rafmacalaba/data-use-mentions-extended huggingface dataset:
lives can therefore have positive impacts on the economy and is important to mitigate declines in GDP growth resulting from a fall in the working age population in aging societies.

**A major consideration when thinking about if and how to support the employment of older persons is the potential impact on the employment of younger persons.** This notion relates to what is often called the lump of labor fallacy, or the belief that there is a fixed number of jobs in an economy, which means that increasing employment opportunities for older persons would decrease employment opportunities for younger persons (Gruber, Milligan and Wise 2010). In practice, the number of jobs in an economy is not fixed, and an increase in labor demand (for older persons or otherwise) translates to an increase in aggregate demand in an economy through an increase in consumption by these workers. Subsequently, this increases the demand for labor further. In other words, a growing economy with an increasing demand for older workers will likely also experience an increase in demand for other workers, including younger workers (Apella 2024). Moreover, older and younger persons are not necessarily substitutes. They are likely to prefer different types of jobs, and have different skills. For example, jobs that are more physically demanding are less conducive for older persons (Acemoglu, Mühlbach and Scott 2022). Thus, older workers are less likely to crowd out younger workers in such jobs.

**The high degree of informality in the Indonesian economy exacerbates concerns of substitutability between older and younger persons.** Following the definition of informal employment put forward by the International Conference of Labour Statisticians in 2017 (ICLS-17), at 76.7 percent, informal employment made up more than two-thirds of total employment in Indonesia in 2023 (see Footnote 6). Since informal work is more likely to be low-skilled and require minimal formal education (discussed in Section 2), there is a possibility that older and younger workers are more substitutable in Indonesia compared to economies with low informal employment
19.7|37.1|–|
|16|_g_ =_k_<sup>2</sup>|–|0.1|–|

# The SDM data

Another popular growth dataset was introduced by Sala-i-Martin _et al._ (2004) (SDM), who model annual GDP growth per capita between 1960 and 1996 for _n_ = 88 countries using _k_ = 67 potential regressors.

The 50 randomly selected estimation subsamples each have 75 observations, and lead to the boxplots in Figure 9. The results are mostly in line with those for the FLS data, but with larger differences between the priors in terms of posterior model size.

26
according to different counterfactual scenarios in order to see the impact it has on reducing prevalence of respiratory illness and severe COVID-19 risk across the country.</mark>

1. Predicting prevalence of risk factors and comorbidities

## _1.1 Handwashing risk_

Using the three variables related to handwashing in the DHS data, we assign each household a hand washing ‘risk’ score. For households with a hand washing facility with water and soap, this was considered equivalent to a hand washing risk of 0. With water but no soap, there was a risk of 0.5, while with no water or no facility (even if soap was present) the risk was set to 1. The mean household level observation was then calculated for each cluster.

Cluster level georeferenced handwashing risk scores were used to predict handwashing risk across the whole country and generate a continuous map using a beta Generalized Additive Model (GAM) which includes a bivariate smooth on latitude and longitude. Population density, night-time light intensity for 2015 and distance to nearest Open Street Map (OSM) road obtained via WorldPop

> 2 Though DHS clusters are intentionally displaced by 0-2km in urban areas and 0-5 km in rural clusters, with a further, randomly-selected 1% (every 100th) of rural clusters displaced by up to 10km (Burgert, 2013), this is taken into account in the interpolation process. The application of interpolation techniques specifically to DHS data is described in the referenced publication (DHS Spatial Interpolation Working Group, 2014).
program impacts on investment over and above a significant and positive trend (with an estimated increase of about 21 m<sup>2</sup> or about 30% of the pre-project mean for the narrowest band).

# **4.2 Results from administrative data**

To see whether insignificant results are attributable to lack of program success or methodological and data limitations, we use administrative data. Results from estimating (2) as reported in columns 1, 3, and 5 of table 5 illustrate that, before policy reform, registration of new leases was strongly biased against women with point estimates for 1 in regressions for female vs. male (-0.25), joint vs. male (-0.48) and joint vs. female (-0.23) registration indicating size of female disadvantage. The combined actions taken as part of ββ the program reversed this pattern. Point estimates for 1 2 , the joint effect of policy reform and systematic registration are all positive and large with coefficients of 0.18 for the female vs. male and 0.37 ββ + ββ for joint vs. male individual as well as 0.18 for joint vs. female rights. While the likelihood of a plot being registered in the name of a woman individually did not change (as an estimated marginal gain of 0.18 was outweighed by a commensurate increase in joint registration), the project is estimated to have changed the marginal likelihood of a newly registered plot to be held (registered) in the name of women - individually or jointly- by 0.55, a meaningful and rather big effect. Table 5 cols. 2, 4, and 6 provides results from (2’) to separate the net effect of policy change 1 2) from that of systematic registration 3 1 2)�. It suggests that 35% and 29% of changes in female (γγ + γγ and joint vs. male individual registrations and 22% of the change in joint
seekers<sup>.81</sup> There are several challenges associated with using central population registers to estimate refugee and asylum-seeker populations, including: consistency of the definition of refugees with the definition in the 1951 Convention and 1967 Protocol; difficulty and cost of establishing and maintaining a population register (UNSD 2014); and confidentiality safeguards.

# **Compilation of statistics on forcibly displaced populations**

Several international organizations are involved in the compilation, analysis and dissemination of statistics on forced displacement including UNHCR,<sup>82</sup> Eurostat, IDMC, OCHA, International Committee of the Red Cross (ICRC),<sup>83</sup> WFP<sup>84</sup> and IOM. Each of these actors has their own thematic focus and specific objectives, and applies their own methodologies.

# **_Asylum-seekers and refugees_**

UNHCR is the principal organization responsible for the compilation, analysis and dissemination of data on asylum-seekers and refugees. UNHCR maintains a publicly available statistical online database<sup>85</sup> with data for the period 1951-2014 on refugees (including people in refugee-like situations), asylum-seekers (pending cases), returned refugees, IDPs protected or assisted by UNHCR, returned IDPs previously protected or assisted by UNHCR, stateless persons and others of concern to UNHCR, disaggregated by country of origin and asylum.<sup>86</sup> Data are also provided on demographics, location, asylum-seekers (refugee status determination and monthly data) and resettlement. UNHCR regularly publishes statistical reports, including “ _Global Trends”_ , “ _Mid-Year Trends_ ”, _“Asylum Trends_ ” and “ _Statistical Yearbook”_ . Additionally, UNHCR hosts interagency information sharing portals for significant emergencies.<sup>87</sup> These portals provide data on populations of concern at regional and country levels, including time series data, demographics, location and accommodation information.<sup>88</sup> Eurostat compiles and publishes data on asylum (applications
trade barrier (TTB) measure exhibits greater measured co-movement with macroeconomic variables than the antidumping measure used in the prior literature. Estimates on antidumping alone fail to capture the true impact of these shocks on import protection, especially with respect to movements in bilateral real exchange rates. The third point worth highlighting is that the estimated IRR for foreign real GDP growth is less than 1 in all specifications of Table 2. This is evidence that, on average, these economies tended to impose new import protection on trading partners that were themselves undergoing a period of weak economic growth or an economic contraction. This result is particularly important for understanding the _differential_ government policy responses during the Great Recession, as we discuss in section 4.

# **3.2 Pre-crisis estimates for subsamples of the US, EU and smaller economies**

Table 3 presents estimation results from the 1988:Q1-2008:Q3 period restricted to subsamples of data based on policy-imposing economies. We consider three subsamples in particular: estimates for the United States, European Union, and a combined sample of data jointly covering Australia, Canada, and South Korea. For each subsample of data, we present results from three different specifications. Columns (1), (4) and (7) of Table 3 present the same baseline model specification as Table 2, specification (1). We then consider robustness checks in columns (2), (5) and (8) in which we substitute the explanatory variables at the second lag ( _t-2_ ) for the first lag. Finally, in columns (3), (6) and (9), we consider the impact of these determinants on the antidumping policy alone, as opposed to the more comprehensive dependent variable that covers all TTBs.

The results for the United States in Table 3 are consistent with the overall results reported in Table 2. Import protection through TTBs is associated with a real appreciation of the US dollar, an increase in the US unemployment rate, and a decline in trading partner real GDP
|Rwanda (RWA)|2010-2017|802_._8|12_,_905|459_._080|84_._5|30|12_._1|14_._2|
|Senegal (SEN)|2010-2018|1_,_547_._1|5_,_732|3_,_641_._8|59_._2|30|27_._3|26_._8|
|Uganda(UGA)|2015-2019|956_._9|16_,_083|587_._080|62_._6|30|15_._1|22_._1|

Note: This table presents summary statistics on firms in the 13 countries in our data. All statistics are from administrative corporate tax records, except for the GDP per capita (column 4) which is from the World Development Indicators. Column (2) shows the years available in the data for each country. We use the most recent year to compute metrics shown in columns (3) to (9). The effective tax rate (ETR) can be larger than the statutory tax rate due to the reintegration of non-taxable deductions in the net profit definition (see Figure 1). This table is discussed in Section 2.1 and 2.2. Appendix B provides additional details on each country’s corporate tax system. _∗_ The maximum statutory tax rate for Honduras is 25%. However, firms paying the asset tax instead of the corporate tax face an STR that is roughly equal to 34% of that on taxable profits.
funding, and characteristics of the founders and investors, among others; it is a cross-section and takes the most recent values or attributes of the firm. This does not allow us to analyze the evolution of these firms over time. Therefore, any reference to the life cycle in this paper refers to the comparison of firms across different age groups.

We complement the analysis with data from Pitchbook (https://pitchbook.com/), another VC/PE private repository that provides detailed characteristics and financial and financing history information on more than 3.6 million companies worldwide. Pitchbook focuses on the entire of investments and collects information on life-cycle mostly investment-ready firms.

Figure 1 shows the spatial distribution of the African digital firms analyzed in this paper. As shown, South Africa, Nigeria, and the Arab Republic of Egypt stand out as digital hubs, where most of the firms in our sample are located. Appendix Figure B1 shows the spatial concentration of African digital firms in the main city of each country. In most countries, a large share of the firms is located in the capital city. From the socalled **_Big Four_** countries (Egypt, Nigeria, Kenya, and South Africa), where most of the digital firms operate, it is only in South Africa where the main city concentrates less than 50% of all digital firms operating in the market.

> 5We also test the robustness of our results with a threshold of five years of age for startups.

6
unit record data (Agénor, Izquierdo, and Jensen, forthcoming).

Concluding remarks are presented in Section 5. An annex provides a summary description of the tools reviewed in this paper.

Table 1 presents a synoptic view of the modeling approaches reviewed in this paper, with a focus on three elements: the treatment of the macroeconomic framework, the modeling of the size distribution, and the variables linking the macroeconomic framework to the model of distribution. The first category has no explicit macro-model. The second embeds distributional mechanisms in a general equilibrium model.

4
for the poorest 40%. Similar improvements are evident for both stunting and wasting in children (comparing Tables 13 and 8).

However, it is clear that these augmented regressions still do a poor job at identifying undernourished individuals within households. While the predictive power is improved, it is not enough to change our conclusion that targeting based on the available household poverty data misses a large share of undernourished women and children.

# **7. Conclusions**

There are multiple constraints on effective policy interventions in practice. Here we have focused on a key informational constraint, and asked whether household poverty might provide a reliable guide for policy efforts trying to reach deprived individuals, as indicated by anthropometric measures of undernutrition, recognizing that poverty is an individual characteristic. We do not claim that information is the only constraint. Even if undernourished women and children are almost solely found 21
currently dominate the field of development economics. By providing responses to these questions, we will be able to rigorously evaluate the impact of rule of law institutions on the prosperity of developing nations in future years.

In this paper, we divide our review into three parts. Part 1 studies the empirical evidence evaluating the impact of justice on development outcomes. Our review focuses on the impact on poverty reduction and firm growth, the prevention of conflict and violence, as well as on the prevalence of corruption and citizens’ trust in institutions. Part 2 discusses the importance of the data revolution in justice. We focus on the potential for judicial administrative and text data to improve the functioning of judicial institutions throughout the world, particularly in developing countries. We argue that the increase in data availability in judiciaries globally, especially since many have been compelled to transition to data-driven systems in light of the COVID-19 pandemic, has the potential to bring the rule of law back to center stage in the development agenda. Part 3 explores new avenues for future research as well as directions for policy makers to leverage the ongoing expansion of data and e-justice solutions in order to address some of today’s most pressing needs.

# **1 The Role of Justice in Development: Empirical Evidence**

In this section we review the empirical evidence that evaluates to what extent justice matters for development outcomes. Specifically, we focus on three main aspects of development: economic growth, conflict and violence, and trust in institutions.

Overall, empirical evidence shows that the judiciary matters–not only for its intrinsic value, but also for its impact on development outcomes. A more efficient judiciary promotes economic growth through better enforcement of contracts and more secure property rights, while also leading to healthier business environments. The judicial system can incentivize entrepreneurship, have a positive effect on firms’ outcomes and growth, and even affect the output of other industries that heavily rely on contracting. A more effective judiciary may also contribute to reducing violence, by creating a deterrent effect on
as has been posited in much of the finance and credit markets literature. Using a structural model on Sri Lankan data, they find that the effects are nonlinear and heterogeneous based on borrower wealth, which in turn, also depends on competitiveness in the credit markets. Their theory and evidence suggest that there might be significant effects on interest rates and profits from improving property rights. However, these appear to come mostly from increased effort rather than increased levels of borrowing. Using the context of the 2009 DRT
# **Access to Credit and Bank Ownership Evidence from Firm-Level Data**<sup>**1**</sup>

Mario di Filippo The World Bank Ugo Panizza Geneva Graduate Institute & CEPR

**Keywords** : G21, H11, O16 **JEL** : Banking, State-owned banks, MSMEs, Credit Constraints

> 1 Ugo Panizza is Professor of International Economics at the Geneva Graduate Institute. Mario di Filippo is a senior financial sector specialist with the World Bank’s Middle East and North Africa region Finance, Competitiveness, and Innovation Global Practice. The authors can be contacted at <u>mdifilippo@worldbank.org</u> and <u>ugo.panizza@graduateinstitute.ch</u>

For helpful comments and suggestions, we would like to thank Roberta Gatti, Martin Melecky, Eva Gutierrez, Abdoulaye Sy, Marina Wes, Djibrilla Issa, Irina Astrakhan, Onur Ozlu, and Randy Kotti. We thank Jorge Rodriguez Meza and Joshua Wimpey of the Enterprise Analysis Unit of the World Bank’s Global Indicators Department and Asif Islam of the World Bank’s Middle East and North Africa Chief Economist Office for providing access to the Enterprise Surveys data. All remaining errors are our own. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

1
.021<br>(0.049)|-0.099<br>(0.086)|-0.I11 *<br>(0.063)|-0.101<br>(0.063)|
|countv_NewMan*year||||0.041**<br>(0.019)|0.066***<br>(0.016)|0.061***<br>(0.015)|
|R Square|0.330|See|note c|0.332|Se|e note c|
|Sample Size|6544|6544|6544|16544|6544|6544|

Source.-- Based on _A Survey of State Enterprises: 1980-1989._

**a** In all OLS and RE estimations of this table (column 2, 4, 5, and 7), we also control the following variables: 9 year dummies, 3 provincial dummies, 9 industry dummies, 4 governance dummies (the firm governed by the central, provincial, city, county government), and the missing indicators for

W_ELASTICITY, marginal retention rate, and share of output under the mandatory plan of the government.

b In all fixed effects estimations of this table (column 3, 6), we also control the following variables: 9 year dummies, and missing indicators for firm-level pay sensitivity, marginal retention rate, and share of output under the mandatory plan of the government.

C For the level model: (1) the F statistic for FE vs. OLS is 14.8 with a P value of 0.000; (2) the Breusch-Pagen test statistic for RE vs. OLS is 6224 with a P value of 0.000; (3) the Hausman (chi square) test statistic for FE vs. RE is 386.5 with a P value of 0.000. For the growth model: (1
force surveys. The HFPS, while timely and informative for looking at how households have weathered during the pandemic on a number of dimensions, is not without limitations. As mentioned in the paper, limited numbers of survey rounds and a respondent sample can only provide a partial (and imperfect) picture of the impacts. In addition, the rapid nature of the phone surveys means that the questionnaires may lack the level of detail necessary to look at precise mechanisms of the pandemic’s economic impact on workers and households. As nationally representative household or labor surveys become available, a more comprehensive assessment of the extent to which the pandemic and its recovery have increased inequality in the region will be important.

Not discussed in this note is the important role that the expansion of safety nets played in many countries in the region in mitigating to some extent these negative impacts. Several countries in EAP have made unprecedented efforts to scale up their social protection systems in a relatively short amount of time. In Mongolia, according to the December 2020 survey, recipients of the Child Money Program have largely used the benefit to purchase food, while a third to save for future usage, potentially increasing their buffer. Among recipients, 8 percent declared that the transfers have completely mitigated the negative impact of 26
functions of wind and water stress, in the supporting information appendix we show that our results are also robust to following Nordhaus (2006) suggestion of an eighth-power relationship between maximum wind speed and damages.

## **Storm Surge Prone Area**

The _f_ damage index provides an informative measure of wind damages and potentially of overall damages because it is also correlated with damage from excess rainfall and storm surge. Nonetheless, because storm surge is often considered one of the most damaging aspects of hurricanes, we further investigate whether coastal lowlands are disproportionately affected by hurricanes. Specifically, we create a coastal lowland indicator variable identifying contiguous areas along the coast which are less than 10 meters above sea level.<sup>4</sup> This storm surge prone area is composed of 7,758 cells (3.6% of all cells). For each of these cells, we additionally calculate the shortest path (Euclidean distance) from the centroid of the cell to the coast. The average distance to the coast is 5.41 km.

# **2.3 Mangrove**

Data on the distribution of mangroves come from two sources. The first is a collection of harmonized maps, 1960 to 1996, that was assembled for the Mangrove World Atlas (Spalding et al., 1997). We use this map to identify areas that have historically supported mangrove habitats. The second source is Giri et al. (2011), who use 1997 to 2000 Landsat data, together with supervised and unsupervised digital image classification, to construct a 30 square meter resolution map of the global distribution of mangrove. We use this second data set to precisely measure the presence of mangrove at the beginning of our sample period.

To rule out that any protection benefits of the mangrove are derived because mangroves grow in areas that are naturally more sheltered, for example, areas that lay in a steeper continental shelf, we begin by excluding from the analysis areas that have not historically supported mangrove habitats. Specifically, for every