rafmacalaba/data-use-mentions-extended huggingface dataset:
of ...nancial dependence for each industry. The expectation is that if ...nancial linkages play a signi...cant role in helping establishments attain greater resilience to credit crunch, the role should be particularly pronounced in industries with greater ...nancial dependence.

The ...nancial dependence variable is measured using the share of capital expenditure not ...nanced with cash ‡ows from operations for the median ...rm in each industry, and captures establishments’dependence on other sources of capital supply such as external loans and intra...rm ...nancing. Similar to the ...nancial linkage variable, the ...nancial dependence measure is computed using the 2000-2004 publicly listed company data from Global COMPUSTAT, with the ranking of industries found relatively stable over time and highly correlated across major countries. Rajan and Zingales (1998) and Braun and Larrain (2005) argue that the measure of ...nancial dependence captures a large technological component that is innate to a sector and is therefore a good proxy for ranking industries in all countries. Industries with the greatest ...nancial dependence according to this classi...cation include, for example, electric machinery, textiles, transport equipment, plastic products, and primary metal.

## 3.4 Other Economic Characteristics

A variety of other establishment-level economic characteristics including sales, labor productivity, age, multi-product status, and exporter status, all lagged by two years, are also taken into account. We use lagged sales to control for establishment size, and labor productivity to proxy for e¢ ciency. We also considered alternative measures such as employment size and market share and found similar results. In addition, we take into account each establishment’s age and multi-product status. The latter variable is an indicator variable that identi...es establishments producing more than one product, and helps control for the e¤ect of product diversi...cation. Finally, we use an
, the loss of revenue would initially present a socioeconomic problem. Such activities could even provide social stability. A study collecting primary data in 2011 in Sierra Leone found that 63 of 64 charcoal-producing villages visited during the data collection had not been involved in charcoal production prior to the civil war. One charcoal trading village consisted entirely of ex-combatants who had turned to cooperative charcoal production as their exclusive source of income (Munro, van der Horst, and Healy 2017).

As the sense of urgency about biomass use driving global deforestation has abated, the last two decades have seen a growing recognition of the high human cost of air pollution from household use of fuels emitting harmful pollutants. The World Health Organization in 2018 estimated, based on updated data from 2016, that 3.8 million a year died prematurely from illnesses attributable to elevated concentrations of PM2.5—particulate matter with an aerodynamic diameter of 2.5 microns or less—caused by emissions from household use of solid fuels and kerosene for cooking.<sup>4</sup> An updated estimation for 2019 shows that the number of premature deaths from household air pollution had declined to 2.3 million (HEI 2020), but there were large regional differences. South and East Asia had made considerable progress in reducing the number of people exposed to household air pollution, driven by China and India, which had the largest reduction in the total number of people exposed between 2010 and 2019. By contrast, despite the declining _percentage_ of the population exposed, several countries in Sub-Saharan Africa with rapidly increasing population saw an increase in the total _number_ of people exposed. Globally, in the 17 countries with more than 50 million people and at least 10 percent of the population living in households with solid fuel use, five countries saw a net increase in the number of people exposed to household air pollution in the two decades, all of them in Sub-Saharan Africa.

In light of the foregoing and additional concerns about the toll—especially on women and children—of
firms have been under-researched in the literature. Hopkins (2002) finds that in Britain about 24% of retailers and manufacturers were burgled in 1993 in contrast to 5.6% of households, implying a higher rate of victimization for firms. Large firms experience more crime than small firms, although small firms face a larger burden of crime in a sample of Latin American countries (Amin, 2009). Also firms owned by immigrants are more vulnerable to crime than native owned firms (Amin, 2010).

We expect a couple of ways in which crimes experienced by firms may differ from those faced by households. A greater police presence, which is measured simply as the total number of police personnel, may have a stronger deterrence effect on crime experienced by firms than individual crime. This may be because criminals are more likely to displace crime targeting firms with less serious criminal activities when police presence may be greater. The deterrence of crime may come in the form of increased police patrols in business areas, or even increases in the capacity to investigate crimes. However, most firms may have the capacity to utilize private security measures to deter criminal activity, although we do find later in this study that controlling for security costs does not alter our main results. Furthermore, one cannot completely rule out the possibility that the presence of a police force may reflect the existence of criminal activity, but have no correlation with crime experienced by firms. Thus, which mechanism dominates is an empirical question.

Empirical studies on crime in developing economies tend to use cross-country crime data from police reports or household crime data from household surveys in a specific city or country. The difficulty of using police reports is that given the level of mistrust of police in developing economies, police reports typically under-report crime (Soares, 2004; Levitt, 1998). The limitation of the household survey studies is that they do not consider crime faced by firms, which tends to 4
However, this section opts for a more specific focus on the federal government budget balance for two noteworthy reasons. Firstly, in Argentina ́s context, the trajectory of the general government fiscal accounts is predominantly influenced by the federal government ́s accounts. With the exception of a brief period in the late-1990s, sub-national administrations (i.e., provinces) have generally maintained balanced or slightly deficit-ridden public accounts ( **Figure 18** ). Secondly, in accordance with Musgrave's seminal work in 1959, the stabilizing function is typically entrusted to the central government.

**Figure 18. National and Subnational fiscal balances (1961-2022)**

<!-- Start of picture text --> 4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>-12<br>-14<br>-16<br>Federal Government Provinces<br>Percent of GDP<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br><!-- End of picture text --> _Source:_ Ministry of Economy.

Conforming to the literature, we categorize taxes into four distinct groups, each exhibiting different elasticities to the economic cycle. Notably, considering that the federal government retains roughly 42 percent of the receipts for various tax categories in Argentina, this study simplifies its analysis by relying on the resulting share of resources officially recorded as central government revenue, an estimation based on data published by the Ministry of Economy ( **Figure 19** ).<sup>2021</sup> These categories are as follows: i) Personal Income Tax (PIT), averaging 2.3 percent of GDP over the past decade, with nearly 1.1 percent of GDP allocated to the Federal
# **Table A7: The effects of temperature on poverty – Grid-level analysis**

|Dependent variable:|Panel|model|Long differ|ences model|
|---|---|---|---|---|
|Poverty rate at $1.90|Baseline<br>(1)|Extension<br>(2)|Baseline<br>(3)|Extension<br>(4)|
|Temperature|0.102***|-2.046***||-0.0006***|
||(0.022)|(0.060)||(0.0001)|
|∆Tempearture||0.870***|0.009***|0.005***|
|||(0.033)|(0.001)|(0.001)|
|Temperature squared||0.092***<br>(0.002)||0.0001***<br>(0.00004)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|No|No|
|Year FE|Yes|Yes|No|No|
|Number of countries|82|82|82|82|
|Observations|1,115,478|1,072,575|42,903|42,903|
|R-squared|0.929|0.555|0.001|0.007|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the country level. Poverty incidence is calculated using subnational GDP from Kummu _et al._ (2018) and the poverty line from WDI. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. *** p<0.
# **3** Conceptual Framework

## **3.1 Explanatory Framework: Delivery Challenges**

The exploration of past research demonstrated the need for a comprehensive joint analysis of the impact that various implementation issues can have on Bank performance and the project outcomes. By taking this approach, we strive to account for possible correlates of project success more thoroughly than would be feasible through a sector- or region-specific examination of project indicators. We achieve this with the data compiled in the delivery challenge framework. The taxonomy provides an overarching examination of critical success factors for World Bank projects. Examining the possible symptoms of failure across three broad phases (planning, implementation, and completion), the more holistic approach employed here allows for a robust analysis of project success while creating parameters that are not bound to sectoral and regional specificities. Thus, the data used offer opportunities for generalization, allowing for the examination of the most critical factors at each phase of the project. This is combined with an exhaustive treatment of the relative impact of each challenge on outcome ratings, overall Bank performance, and quality of supervision. By testing the delivery challenges against pre-existing macro- and micro-determinants of project success, this paper offers a systematic breakdown of the location, frequency, and impact of the most challenging obstacles faced by practitioners throughout the disbursement process.

Delivery challenges are central to understanding the intricate underpinnings of project success. Complex linkages between a diverse series of factors have led to an unfortunate reliance on allencompassing jargon such as “institutional weakness” or “lack of political will.” These heuristics provide a general sense of the underlying difficulties faced, but lack an important element of analytical rigor that would allow them to be compared across different projects and in a variety of different contexts. Simply put, current metrics for “what went wrong” have been defined with insufficient specificity, in a way that does not properly capture the extent of variation across different delivery projects. One issue involves the inherently subjective conceptual framework that underlies many delivery challenges.
we expect lower quality data to drive larger forecast errors. The identification strategy leverages cross-country variation across time in both forecast errors and the LLII) SCI. There are two main concerns of endogeneity. One is the possibility of simultaneity bias or reverse causality. It seems unlikely that the forecast errors will affect the quality of the data systems. However, one cannot rule out the possibility that as a response to growth forecast errors, policy makers may enact reforms to improve data systems. To limit this, we use the lagged value of the SCI. The second concern is that of omitted variable bias. We address this by accounting for as many relevant covariates as possible, as discussed below. We cannot completely obviate the endogeneity concerns in the study, but we do alleviate them as much as possible.

To account for omitted variable bias, explanatory variables employed include several covariates. The log lagged GDP per capita in constant 2010 US dollars ( L L L ) is included as a proxy for the level of economic development. According to Loungani (2001), on average, forecast errors are positive LLFF (optimistic), and more pronounced in developing countries. Due to their greater susceptibility to shocks, smaller economies may exhibit greater volatility than larger ones. Developing economies may be more susceptible to severe shocks that might result in significant forecast errors since they may be less diversified and dependent on the fluctuations of commodity prices (Eicher et al., 2019). Therefore, we include the lagged log of the total number of population in an economy (L L L ) as a proxy for country size, and the absolute value of log change in export commodity prices ( A ), which captures both exposure to commodity exports and the fluctuations of the international prices. An internal conflict dummy variable ( I I ) for internal conflict country-year pairs - as defined using the AA∆L LLFFL LLFFppFFppAA UCDP-Prio Dataset is included in our analysis. Given the significant macroeconomic repercussions of FFppFFI IIL IIppF conflict, growth in countries that frequently experience
MOB and the fact that the CES are consumption-based surveys, we use the consumption-based partition as the sample to calculate a pass-through rate for India. Our preferred estimate of the pass-through rate thus uses the global sample of comparable consumption survey spells in combination with per capita HFCE growth. There are 471 spells in this sample, and the regression using this sample yields a pass-through rate of 0.67 with a 95 percent confidence interval between [0.59, 0.75].<sup>25</sup> While it is impossible to know the true pass-through rate for India over this period, 95 percent of pass-through rates using global consumption-specific survey spells with the per capita HFCE aggregate will fall within this confidence interval. As determined by the

> 23 As noted above, Lakner et al. (Forthcoming) use per capita GDP growth as their main dependent variable (as opposed to per capita HFCE growth used here). They find that in addition to partition by data type, further partitions by median income, level of inequality, and geographic regions yield significantly different pass-through rates when using income-based surveys. However, similar to what we find, they find that within the sample of consumption surveys, there are no further significant splits.

> 24 Lakner et al. (Forthcoming), using per capita GDP growth rate, calculate a slightly higher pass-through rate of 0.72 for global consumption-specific comparable survey spells.

> 25 Table A.5, which is further discussed in the Appendix, compares the estimate from the global consumption-specific comparable sample with estimates using various alternative samples. The difference in the pass-through estimate between the MOB of 0.66, reported in Table 4, and the 0.67 estimate reported here and in Table A.5, is due to the difference in sample size – 457 observations in the former and 471 in the latter. The MOB is more taxing on the data as we use several variables to check for partitioning and there might be missing values in some cases. In this
# **5.2 Informal firms have lower productivity, with consistently lower levels of management and technological capabilities across all types of informal firms**

Consistent with the finding in the literature that informal firms are highly inefficient (La Porta and Shleifer, 2014), the BPS data for South Asia shows that informal firms are largely present in the first two terciles of pre-pandemic labor productivity (Figure 3, panel a)<sup>15</sup> and that there is a clear negative relationship between the degree of informality and productivity (Figure 3, panel b).<sup>16</sup> Figure 3: Informality according to normalized polychoric **PCA 5** vs. labor productivity <!-- Start of picture text --> (a) Proportion of formal and informal by tercile of labor (b) Labor productivity vs. Informality<br>productivity<br>.4 Formal .4<br>Informal<br>.3 .2<br>.2 0<br>.1 -.2<br>0 -.4<br>1 2 3 -.2 0 .2 .4 .6 .8<br>Terciles of initial labor productivity PCA Informality<br>Proportion of firms<br>Labor productivity standardized at country level<br><!-- End of picture text -->

**Notes:** Panel (a) classifies as informal firms those with a value above the median in our continuous measure of informality ( _PCA 5_ ) Panel (b) controls for size, sector, age, country, and severity of the crisis. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

These results hold consistently for other predictors of firms’ total factor productivity, such as managerial capabilities (see Bloom et al., 2019, 2022; Grover and Torre, 2019) and technological readiness.<sup>17</sup
; Munji et al. 2014; Nguyen 2014.) While significant losses due to human actions are likely to continue in the future, it is projected that stresses on mangroves may be further aggravated in the 21<sup>st</sup> century due to climate change. Continuation of the present rate of global warming may even threaten the survival of mangroves. Climate change poses a number of threats to mangroves: rise in sea level, rise in atmospheric CO2, rise in air and water temperature, and change in frequency and intensity of precipitation/storm patterns due to climate change (discussed in Alongi 2008). Among these threats from climate change, sea level

> 3 Data for extended periods are available for some countries.  For example, coastal development in the Philippines has led to more than a 50 percent loss of mangroves since 1900, mainly due to conversion for aquaculture (Primavera 2005, Primavera et al., 2014). Vietnam’s mangrove forests declined about 75% from 1950 to 2000, falling from roughly 400,000 hectares to 100,000 hectares (MONRE 2002).

3
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vote margin was within a 5 percentage-point bandwidth of the zero threshold.<sup>16</sup> In these municipalities, a total of 521 incidents occurred, leading to 315 casualties.<sup>17</sup> Information on the other municipality characteristics included in the estimation were obtained from the 2000 Philippines Census, available from the National Statistics Office of the Philippines.

> 16A violent incident is defined as an incident that leads to at least one casualty.

> 17As Crost et al. (2014) noted, it is possible that the AFP troops misreported the number of casualties related to an incident. This type of misreporting, however, is likely to be limited since the field data were originally collected to be used internally by the AFP for intelligence purposes and for the planning of future operations. Moreover, casualties are typically easy to verify which makes misreporting more difficult, even if individual AFP units had an incentive to do so.

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women’s work compared to that of men in explaining the gender wage gap, a survey experiment was included in three nationally representative surveys in Central Asia in 2022. The experiment took the form of vignettes about workers with randomly assigned characteristics that were read aloud to approximately 4,500 residents of Kazakhstan, Kyrgyz Republic, and Uzbekistan. Each respondent was asked about a set of 8 vignettes per interview, each of the eight focused on a specific occupation. The list of occupations was standardized such that every respondent was told one vignette for the same 8 occupations in the same order. Following each vignette, respondents were asked whether they thought the person described was: “severely underpaid, underpaid, fairly paid, overpaid, or severely overpaid.” Over the course of two months more than 70,000 evaluations of fair earnings were gathered in this form.

The results show a clear and systematic bias against the value of women’s work. Even though the prompts were identical in every other respect, respondents were 13 percent more likely to say wages were “severely overpaid” when the person described in the vignette (subject) was a woman, and 34 percent more likely to say they were “severely underpaid” when the subject was a man.

The findings suggest that the social context is on average biased against women with the same profile as a man, which likely plays a significant role in perpetuating gender inequality in the labor market and the size of the gender wage gap in Central Asia. The presence of this systematic bias against women reinforces the importance of legal provisions to ensure equal pay for work of equal value, and the importance of challenging and changing the norms that perpetuate gender inequality more broadly.

> 3 Details from the World Bank’s Women Business and the Law report are summarized here:

> - <u>https://wbl.worldbank.org/content/dam/documents/wbl/2021/snapshots/Kyrgyz republic.pdf, accessed October 27, 2022.<
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