rafmacalaba/data-use-mentions-extended huggingface dataset:
are usually available for firms listed in stock exchanges across a wide set of economies, but not for unlisted firms that conduct issuances and constitute about 60 percent of all corporate bond issuers (Table 1).<sup>6</sup> As an alternative measure of size that is comparable across firms and covers the whole universe of issuers, we proxy firm size by the average amount raised per issuance, measured over all issuances per firm during 1990-2016.

To make sure that the average amount raised is a reasonable proxy for issuer size, we plot the average amount raised and the average assets (also from SDC) for listed firms. The scatter plot shows a high correlation between the two size variables (Appendix Figure 1, Panel A). Regressions of the log of average assets on the log of the average amount raised per issuance yield a point estimate of 0.99, not statistically different from one but statistically different from zero. To the extent that a similar correlation exists for unlisted firms, the average amount raised should be a reasonable proxy for the analysis of issuer size. A comparison of the firm size distribution (FSD) of listed and unlisted bond issuers illustrates the importance of using a comprehensive and complete measure of firm size. Focusing on the bottom quantiles, the FSD of unlisted bond issuers lies to the left of the FSD of listed issuers, indicating that the smallest firms tapping bond markets are unlisted (Appendix Figure 1, Panel B). If we were to use assets from only listed firms, we would be disregarding the smallest bond issuers.

To link capital raising issuances with firms’ balance sheet and income statement data, we merge the transaction‐level data from SDC with financial statement information for publicly listed firms from Worldscope. Using these data, we could also check that our results on firm size are not specific to the proxy we use. We explored two alternative measures of firm size from the Worldscope data: total assets and net sales, taking the end-of-year values reported by firms
modeled as being additive in TFP, rather than as interacting with the returns to capital, labor and material inputs.

6 Some studies suggest women indeed have more difficulty accessing finance than men (e.g. Brush 1992, Carter, 2000), while others detect no gender differences (Blanchflower et al., 2003, Storey, 2004, Cavalluzzo and Wolken, 2005).

7 Thus, in many cases the term “activity” might have been more appropriate than the term enterprise.

8 Loening et al (2008) demonstrate that the rural non-farm sector in the Amhara region is very similar to the rural nonfarm sector in other regions, although its composition is slightly skewed towards manufacturing activities, whereas in other regions of Ethiopia trade and services enterprises dominate.

9 Only three enterprises in the Ethiopian dataset employ more than 10 workers. Since the enterprises in this dataset are all household-based we might miss out on fully commercial enterprises owned or managed by individuals not living in these communities. However, from the community level dataset one can infer that there are not more than a dozen firms with more than 20 employees in a radius of one hour commuting distance from the 179 surveyed communities. It 30
|

8 decisions about large issues - should a system be presidential or parliamentary? — and smaller but still important issues - how high should voting thresholds be, which rules of proportional representation should be adopted - tend to rest on intuition and political self-interest, uninformed by empirical evidence about the later consequences of these choices for growth, development, or social conflict. Variables in the DPI should contribute to accelerated empirical research on such questions.

# The Variables

The variables in this dataset provide considerable scope for improving our ability to answer these questions. The more than one hundred different variables can be divided into seven general categories. Each category, if not each variable, is described in this section.

## 1. Political system and electoral competitiveness

Two fundamental characteristics of a political system are the relationship of the executive and legislative branches, and the competitiveness of elections of the political actors who occupy them. The DPI has information on both. With regard to the first, the variable SYSTEM was created to capture whether countries are presidential, assembly-elected presidential, or parliamentary. Determining whether political systems are presidential or not depends on answers to a number of questions. Our judgements are similar to those-of others, but were modified to take into account our particular sources.

The first question is straightforward: is there a single executive elected by popular vote? If both are true, the system is coded as “presidential.” However, there are countries in which both a prime minister and a president are in place, or where the executive is elected by the legislature but is called a president and enjoys the same degree of independence from the
# **References**<sup>*****</sup>

Agamile, P., Ralitza, D., & Golan, J. (2021). Crop Choice, Drought and Gender: New Insights from Smallholders’ Response to Weather Shocks in Rural Uganda. _Journal of Agricultural Economics_ . 72(3), 829-856. **[1]** <mark>Alam, G. M., Alam, K., & Mushtaq, S. (2016). Influence of institutional access and social capital on adaptation decision: Empirical evidence from hazard-prone rural households in Bangladesh.</mark> _<mark>Ecological Economics</mark>_ <mark>,</mark> _<mark>130</mark>_ <mark>, 243-251.</mark> **<mark>[</mark> 1]** <mark>Auffhammer, M. (2018). Quantifying economic damages from climate change.</mark> _<mark>Journal of Economic Perspectives</mark>_ <mark>,</mark> _<mark>32</mark>_ <mark>(4), 33-52.</mark> **<mark>[</mark> 3]** <mark>Auffhammer, M., Hsiang, S. M., Schlenker, W., & Sobel, A. (2013). Using weather data and climate model output in economic analyzes of climate change.</mark> _<mark>Review of Environmental Economics and Policy</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(2), 181-198.</mark> **<mark>[</mark> 3]** <mark>Azzarri, C., Haile, B., & Letta, M. (2022
.0|0.0|
|Absolute poverty rate, p.p.|0.0|0.0|0.0|0.0|
|Gini index, p.p.|0.0|0.0|0.0|0.0|
|Total budget balance, %ofGDP|0.0|0.2|0.4|1.1|
|||Scenar|io 1B||
|Income of 1st decile, %|0.0|-0.3|-0.6|-1.5|
|Income of the formalized, %|0.0|-16.4|-16.2|-17.2|
|Income total, %|0.0|-0.3|-0.6|-1.4|
|Absolute poverty rate of the formalized, p.p.|0.0|14.9|13.4|16.6|
|Absolute poverty rate, p.p.|0.0|0.3|0.6|1.4|
|Gini index, p.p.|0.0|0.1|0.1|0.4|
|Total budget balance, %ofGDP|0.0|0.2|0.4|0.9|
|||Scenar|io 1C||
|Income of 1st decile, %|0.0|-0.1|-0.2|-0.5|
|Income of the formalized, %|0.0|-5.1|-5.0|-5.3|
|Income total, %|0.0|-0.1|-0.2|-0.4|
|Absolute poverty rate of the formalized, p.p.|0.0|5.1|4
opportunities.

To provide such context, this paper relies on the Rwandan Integrated Household Living Conditions Survey ( _Enquête Intégrale sur les Conditions de Vie des Ménages au Rwanda or EICV)_ conducted by the Statistics Department of the Ministry of Finance and Economic Planning between October 1999 and July 2001 (Ministry of Finance, 2002). While the EICV — a _multipurpose_ household survey — did not focus on the question of interest here, it is nonetheless based on a representative sample of the population and replete with information regarding Rwandan households.

After identifying the main determinants of participation in the non-farm sector, we use propensity score methods to investigate the differences in outcomes (earnings and consumption) between individuals (households) who engage in non-farm sector activities

> 1 By non-farm sector, we mean both self-employment and wage work in non-farm activities. Rural non-farm self-employed and wage employees are defined as individuals who live in rural areas and work in the non-farm sector. We do not have information on where this activity is taking place. This means that some of these individuals may live in a rural area, but work in a urban area.

> 2 Reardon (1997) suggests four possible advantages from wider income diversification: (1) reduction of income risk (due to the uncertainty linked to their farming activities) by ex ante diversification; (2) maintaining food security in the face of low farm productivity or shocks; (3) ex post diversification because of insurance market failure, and (4) earning cash income to finance farm investments in case of credit market failure.

2
not publicly reported.<sup>10</sup> By implication, our estimates of 1,211 unprotected species for India and 2,156 for China must be significantly higher than the actual numbers. Nevertheless, we believe that analyses for the two countries are worthwhile because our methodology can provide comparative templates that may be useful for colleagues in India and China whose information is more complete than ours. In particular, the inclusion of many new species in our GBIF database may provide new insights about priority areas for protection in the two countries.

# **India**

Our Indian case methodology goes through 175 iterations to identify candidate areas for protecting all of the 1,211 species that are not covered by publicly-reported protected areas. Table 6 displays results for the first 40 iterations, which account for 958 (79.1%) of the unprotected species. The distribution is very skewed, with the first 3 areas accounting for nearly 25% of unprotected species and 11 areas accounting for 50%. Within the new protected areas, the diversity of species representation is striking. Representation varies from 0 to 87.5% for plants, 0 to 82.9% for vertebrates, 0 to 100% for arthropods and 0 to 100% for other species.

> 10 Citation:  Protected Planet (2024). “China chooses to restrict some data on its protected areas. As a result, data on 2,960 protected areas is not publicly-available and cannot be viewed or downloaded on this page. While these sites are included in the coverage statistics presented here, all other statistics (e.g. number of protected areas; breakdown by governance type etc.) on this page are based upon the publiclyavailable data only.” <u>htps://www.protectedplanet.net/country/CHN</u>

36
In order to test these predictions we constructed a data set of 56 countries between 1977 and 1997. In addition to financial liberalization and transparency variables, which we describe below, the data set includes macroeconomic and financial variables that are identified by Demirguc Kunt and Deteragiache (1998b) as increasing the probability of a financial crisis. The macroeconomic variables are: GDP growth rate, inflation rate, change in terms of trade, real interest rate, and exchange rate depreciation rate. The financial variables are: M2 to gross international reserve ratio, claims on private sector as percent of GDP, bank liquid reserves to bank assets ratio and real credit growth two years ago. The source of the data is the International Financial Statistics and the World Bank GDF & WDI.

Data on financial liberalization episodes are based mainly on Williamson and Mahar (1998), Lindgren, Garcia, and Saal (1996), and Drees and Pazarbasioglu (1998). Williamson and Mahar survey 51 financial liberalization episodes in 28 countries between 1977 and 1994, Lindgren, Garcia, and Saal survey 6 financial liberalization episodes in 3 countries, and Drees and Pazarbasioglu survey 4 financial liberalization episodes in 2 countries between 1986 and 1992. Data on banking crises are constructed based on the data and description in Lindgren, Garcia, and Saal (1996) and Demirguc-Kunt and Detragiache (1998)3. Since the crises may affect the macroeconomic variables, we delete the five years following the crises. If the crisis lasts longer than five years, we drop the observations while the crisis lasts.<sup>4</sup> Table 1 presents the countries in our sample, the periods and types of financial liberalization, and the periods of banking crises. The financial liberalization varies in their scope and magnitude. Some liberalization episodes were a move from very control financial

> 3We drop three countries, Guyana, Papua New Guinea, and Zaire in our regression estimations because of lack of macro/financial data. Therefore, there are 89 financial liberalization and 57 banking crises from 56
5 from this study shows the effect of educat'Ln. For example, Ghanaian women with no schooling had fertility 87% of what it would have been if th- had been married through out their reproductive life. Regional averages for this value show that age of marriage has a greater effect on reducing fertility in Latin America and the Caribb- (.73) than in Africa (.8'' and Asia (.78). The educational differentials in tnese factors can be show.. by looking at the marriage index for women of different education groups relative to that for the uneducated. Fcr example, in Ghana women with 1-3 years of school have a marriage index 11% lower than women with no school (i.e. 89% as high).

In Africa the marriage index for those with all levels of schooling is always lower than that of the unschooled, but Latin America and Asia there are several countries where those with some school marry earlier than those with no school (Jamaica, Haiti, Costa Rica, Philippines, Korea and Jordan). Those with 7 or more years of schooling have reduced fertility due to marriage behavior relative to those with no school in every country except Jamaica. On average those with 7 or more years of schooling have fertility only 74% of that of those with no school because of their tendencies to marry later. In Africa, however, education has a somewhat lesser effect than elsewhere. (Here, however, only 5 African countries are used. In other studies where more African countries are used the education effect on marriage is shown to be larger in Africa than elsewhere. U.N., 1987 and Cochrane and Farid, 1985).

The indices for contraceptive use show much wider variations than do those for marriage. Fertility is reduced by only 2% among the least educated in Africa (.98) compared with 21% (.79) among ther least educated in Latin
|Self-employed|22|2|3|2|3|23|24|23|22|22|23|23|23|22|0||0|0|0||0<br>0|
||Not salaried|4||4||4|4|4|5|4|4|4|4|4|4|0||0|0|0||0<br>0|
||Unemployed|8||9||9|9|10|13|7|9|9|9|9|13|0||0|0||0|0<br>0|
|**Household**|Average hh size|5||4||4|4|4|4|4|4|4|4|4|4|0||0|0||0|0<br>0|
|**characteristics and**|Access to electricity (%)|99|9|9|9|9|99|99|99|99|99|99|99|99|99|0||0|0||0|0<br>0|
|**access to services**|Accessto improved water(%)|96|9|7|9|7|97|97|97|96|97|97|97|97|97|0||0|0||0|0<br>0|

Source: SEDLAC (World Bank and CEDLAS).

Note: Note: Since the numbers presented here are based on SEDLAC, a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC aggregate is based on 18 countries in the region for which microdata are available. In cases where data are unavailable for
**Figure 4: National Household Access to Electricity for Census and Statutory Towns**

<!-- Start of picture text --> 100%<br>80%<br>CT ST<br>60%<br>40%<br>20%<br>0%<br>10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Level of Household Electrification<br>Source: Census 2011<br>Share of settlements<br><!-- End of picture text --> However, in the four states being studied, the situation is not as clear. As seen in Figure 5, there is considerable variation between states regarding electricity supply in STs and CTs.

**Figure 5: State Wise Household Access to Electricity for Census and Statutory Towns**

<!-- Start of picture text --> Bihar Jharkhand<br>100% 100%<br>90% 90%<br>80% CT ST 80%<br>70% 70% CT ST<br>60% 60%<br>50% 50%<br>40% 40%<br>30%<br>30%<br>20%<br>20%<br>10%<br>10%<br>0%<br>0%<br><!-- End of picture text --> <!-- Start of picture text --> 100% West Bengal 100% Odisha<br>90% 90%<br>80% 80%<br>70% CT ST 70% CT ST<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<
# **Introduction and Program Description**

Afghan refugees form one of the largest protracted refugee populations in the world, with many Afghans having lived in exile for decades. Despite the ongoing conflict, roughly 2.4 million displaced Afghans have returned to Afghanistan since 2014, including over half a million registered refugees who returned under the voluntary return program of the United Nations High Commissioner for Refugees (UNHCR), which provided them with immediate humanitarian assistance. More than 370,000 Afghan refugees returned to Afghanistan in 2016 alone. Between 2016 and 2018, UNHCR assisted more than 458,000 documented Afghan refugees who returned from Pakistan.<sup>1</sup> The majority of refugees arrived at the Afghan border without any assets or income-generating means. A significant share of returnees were women accompanied by young children, which complicated reintegration.

The majority of Afghan refugees moved to Pakistan during the Soviet invasion in 1979–80, according to the Population Profiling Verification and Response (PPVR) survey conducted in 2011. The protracted nature of the Afghan refugee situation, the large return wave since 2016, ongoing conflict, low economic growth, and weak job creation raise questions about how best to absorb and reintegrate the large number of returnees.

As a response to these challenges, UNHCR offers returning refugees under their mandate cash assistance for reintegration and transportation. Returnees receive two types of cash assistance. First, UNHCR pays transportation assistance, based on the distance traveled and the province of origin. The median transportation assistance received by Afghan returnees in our sample was $45 per returnee. Second, returnees receive reintegration assistance.

Between July 2016 and March 2017, UNHCR increased its reintegration assistance amount from $150 per returnee to $350 per returnee because of the increased needs of returning Afghan refugees and the need to ensure dignified reintegration. The amount of assistance was returned to $150 per returnee in April 2017. The amount of reintegration assistance is thus determined purely by the timing of return, with returnees returning between July 2016 and March
; and Uganda’s HFPS Round 5 (in February 2021), when households were asked to compare their current monthly income to their average

> 10 Furbush et al. (2021) show that the proportion of households reporting income loss relative to the previous HFPS round (typically about a month ago) declined in more recent HPFS rounds in the four countries that we analyzed. They show that over time, NFE income losses remained most common and by August 2020, more than 50% of households still reported losing NFE income compared to the previous phone survey (ibid.).

9
:II and<br>zero<br>otherwise if<br>there is an<br>increase in<br>the slope of<br>the trend, or<br>_DT_<br>would take a<br>decreasing<br>number|(F) Ratio of<br>foreign<br>currency<br>denominated<br>deposits to<br>total<br>liquidity, (X)<br>expected<br>change in<br>exchange<br>rate, (i*-i)<br>interest rate<br>differential,<br>(FCD) total<br>holdings of<br>foreign<br>currency<br>deposits in<br>local banks,<br>(FCDA) ~ in<br>banks abroad,<br>M2, (LE) log<br>of tertiary<br>exchange<br>rate,<br>(LESPT) ~<br>to official<br>exchange rate|F, LE,<br>LEPST are<br>I (1) all<br>others were<br>I(0)|ECM|10 models:<br>_1) F = LE +_<br>_LDIF_<br>_2) F = LE_<br>_3) F = DEP +_<br>_LDIF_<br>_4) F = DEP_<br>_5) F = LE SPT_<br>_+ LDIF_<br>_6) F = LESPT_<br>_7) F = DEP +_<br>_PBV_<br>_8) F = PBV_<br>_9) F = LESPT_<br>_+ PBV_<br>_10) F = LDIF_|In this study, quarterly data for Egypt<br>covering the period 1981:IV to 1993:IV is<br>used to analyze
treating the nonlagged-dependent-variable regressor both as an exogenous variable and as a predetermined variable.

Inspection of table 3 suggests convergence in inequality levels, with the estimators that take into account the possibility of fixed effects resulting in a faster speed of convergence. For example, while the pooled estimator suggests a half-life of about 20 years, the other estimators suggest a half-life of between 1 and 5 years. These results are consistent with the findings of Benabou (1996) and Ravallion (2002)<sup>15</sup> who also find inequality convergence. Further, their estimated coefficients for the speed of convergence are of a similar order of magnitude to our pooled estimator. Two possible interpretations behind the inequality convergence given by Ravallion are related to (i) the implications of the neoclassical growth model (which in addition to predicting income convergence across countries would also predict inequality convergence), or more pragmatically to (ii) the widespread convergence of economic policy during the 1990s. This second argument is likely to weigh less, however, when we take into account the fact that we also find inequality convergence when we control for policy changes, and therefore for policy convergence. As for the impact of initial income on the changes in inequality, table 3 does not find any significant effect, although admittedly, given how parsimonious the models are, this could be due to omitted variables bias.

> 14  In equation 10 the instruments are 3 lagged growth observations and inequality with 3 lagged values of inequality. In equation (11) lagged inequality is instrumented with lagged changes  in inequality and initial per capita income in equation (10) is instrumented with 3 lagged growth observations.  These instruments would be valid even in the presence of fixed effects.

> 15 Ravallion uses a different estimation technique. Rather than relying on the relation between initial Gini and the subsequent change in the index, Ravallion (2002) measures the speed of convergence by comparing estimated trends in inequality with predicted initial levels of inequality.

-12-
|**of total**<br>**ent budget**<br>**of GDP**|
||75<sup>th</sup><br>percentile<br>of five-|75<sup>th</sup><br>percentile|75<sup>th</sup><br>percentile<br>of five-|75<sup>th</sup><br>percentile|75<sup>th</sup><br>percentile<br>of five-|75<sup>th</sup><br>percentile|75<sup>th</sup><br>percentile<br>of five-|
||year||year||year||year|
||average<br>growth||percentage<br>point<br>growth||percentage<br>point<br>growth||percentage<br>point<br>growth|
|Low- and middle-<br>income total|43|5.5|0.6|19|1.6|28|3.2|
|UMICS|32|5.3|0.6|19|1.4|30|3.0|
|LMICs|41|6.3|0.6|20|1.6|26|3.8|
|LICs|60|4.8|0.8|19|2.4|22|2.4|

_Source:_ World Bank calculations based on UIS and IMF data. _Note:_ The 75<sup>th</sup> percentile is defined separately for each indicator. The averages for the share of education and the total government budget are based on the country period averages for 2010 **–** 13 and 2014 **–** 17. LIC