and intangible capital in production are 32%, 7% and 18% respectively; if we limit our sample to developing countries the shares are quite similar. However, when we limit the sample to OECD countries, we find the striking result that the only statistically significant factor of production is intangible capital, with a 50% share. This finding supports the conjecture in Ferreira and Vincent (2005) that intangible factors, rather than produced or natural capital, are the principal sources of consumption growth in high-income countries.

We also attempted to decompose the constituents of intangible capital using data on institutional quality (as measured by a rule of law index) and human capital. Panel regression analysis suggests that the contribution of human capital is indeed important. A one-point increase in our human capital indicator is associated with an increase in intangible wealth of over $92,000 for the average country in our sample. This implies a difference in intangible capital of over 17
This simplified approach would draw on the World Bank’s POVCAL database, which includes income and/or consumption survey data for more than 180 countries, including 67 IDA/blend countries. Availability of the underlying microdata for each country depends on the agreements that have been made between countries and the World Bank, but, in many cases, it is anticipated that the microdata would be accessible via the respective country offices. While income data is available for many of the countries in the full database, income data is only available for five IDA/blend countries. Consumption data is available for 62 IDA/blend countries. In addition to the microdata, the POVCAL database provides, for each country, cumulative income or consumption distribution results across percentiles. Datasets are from between 2014-2019 for the vast majority of countries.

While the underlying microdata files would be required for each country to build a full microsimulation model, the publicly available percentile distributions can be used under a simplified modelling approach.<sup>38</sup> The simplified modelling approach would apply the PIT rules in place for single individuals to calculate the average tax liability applicable for each of the 100 percentiles. The income distribution and tax liability data would then be used to calculate Kakwani and Reynolds-Smolensky indicators of progressivity and redistribution. The modelling would account for any basic allowances or deductions, zero rate brackets, and the progressive PIT rate schedule. However, it would not account for any tax provisions that are contingent on family size – such as a child tax credit. While this simplification would be a significant concern for high-income countries, it is likely to provide less inaccuracy for IDA countries that apply far more limited use of such targeted support. A fixed adjustment would also be applied to the consumption survey percentile results to adjust for savings patterns and thereby better proxy for income.

The modelling results can be compared to the small number of countries for which full microsimulation models will be constructed as part of other projects over the period of the project. For example, a