rafmacalaba/data-use-mentions-extended huggingface dataset:
mark>Knittel, Christopher R., and Shinsuke Tanaka.</mark> _<mark>Driving behavior and the price of gasoline: evidence from fueling-level micro data</mark>_ <mark>. No. w26488. National Bureau of Economic Research, 2019.</mark> <mark>Metcalf, Glibert.</mark> _<mark>An equitable tax reform to address global climate change</mark>_ <mark>. Discussion Paper 2007–12 (The Hamilton Project, The Brookings Institution 2007), 2007.</mark> <mark>Metcalf, Gilbert E. "On the economics of a carbon tax for the United States."</mark> _<mark>Brookings Papers on Economic Activity</mark>_ <mark>2019, no. 1 (2019): 405-484.</mark> <mark>Metcalf, Gilbert E., and James H. Stock. "Measuring the macroeconomic impact of carbon taxes." In</mark> _<mark>AEA Papers and Proceedings</mark>_ <mark>, vol. 110, pp. 101-06. 2020.</mark> <mark>Moran, Daniel, Peter-Paul Pichler, Heran Zheng, Helene Muri, Jan Klenner, Diogo Kramel, Johannes Többen et al. "Estimating CO 2 Emissions for 108,000 European Cities."</mark> _<mark>Earth System Science Data Discussions</mark>_ <mark>(2021): 1-23.</mark> <mark>Rausch, Sebastian, Gilbert E. Metcalf, and John M. Reilly. "Distributional impacts of carbon pricing: A general equilibrium approach with micro-data for households."</mark> _<mark>Energy economics</mark>_ <mark>33 (2011): S20-S33.</mark> <mark>Reiss, Peter C., and Matthew W. White. "Household
United Kingdom (92%), France (94%), Switzerland (93%), China (92%), and India (90%), among others.<sup>9</sup> Patent data do not provide unique identifiers for inventors appearing in more than one application. Although this is a drawback, I follow Kerr (2008) and treat each record as if it were a different individual, and compute the migration variables aggregating by country pairs and moving time-windows of five years.<sup>10</sup> Out of all records with complete information, about 5 million, around 9–10% have a migratory background, that is, residence different from their nationality. Figure 1 depicts the evolution of the share of inventors with migratory background (solid line), alongside the same figures broken down by a number of selected receiving countries/areas. As can be observed, the share of worldwide migrant inventors has steadily increased over time.<sup>11</sup> Among the most receiving countries of the world, 9. Compared to other highly patenting countries, the United States, Canada, and the Netherlands present relatively low coverage rates. This is because numerous applications with inventors from these three countries were applied to the USPTO before being extended internationally through the PCT system. In consequence, the United States was not mentioned in the applications as designated state and the inventors were only inventors and not applicants at the same time, which exonerates them from providing nationality information. In principle, there is no reason to believe that this less than complete coverage in these countries may bias the analysis one way or another. However, in order to address this inconsistent coverage of migration information, I repeated the analysis splitting the sample into shorter time windows and excluding some countries. No important differences arise regarding the main conclusions of the study. 10. The priority date of applications is used to allocate individuals in time. By “priority date” I mean the first
a land phone|0.01||0.006|0.07|0.02|0.02|0.13|
|Household has electricity|0.29|0.65|0.55||0.38|0.82|0.74|
|Household has a radio|0.05|0.35|0.14|0.65|0.43|0.02|0.48|
|Household has a television|0.12|0.22|0.23|0.93|0.13|0.29|0.60|
|Household has a fan|0.19|0.06|0.40|1.00|0.12|0.78|0.22|
|Household has a sewing machine|0.03|0.01|0.05|0.55|0.04|0.25|0.17|
|Household has a bicycle|0.16|0.04|0.59|0.30|0.28|0.23|0.35|
|Drinking Water from Hygienic<br>Source|0.96|0.98|||0.75|0.86|0.87|
|Availability of Proper Sanitation<br>Facilities|0.33|0.61|||0.08|0.34|0.79|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). All reported summary statistics are population-weighted.

20
Policy Research Working Paper 5563

# **Abstract**

This paper examines _de jure and de facto_ measures of regulations, finding the relationship between them is neither one for one, nor linear. “Doing Business” provides indicators of the formal time and costs associated with fully complying with regulations. Enterprise Surveys report the actual experiences of a wide range of firms. First, there are significant variations in reported times to complete the same transaction by firms facing the same formal policy. Second, regulatory compliance appears “under water” as firms report actual times much less than the Doing Business reported days. Third, the data reveal substantial differences between favored and disfavored firms in the same location. Favored firms show minimal variation, so Doing Business has little predictive power for the times they report. For disfavored firms, the variation is greater, although still not significantly correlated with Doing Business. Fourth, where multiple Enterprise Surveys are available, there is little association over time, with _reductions_ in Doing Business days as likely to be accompanied by _increases_ in Enterprise Surveys days. Comparing these two types of measures suggests very different ways of thinking about policy versus policy implementation, what “a climate” for firms in a country might mean, and what the options for “policy reform” really are.

This paper is a product of the Office of the Chief Economist, Financial and Private Sector Development. 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 author may be contacted at mhallward@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
.151)|
|Observations|31,768|31,768|153,155|153,155|

_Source:_ Calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011, and Sri Lankan 1992–2002, 2008, 2011, and 2012 Labor Force Surveys. _Note:_ Standard errors are in parentheses. Standard errors are clustered at the industry level. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.

The grand mean effects of the industries are calculated; post-MFA is a dummy equal to 1 for years 2005 and later; additional controls include age squared, industry, and occupation dummies. Sri Lanka Household Income and Expenditure Survey was excluded from the analysis as hours worked were not available in this survey. Occupation dummies are according to 1-digit ISCO-08; the omitted category is managers. Industry dummies include 15 categories: Agriculture and Forestry; Mining; Food, Beverage, and Tobacco; Textile and Apparel; Wood; Other Manufacturing; Utilities; Construction; Sales; Transport; Financial, Insurance, and Real Estate; Public Administration; Social Services; and Other Services. Log wages are measured as logarithm of real monthly individual labor income. Log wages are measured as logarithm of real monthly individual labor income in 2005 prices. The omitted time category in cols (1) and (3) is 2004 and earlier; the omitted time category in cols (2) and (4) is 1998 and earlier.
|and conventions on economic, social,<br>and cultural (ESC) rights (including<br>the right to education and to highest<br>attainable standard of physical and<br>mental health) ratified (2)<br>•Percentage of nominal annual budget<br>allocation in line with PRS priorities<br>(4)<br>•Number of formal initiatives<br>supporting free access to information<br>on service entitlements per year (1)<br>•Number of formal initiatives<br>supporting free access to information<br>on government service delivery<br>performance (1)|with PRS priorities (e.g. government expenditure on<br>health and education as percentage of GDP) (4)<br>•Number of public consultations on policy proposals/<br>formulation per year<br>•Number of women attending public consultations on<br>policy proposals/ formulation per year<br>•Number of inclusive platforms for participation in<br>service delivery<br>•Number of formal legal actions upholding ESC<br>rights with respect to government conduct per year<br>•Number of reported cases of corruption among<br>“street level” bureaucrats per year<br>•Corruption Perception Index (3)<br>(transparency/accountability/freedom from<br>corruption among “street level” bureaucrats, public<br>officials, and professional groups, including<br>investment in capacity building)<br>•Percentage of total population unable to access at<br>least one basic service in the previous year due to<br>cost (4)<br>•Percentage of total population unable to access at<br>least one basic service due to physical distance (4)<br>•Percentage of total population unable to access at<br>least one basic service due to social distance (4
# Individual Wealth and Time Use: Evidence from Cambodia

Ardina Hasanbasri<sup>†</sup> , Talip Kilic<sup>‡</sup> , Gayatri Koolwal<sup>#</sup> and Heather Moylan<sup>*1</sup> JEL Codes: C21, C83, D31, J16, J22. Keywords: Asset Ownership, Wealth, Time Use, Labor, Gender, Household Surveys, Cambodia.

> 1 † University of Michigan and Independent Consultant, Development Data Group (DECDG), World Bank, ardinah@umich.edu; ‡ DECDG, World Bank, <u>tkilic@worldbank.org;</u><sup>#</sup> Development | Science and Independent Consultant, DECDG, World Bank, <u>gkoolwal@worldbank.org;</u><sup>*</sup> DECDG and Gender Group, World Bank, hmoylan@worldbank.org. The research was funded by the World Bank Living Standards Measurement Study – Plus (LSMS+) program. The authors are grateful for the exceptional professionalism and hard work on the part of the National Institute of Statistics of Cambodia management and survey teams in successfully implementing the 2019-20 Cambodia LSMS+ survey. The authors would like to thank Dalila Figueiredo, Magnus Hattlebakk, Henrike Sternberg, Kimsun Tong, and the seminar participants at the World Bank Development Data Group (DECDG), the 2021 Nordic Conference in Development Economics (NCDE), and the 2021 International Association for Feminist Economics (IAFFE) Conference for their comments.
and those who do not. Our main finding are as follows. First non-farm self-employed individuals in rural Rwanda have significantly higher earnings than farm workers and non-farm formal employees. Second, the benefits to non-farm self-employment are much higher among the non-poor than the among the poor. Third, we show that diversified households, those with a farm and a non-farm enterprise, are less likely to be poor. Finally, farm households who do not participate in the market have significantly lower consumption levels than households that do. However, the benefits to market participation appear less palpable for the poor than for the non-poor. We find little difference in expenditures between market participants and non-market participants for comparable households in the bottom 40% of the expenditure distribution.

Following this introduction, section 2 provides background information on the non-farm sector in Rwanda; sections 3 and 4 describe the data used and the methodology implemented while section 5 presents the results of the analysis: first the determinants of non-farm sector participation are discussed and second the size of earnings differentials are examined as we compare outcomes for participants and non-participants in the nonfarm sector, at both the individual and household levels. Section 6 concludes by drawing policy implications to inform any future reform efforts.

# **2. Data description**

For the analysis presented in this paper we use the Rwanda Household Living Conditions Survey (EICV). This survey provides information on a wide range of economic and social issues (e.g., unemployment, health, education, incomes, expenditure, subsistence agriculture, migration and access to services). It is modeled on the World Bank’s Social Dimensions of Adjustment data collection and analysis format.<sup>3</sup> The design was completed in 1997 and piloted in 1998; 6420 households were interviewed between October 1999 and July 2001.

The sample design is a stratified two-stage design with three main strata: (i) Kigali urban, (ii) other prefecture urban centers and (iii) rural prefecture. Stratum (iii) is
receiving country. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia, and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800. The lower number of observations between columns (1), (2) and (3), (4) is due to the inclusion of fixed effects in pseudo-maximum likelihood estimations: the PPML method automatically drops the country-specific fixed-effects (and their corresponding observations) for which the country has zero recorded inventors’ flows to every other country in the sample in order to achieve convergence. Results are comparable to other count data methods without removing these observations (see Santos Silva and Tenreyro 2010 for further details).

22
|Remittances from abroad|-0.011|-0.116|-0.294***|-0.184*|-0.153|
|---|---|---|---|---|---|
||(0.074)|(0.115)|(0.111)|(0.111)|(0.118)|
|Remittances from abroad within the same||||||
|enumeration area (EA)|0.0102|0.108*|0.057|-0.086**|0.001|
||(0.037)|(0.065)|(0.071)|(0.041)|(0.058)|
|Remittances from family within the same||||||
|enumeration area (EA)|0.068**|-0.042|0.127***|0.116**|0.087|
||(0.031)|(0.053)|(0.048)|(0.046)|(0.056)|
|Observations|934|802|802|802|802|

Note. Results are presented in margins and clustered standard errors in parentheses. Individual-level survey weights used. Statistical significance at: *** p<0.01, ** p<0.05, * p<0.1. Enumeration areas fixed effect included. We also controlled for the following variables: 19-22 years, Currently neither working or attending school, Interesting job to do, STEM related work, Own account worker, Quintile 2, and Remittances from family within the country, Knowing someone in the community that has your dream job, and Urban area. Results for these variables were either not statistically significant or not discussed in the
# **Appendix**

# **A Additional figures and tables**

Figure A1: Urbanization, poverty, and inequality <!-- Start of picture text --> (A) GDP and poverty (B) GDP and urbanization<br>80 100<br>60 80<br>60<br>40<br>40<br>20<br>20<br>0<br>7 8 9 10 11 12 7 8 9 10 11 12<br>Log of GDP per capita Log of GDP per capita<br>R2 = 0.5141 R2 = 0.5422<br>(C) Urbanization and poverty (D) GDP and Gini index<br>80<br>60<br>60 50<br>40 40<br>20 30<br>0 20<br>0 20 40 60 80 100 7 8 9 10 11 12<br>Urban population share (%) Log of GDP per capita<br>R2 = 0.2984 R2 = 0.1502<br>Non−SSA SSA<br>Poverty rate (%)<br>Urban population share (%)<br>Poverty rate (%) Gini coefficient<br><!-- End of picture text --> Source: World Development Indicators (World Bank) Note: Data is derived from World Development Indicators, selecting the most recent year with available data. 163 countries are included in panel (A), (C) and (D), and 193 countries are included in panel (B). Poverty is measured using the $2.15 poverty line. GDP per capita is measured in PPP (constant 2017 international $).

28
with the businessman as the “line player” to whom the first entry in each cell corresponds and the bank as the “column player” to whom the second entry in each cell corresponds.

**<u>Table 4: Game matrix</u>**

|||Ba<br>N|nk<br>C|
|---|---|---|---|
|Bi|L|21.43 / 28.57|29.14 / 20.86|
|usnessman|H|57.14 / -14.29|30.86 / 12.00|

The Nash equilibrium is given with the combination H/C, where the businessman chooses project H at an expected cash flow of 30.86 and the bank offers contract C at an expected cash flow of 12.00. Neither the businessman nor the bank will deviate from this choice as a deviation for either would lead to lower expected cash flows (29.14 and -14.29, respectively). Taking strategic considerations into account, the businessman thus chooses project H and not, as was calculated under the efficiency approach, project L.

This erroneous solution under the efficiency approach clearly results from negligence of the credit contracts, which determine the distribution of payoff between the two players. Only in cases when nothing is to be distributed does this approach make sense, such as in the case of 100 percent equity financing. However, in the case of debt financing, as in our example, the bank bears part of the costs and therefore the businessman has incentives to choose the high risk project (H). That is exactly what happened, ultimately leading to the financial crisis.

Note that statistical data are needed in both models to calculate the expected cash flows per player in each action combination. However, whereas in the efficiency approach the mere existence of statistical data is sufficient, the strategic approach goes much further by allowing each player
084 samples in 145 countries.<sup>3</sup> Our sample includes 38 high, 81 middle and 26 low income countries, which allows us to examine whether the returns vary non-linearly with economic development. Second, we account for several empirical challenges in estimating the returns. We use two methods inspired by Heckman and Robb (1985) to attempt to reduce the experience-time-cohort problem when estimating returns to experience.<sup>4</sup> Our large data sets allow us to include decadal cohort fixed effects or country-specific

> 2See Rubinstein and Weiss (2006), Rosen (2008) and Polachek (2008) for surveys of the literature.

> 3Lagakos et al. (2018b) use for robustness checks an expanded set of 263 samples in 35 countries. In a related paper, Islam et al. (2019) use the same 1,084 samples as in the present study to investigate withincountry gaps in the returns to experience between sectors and locations.

> 4Due to collinearity, experience, time and cohort effects cannot be simultaneously estimated. If year fixed effects are added, the estimated experienced effects may then also capture cohort effects.
the genocide, particularly for younger women. For instance, the proportion of widows among women of age 25-34 years doubled between 1992 and 2000. Moreover, the proportion of widows rises steadily with older birth cohorts; a similar pattern is apparent for divorced women. This may indicate that once a

> 6 The original question was ‘How many years ago did [name of sibling] die?’ The 2005 RDHS was collected between February and July 2005, while the genocide occurred between April and July 1994. Hence, a woman interviewed in February 2005 whose sibling died in May 1994 lost her sibling ten (discrete) years before the interview, which translates into 1995. In contrast, the 2000 RDHS was collected between June and November 2000, so most respondents who lost a sibling during the genocide period would have reported the death occurring six years ago, which translates into 1994.

> 7 Verwimp and de Walque (2010) use the same RDHS data from 2000 to analyze excess mortality patterns related to the 1994 genocide. They find that the mean and median of both the siblings’ sex and date of birth are similar and conclude that there is no evidence for a systematic bias in the reporting of sibling deaths.
reports the coefficient on ELA from a regression of the Ebola measure of interest on treatment assignment. The coefficient in Column 4 is obtained controlling for: number of dwellings, whether the village is a political stronghold (i.e. it is the residence of a chief), number of NGOs active pre-Ebola, average PPI score, share of Christians, distance from Freetown and distance from Kailahun (where the first Ebola case was recorded). In Column 5 distance from the nearest facility of interest (PHU in Panel A, Secondary School in Panel B, market structure in Panel C, all in panel D) and interaction between ELA assignment and these distances are added as regressors. None of the coefficients on the interaction between ELA and distance from each facility is statistically significant (not shown). Column 6 includes all regressors and interactions employed in Column 5, plus features selected by a penalized regression (Elastic Net) of the Policy index (dummy) on all village characteristics and their interactions. The Primary Health Unit functioning score is assessed on a monthly basis between July 2014 and September 2015, and later aggregated into an index ranging between 0 and 100. Secondary schools were considered as having re-opened on time if they were open in April 2015. The Disruption Index is constructed following Anderson [2008]. It aggregates variables in Panel A and B, and assigns higher values to communities more exposed to disruptions.
critical mass of training data to estimate predictive models. In general, when the number of images we used to train the model fell below 10% of the population, predictive performance deteriorated rapidly. However, the results were far more robust to restricting the size of the sample used to generate the training data labels. Furthermore, prediction accuracy remained high in Burkina Faso, despite a reduced effective sample size of the training data due to linking satellite images to survey data at a much higher geographic level. Future work could investigate methods to further improve performance when training transformer models using the types of small samples typically collected for household surveys.

Finally, the results demonstrate the potential of using transformer models to predict changes in wealth and household wellbeing more generally. Future work can examine the extent to which the parameters in change models are stable across time and/or space. This could point the way toward the use of geospatial data to generate approximate micro estimates of welfare change in settings where survey data are unavailable.

# **Methods**

We describe the details of our large-scale, multi-resolution, and multi-temporal wealth dataset, wealth and its change prediction approaches, and evaluation methods.

## **Multi-resolution and multi-temporal wealth dataset.** We

utilize data from four low-income countries (Malawi, Mozambique, Burkina Faso, and Madagascar) and two cities in Malawi as study areas in Africa. These countries were selected due to the availability of location identifiers in available census extracts. This allows us to pinpoint models on the most comprehensive scale to date, to tune a general model to specific countries and even cities, and to robustly simulate the impact of data scarcity on model performance across three spatiotemporal scenarios: country-level wealth level prediction, decadal country-level wealth change prediction, and city-level wealth level prediction.

**Asset wealth index (AWI).** We construct the asset wealth index using data from the national census questionnaire. We utilize full census data in Mozambique and Madagascar, and census extracts in Malawi and Burkina Faso,