rafmacalaba/data-use-mentions-extended huggingface dataset:
models are close to our estimation results (Table 5, row 4). We use the unrestricted sample containing households with more than five adults and three children and estimate our main regressions. Estimation results for children are no longer statistically significant for the BUC model, and are only statistically significant in the POL model (Appendix A, Table A.11). At the same time, the estimates for the adult scale parameter remain similar at about 0.6 (Table 5, row 5).

# **6.2. Role of Pensioners**

Our earlier analysis has focused on household sizes and children, but has not discussed the impacts of elderly pensioners on the total household income. Pensioners may have disability or health issues and thus can impose significant costs on the household. On the other side, pensioners often consume less than a working-age adult and can contribute their pension salary to the household income. Our estimates from the RLMS suggest that the share of individuals (in total population) who received any pension in the past month hovers around 30 percent over the period 1994-2017. The majority of these pensioners (more than 70%) receive retirement or old-age pensions.

20
## _Variables in the “Large Model”_

For **human capital** (education) we rely on the log human capital index, _lhc_ , from PWT10.0, which linearly interpolates data on educational attainment from Barro and Lee (2013) and assumes a certain rate of return for primary, secondary, and tertiary education. Human capital is widely assumed to be a key driver of cross-country income differences (Mankiw et al., 1992; Glaseser et al., 2004; Hanushek and Woessmann, 2015) but problems in adequately measuring human capital across a wide range of countries have also been pointed out in the literature (e.g., Pritchett, 2001; Hanushek and Woessman, 2012).

To measure **inequality** , we take the Gini coefficient of market and disposable incomes, _gini_mkt_ and _gini_disp_ , respectively, from the Standardized World Income Inequality Database (Solt, 2019). It should be noted that this data is severely over-imputed.<sup>18</sup> Following the literature (e.g., Bruckner and Lederman, 2018; Scholl and Klasen; 2019), we do not log-transform this variable. As discussed in those studies, there are several approaches to address apparent endogeneity concerns for this variable and several arguments suggest various functional forms (changes vs. levels, quadratic and interaction terms; e.g., Gründler and Scheuermeyer, 2018; Blotevogel et al., 2022). We opt for changes in the Gini of market incomes, _dgini_ , in our regression models.

# **5. Reference Models and Results**

## **5.1 Baseline Results from Least Squares Estimation**

Table 2 contains the results of the fixed effect regression, as specified in equation (1), with the variables introduced above. The “small model” in column (1) captures fewer variables, which allows for a larger sample size (~1,500 observations from 168 countries). At each column
percent of household income. Thus the departure from proportionality represents only a small fraction of the total sums disbursed in the bail-out.

Ironically, a policy which begins by financing liquidity problems of the banking system (and foreign exchange outflows) arising from depositor withdrawals but is then followed by suspension and not paying the remaining depositors (or paying them only in depreciated currency) after the bank fails, or the currency collapses, is likely to be much more regressive. As is comprehensively documented for the cases of Argentina, Ecuador and Uruguay by Halac and Schmukler, it is the largest depositors who withdraw first (even while smaller depositors are still adding to their deposit holdings). Likely the large and well-informed depositors who withdraw early are mostly corporates, but wealthy individuals will also be in this category.<sup>7</sup> Their relative gains are unlikely to show up in surveys of income and expenditure.

> 5 This information is available for some advanced economies.  Interestingly, bank deposits form a much lower proportion of financial asset holdings for higher income deciles in advanced economies, reducing the dispersion of deposit holdings.

> 6 At one extreme, a recent Tanzanian survey found that 94 percent of households had no bank deposits. 7  In Uruguay, most of the withdrawals were by non-residents, presumably from Argentina.

7
## **3.2 Plant Size**

The WBES only interviews enterprises with 5 or more employees and Turkish enterprises have 3.6 employees on average. Consequently, the WBES dataset cannot be used to analyze the plant size distribution of Türkiye. Instead, I use the Annual Industry and Services Statistics provided by the TurkStat.<sup>6</sup> This data is confidential and contains detailed information for all the enterprises in Türkiye. TurkStat annually reports summary statistics, instead of providing micro data. Table 3 reports the plant size distribution in Türkiye and the US.<sup>7</sup> Note that these numbers are the average over the period 2009-2014 for Türkiye and 2009-2015 for the US.

The mean plant size in Türkiye is almost one-fourth of that in the US. Given that the unit of observation in Türkiye is the enterprise whereas in the US it is the establishment, this difference would be bigger if establishments were measured in Türkiye. This is because enterprises may contain more than one establishment. There are only 3.6 employees in an enterprise, on average, in Türkiye. As many as 97.3% of enterprises have 19 employees and 99.6% of enterprises employ fewer than 100 workers. However, enterprises with more than 100 employees employ 43.6% of all workers.

Compared to the US, the plant-size distribution in Türkiye is skewed to the left. However, Türkiye’s total employment size distribution for big plants is similar to that of the US. Türkiye’s frequency of plants with size between 20 and 99 workers is only 2.2% but it is 13.2% in the US. Türkiye’s plant size distribution is an example of the “missing middle" literature proposed by Tybout (2000) and Krueger (2007), there are very few middle-sized firms, and employment is concentrated in a few big firms, and there is a large number of small firms.

# **4 Model**
this is not the case. In Chile, the assets of insolvent institutions amounted to approximately 22 percent of GDP, but bailout costs were twice as high, 41 percent. In Colombia, assets amounted to § percent of GDP, but the fiscal costs of resolving the crisis were lower, 5 percent, Deposits in insolvent institutions were approximately 5 percent of GDP in Uruguay, where bailout costs were 7 percent of GDP; they were 9 percent of GDP in Malaysia in 1985, where the bailout amounted to only 4.7 percent of GDP (asset and deposit information from Caprio and Klingebiel, 1997 and Beck, Demirgit¢-Kunt and Levine).
# **Appendix Al Estimation Difficulties**

This section considers practical and theoretical difficulties involved in estimating beta coefficients from available data. There are several explanations of why beta coefficients may be biased, and some of these reasons cause severe problems for international comparisons of estimated beta values. When these estimates are derived using data from different stock markets, the extent and direction of bias in each estimate may differ and the comparison is likely to be very misleading unless corrections can be made for these distortions.

# **AM.l Data format and availability**

# **AL.I.A No trade periods**

A fundamental assumption of the capital asset pricing model (CAPM) is that all securities are continuously traded and that the price at which such trades may take place is known at each moment in time. In practice, however, shares are traded at discrete times and there are many periods during which no trades take place.

In many stock markets, the measured price for a given share is the price at which its latest trade took place. This method opens up the possibility that the notional price at which a trade _would_ occur may change without any actual trade taking place, resulting in discrepancies between measured and 'true' values. In more sophisticated stock exchanges data is available for quotation as well as transaction prices, which overcomes the no-trade problem in these markets but may distort comparisons with beta values estimated from transaction data.

33
slowed, falling behind GDP growth. After 2008, the size of the PPP portfolio as a percentage of GDP started growing again. At the end of 2020, cumulative investment in the active portfolio of PPP projects was about $1.66 trillion,<sup>4</sup> according to the World Bank Private Participation in Infrastructure Project Database (PPI),<sup>5</sup> which corresponds to about 5.25 percent of the developing world’s GDP.

> 3 The GDP of the developing world was calculated as the total GDP of low- and medium-income countries as classified by the World Bank, plus Romania, for which data are available until the end of first half of 2020 but which was reclassified as a high-income country in 2020.

> 4 All dollar figures in the study are expressed in 2020 US dollars, inflated using the US Consumer Price Index (CPI) series.

> 5 See https://ppi.worldbank.org/en/ppidata.

6
the price of purchasing a calorie depends on the consumption patterns _observed in the survey period_ . Hence, instead of updating over time the price of a calorie for inflation, the Egyptian poverty lines update the price of a calorie as a function of the updated food consumption behavior. This implies that the price of a calorie, and thus the food component of the poverty line, is not necessarily kept fixed in real terms over time. When growth changes consumption possibilities, households may switch their food habits and consume calories of a different quality (say, substitute wheat for animal sourced proteins), thereby changing the real value of the price of a calorie. This feature implies that the Egyptian poverty lines are neither purely absolute nor purely relative.<sup>2</sup> Rather these lines are hybrid, or “weakly relative” (Ravallion and Chen, 2011). Egypt is not an exception in that regard, as the official poverty lines of other developing countries, such as Indonesia or Kenya, have the same feature. This feature considerably complicates poverty comparisons.

The second non-standard feature relates to the equivalence scale implicit in Egypt’s official poverty lines. In line with the CBN method, the non-food component of the poverty line is computed as a fraction of the food component. However, this fraction is not constant but rather depends on the household composition and the consumption patterns _observed in the survey period_ . This implies that the equivalence scale implicit in the official methodology is not fixed, but rather endogenous. In other words, the equivalence scale changes over time and across space. This non-standard feature again makes it challenging to interpret official poverty figures and trends. It is unclear to what extent the departure from more standard poverty lines impacts poverty trends and the distribution of poverty across regions. In this paper, we provide a conceptual and quantitative understanding of the two non-standard features inherent in the official poverty lines of Egypt. We study which of these two non-standard features has the largest impact on poverty figures. We
0.0183)|(0.0215)|(0.0273)|
|**preneur**<br>**Model**|Ever been a business||0.200***|0.142**|0.200***|0.341***|
|**Entre**<br>**Role**|owner||(0.0656)|(0.0590)|(0.0630)|(0.0963)|
|**e skills**|Digitspan score|||0.0137<br>(0.0245)|0.0282<br>(0.0402)|0.0212<br>(0.0469)|
|<br>**itiv**|Raven test score|||-0.0434**|-0.0257|-0.0348|
|stics<br>**ogn**||||(0.0183)|(0.0158)|(0.0271)|
|teri<br>**n-c**|Personal intitative|||-0.0935|-0.182*|-0.147|
|arac<br>**no**||||(0.113)|(0.0990)|(0.181)|
|Ch<br>**and**|Error competence|||0.173|0.166|0.145|
|iner<br>**ve**||||(0.134)|(0.185)|(0.166)|
|Tra<br>**Cogniti**|Prosocial orientation|||0.153<br>(0.113)|0.146<br>(0.164)
advantages. Finally, we also want to test whether certain policies, such as the accumulation of international reserves, help reduce the negative consequences of sudden stops. To this end we will add the interaction of the different variables in equation (1) with measures of policy responses and test whether indeed these interactions help explain differences in the average impact of sudden stops or differences in their impact across industries.

# **3. Data**

For our main indicator of sudden stops, we follow Guidotti, Sturzenegger, and Villar (2004) in defining a sudden stop _SSc t_ , as a year in which the annual change in the capital account (scaled by GDP) is one standard deviation below the average and also below 5 percent of GDP. Both the standard deviation and the average are country specific and computed using all available data during 1975-2005 from the International Monetary Fund (2008), International Financial Statistics (henceforth IFS).<sup>5</sup> In addition to this baseline definition, we also consider three alternatives ways of identifying sudden stops that have been previously used in the literature. First, we follow Rothemberg and Warnock (2006) and Calvo, Izquierdo and Mejia (2008) and define a sudden stop considering annual changes in the capital account at least one standard deviation below average that eventually reaches two standard deviations below average (regardless of the size of the decline as a share of GDP). Second, we further follow Rothemberg and Warnock (2006), Calvo, Izquierdo and Mejia (2008), and Gallego and Tessada (2009) and construct rolling standard deviations to define time varying thresholds. When following this procedure we use quarterly data from IFS to compute meaningful rolling

> 5 Changes in the capital account correspond to changes in financial flows as reported in series 78BJ..DZF (Financial Account N.I.E.) from the IFS. GDP in US dollars also comes from the World Development Indicators.

11
Policy Research Working Paper 9195

# **Abstract**

This paper explores the link between the prevalence of violent conflicts and extremely low female labor force participation rates in South Asia. The Labor Force Surveys from Bangladesh, Sri Lanka, India, and Pakistan are merged with the Global Terrorism Database to estimate the relationship between terrorist attacks and female labor supply. Geographical data on exposure to violence are used to compare administrative units exposed to attacks with those not exposed. The analysis finds that one additional attack reduces female labor force participation rates by about 0.008 percentage point, on average. Violence has less impact on male labor participation, thus widening the gender labor participation gap. The paper tests the added-worker effect theory—which posits that violence might increase female labor force participation as women try to make up for lost household income—and finds mixed evidence: greater prevalence of attacks may encourage married women to work more hours, but when the environment gets more risky, all women work fewer hours. The paper also finds that violence decreases female labor participation less where it was already higher and has a progressively greater impact on lowering female labor participation where the number of attacks is higher.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gacevedo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily
**Figure 11 The gap between weighted and un-weighted labor productivity, 1990s**

<!-- Start of picture text --> The Gap Between Weighted and Un-Weighted<br>Labor Productivity, 1990s<br>Five-Year Differencing, Real Gross Output, Manufacturing<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Data for Hungary, Indonesia and Romania use Three-Year Differencing.<br>Excluding Brazil and Venezuela.<br>Argentina ChileColombia UK oldFinlandNetherlandsWest GermanyFrance Portugal USA TaiwanKoreaIndonesia SloveniaLatviaRomaniaHungaryEstonia<br><!-- End of picture text --> Figure 12 presents the evolution over the 1990s in the transition economies. As may be seen, the transition economies all start out at very low and sometimes negative allocation effects. A negative effect means that allocation was worse than that resulting from a toss of the dice: for some reason resources were disproportionately allocated towards poor productivity firms. However, the transition countries generally exhibit a rapid increase of this ratio over time consistent with the view that allocative efficiency improved substantially in the transition economies over this time period.

**Figure 12   The evolution of the gap between weighted and un-weighted productivity in transition economies over the 1990s**

<!-- Start of picture text --> The Evolution of the Gap Between Weighted<br>and Un-Weighted Labor Productivity<br>in Transition Economies over the 1990s<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Five-Year Differencing, Real Gross Output, Manufacturing.<br>Data for Hungary and Romania use Three-Year Differencing.<br>Estonia, 1995Estonia, 2001 Hungary, 1992Hungary, 1995Hungary, 2001 Latvia, 1996Latvia, 2001 Romania, 1995Romania, 1999 Slovenia, 1992Slovenia, 1995Slovenia, 2001<br><!-- End of picture text --> 41
# 1 INTRODUCTION

Since the end of the last century, countries around the world have enacted a plethora of power sector and renewable energy policies at different points in time, in different combinations, to advance one or several environmental, economic, security, or equity policy goals. These policies encompass instruments ranging from feed-in tariffs for renewable energy generation to renewable energy targets and from biofuel blend mandates to carbon pricing mechanisms. More recently, these policies were also motivated by the growing challenge of mitigating climate change on a global scale.

We offer the first comprehensive and systematic assessment of how energy policies, largely related to the deployment of renewable energy, shaped the decarbonization of the energy mix 3 to 7 years after their implementation across more than 100 developing countries and four decades.

In doing so, our study fills an important knowledge gap. A systematic review of published research that assessed the impacts of a wide range of decarbonization policy instruments related to renewable energy indicated that most existing studies focus on advanced industrialized countries (Peñasco et al., 2021).

We consider five inter-related decarbonization outcome indicators of the energy mix, all in relative terms as a share of the total: (1) renewable energy consumption; (2) renewable electricity output; (3) fossil fuel energy consumption; (4) oil, gas, and coal energy electricity production; and (5) oil electricity production.

Our policy variables are grouped into seven energy policy packages leveraging the World Bank Regulatory Indicators for Sustainable Energy (RISE) data.<sup>2</sup> The dataset includes over 75 policy instruments related to renewable energy, power, and fuel use implemented

> 2 For a detailed description of the database, please refer to its official website: https://rise.esmap.org/.

2
Brown and Dinc (2004) use data on individual bank failures in developing countries to investigate the impact of political factors on bank fragility. They find that political concerns play a significant role in delaying government intervention in failing banks. For instance, failing banks are less likely to be taken over by the Government or lose their licenses before elections than after elections. This effect becomes even stronger when the ruling party is politically weak.

This brief summary of the recent additions to the bank crisis literature reveals that there has been significant interest in how institutions – economic, financial or political – affect bank fragility. Another broad area of focus has been the impact of the policy framework – financial liberalisation, exchange rate regime, policy on foreign bank entry – on bank stability. Most of the research on these themes uses the multivariate probability model and low frequency data, since institutional and structural variables change slowly over time. Because of this literature, we now know much more and will no doubt continue to learn more about the fundamental reasons underlying financial crises. But what are the economic consequences of banking crises? We turn to this question next.

# **6. The effects of banking crises**

# _The credit crunch hypothesis_

A number of empirical studies of banking crises examine not only what causes crises but also how crises affect the rest of the economy. For example, summarising several case studies, Lindgren, Garcia, and Saal (1996) conclude that bank fragility has adversely affected economic growth. More systematic empirical investigations have also shown that output growth and private credit growth drop significantly below normal in the years around banking crises (Kaminsky and Reinhart, 1999; Eichengreen and Rose, 1998; Demirgüç-Kunt _et al_ ., _forthcoming_ ).

22
- Studies should achieve at least 45% of the maximum possible score to be ranked as intermediate methodological quality.

- Studies that achieved less than 45% of the maximum possible score should be ranked as limited methodological quality.

This means that studies must have at least two of the three double-weighted criteria in order to be eligible for the high methodological category—because the maximum possible score for studies that are missing two of the double-weighted criteria is 7 points.

# **The basis for the eight criteria: Representativeness and the role of chance**

As mentioned previously, the most frequently used epidemiological design for HIV research is the cross-sectional or survey type. This design is applied to a great variety of themes, most frequently serological and behavior surveys. The following is a review of important methodological concepts that need to be taken into account when interpreting cross-sectional studies.

## **Inclusion and exclusion criteria**

Inclusion and exclusion criteria define the universe of eligible subjects that are established by a study. It is very important to have these criteria in mind when making inferences or

**_Inclusion and exclusion criteria define the universe of eligible subjects, a distinction which controls the applicability of the study results._**

generalizations based on the findings in a study. For example, in a serological survey carried out on female sex workers, the inclusion criteria established that the sex workers belong to city brothels, an approach that excluded streetwalkers. Thus, the findings in that particular study cannot be generalized to all female sex workers of the city, but must be limited to the ones working in brothels.

## **Sample size**

Adequate sample size gives a study the ability to prove its hypothesis; in epidemiology this attribute is called “power of the study”. Adequate size in a sample produces more precision in a study’s findings. The larger the sample

**_The power of the study and accuracy of the results depend on the sample size._**

size, the narrower the resulting