rafmacalaba/data-use-mentions-extended huggingface dataset:
|2014-12|Jiangsu Changjiang|STATS<br>ChipPAC|Singapore|Technology|1660|
|---|---|---|---|---|---|

Source: China Global Investment Tracker.

Table 2: China’s top 10 construction projects in BRI countries since 2014

|Year-<br>Month|Constructor|Transaction<br>Partner|Country|Sector|Subsector|Value<br>(million<br>USD)|
|---|---|---|---|---|---|---|
|2017-04|China Railway<br>Engineering||Indonesia|Transport|Rail|3190|
|2016-08|China Railway<br>Engineering||Bangladesh|Transport|Rail|3140|
|2015-12|State Construction<br>Engineering||Pakistan|Transport|Autos|2890|
|2015-08|China Railway<br>Construction and<br>China Railway<br>Engineering||Thailand|Transport|Rail|2840|
|2016-01|Shanghai Electric||Egypt, Arab<br>Rep.|Energy|Coal|2640|
|2017-04|CNPC||Russian<br>Federation|Energy|Gas|2520|
|2016-01|Three Gorges||Pakistan|Energy|Hydro|2400|
|2016-06|Harbin Electric|DEWA|United Arab<br>Emirates|Energy|Coal|2150|
|2016-11|China Railway<br>Contruction, China<br>Railway Engineering,<br>and China<br>Communications<br>Construction||Malaysia|Transport|Rail|2120|
|2016-03|Sinomach|Electricite<br>du Laos|Lao PDR|Energy||2100|

Source: China Global Investment Tracker.

# 3.2 Transportation Cost

To measure transportation costs, we construct detailed transportation cost data by the mode of transportation. First, we measure
In contexts where legal reform has been enacted, observational studies can measure the causal impact of introducing gender equal inheritance law. Women in Western countries progressively gained property and inheritance rights in the 19<sup>_th_</sup> and 20<sup>_th_</sup> century. Shammas (1994) argues that the Married Women Property Acts which were progressively adopted by US states from 1839 improved the legal situation of women. According to the historian, the percentage of wealth owned by women in the US increased more between 1860 and 1890 than in the two centuries preceding the acts. The quantiative evidence on the effectiveness of legal reform is at best mixed, though: the experience of India illustrates that policy makers’ attempt to increase women’s access to inheritance may backfire when restrictive gender norms prevail. Using a difference-in-differences approach Rosenblum (2015) shows that granting daughters the right to inherit ancestral lands in five Indian states increased the mortality of girls - the law seems to have incentivized parents to invest less in the health of their daughters. Bhalotra et al. (2020) confirm that the introduction of gender equal inheritance rights led to higher female infant mortality and an increase in the use of sonbiased fertility stopping rules in India. Roy (2015) argues that the reform at best remained ineffective - in reality, daughters’ access to ancestral land did not increase. By contrast, families compensated them with higher dowries or increased investment in their daughters’ education.

Gender discrimination in wealth transmission may persist even where the law is gender equal: Bessi`ere and Gollac (2023) show that gender discrimination in wealth transmission is commonplace in the US and Europe and has even increased in France over the past two decades. Inter-vivo gifts can be one way for families to favor sons. Moreover, land, real estate property or family businesses are most commonly passed on to (first-born) sons with daughters being paid out “their” share. According to Bessi`ere and Gollac (2023), notaries often
wards emissions reductions than privately-owned companies. Similarly, foreign companies may be more amenable to complying than domestic firms although some argue that foreign companies disregard local communities’ preferences.

The paper focuses on understating the drivers of a firm’s compliance, and especially to contribute to the value of regulatory discretion: estimates from environmental inspections and self-reports in India. Given the binary nature of “compliance,” the paper estimates the effect on the probability of complying. For this purpose, the empirical strategy uses a probit estimator as follows: where G is a normal distribution. The variable "self" captures the differential compliance in self-reports and actual inspections. Important characteristics for policy insights are added like the private or public ownership of the firm, the "category", referring to air and water pollution differences, and the effect of the firm’s age on compliance levels. Industry and year fixed effects are used except in the analysis to estimate industry-level regressions. Since in India self-reporting cannot be used to penalize a firm, one would expect higher levels of compliance.

# **5 Results**

This section presents the results based on the inspection and self-reported data. One can start by studying the differences between both types of data to assess the validity of selfreported data. Once this characterization is done, it is easy to then carry out the analysis by differentiating both types of information.

## **5.1 Self-Reporting vs Inspection Data**

Table 3 presents the regressions with the pooled data, using both inspection and selfmonitoring data. In all regressions of the table, and in the pooled data, a dummy is used for self-reporting. This allows one to capture whether firms tend to report compliance more or less than when firms are inspected (note that the dummy for inspection data is omitted). Columns 1 to 6 sequentially add a different set of fixed effects to control for confounding factors. These include the air and water categories, district, industry, firm, and year fixed 13
<!-- Start of picture text --> TriflicrWn<br>EUamDIniuanl SyfcrEhnkbmn<br>15 Si<br>-0.5<br>v >, O: > Os >- O) I CD I~ m I CD >E C > O~ C) > CD<br>D :gEIZD l Z D~ Z ! M D~ ) Z >. 31 D Z 5 D~0<br>X CO C C c C C L C_<br>< Figure 17 ><br><!-- End of picture text --> A comparison of the excess demand for loans with the survey results of the external financing conditions facing small and medium-sized firns in manufacturing in Figure 18 shows that the trend of the excess demand seems reasonably consistent with the extent of the credit market tightness. The survey has been undertaken every month since January 1994 by the Industrial Bank of Korea with a view to examining the external financing conditions of small and medium-sized firms operating in manufacturing. The number on the left scale in Figure 18 represents the percent of all the respondents who identified greater difficulties in getting access to external financing including bank loans relative to the immediately previous month. While there is one appreciable caveat on the similarities between those two trends during the excess supply period of the first half of 1995, the pattern of the excess demand during other periods is very similar to that of the survey measure.

In particular, it is noteworthy that the ratio of the respondents reporting greater difficulties in getting access to credits considerably increased (almost three times compared to other periods) during the crisis. This result is broadly in line with the occurrence of the substantial excess demand for bank loans owing to the credit crunch during the crisis period starting November 1997.

26
and 54 percent of Vanuatu RSE households had only one seasonal worker spell during the two years of our study.

We then begin with panel data regressions of the impact of the RSE in each country, using the full sample of households from each country. Letting _Yi,t_ be an outcome of interest for household _i_ in survey round _t_ , we begin with the following difference-in-differences specification: Where _EverRSEi_ indicates whether household _i_ ever participates in the RSE over the four waves of our sample, and δt are survey round dummies. The coefficient of interest is then γ, which gives the average treatment effect of participating in the RSE. We do not include additional timevarying controls in this regression, since we have few time-varying variables that are not potentially themselves affected by the RSE. Standard errors are clustered at the household level to account for autocorrelation in the error term εi,t across survey waves. The same equation is also estimated using _RSEDurationi,t_ in place of _RSEi,t,_ , in which case γ gives the average household-level impact of one month’s duration in the RSE.

Difference-in-differences controls for any baseline level differences in the outcome _Yi,t_ at the group-level. An alternative approach is to control for baseline differences at the household-level through the addition of household-level fixed effects. We estimate this via the following specification: Where μi is the fixed effect for household _i_ . Again we estimate this using both our measures of household-level RSE participation, and cluster the standard errors at the household level.

The underlying assumption in both the difference-in-differences and the fixed effects specifications is that after controlling for level differences among households, they would have exhibited the same trends in the outcome variables in the absence of the RSE. However, this assumption is less credible when the households we are comparing have very different characteristics. We therefore follow the recommendations of Crump et al. (2009), who recommend estimating a propensity score, and then dropping observations with estimated propensity scores outside the range [0.1,0.9]
months. De-identified data were scored using the CREDI Scoring App (McCoy et al., 2018). Non-yes/no responses are treated as missing, and observations with fewer than 5 yes/no responses do not receive a score from the app. Scores were standardized by age group, and differences in scores were interpreted in standard deviation units. Internal consistency for each age group was adequate (Cronbach’s alpha coefficient ranging from 0.62 to 0.94).

# _School readiness_

School readiness, defined as children’s development and early learning skills across multiple dimensions of development relevant for school entry, including early academic skills, socialemotional development, and motor development (Snow, 2006; Wolf et al., 2017), was assessed for children aged 36-72 months using adapted items from the Measuring Early Learning Quality and Outcomes (MELQO)-Measure of Development and Early Learning (MODEL) Teacher/Caregiver report (UNESCO, 2017) following expert guidance to ensure socio-cultural relevance. The tool included 25 yes/no questions completed by the respondent caregiver about the child to evaluate school readiness skills across three subdomains: math knowledge and skills, language and literacy knowledge and skills, and socio-emotional development. Twenty of the 25 items are included in the Anchor Items for Measurement of Early Childhood Development (AIM-ECD) tool (World Bank, 2022) which has previously been administered across Pakistan (Ansari et al., 2024). ‘Don't know' responses were treated as missing. Based on the distribution of missingness, minimum

> 3 Missingness in the MELQO-MODEL domain scores for language and literacy knowledge and skills and socioemotional development was associated with lower psychosocial stimulation z-scores. No associations with missingness were found at the item level.

6
by high transport costs.

Despite a well‐developed road, rail, port and airport network South Africa ranks poorly in the Doing Business index for “Trading across Borders” (100<sup>th</sup> out of 189 countries), reflecting the high cost of exporting (World Bank, 2015). Exporting a container from South Africa is more expensive than in OECD high‐income economies as well as key comparator countries ( Figure 2). Figure 2 reflects the inland concentration of economic activity as well as the high cost of our ports. South Africa’s exporters and importers cite the high cost and delays caused by domestic transportation as the most problematic factor for trade (World Economic Forum, 2014). These findings show that the competitiveness of South African firms are eroded before they take into account the cost of shipping goods from South African ports to destination markets (where South Africa is disadvantaged by global geography).

**Figure 2: Cost to export a 20ft container (US$), 2015**

<!-- Start of picture text --> OECD high‐income<br>Singapore 120 150 50 140 460 economy average<br>(US$1 080)<br>Chile 220 190 100 400 910<br>Mexico (Mexico City) 200 200 150 900 1 450<br>Durban 355 285 90 1 100 1 830<br>Brazil (Sao Paulo) 325 500 400 700 1 925<br>Ngqura 355 330 95 1 200 1 980<br>Port Elizabeth 355 334 95 1 200 1 984<br>Cape Town 355 323 100 1 300 2 078<br>Document preparation Port and Terminal handling Customs Inland transport and handling<br><!-- End of picture text --> _4_
**Table A1:  Countries from the Demographic and Health Surveys (DHS)**

|**Region**|**Country**|**Used**|**Region**|**Country**|**Used**|
|---|---|---|---|---|---|
||Cambodia|Yes||Angola|No|
||Indonesia|Yes||Benin|Yes|
||Lao PDR|No||Botswana|No|
||Myanmar|No||Burkina Faso|Yes|
|East Asia &|Papua New Guinea|No||Burundi|Yes|
|Pacific|Philippines<br>|Yes<br>||Cameroon<br>|Yes<br>|
||Samoa|No||Cape Verde|No|
||Thailand|No||Central African Republic|No|
||Timor-Leste|No||Chad|Yes|
||Vietnam|Yes||Comoros|No|
||Albania|Yes||Congo|Yes|
||Armenia|Yes||Congo Democratic<br>Republic|No|
||Azerbaijan|No||Cote d'Ivoire|Yes|
||Georgia|No||Equatorial Guinea|No|
||Kazakhstan|No||Eritrea|No|
|Europe &|Kyrgyz Republic|Yes||Eswatini|No|
|Central Asia|Moldova|No||Ethiopia|Yes|
||Romania|No||Gabon|Yes|
||Tajikistan|Yes||Gambia|No|
||Turkey|Yes||Ghana|Yes|
||Turkmenistan|No|SbSh|Guinea|Yes|
||Ukraine|No|u-aaran<br>|Kenya|Yes|
||Uzbekistan|No|Africa|Lesotho|Yes|
||Bolivia|Yes||Liberia|
We next construct a proxy for the change in political power (PolChange). Fifty-four percent of countries had parliamentary elections in any one year, consistent with a 4-year election cycle in the democratic world. OECD high-income countries had an average of 0.250 elections a year, the highest in the world. However, Brunei, China, Eritrea, Fiji, Guinea, Qatar, West Bank and Gaza and the Republic of Yemen did not have a single parliamentary election during 2003–2016. Most of these economies have presidential or hereditary royal systems.

In testing the effects of political change, we use as our main explanatory variable not just the occurrence of elections, but when these elections lead to a change of control over parliament from one party or coalition of parties to another. During the sample period, 223 changes of power took place.

The data suggest that such change of power varies widely across countries by income group. More than 60 percent of political changes during the sample period took place in high-income and upper-middle-income countries, while only 12 percent of changes occurred in lower-income countries. Serbia had the highest number of political changes in the sample period (five), Bulgaria, Latvia and Slovenia had four changes of power.

In extended regressions, we use variables for population growth, the inflation rate, and public spending on education and health care to test the robustness of our analysis. These data come from the World Bank’s World Development Indicators database.

**Table 1**

Variable definition.

|Variable|Description|
|---|---|
|Poverty|Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population).<br>_Source:_WDI, World Bank.|
|Business formation|To measure entrepreneurial activity, annual data was collected from<br>143 company registrars on the number of newly registered firms. New<br>business entry is defined as the number of newly registered<br>corporations per 1000 working-age
1 percent in 2008. Though these numbers are encouraging, a detailed analysis at a sectoral level reveals a gloomy picture. As shown in Table 9, of the total women workforce employed in the registered sector in Nepal in 2008, almost 83 percent of women were engaged in agriculture, hunting and forestry activities, followed by other sectors such as electricity, gas and water supply (4.9 percent), hotels and restaurants (3.9 percent), and transport, storage and communications (1.6 percent). As highlighted by Tanaka and Muzones (2016), this is primarily because majority of the women entering the labor force in Nepal have a minimum level of education, thus ending up in low skilled and low paid jobs in the informal sector. Around 61 percent of employed females aged 15 and above had never attended school, 20.3 percent had attained less than primary to primary level education, and only 15 percent had attained lower secondary to secondary level education (Nepal labor force survey 2008).

Like other low-income countries, women in Nepal must work out of necessity and poverty In Nepal, majority of the women are burdened with household work and have less qualification in education and skills and mostly they are employed in informal sectors and do not have well-paying jobs in the formal sector. Thus, women’s share is large in all lower skill occupations working mainly in ‘service workers’ category and negligible in higher skill jobs. Under registered sectors, non-financial sector is the largest employer, followed 22
# **References**

Ahmed, S., B. Coulibaly, and A. Zlate. 2017. “International Financial Spillovers to Emerging Market Economies: How Important Are Economic Fundamentals?” _Journal of International Money and Finance_ 76 (September): 133-152.

Aizenman, J., M. Binici, and M. M. Hutchison. 2016. “The Transmission of Federal Reserve Tapering News to Emerging Financial Markets.” _International Journal of Central Banking_ 12 (2): 317–356.

Albagli, E., L. Ceballos, S. Claro, and D. Romero. 2019. “Channels of US Monetary Policy Spillovers to International Bond Markets.” _Journal of Financial Economics_ 134 (2): 447-473.

Arbatli, E.C., M. Firat, D. Furceri, and J. Verrier. 2022. “U.S. Monetary Policy Shock Spillovers: ” Evidence from Firm-Level Data _._ IMF Working Paper 22/191, International Monetary Fund, Washington, DC.

Arteta, C., M. A. Kose, F. Ohnsorge, and M. Stocker. 2015. “The Coming U.S. Interest Rate Tightening Cycle: Smooth Sailing or Stormy Waters?” Policy Research Note 15/02, World Bank, Washington, DC.

Aruoba, B., and T. Drechsel. 2022. “Identifying Monetary Policy Shocks: A Natural Language Approach.” CEPR Discussion Paper 17133, Centre for Economic Policy Research, London.

Bagliano, F.C. and C.A. Favero. 1999. “Information From Financial Markets and VAR Measures of Monetary Policy.” _European Economic Review_ : 43(4-6): pp.825-837.

Bauer, M., and C. J. Neely. 2014. “International Channels of the Fed’s Unconventional Monetary Policy.” _Journal of International Money Finance_ : 44 (June): 24-46.

Bernanke, B.S. 1986. _Alternative Explanations of the Money-Income Correlation.
a set of pre-selected questions. As such, the emphasis of the standard PMT approach is accuracy, rather than on cost reduction.

The focus of this paper is on modelling, but note that targeting survey design and eligibility determination are also impacted by modelling choices: the targeting survey consists primarily of variables selected, and the predictive model is used to score eligibility. The key statistical challenge in PMT design is model selection, particularly to limit the number of independent variables without sacrificing accuracy. The number of possible variable combinations is enormous in most settings due to the wide extent of the sample population survey. It is computationally infeasible to try out any but a fraction of these combinations within a chosen predictive model, and PMT manages this challenge with the following algorithm.

8
enough to have an overview of management practices but not so senior as to be detached from day-today operations.

# _2.3 RUSLANA as Sampling frame_

The data on firm performance in Russia are basic, substantially limiting the ability of researchers to apply the latest techniques for measuring and understanding firm productivity (see Kilinc, 2019). Consultations with the authorities and academics confirmed that nearly all analysis is done with firms’ financial/accounting data, which are available from Rosstat via third-party aggregators like Ruslana, Spark, and Integrum. These data sets allow for very basic analysis of productivity (e.g., output per worker) or require analysts to impose fairly strict assumptions/theoretical structure on the data. Although Rosstat collects very detailed data on firm inputs and outputs, as well as quantities and prices, these data are confidential by Russian law and hence they are difficult to access (even in anonymized form). Therefore, researchers generally shy away from using them – creating a perception that data quality may be uneven and disabling positive feedback loops from data users to data producers.

Ruslana is a commercial data set, which contains administrative data covering more than 9 million companies in Russia (Ruslana is a subset of Bureau van Dijk’s global Orbis data set, comprising of Russia, Kazakhstan, and Ukraine). Cusolito and Winkler (2015) use this data set for the period 2005-2013, comprising of 30,000 to 40,000 firms per year, amounting to a total of more than 300,000 firm-year observations. Since Ruslana is not a census nor a representative survey, the results based on an analysis of these data could be biased. For example, it represents only about 10 percent of total wage employment in the Russian economy and small firms are particularly under-represented. Likewise, Cusolito and Winkler (2015) show that while the share of firms in many sectors covered in RUSLANA is similar to Rosstat, there are important differences. In particular, wholesale and retail trade firms are over-represented in Ruslana, while
in the brain drain is provided by Docquier et al. (2005).

> 6 Carrington and Detragiache used the 1990 US census data to estimate the brain drain for a number of developing countries in 1990. Docquier and Marfouk (Chapter 6 in this volume) improved the measurement of the brain drain by expanding data sources to all OECD countries, estimating the brain drain for a larger number of developing countries, and doing so for the year 2000 as well as for 1990. They also provide estimates of the brain drain between developed countries.

3
15 Figure 4: kWh of electricity consumed per month by expenditure quintile and location <!-- Start of picture text --> 120<br>Bottom quintile Quintile 2 Quintile 3 Quintile 4 Top quintile<br>100<br>80<br>60<br>40<br>20<br>0<br>Madagascar Mozambique Rwanda São Tomé and Príncipe<br>kWh per month<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data. _Note_ : The survey in São Tomé and Príncipe had many households reporting exceptionally low monthly consumption, including those in the top quintile. They are reported to illustrate difficulties encountered in obtaining information on electricity consumption. Rwandan consumption is calculated from expenditures and the unit price in effect at the time of the survey.

The results show the predictable pattern of increasing consumption with increasing quintile, and significantly higher consumption by the top quintile in the first three countries where the results seem more plausible than in São Tomé and Príncipe or Togo. Rwanda, the only country in which kWh consumed was calculated from reported billed amounts, shows low consumption compared to Madagascar or Mozambique. In addition to seeming confusion about units for electricity consumption, another reason for caution in interpreting the results is the very small sample size in lower quintiles, especially in rural areas, leading to results that are not meaningful. For example, the bottom three quintiles in rural Mozambique had a total of only 11 households reporting kWh consumed. The bottom two quintiles in Rwanda similarly had only 7 and 12 households, respectively, connected to the grid with positive expenditures.

# Spending on Electricity

The share of household expenditures spent on electricity among those with non‐zero spending on electricity provides an indication of how much households are willing to spend, balancing affordability with the perceived value of electricity. The results are summarized in Table 2 for the share of household expenditures that include imputed values of freely acquired items, largely home‐grown