rafmacalaba/data-use-mentions-extended huggingface dataset:
12 combines information on the satisfaction of various basic needs. The index can be produced at a number of different levels of geographical aggregation, ranging from regions and provinces down to municipalities. The smallest geographical units for which such information was available are neighborhoods (or barrios). On average, these are units of some 300-400 households.

In order to decide whether it was desirable to allocate water subsidies on a zonal basis, it was important to establish how successful a geographical criterion was likely to be at reaching the poor households in the target group. Using data from the 1997 national Living Standards Measurement Survey, a model was developed to simulate the effects of a zonal subsidy. The simulation model picked out all households residing in zones that met a particular set of socio-economic criteria and then counted how many of these households were genuinely poor.

Targeting errors can take one of two different forms. Perhaps the most serious of these are the Type I errors which arise when members of the target group are not captured by the eligibility criteria and hence fail to receive the subsidy. A subsidy scheme with a high Type I error clearly fails on its own terms. Also of concern is the possibility that people outside of the target group may fortuitously comply with the eligibility criterion and consequently receive the subsidy. A high level of Type II error thus indicates leakage of funds to unintended beneficiaries, and suggests that the subsidy scheme is comparatively inefficient in reaching its target population.

Table 4.1 below presents the results of one such simulation. In this case, a subsidy was allocated to all households living in a zone where more than 50% of households were below the extreme poverty line, and where telephone coverage was below 30%4. The results indicate that using a zonal subsidy of this kind is a very imperfect method of targeting subsidy funds towards the poor. In the first place, the level of Type I error is extremely high at 94%; which is to say that only 6% of households in extreme poverty (1
money agent networks thus need to consider MNO’s incentives and commercial interests.

# **References**

Aker, J C, R Boumnijel, A McClelland and N Tierney. 2016. “Payment mechanisms and antipoverty programs: Evidence from a mobile money cash transfer experiment in Niger.” _Economic Development and Cultural Change_ , 65(1): 1-37.

Allen, Franklin, Elena Carletti, Robert Cull, Jun QJ Qian, Lemma Senbet, and Patricio Valenzuela. 2013. “Improving Access to Banking: Evidence from Kenya.” _Review of Finance_ , 25(2): 403-447.

Anderson, Michael. 2008. “Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects.” _Journal of the American Statistical Association_ , 103(484): 1481–1495.

Andersson-Manjang, S.K. 2021. “The Mobile Money Prevalence Index (MMPI): A Country-Level Indicator for Assessing the Adoption, Activity and Accessibility of Mobile Money.” GSMA Mobile Money Research Working Paper.

Akinyemi, Babatope E. and Abbyssinia Mushunje. 2020. “Determinants of mobile money technology adoption in rural areas of Africa.” _Cogent Social Sciences_ , 6(1).

Batista, Catia and Pedro C. Vicente. 2022. “Is Mobile Money Changing Rural Africa? Evidence from a Field Experiment” Mimeo. Nova School of Business and Economics.

Bank of Uganda. 2017. _Financial Stability Report 2017_ . Available at: <u>https://www.bou.or.ug/bou/publications_research/Financial_Stability_Report.html</u> Blumenstock, Joshua E., Nathan Eagle and Marcel Fafchamps. 2016. “Airtime Transfers and Mobile Communications: Evidence in the Aftermath of Natural Disasters.” _Journal of Development Economics_ , 120: 157-181 18
flooding (e.g. NASA 2006) that the flood impacted close to 285,000 people, who are living in the Zambezi valley (AFROL.COM 2009). These findings demonstrate that the model can be used to emulate the various and inter-seasonal flow in the Zambezi. Furthermore, findings illustrate the potential application of the BWI to predict extreme events in numerous basins around the world, particularly when precipitation and gauge data are limited.

# **DEMONSTRATING USE OF THE MODEL FOR ECONOMIC AND INSTITUTIONAL ANALYSIS**

How can the results of the flow probability be utilized for policy purposes in each of the basins? We first discuss a framework to incorporate economic considerations for dealing with flows above and below normal (or in other words, above and below the long term mean flow level that is the basis of the treaty). The framework builds on Peck and Adams (2012).

In this simple framework, countries rely on water for their economic development. Annual water allocation decisions are made in two stages a basin, where a water treaty regulates the water allocation of water by country. Water flows much below and above the mean may inflict damage to socio-economic infrastructure (noted as the first stage), or conversely, the over capacity cost of infrastructure to regulate water resources can put economic pressure on the society (noted as the second stage). Policy makers can reduce the damage by considering different allocation policies and the cost and benefits of these two decision making solutions (i.e. first and second stage). The simple country level model is: (4) s.t.

(5) (6) (7) (8) where,

- = the welfare from water use

18
This heterogeneity complicates the comparison of program effects across studies. We report the definition and reference time used in each paper (when available) in our review; we also use child labor and children engaged in an economic activity interchangeably throughout the paper.

It is important to note that there could be unexplained inconsistency in child labor statistics even when a single definition of child labor is used. Guarcello et al. (2010), for instance, documents large discrepancies in child labor statistics between independent national surveys within the same country that ranges from 20 to 30 percentage points, even after accounting for differences in sample design. For instance, in Cameroon, a comparison between the Multiple Indicator Cluster Survey (MICS 2000) and a Priority Survey (2001) shows a decline in child labor from 64 percent in the MICS survey to 16 percent in the Priority Survey one year later. In Senegal, the Demographic Health Survey (DHS 2005) reports 35.2 percent of children as engaged in an economic activity while the Statistical Information and Monitoring Programme on Child Labour (SIMPOC 2005) survey of the same year reports 22.3 percent of children as working.

Despite the increasing sources of information on child labor over the past decade, there is not much evidence on the validity of data collection methods (Edmonds 2008). Child labor could be affected by measurement error due to several factors, for example, the survey information is collected primarily using standard household surveys that target adult work, i.e., formal jobs rather than unpaid and family work/enterprise jobs. Likewise, due to budgetary constraints 5
(2015) find a consistent trend over the lifecycle of firms; however, start size explains the vast majority of variation in current size. Indeed, a specific advantage of using micro-data is to see if these effects are heterogeneous within economies.

A positive correlation between firm size and productivity has been found in developing countries, suggesting the existence of “static” allocative efficiency. Based on the World Bank Enterprise Surveys for developing countries, Ayyagari, Demirgüç-Kunt, and Maksimovic (2015) documented that large firms are typically more productive than small and medium firms.<sup>4</sup> Li and Rama (2013) also find that productivity dispersion and growth prospects vary a lot in developing countries compared to advanced economies, but not as clearly correlated with firm size. Restricting the sample to manufacturing firms from nine SubSaharan African countries, Van Biesebroeck (2005) finds that large firms achieve higher productivity levels and are more likely to survive. In contrast, the commonly found higher growth rates for small firms are not replicated in his African sample, and the distribution of firms changes very little over time. Firms are more likely to have started out large than to have grown. We find that larger firms are more productive, but that the positive relationship between size and productivity is driven by large firms, consistently with the studies above.

> 4 It has to be noted that WB ES may not include micro firms (1-4 employees); in some countries, including Ghana, registered micro firms have also been sampled.

6
ht Ad 10 t 22 i 2008|6.32|6.46|-0.14|
|oung coor (ge o n )|(0.06)|(0.04)|(0.07)|
|Diff|1.52|2.71|-1.20|
|erence|(0.13)|(0.08)|(0.16)|

Note: Standard errors are in parenthesis, all estimated coefficients are statistically significant at 1 percent Table 4.1.1 reports average years of education for both age-cohorts and a war prevalence dummy, which takes the value of one if a department (of birth) experienced at least one conflict event, zero otherwise. The war prevalence of a department reflects the total number of conflict events between 2002 and 2006. For both age-cohorts, the average years of education in conflictaffected departments is higher compared to the rest. However, the gap in average years of education is negligible for the young cohort. Two possible explanations can be offered. First, the war zones (departments that experienced conflict) traditionally had higher average years of education and this could be due to better educational facilities or better job prospects. Second, 11
Wealth will be measured by whether the families possess any land since about 40 per cent of the households are landless. To measure the human capital of the husband and wife we use their years of schooling and their ages at marriage. Alternatives in the marriage market are measured by the date of the marriage (to capture exogenous trends), and the ratio of the number of women to the number men at marriageable ages (defined as women aged 10-19/ men aged 2029) measured at the year of the marriage for the bride's home district. In addition to these variables I also include dummies for whether the family is Muslim or belongs to a disadvantaged or "Scheduled" caste.

# Data and Results

The sample consists of 800 households randomly chosen from five districts spread across North and South Karnataka State. Seven villages from each district were randomly chosen and twenty households then randomly selected from each village. The marriage data that are employed in this paper were collected retrospectively from the married women in the sample. About a third of the sample did not have any ever-married women of reproductive age and therefore did not answer the women's questions. Of those that did have eligible women, many did not provide complete answers to the retrospective marriage questions, forcing us to drop them from the analysis. After eliminating about ten outliers, we are left with a sample of about 300 women spread across the five districts. There are no significant differences between this subsample and the complete sample for those questions that were answered by all the households.

Table 1 reports summary statistics from the survey data. The average wedding celebration expenses are about 3,000 rupees, 11 per cent as large as the average dowry which is 26,000 rupees. Average schooling levels for men are 4.3 years and 2.9 years for women. The average age at marriage for men is about 25 years and 17 years for women. The mean marriage took place in 1980, with a standard deviation of 8.4, showing that
# **1. Introduction**

The economic impact of trade reforms has been one of the most studied topics in economics in the last decades (e.g. Pavcnik (2002); Amiti and Konings (2007); Caliendo and Parro (2015); De Loecker et al. (2016)). The empirical literature has mainly focused on the changes in import tariffs across the 1980s and 1990s, as tariffs were the main trade policy instrument for most of the 20th century.

Modern trade policy, however, has become more complex. Greater product sophistication and increasing public concern around health and safety have resulted in the proliferation of non-tariff measures (NTMs).<sup>1</sup> By 2017, NTMs were applied to goods covering around 60 percent of import values in developing countries and over 80 percent in high-income countries (UNCTAD and World Bank, 2018). In the same year, data from the _Global Trade Alert_ suggest that import tariffs accounted for just 20 percent of all trade policy measures adopted globally.

Despite their extensive use, little systematic evidence exists on the economic impact of NTMs. The few existing studies on the topic use aggregate measures of NTMs, which lump highly heterogeneous regulations together (Kee et al., 2009; Webb et al., 2020), or focus on one-off liberalization of a specific NTM, usually quotas (Khandelwal et al., 2013; Harrigan and Barrows, 2009), which however represent only around 2 percent of all applied NTMs (UNCTAD and World Bank, 2018). An important reason behind the paucity of evidence is the difficulty in collecting sufficiently comprehensive and detailed data on NTMs (Goldberg and Pavcnik, 2016).

To help fill this gap, we build the first available time-varying dataset on all NTMs applied to the universe of goods produced in Indonesia from 2008 to 2015. We exploit the experience of Indonesia because it fits remarkably well the general pattern of trade policy over the past three decades: import tariffs liberalization since the 1990s
- 36 -

- Krueger, Anne O., "Trade Policies in Developing Countries",in R. W. Jones and P. B. Kenen, eds. Handbook of International Economics, Elsevier Science Publishing (1984).

- Landau, Daniel, "Government Expenditures and Economic Growth: A CrossCountry Study," Southern Economic Journal 49 (January 1983): pp. 783-92.

- Landau, Daniel, "Government and Economic Growth in the Less Developed Countries: An Empirical Study for 1960-80," E5onomic Development and Cultural Change (October 1986): pp. 35-75.

- Lucas, Robert E., "On the Hechanics of Economic Development", mimeo, University of Chicago (1985).

- Manas-Anton, Luis A., "Structure of Taxation and Growth in Developing Economies", Mimeo, IMF Fiscal Affairs Department (September, 1985).

- Marsden, Keith, "Links Between Taxes and Economic Growth: Some Empirical Evidence," World Bank Staff Working Paper #605, (August, 1983).

- Martin, Ricardo, "Fiscal Variables and Growth: A Cross Sectional Analysis," mimeo (n.d.).

- Ram, Rati, "Government Size and Economic Growth: A New Framework and Some Evidence from CrossSection and TimeSeries Data," American Economic Review 76 (March 1986a) p. 191-203.

- Ram, Rati, "Causality Between Income and Government Expenditure: A Broad International Perspective," Public Finance/Finance Publigues (1986) 41(3): pp. 393-413.

- Robinson, Sherman, "Sources of Growth in Less Developed Countries: A Gross-Section Study", Ouarterly Journal of Economics (1971) pp. 391-408.

- Schneider, Friedrich, and Bruno S. Frey, "Economic and Political Determinants of Foreign Direct Investment," World DeveloRmet 13 (1985): pp. 161-175.

- Seidman, Laurence S., "Conversion to a Consumption Tax: The Transition in a LifeCycle Model," Journal of
br>�|(0.073)<br>_I_<sup>_MT I−T_</sup><br>_zy_<br>= (_−_1)_× I_<sup>_MT I_</sup><br>_zy_<br>[0.085]|0.12|[0.731]|
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I−T_</sup><br>_zy_|-0.010<br>_I_<sup>_F P E_</sup><br>_zy_<br>=_I_<sup>_MT I−T_</sup><br>_zy_<br>(0.008)<br>= (_−_1)_× I_<sup>_MT I_</sup><br>_zy_<br>[0.239]|0.23|[0.794]|
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I_</sup><br>_zy_|-0.001<br>(0.007)<br>[0.930]|||
|N|24,898|||

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (DHS) in years 2005, 2011, and 2016. _Note_ : MTI = mother tongue instruction. Column 1 is reproduced from Table C.9 in this supplementary online appendix. The dependent variable is years of schooling. The sample includes women in birth cohorts from 1970 to 1988. The regression includes birth year and zone fixed effects, zonespecific linear trends, and a cubic for age when multiple survey waves are included. Standard errors are clustered at the zone level and shown in parentheses; _p_ -
the parameters have been calibrated using databases and models developed for high-income economies.

Recently, the advent of satellite-based GHG measurement has greatly expanded the potential for empirical assessment. High-resolution observations of atmospheric GHG concentrations are now available from several platforms, including NASA’s OCO-2 and OCO-3 instruments, the European Space Agency’s METOP-A and TROPOMI (Sentinel-5P) platforms, and the Japan Space Exploration Agency’s GOSAT and GOSAT-2. Detailed technical assessments of measures from these platforms have verified that they provide useful and comprehensive information for global carbon emissions analysis (Pan et al. 2021; Wu et al. 2020; Labzovskii et al. 2019). Along with research verification, the first global satellite-based GHG studies have begun to emerge for small samples of cities (e.g., Wu et al. 2020).

In this paper, we extend the domain for analysis with a panel of satellite-measured CO2 concentrations for 1,236 urban areas with populations greater than 500,000. The database covers the period 2014-2020 and includes cities in 138 countries in Africa (41), the Americas (22), Asia (44), Europe (29) and Oceania (2).

Second, we provide the first test of an Environmental Kuznets Curve (EKC) relationship for CO2 based on actual observations. We specify an econometric model of cities’ atmospheric CO2 concentrations that incorporates a large set of georeferenced determinants. We find evidence for an EKC that reaches a peak near or above $40,000 per capita, which is at the 90th percentile internationally. We also explore the relationship between population density and urban CO2 emissions at different levels of economic development.

3
to a treated household and the number of CFLs that household has at follow-up.

# **7.2 Checks of aggregate reliability effects**

To ensure that in Section 6.2 we are indeed measuring a local aggregate reliability effect, we perform two checks.

First, we calculate the intra-cluster correlation in the number of reported outages at followup. We have argued that intense saturation of treatment households within a transformer is causing a reduction in outages at the transformer-level. Then, household reported outages should be correlated with other household responses within the transformer. Our calculation indicates a intra-class correlation of 0.56, which means that responses within transformers are indeed highly correlated.<sup>60</sup> Second, we consider the estimated reduction in outages in conjunction with the difference between the expected and the estimated reductions in household electricity consumption to confirm that our series of results tell a consistent story. We make this comparison for the winter months, as that is the season for which we collected outage data.<sup>61</sup> In Section 5, we expected a reduction in households’ winter electricity consumption of approximately 42 kWh per month (Appendix Table 3). However, what we actually estimated was a winter reduction in electricity consumption of approximately 26 kWh per month (Appendix Table 4). This is a difference of 16 kWh per month, which is equivalent to approximately 14.4 hours of additional electricity consumption. In Section 6, we found the aggregate impacts of these electricity savings to be two fewer days without electricity per month due to unplanned outages. In conjunction with the previous result, this would mean two outages of approximately seven hours in duration each. This is very reasonable and assures us of the consistency across results.<sup>62</sup>

> 60Responses won’t be perfectly correlated with one another, as households in a transformer were surveyed on different dates and therefore the reference point of “past month” differs across households within a transformer.

>
# **4. Official lending in wars, financial crises and natural disasters**

In this section, we match our data on official sovereign lending with data on the occurrence of disasters, more specifically of wars, financial crises and severe natural catastrophes such as earthquakes. This allows us to study the international response to crises across different eras of the international financial system. We begin by compiling a comprehensive database on disaster and crisis events in the past 200 years and then match this with our novel dataset on official lending.

# **4.1 Identifying disasters and defining crisis events**

To identify financial crises, military conflicts and severe natural disasters, we make use a variety of sources. This section provides an overview, while details are described in Appendix I. Our approach focuses on actual disaster dates and events, in contrast to a related macroeconomic literature that defines “macroeconomic disasters” as drops in consumption or GDP beyond a specific threshold (see Barro and Ursua 2010). The advantage of using event data is that it allows for much broader geographic and time coverage, given that long-run consumption and GDP data is only available for advanced countries and a few select emerging market countries. Furthermore, event-based data allows for a more granular analysis, e.g. by differentiating between different types of shocks and disasters.

To identify _financial crises_ , we rely on the data of Reinhart and Rogoff (2009), and the data update by Meyer et al. (2019). We consider banking, currency and external sovereign debt crises and follow the crisis definitions brought forward by Reinhart and Rogoff (see Appendix I).

Our definition of _wars_ follows the Correlates of War (CoW) project, namely a sustained combat, involving organized armed forces and resulting in a minimum of 1000 battle-related fatalities over the course of a 12 months period (Sarkees and Wayman 2010). The CoW data collection distinguishes interstate and intra-state conflict, both of which are included as disaster events here. International or interstate conflicts are defined