rafmacalaba/datause-encoder-data huggingface dataset:
- 39 -

interest, though they have received little attention. For related discussion see Ravallion (1988c) and Datt and Ravallion (1989).

18. This assumes that the changes in G are such that the new Lorenz curve is - g.,en by L(p) X[p L(p)]. As nGted, this assumption fits the data in Table 4 well (footnote 16). Then 60 = to(z - p)/z and

- e _=?a_ + UIAPa.-1/(ZPa) for a > 1, and it is readily verified from the following formulae that 8va/<sup>8</sup> G > 0 (all a) for these data.

19. This was found by Ravallion (1988a) in a study of poverty in East Java based on the 1981 SUSENAS which (unlike 1984 and 1987) spanned an entire year.

20. Though harvests in 1987 were depleted, this occurred after the SUSENAS interviews.

21. For a recent discussion and some evidence for Indonesia see Deaton and Muellbauer (1986).

22. Noting that the distribution of consumption will also depend on how households adjust in their intertemporal behavior.

23. There is some evidence of a systematic bias, whereby the "poor" tend to overestimate food expenditure over a 7 day recall, while the "rich" underestimate it. The turning point (at which there is neither underor overestimation on average) is such that standard poverty measures for Indonesia will tend to be underestimated, though the magnitude of the error is probably farily small. For detailed results and an interpretation see (Ravallion (1989b).

24. The index tracks changes in the cost of an average consumption bundle in each of 17 Provincial capital cities; for other capital cities they have used shares for the city amongst the 17 which is considered to be most similar in this respect (usually the closest).
# **Appendix A: Data Samples**

We use several samples of the merged data described in Section 3 in different parts of the paper, each with a different size and coverage (as shown in Appendix Table 1). To construct the samples of firms, we start from all legal and formally registered firms in the economy (602,882 firms) with a tax identification, which we call _formal firms_ .<sup>75</sup> The first sample, which we call _active firms_ , is constrained to include only firms with positive sales in 2019, which amounts to 449,632 firms. We use this sample to conduct the aggregate analysis of the paper and the mapping between micro and macro patterns. This sample represents 75% of firms, 92% of private sector employment, 82% of the debt outstanding, and 100% of positive value added in the economy. Among active firms, 97% are SMEs and contribute 43% and 27% of total employment and credit in the economy, respectively. The remaining 3% of active firms are large firms (2%) and mega firms (1%).

The second sample is used to estimate the default probability models that measure firm-level default risk. Starting from the active firms sample, this second sample includes only firms with available data on default during 2019, plus sales, number of workers, value added, firm age, municipality, and industry in December 2018. Firms in default are those with loans past due 90 days. We consider only firms with a previous loan to estimate the model, i.e., firms with outstanding debt as of December 2019 or receive a loan over the period 2012–2019. Firms with previous loans constitute 36% of active firms (capturing 79% and 87% of employment and value added, respectively).

The third sample adds further restrictions to the active firms sample. We restrict the sample to all firms with the relevant observables to perform the main regression analysis, including a measure of default risk. This sample excludes firms that use the employment program before the public credit guarantee program starts (end of April 2020) to compare the two policies more equally. We call this the _selection and leverage model_ sample. This sample represents 20% of firms, 50% of employment, 44% of the debt outstanding, and 74% of value added. Although this sample is smaller than the others, it provides detailed information at the firm level that is unavailable for other firms and is essential for the regression analysis we perform.

The fourth sample starts from the selection and leverage model sample. It imposes the eligibility constraints from the public credit guarantee program, namely that firms must

> 75We exclude natural persons who use their personal tax identification to borrow as a firm. For these natural persons, we do not have the same scope of information as we do for active firms, and we exclude 818,572 tax IDs for this reason. These natural persons are only included in our aggregate analysis when we report the total value of the program and in our estimate for total expected credit loss (Table 7).

1
23 substantial (17%) reduction in infant – although not in neonatal – mortality rates in rural Mexico (see Barham, forthcoming).

On balance, CCTs have transformed the social assistance landscape in (and, increasingly, beyond) Latin America in large part because they greatly enhanced the State’s capacity to target resources effectively to the poorest people in society. As we saw in Section 2, the bottom fifth of the income distribution had been effectively excluded from any serious state assistance throughout Latin America’s history, and this only started to change with the introduction of NCSIs and CCTs in the 1990s. Furthermore, this appears to have been the case under very different targeting systems, ranging from the careful, multi‐layered geographical‐cum‐proxy‐means approach of _PROGRESA_ – rural _Oportunidades_ , to the enormously decentralized and much less rigorous approach of _Bolsa Família_ . The fact that the latter program also appears to be very well‐targeted would seem to warrant further research on the determinants of CCT targeting.<sup>25</sup> CCTs have also contributed to rationalizing and integrating social assistance programs, and reduced the role of untargeted programs and consumption subsidies. The classic example is Brazil’s _Bolsa Família,_ which consolidated four other programs ( _Bolsa Escola_ , _Bolsa Alimentação_ , _Cartão Alimentação_ , and _Auxílio Gás_ ), and is now the largest program in Brazil after the social pensions for the elderly and disabled (BPC) (see World Bank, 2009a). Another example is Mexico’s _Progresa_ (now _Oportunidades_ ) that, when established in 1997, replaced consumption subsidies for tortillas.

Although CCTs have been very successful, there is always room for improvement. RRW indentify three areas where more thinking is required: (i) improving coordination between CCTs and the supply side in health and education; (ii) modernizing procedures for enrolling and “graduating” beneficiaries in a timely fashion; and (iii) adapting programs to urban settings.

On the supply side, the quality of education and health care remains low in most countries in the region. Hence, even if individual investments in education and the consumption of health services increase as a result of CCTs, social benefits will be constrained in the absence of structural reforms in education and health systems. There is also room to improve the administration of CCTs, and in

> 25  It is possible, for instance, that the five thousand mayors who have overseen the targeting of Bolsa Família in Brazil have exerted greater effort in ensuring that the money reaches its intended beneficiaries because the conditionalities (or co‐responsabilidades, in the Spanish usage) associated with them reflect a new political economy equilibrium, whereby middle‐class voters are much more supportive of transfers if they are (i) not seen to be unconditional handouts, and (ii) not seen to be captured by politicians and their cronies. See Levy and Rodríguez (2004), and Chapter 2 in Fiszbein and Schady (2009).
Table 2.1 Environmental Policies and Institutions Indicators for Four African Countries

||Country|Chad|Kenya<br>|South<br>Africa|Nigeria|
|---|---|---|---|---|---|
|1|Global Emissions|0.45|1.35|8.43|7.26|
|2|Pollution|1.09|1.89|1.14|12.92|
|3|Fragile Lands|0.94|3.27|4.60|12.52|
|4|Biodiversity|0.53|4.15|2.90|6.67|
|5|Water Resources|0.44|1.27|4.15|2.77|
|6|Overall Environmental Indicator|0.69|2.39|4.24|8.43|
|7|Institutional Development Problems|5.00|3.50|2.00|3.00|
|8|Environmental Policies and Institutions<br>Indicator|3.44|8.35|8.49|25.29|

# **3.  Country Experience with Project Implementation**

The World Bank lends to countries that have highly-varied experiences with implementation. To incorporate this factor, we draw on a database maintained by the World Bank's Operations Evaluation Department (OED). The database rates the outcomes of 3,075 World Bank projects implemented in 146 countries since 1990. OED rates projects in eight categories: highly satisfactory, satisfactory, moderately satisfactory, marginally satisfactory, marginally unsatisfactory, moderately unsatisfactory, unsatisfactory, and highly unsatisfactory. We interpret the highest two ratings as "successful" for our purposes, and define our country risk indicator as the percentage of projects rated successful by OED. Table 3.1 displays the distribution of our results by region. Although the estimated success rates are generally highest in Eastern Europe/Central Asia and lowest in Sub-Saharan Africa, countries in all Bank regions except South Asia exhibit a wide range of variation.

8
<!-- Start of picture text --> 0.11<br>0.105<br>0.1<br>0.095<br>0.09<br>0.085<br>0.08<br>0.075<br>0.07<br>0.065<br>0.06<br>1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Y ear<br><!-- End of picture text -->

**Figure 5. Trend of the non-deposit funding share.** The non-deposit funding share is the share of non-deposit short-term funding in total deposits & short-term funding. This figure displays the trend of the non-deposit funding share from 1999 to 2007. The non-deposit funding share data are yearly averages. Data are from Bankscope.

61
- Au, Chun‐Chung, and Henderson, J. Vernon. 2006. “How Migration Restriction Limit Agglomeration and Productivity in China.” _Journal of Development Economics_ , 80: 350‐88.

- Bandiera, Oriana. 2007. “Land Tenure, Investment Incentives, and the Choice of Techniques: Evidence from Nicaragua.” _The World Bank Economic Review_ , 21(3): 487‐508.

- Benjamin, Dwayne and Loren Brandt. 2002. “Property Rights, Labour Markets, and Efficiency in a Transition Economy: The Case of Rural China,” _Canadian Journal of Economics_ , 35(4): 689‐716.

- Benjamin, Dwayne, Loren Brandt and John Giles. 2005. “The Evolution of Inequality in Rural China,” _Economic Development and Cultural Change_ , 53(4) (July 2005): 769‐824.

- Besley, Timothy. 1995. “Property Rights and Investment Incentives: Theory and Evidence from Ghana,” _Journal of Political Economy_ , 103(5): 903‐37.

- Besley, Timothy, and Ghatak, Maitreesh. 2009a. “The de Soto Effect.” Center for Economic Policy Research Discussion Paper 008.

- Besley, Timothy, and Ghatak, Maitreesh. 2009b. “Property Rights and Economic Development.” Center for Economic Policy Research Discussion Paper 7234.

- Brandt, Loren, Rozelle, Scott, and Turner, Matthew. 2004. “Local Government Behavior and Property Right Formation in Rural China.” _Journal of Institutional and Theoretical Economics_ , 160(4): 627‐49.

- Brasselle, A‐S., Gaspart, F., Platteau, J‐P. 2002. “Land Tenure Security and Investment Incentives: Puzzling Evidence from Burkina Faso,” _Journal of Development Economics_ , 67: 373‐41.

- Cai, Fang, Wang, Dewen and Du, Yang. 2002 “Regional Disparity and Economic Growth in China, the Impact of Labor Market Distortions” _China Economic Review_ 13: 197‐212.

- Cameron, Colin A. and Douglas L. Miller. 2011. "Robust Inference with Clustered Data," in A. Ullah and D.E. Giles eds., Handbook of Empirical Economics and Finance, CRC Press.

- Cameron, Colin A. and Douglas L. Miller. 2013. " A Practitioner's Guide to Cluster‐Robust Inference,” _Journal of Human Resources_ (in press).

- CRLSRT (China Rural Local Self‐governance Research Team). 2000. Study on China’s rural self‐governance. [In Chinese]. Beijing: China Agriculture Publishing House.

- de Brauw, Alan, and Mueller, Valerie. 2012. “Do Limitations in Land Rights Transferability Influence Low Mobility Rates in Ethiopia?” _Journal of African Economies_ , 21(4) (August): 548‐79.

- de Brauw, Alan, and Giles, John. 2008. “Rural to Urban Migration in China: How Do Migrant‐Sending Communities Benefit?” World Bank Policy Research Working Paper 4585.

_Land Tenure Insecurity and Migration, Page 31_
2015c). Still, the vision is supported in pieces, and the most recent impact studies make a case for guarded optimism (e.g., Breza and Kinnon 2017, Banerjee et al. 2015a).

As debates on microfinance proceed, many are turning to broader notions like “financial inclusion” that bring microfinance together with efforts to provide saving, insurance, and payment services in under-served communities. This broadening should also inspire an expanded vision for microfinance itself. In this view, microfinance is not exclusively about fueling small businesses. Instead, microfinance makes life easier by enhancing financial liquidity, making it more likely that households can get hold of money when they need it. Here, microfinance is often worth paying for, and perhaps worth subsidizing, even though it rarely transforms. In this expanded vision, microfinance helps households with the challenges of managing the ups and downs of lives in poverty and near-poverty, even when poverty persists (Collins, et al. 2009). From customers’ viewpoints, microfinance is already seen as providing basic household finance—even though in public documents lenders may describe microfinance as a tool strictly for entrepreneurial finance.

Recognizing that microfinance is akin to consumer finance makes it clearer that donors and investors must also recognize that access to microfinance is not always benign (e.g., Karim 2011). In the 1980s and 1990s, the loan repayment rate coupled with evidence of steady demand had been used as a rough indicator of impact. After all, why would customers borrow repeatedly—and how could they repay their loans steadily and with interest—if the borrowed funds were not yielding high returns to investment? But as microfinance markets saturate and borrowing is partly driven by liquidity needs, it is easier to see how debt problems arise amid sustained demand (Schicks 2013). The past decades give examples of over-lending by providers and over-borrowing by customers—with microfinance crises emerging in economies including 4
# **1. Inequality in ECA: Literature Review**

Inequality considerations are important to policymakers not only because they are linked to the economic state of affairs, but also to the social and political conditions of a given country. This is even more so in the countries of the Eastern Europe and the former Soviet Union that underwent transition from state-control to market economy in the era of globalization. However, there are a limited number of rigorous empirical studies on the evolution of inequality in the transition economies. Although there is a lack of consensus on the impact of inequality on economic growth, the limited empirical evidence that has recently become available for transition countries shows that the effect of inequality on growth can be negative and robust (e.g., Ferreira,1999; Ivanova, 2006; Sukiassyan, 2007).

This section provides a brief review of the literature on the determinants of inequality in transition countries, with particular emphasis on the role of institutions and government policies pursued under the new economic order following the transition. The paper then attempts to investigate the causes of, and establish some stylized facts on the changes in inequality, using a rich data base of household surveys collected over 16 years (1990-2005).

The empirical studies on inequality in transition countries are relatively few in number despite the importance of the topic. There are only several studies that have attempted to systematically and empirically investigate inequality in the transition countries and provide some possible explanations for its evolution since the beginning of transition (Mitra and Yemtsov, 2006; Ferreira, 1999; Milanovic, 1999; Ivaschenko, 2002; Giammatteo, 2006). These studies on the distribution of income immediately, during and after the transition show that there has been appreciable increase in inequality in most of Eastern Europe and the former Soviet Union countries, albeit at varying degrees in magnitude and pace. A widespread view is that the transition to market economy, which entailed several transformations including liberalization capital, goods and services, and 2
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE in Juba—the two largest cities in South Sudan—indicate that power is the largest impediment to their business. Around 83 percent of firms in Juba own or share generators to combat erratic power supply. Generators supply as much as 93 percent of the total power consumption in Juba (World Bank 2009b) (see figure 15).

**Figure 15. Electricity is a large obstacle to business activity in South Sudan**

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Malakal Juba<br>% firms that indicate electricity is a major obstacle % of firms that own generator % of power from generator<br>Share of firms (%)<br><!-- End of picture text --> _Source:_ World Bank 2009a.

South Sudanese power prices range between $0.18 and $0.29 per kilowatt-hour (kWh). This is twice as much as the average African consumer pays, five times what is paid in other developing countries, and more than two times what is paid in Sudan (figure 16).

**Figure 16. South Sudan’s power tariffs are among the highest in Africa**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Source: Briceño-Garmendia and Shkaratan 2009; South Sudan’s estimates were provided by World Bank staff.<br>US cents per KWh<br>Liberia Chad South Sudan Cape Verde Senegal Burkina Faso Mali Uganda Kenya Congo, Rep. Rwanda Benin Niger Cote d'Ivoire Namibia Madagascar Cameroon North Sudan Ghana Botswana Mozambique Lesotho Tanzania Zimbabwe South Africa Ethiopia Botswana Nigeria Malawi DRC Zambia<br><!-- End of picture text --> The high prices reflect that South Sudan has one of the highest costs of power production in Africa (figure 17). The average power cost in South Sudan is as high as $0.37 per kWh, essentially double the average cost of power in Sub-Saharan Africa, which has been estimated at $0.18 per kWh. Part of the explanation behind the high power costs lies in the use of small-scale generation that precludes the system from benefitting from the substantial scale economies that exist in this sector. African countries with power systems as small as South Sudan’s typically face power-generation costs that can be three times as 30
etc.). However, the model assumption of proportional hazard is not satisfied. The main issue at this stage is that CDT data population presents discrete variability patterns that are hardly modeled by parametric or semi-parametric methods.

Stratification can be used for a better fit of parametric or semi-parametric models. Moreover, other treatments (e.g. using time dependent covariate (Collett 2003)) can be applied.

# **Parametric fit using discrete models: Logistic regression**

Continuous models being unsuccessful in modeling CDT data, discrete analysis of CDT is attempted. The objective is to identify which are the most significant determinants of very long and abnormal CDT in the list of variables identified in earlier sections. We transform first the CDT into three discrete levels (categories); (1) CDT less than or equal to 30 days, (2) CDT between 31 and 90 days, and (3) CDT greater than 90 days. An (Ordinal/Multinomial) Logistic regression model is then fitted with the CDT as categorical dependent variable (see Annex for details).

# **Interpretation of modeling results**

Container type is significant in both sub-models (level 2 vs. level 1 and level 3 vs. level 1). This demonstrates that “last-trip” containers, i.e. those containers that are purchased with cargo at a negotiated rate with shipping line (about $2,000 for a twenty feet container) are expected to stay longer in the port with a significant confidence level.

Last ports of call are also significant in both models. Cargos originating from Dubai for example are likely to stay longer as compared with other ports with a justification that needs to be further investigated.

Fiscal regime is also significant: Containers with finished goods and semi-finished goods are expected to have longer dwell time as compared to other categories of goods, which is probably to be linked to the high cost of customs duties that need to be paid as compared to lower fiscal pressure for raw materials for example.

Containers with higher density of value are also likely to stay longer than containers with lower density value probably for similar reasons. It is worth noting that the consideration of logistics cost would lead to the inverse relationship since cargo with high density of value are also those with highest inventory costs, which corroborates our earlier comment on low awareness of total logistics cost.

25
The companion dataset CRU TS 2.0 may be used in conjunction with TYN SC 2.0 to provide complete time series for the period 1901–2100. The control scenario for the 21st century may be duplicated into the 20th century, which provides a time series for the period 1901–2100 without any long-term climate change.

The purpose of providing 20 different futures is to enable environmental modelers to represent the uncertainty in climate impacts arising from two distinct sources of uncertainty: uncertainty in the future emissions of greenhouse gases, and uncertainty in climate modeling. Each of the 20 permutations should be treated as equally likely. Between them, the 20 scenarios cover 93% of the possible range of future global warming estimated by the IPCC (Intergovernmental Panel on Climate Change) in their Third Assessment Report (Houghton et al. 2001). The control scenario may be useful for tuning models, and for establishing baselines.

The TYN SC 2.0 dataset comprises monthly grids of modeled climate, for the period 2001– 2100, and covering the global land surface at 0.5 degree resolution. There are five climatic variables available: cloud cover, diurnal temperature range, DTR, precipitation, temperature, vapor pressure. There is one control scenario and there are 20 climate change scenarios which should all be treated as equally likely.

# _Definition_

Each datum ( _x_ ) in any scenario is defined by the following equation: The calculated value must be checked to ensure that it lies within the permissible range. If the value is out of range it must be corrected to the minimum or maximum permissible value, as appropriate.

The symbols used in the above equation are given in the two tables below Tables 5a and 5b: main characters in the first table, subscript characters in the second table. The ‘units’ given in 18