rafmacalaba/data-use-mentions-extended huggingface dataset:
There are very few studies in the literature that obtain or estimate the average unit cost based on data from the utility companies actually serving

> 4 Foster (2004) uses data from a household survey that asked respondents to present their electricity/water bills, when available: 28% of the respondents were able to show their water bill and 35% were able to show their electricity bill. At the same time, 42% were unable either to present a water bill or to report their water expenditure; and 35% were unable to show their electricity bill or report their electricity expenditure.

6
Celsius. This relationship is more pronounced in urban areas and not significant in rural areas. For flooding, an additional flood is associated with 0.01 fewer employees in urban and rural areas.

We contribute to the literature on the distributional implications of climate change. Many studies find that the poor are impacted more severely by extreme heat, floods and other climate shocks.<sup>1</sup> (Kahn 2005; Hallegatte et al. 2016; Triyana et al. 2024). But as noted in a recent review (Triyana et al. 2024), evidence on differential exposure to climate shocks is comparatively limited and mixed. Research into this question has been constained by limited spatial detail in available data on household poverty and wealth. In effect, most existing studies have used poverty or wealth estimates that only vary across admistrative units such as districts and subdistricts. This is a significant drawback because there is sizable varation in wealth within districts and subdistricts.

Recent work has benefited from the growing availability of more spatially detailed data on poverty. Notably, Park et al. (2018) use 0.5 by 0.5 degrees (approx. 50 by 50 kilometers at the equator) resolution geo-referenced household survey data to examine the exposure of poor households to extreme heat in 52 countries and find that the poor tend to be more exposed to heat than the nonpoor in hot countries. Using data at a similar spatial resolution, Winsemius et al. (2018) find that globally, the poor are more exposed to flooding.

Use of the RWI allows our study to exploit even more granular (2.4 by 2.4 kilometers resolution) variation in relative wealth. Moreover, as it is constructed using a machine learning algorithm, the RWI is a standardized measure which does not rely on researcher discretion in the choice of survey questions used to index wealth. Additionally, we use a definition of urbanicity that is comparable across countries (Nelson et al. 2019). These comparable definitions allow for an
# **3.3 The impact of good people management**

In this subsection, we use the theoretical framework to give a possible explanation for why schools with good people management may produce better student outcomes. In Section 3.3.1, we decompose the test score gain from people management into two effects: teacher selection and teacher incentives. If this decomposition is correct, then we should see evidence of these mechanisms in intermediate school outcomes. We develop this argument, and present corroborative evidence from our PISA dataset, in Section 3.3.2.

## **3.3.1 Decomposing the test score gain into teacher selection and incentives**

A school of management type _i_ hires the teacher if, given her ( _τ, θ_ ) type, she expects to receive a higher payoff teaching in this school compared to other schools or working in the outside sector. Let the set of ( _τ, θ_ ) types hired by a school of management type _i_ be denoted by _T_<sup>_i_</sup> . The expected learning outcome of a representative student (i.e. _ex ante_ , prior to occupational and effort choices) can therefore be written as where 1 _{_ ( _τ,θ_ ) _∈T i}_ and 1 _{_ [( _τ,θ_ ) _∈T/ i}_ are indicator functions for the hiring and not hiring events. In keeping with the empirical application, we will refer to E [ _y_<sup>_i_]</sup> as the expected test score in school _i_ .

The difference in expected test score across high and intermediate management schools—that is, the impact of people management holding operations management constant—can be written as where the equality follows from the fact that people management only impacts test scores when the teacher is hired (the effect of household effort and the error term difference out). It is helpful 17
1975.<sup>18</sup>

# **(6.2) Defining Market Access**

Many studies rely on the distance to the nearest city/town as a measure of market access. It seems intuitive in the context of the simple theoretical model developed above where distance to the nearest city ( _K_ or _J_ ) is a sufficient statistic for market access of a location.

> 17We have data on 1975 which is used for initial conditions including the 1975 population density as a measure of agglomeration.

> 18The DID results do not change in any significant way if we exclude arc distance to the nearest route from the regressions.

20
FAO. 2008. Global Change in Net Primary Productivity (1981-2003). Rome: Food and Agricultural Organization.

- FAO. 2010. Global survey of agricultural mitigation projects. Rome: Food and Agricultural Organization. Available on the internet at http://www.fao.org/docrep/012/al388e/al388e00.pdf.

- FAO. 2010. Land use systems of the World – East Europe and Central Asia.  Available on the internet at: www.fao.org/nr/lada/

- FAO. 2011. FAO statistical databases. Food and Agriculture Organization. Available on the internet at faostat.fao.org

- FAO-UNESCO. 2007. Soil Map of the World: Organic carbon pool – Topsoil. Available on the internet at www.fao.org

- Ghosh, P. and J. Puri (eds). 1994. Joint implementation of climate change commitments: Opportunities and apprehensions. Tata Energy Research Institute, New Delhi.

- Gold Standard Foundation. 2010. The Gold Standard Premium quality carbon credits requirements. Available on the Internet at www.cdmgoldstandard.org.

- Graff-Zivin, Joshua and Leslie Lipper. 2008. Poverty, risk, and the supply of soil carbon sequestration. Environment and Development Economics 13(3), 353-73.

- Grubb, Michael, Christiaan Vrolijk and Duncan Brack. 1999.  The Kyoto Protocol: A Guide and Assessment. London: Earthscan/ James and James.

- Gulbrandsen Lars H. and Steinar Andresen. 2004.  NGO influence in the implementation of the Kyoto Protocol: Compliance, flexibility mechanisms, and sinks. Global Environmental Politics 4(4), 54-75.

- Haites, Erik, Maosheng Duan and Stephen Seres. Technology transfer by CDM projects. Climate Policy 6(3), 327-344.

- Hall, Anthony. 2008. Paying for environmental services: the case of Brazilian Amazonia. Journal of Internation Development 20 (7), 965-981.

- Hamilton Katherine, Milo Sjardin, Molly Peters-Stanley and Thomas Marcello. 2010. Building
0.14|
|N|22956|22956|22742|22956|

Note: OLS regression. The dependent variable is whether the firm has introduced a new or improved product or service over the last three years ("innovation diffusion or new to the firm"). Robust standard errors clustered at the sector level are reported in brackets. ***, **, * represent significance at the 1%, 5%, and 10% level.

# **3.1. Addressing endogeneity concerns about access to credit**

The link from access to credit to innovation can be subject to possible reverse causality and endogeneity issues. We apply the following method to identify a weakly exogenous credit access shock at the firm level. First, we identify countries in our sample with more efficient banking markets.<sup>4</sup> Second, we estimate a market scoring model for those efficient markets using "Does the firm have a credit line" as the dependent variable and all other variables from the World Bank Enterprise Survey as potential explanatory variables. We use LASSO as the dimension-reduction technique to arrive at an estimate of a parsimonious credit scoring model. Third, we take the OLS estimated credit scoring model based on LASSO-selected covariates for efficient banking credit markets to the remaining countries in our sample and calibrate it for each firm. Fourth, if the calibration indicates that the firm belongs to the lowest 25<sup>th</sup> percentile of scored firms, we assume it is not creditworthy and should not have access to credit. Fifth, if any such not-creditworthy firm did get credit, we identify this as a positive credit shock (0/1 dummy). Finally, we use this positive credit shock instead of the credit line access variable in our estimations. The results are reported in Table 3. The estimation results reinforce the significance and magnitude of the baseline findings regarding the impact of state ownership and foreign state ownership variables on firm innovation. Using the credit shock helps better
interaction term between bank credit and a proxy measure of the level of competition in the banking industry which equals the assets of the five largest banks in the country-year as a share of total commercial banking assets ( _5-bank asset concentration_ ). For robustness, we also report results using a similar index for the three largest banks (3 _-bank asset concentration_ ). Higher values of the concentration indices imply less competition in the banking industry. The data source is GFDD, World Bank.

In all the heterogeneity estimations, we include as controls interaction terms between bank credit and the following variables (defined above): log of GDP per capita, log of number of workers at the firm (firm size), log of age of the firm., and labor productivity of the firm (logs). These controls guard against the possibility that our main interaction term (as stated in Hypotheses 1-3) is spuriously picking up the differential effect of bank credit in countries with high vs. low GDP per capita, and firms of different size, age, and labor productivity.

# **4. Base regression results**

Base regression results are provided in table 1. Panel A in table 1 contains the estimated log odds ratios while Panel B contains the estimated marginal effects of the bank credit to GDP ratio.<sup>4</sup> Table 1 shows that regardless of the controls, the relationship between the bank credit to GDP ratio and the likelihood (log odds ratios and marginal effect) of a having a woman top manager is positive and statistically significant at the 1 percent level. Without any controls (except for country, year, and industry fixed effects), the odds of having a woman top manager increase by 2.0 log points due a unit increase in the bank credit to GDP ratio (column 1, Panel A). The corresponding increase with various baseline controls included (columns 2-5) is larger, equaling 2.7 log points for the final

> 4 Unless stated otherwise, all
40 Since the household survey contains information on which radio stations households listen to, it is possible to ask directly whether listening to stations that broadcast education programming influences household investment decisions. This is yet another link in the causal chain from radio access to education outcomes. Of households with children in school, 67 percent listen to community radio. Though this ratio is almost precisely the same (66 percent) for households that do not have children in school, it is nevertheless possible that the decision to listen to community radio is potentially endogenous to unobserved household characteristics that influence education investment. To address this possibility, radio access can be used as an instrument for listening behavior, assuming that radio access is exogenous to households and influences education investments only through its effect on household listening behavior.

The third column in Table 7 therefore asks whether households that report listening to at least one community radio station, using access to community, private commercial, public and religious radio stations as instruments for station preference. All types of stations are used as instruments since the decision to listen to any one type of station is dependent on the availability of other kinds. As the lower rows of Table 7 demonstrate, station access has a significant effect on whether households listen to community radio; the _F_ -statistic associated with the instruments implies that the probability that the instruments do not add explanatory power to the first-stage regression is less than two percent. Evidence from the Hansen‘s _J_ -statistic (with a _p_ -value of .92) is also supportive of the exclusion restriction: the effect of access variables on book-buying is only through household listening behavior. Using these instruments to control for the endogeneity of listening behavior, then, the estimated effects of listening to community radio are large: households that listen to community radio purchase of books for 1.8 additional children.

The fourth and fifth columns of Table 7 display the results for fee payments reported by parents. The fourth column shows the OLS estimates of the effect of living in a village
Policy Research Working Paper 6025

# **Abstract**

This paper provides the first analysis of the Global Financial Inclusion (Global Findex) Database, a new set of indicators that measure how adults in 148 economies save, borrow, make payments, and manage risk. The data show that 50 percent of adults worldwide have an account at a formal financial institution, though account penetration varies widely across regions, income groups and individual characteristics. In addition, 22 percent of adults report having saved at a formal financial institution in the past 12 months, and 9 percent report having taken out a new loan from a bank, credit union or microfinance institution in the past year. Although half of adults around the world remain unbanked, at least 35 percent of them report barriers to account use that might be addressed by public policy. Among the most commonly reported barriers are high cost, physical distance, and lack of proper documentation, though there are significant differences across regions and individual characteristics.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at Ademirguckunt@worldbank.org and lklapper@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 represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World
# **I. Introduction**

Between 1975 and 1978, under the regime of the Khmer Rouge, Cambodia experienced a dramatic political shock: massive killings and starvation, large-scale population transfers, forced labor on collective farms, and an almost complete destruction of the school system.

The extreme atrocities endured by the Cambodians have been documented and identified as a genocide (Banister and Paige Johnson 1993; Chandler 1996; Kiernan and Boua 1982; Kiernan 1993; Kiernan 1996; Kiljunen 1984; Sliwinski 1995; Vickery 1984), however, few studies have quantified the extent or measured the long-term impact of these events. Using diverse micro-level representative surveys of Cambodia (Cambodia Socio-Economic Survey 1997, Demographic and Health Survey 2000, Labor Force Survey 2001), this paper investigates the long-term consequences of the Khmer Rouge period on a range of outcomes in today's Cambodia.

The aim of the analysis is to determine the legacy of the Khmer Rouge period on the current population structure, health status, and schooling levels in Cambodia. The excess mortality of the 1970s dramatically affected the population composition. Using data from siblings’ mortality in the Cambodia Demographic and Health Survey, I show that excess mortality was heavily concentrated in the 1974-80 period. I also show that adult males were most likely to die, indicating that violent deaths represented a large share of the excess mortality. Individuals with an urban or educated background were more likely to die, establishing that they were especially targeted. Infant and under-five mortality was also at very high levels around the Khmer Rouge period. For cohorts exposed to the risk of violence during the period, disability rates are much larger for males than for females and this is mainly due to disability caused by landmines and other weapons.

Fertility and marriage rates were very low under the Khmer Rouge but rebounded dramatically immediately after. Since the death toll was extremely heavy for adult males, the marriage market seems to have adapted by reducing the age difference between partners.

As the school system was ruined under the Khmer Rouge regime,
and private (Assistencia Tecnica Privada-ATP2). While the first two are provided by INTA's staff, the third is carried out by private extension firms. At this stage ATP I and ATP2 are still subsidized by INTA.

> 5INTA's mandate is to reach small and medium farmers. These include some 170,000 out of a total of 243,000 rural families.

> 6 However, based on FIDEG (1995) Only 8.1% of the 313,845 farmers in Nicaragua received extension of any kind from any source. Ofthe 8.1% receiving extension, 24.1% (6126) were served by INTA. This discrepancy is explained by improper documentation of producers served by INTA prior to the 1993 reorganization. For example, INTA's 1997 budget targets 26,000 producers through ATPb and over 5,000 producers through ATPI. Additional 15,000 producers are targeted via ATP2.
industry. Then equation (11) leads to an estimable stochastic equation such as the following where _i_ and _t_ index the individual establishment and timing of each observation: where β� �β��σ��0 and u�� is a random error term that may include unobserved establishment effects.

# _B. Data and Measurement Issues_

Our data are drawn from an 11‐year, annual census of manufacturing firms from Ethiopia.<sup>4</sup> This panel contains production data for the period 2000 to 2010 and covers all manufacturing firms employing 10 or more employees.<sup>5</sup> Industries are classified at the 4‐digit International Standard of Industrial Classification (ISIC) level. We analyze data from eleven 4‐digit ISIC industries on which sufficient observations are available in the data per annum over several years for estimating parameters of the production function at the level of the individual industry. Importantly, the data include producers’ physical outputs, q� , along with their respective prices, p� . This allows us to distinguish between revenue‐based measures of total factor productivity (TFPR), defined as the value of revenue �p�q�� per input unit �x�� and its physical counterpart (TFPQ), defined as the number of physical units produced per unit of output �q�/x�� . We analyze data on TFPQ and TFPR on only four of these eleven industries. These industries are: 1) cinder blocks (ISIC 2695); 2) cooking oil (ISIC 1514); 3) grain mills (1531); and 4) bakeries that produce white bread (ISIC 1541).

We limit our analysis to changes in TFPR for the remaining seven industries. We group these seven industries into two sets. The first set is “textiles” which includes weaving and spinning activities (ISIC 1710), manufacturing of wearing apparel (ISIC 1810) and the footwear industry (ISIC 1920). The second set
from foreign countries. Hovhannisyan & Keller (2010) analyze data on international business travel from the U.S. to 74 other countries, and find that it has an effect on innovation in these countries beyond the usual technology transfer through trade and FDI. The stock of migrants itself may also induce network effects from increased trade (Rauch 2001) and knowledge transfer (Kerr 2008; Kerr & Lincoln 2010). Even the fact of immigration can be a source of growth for the recipient nation, as migrants tend to be self-selected as industrious and seeking opportunity (McCraw 2010).

# **3 Empirical measures of growth polarity**

## **3.1 Measure construction**

The most straightforward measure of a growth pole is a given economy’s contribution to global growth: where _yit_ is the GDP of country _i_ at time _t_ , _Yt_ =<sup>�</sup><sup>_N_</sup> _j_<sup>_yjt_is global GDP which simply aggregates</sup> GDP for all _N ∈ C_ countries, and ∆ _yit ≡ yit − yi,t−_ 1 is the change in GDP of economy _i_ . The above can be rewritten as where _s_<sup>_y_</sup> _it_<sup>and</sup><sup>_g_</sup> _it_<sup>_y_aretheoutputshareandgrowthrateofcountry</sup><sup>_i_attime</sup><sup>_t_,whichmeans</sup> that a growth pole as defined in (1) is simply the size-adjusted growth rate of the economy. (1) is the standard approach to decomposing the relative contribution of each country to global growth. While intuitive and direct, such a measure would be incomplete. Critically, it fails to embody the manner by which growth poles exert their _polarity_ , in the sense of capturing the transmission and spillover mechanisms for the country’s growth to others in its economic space.

The natural extension of (1) is then
.4|1.1|1.2|
|Expenditures per capita|1.3|1.2|1.2|1.0|1.3|1.0|1.0|1.1|1.4|1.2|
|Household expenditures|1.0|0.67|0.87|0.85|0.7|4<br>0.87|0.79|0.97|0.81|0.90|

_Source_ : World Bank staff calculations using household survey data. _Note_ : The ratio for a given item is that of those using LPG and had abandoned polluting fuels to that of those citing LPG as the primary cooking fuel and retained the use of polluting fuels. Expenditures on LPG and electricity are not deflated to avoid geospatial deflators markedly altering what households had spent. Ratios exceeding 1 (namely metrics for those who had abandoned polluting fuels were larger than those who had not) are shaded in light blue. To avoid giving a false sense of accuracy, only two significant figures are shown for all statistics except the percentage of the total population in the first line for each quintile, for which no decimals are retained.

a. Urban households only.

In a given expenditure quintile, households using LPG as their primary cooking fuel but retaining polluting fuels could even have nearly twice as many members as LPG-using households that had stopped fuel stacking, and in no country were fuel-stacking households on average the same size or smaller than
is a decrease of 7.4 and 7.2% in the lower and upper thresholds, respectively.

Table 3. Decomposition of changes to middle-class thresholds

|**Threshold**|**s in 2011**<br>**PPP**|**Original**|**Proposed**<br>**approach with**<br>**3 countries as**<br>**in Lopez-Calva**<br>**& Ortiz-Juarez**<br>**(2014)**|**Proposed**<br>**approach**<br>**with all 15**<br>**countries**<br>**available**|
|---|---|---|---|---|
|**Lower**||13|13.5|12.5|
|**Upper**||70|78|72.4|

Source: Own estimations based on SEDLAC (CEDLAS and the World Bank).

# **V. Impacts of 2017 PPPs on poverty, vulnerable, and the middle class**

In this section, regional, sub-regional and country-level poverty, vulnerable, and middle-class estimates are presented over time with the 2017 PPPs, in comparison with the 2011 PPPs. This analysis helps understand how incidences and the geographic distribution of the poor, vulnerable, and middle-class populations would change when using 2017 PPPs and higher thresholds. Profiles for these populations are presented over time with the 2017 PPPs, relative to the 2011 PPPs. It is important to assess whether this update of PPPs and thresholds significantly impacts the characteristics of these populations. Marked changes in what the poor and the vulnerable look like affect policy design.

16