rafmacalaba/data-use-mentions-extended huggingface dataset:
differences regression comparing the UCT and control groups. Across the 32 regressions, we find no statistically significant difference in 30, while we find differences between the CCT and control groups in terms of polygamy and whether the household’s religion is animism.

Table 3b presents a similar attrition analysis for child level variables. Across most characteristics (with the exception of math test scores), children from attritor and non-attritor households look similar. In comparing whether the characteristics of attritors differ across groups, we find no statistically significant difference in 23 of the 26 cases. We find differences between the control group and the intervention groups in terms of child age (both CCT and UCT) and parental self-reports of enrollment (CCT). This last result suggests that differences in selfreports of enrollment between attritors and non-attritors are not similar across control and treatment groups and justifies our attrition-related robustness checks discussed in Section 5.5. _5.2. Impacts on Enrollment_ To analyze the impact of cash transfers on school enrollment, we use two measures of enrollment as dependent variables. The first comes from parental self-reports in the household survey. The second comes from school administrative ledgers we collected at each school. By using two measures, one collected at the household-level and potentially prone to self-reporting bias as highlighted by Baird and Özler (2012) and one collected at the school-level and potentially more objective, it reinforces the robustness of our analysis. The correlation between the parental selfreport and the school-based measure is 0.79.

In Table 4, we analyze the impact of cash transfers on enrollment for all children ages 7 to 15 using the three specifications in Equations 1-3. The Equation 1 specification uses only the round 3 cross-sectional data and relies on the random allocation of interventions but does not control for potential residual baseline variation. For the parental self-report measure (column 1), 17
# **5. Conclusion**

Until the end of 2015, currency controls in Argentina led to a widening spread between the official exchange rate and parallel market rates. On December 16, 2015, the government announced its decision to remove the currency and capital controls and currency restriction and let the peso to float within certain limits. As a result, the currency immediately depreciated by about 30 percent and the gap with parallel rates virtually closed.

The removal of the controls boosted the debate on exchange rate equilibrium levels for Argentina. This study aims at contributing to this debate by: (a) identifying which are the macroeconomic fundamentals affecting the exchange rate equilibrium level in Argentina; and (b) estimating an equilibrium exchange rate, based on these fundamentals.

With this objective, the study adopts the behavioral equilibrium exchange rate (BEER) approach using monthly data for the period 1980-2015 and takes into account important features specific to the Argentine economy during the period considered, such as the relevance of agriculture exports and the potential underestimation and overestimation of inflation and economic activity figures, respectively.

According to the analysis, the set of fundamental factors driving Argentina’s real exchange rate are identified as: the terms of trade, productivity differentials with trade partners, US real exchange rate, trade openness, and reserves. Based on the data available for these fundamentals, the peso was overvalued by 39%, as of October 2015. To ensure robustness of results, the analysis is repeated using different specifications, frequencies, and estimation approaches.

Moreover, estimation results shed light on the long-run impact of economic fundamentals on the equilibrium exchange rate in the context of Argentina. The analysis suggests that, for every 1% increase in real commodity (soybean) prices, the real exchange rate appreciates by approximately 0.6% in the long run. A 1% increase in the ratio of CPI-to-PPI relative to trade partners results in nearly a 1.5% appreciation. A 1% increase in the level of real reserves implies a 0
this does not preclude other potential impacts. For instance, the body’s reaction to stress also involves parts of the brain that control mood, fear and motivation, opening the potential to analyze outcomes beyond physical health. Analyzing these outcomes is not possible with DHS data but it could feed future work.

The main results were unlikely to be driven by a change in the composition of mothers, either because of migration or fertility changes. They were also robust to alternative specifications that tested for the possibility of being driven by concurrent shocks, albeit with the significance of the effect faded when focusing only on singletons or on babies born within healthy birthweight ranges. Given variation in the size of the estimate across robustness specifications, the known problems with the report of birthweight in household surveys, and the low sample sizes at higher degrees of disaggregation (for instance, the group of low educated mothers), it may be relevant for future work to validate some of the headline findings with administrative data from birth records, which cover the universe of births.

Another important result was the absence of change in fetal mortality or in the gender ratio at birth associated with a violence shock. These results complement those from earlier studies in poorer countries and would support the hypothesis that the balance between scarring and selection effects differs across countries, and particularly, that in a middle-income developing country such as Colombia the scarring of health caused by a violence shock is stronger.

A gender split in the results would be in line with the medical literature where it is well established that male fetuses are more vulnerable to in utero shocks. However, unlike previous related work where the effects were mostly concentrated on boys, here I also found some impact of in utero violence exposure for girls.<sup>64</sup> It has been hypothesized that the mechanism for the intergenerational transmission of exposure to violence is the hormonal imbalance resulting from the maternal experience of stress. The effect for girls was not visible when exposure to violence was in the first trimester of
# **III. Data**

This paper uses harmonized cross-section microdata for 15 countries in Latin America and the Caribbean (i.e., Argentina, Bolivia, Brazil, Colombia, Costa Rica, Chile, Dominican Republic, El Salvador, Ecuador, Honduras, Mexico, Panama, Paraguay, Peru, and Uruguay) for the series starting in 2010 and ending in 2019.<sup>11</sup> These data are from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), a joint effort of the World Bank and the Center for Distributive, Labor, and Social Studies (CEDLAS) at the National University of La Plata in Argentina.<sup>12</sup> The selection of these countries is based on the availability of at least two comparable data points between 2010 and 2019 within a two-year interval and on the accessibility of the necessary variables to conduct the estimation. More than 85 synthetic panels were constructed for the selected countries with a two-year length following Balcazar et al (2018). Table 1 summarizes the countries, initial and end years, household-survey names in SEDLAC, and the total number of synthetic panels. This study considers information for small and large economies in the region; lower-middle, upper-middle, and high-income countries, and represents 94 percent of the LAC population. The countries excluded from the exercise are those whose data is non-existent or not available circa the end year of the interval.

Table 1: Available cross-section surveys

|Country|Years|Survey|Total #<br>SP|Classification|
|---|---|---|---|---|
|Argentina|2010,2011,<br>2012,<br>2013,<br>2014,2016, 2017, 2018, 2019|Encuesta Permanente de<br>Hogares- Continua|6|H|
|Bolivia|2011, 2012, 2013, 2014, 2015,<br>2016, 2017, 2018
, for each state. Data are drawn from the tender-participant table, and encompass all types of purchases, including tender waivers, for both products and services. Here, we consider only the winners’ (suppliers) information. We match this dataset with the _Cadastro Nacional de Pessoas Jurídicas_ (CNPJ), a dataset provided by the Ministry of the Economy that contains information on every firm registered in Brazil (we use this information as of 2019, the earliest available year). Red dotted line marks the average value of the distribution. The temporal coverage of the data ranges from 2014 to 2021. See Table 1 for more details on our data coverage.

26
# **References**

Angrist, J., & Pischke, J.-S. 2009. “Mostly harmless econometrics: An empiricist’s companion.” Princeton University Press. https://doi.org/10.2307/j.ctvcm4j72 Apedo-Amah, M. C., Avdiu, B., Cirera, X., Cruz, M., Davies, E., Grover, A., Iacovone, L., Kilin, U., Medvedev, D., Okechukwu Maduko, F., Poupakis, S., Torres, J., & Tran, T. T. 2020. “Unmasking the impact of COVID-19 on businesses: Firm level evidence from across the world.” Policy Research Working Paper 9434, World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/34626.

- Autor, D., Cho, D., Crane, L.D., Goldar, M., Lutz, B., Montes, J., Peterman, W.B., Ratner, D., Villar D., & Yildirmaz, A. 2022. "The $800 Billion Paycheck Protection Program: Where Did the Money Go and Why Did It Go There?" Journal of Economic Perspectives, 36(2): 55-80. https://doi.org/ 10.1257/jep.36.2.55

- Bertelsmann Stiftung Transformation Index (BTI). 2020. “BTI 2020 Codebook for Country Assessments.” https://bti-project.org/en/methodology#Index%20aggregation.

- Bloom, N., Brynjolfsson E., Foster L., Jarmin R., Patnaik M., Saporta-Eksten I., & Van Reenen, R. 2019. “What drives differences in management practices?” American Economic Review, 109(5): 1648– 1683. https://doi.org/ 10.1257/
capital ($20 million) needed in any given year to pay losses. Insurance loss modeling develops data on these two main figures -- annual average loss and probabilities of maximum losses -- from data on multiple meteorological or seismological events.

# **_The_** _Nature of Catastrophic Risk_

Catastrophic risks in primary markets tend to be spread temporally more than spatially. For example, prior to Hurricane Andrew, insurance companies added a 14 percent catastrophic property factor to insurance premiums in Florida. With residential insurance premiums totaling $1.2 billion in 1992, this resulted in the catastrophe portion of premiums of $168 million per year. This factor was based on loss data for 30 to 40 years. The assumption was that catastrophe losses would average out to 14 percent of the premium over 40 years. In any single year, a large hurricane could cause losses many times the $168 million in premium. However, the companies assumed that over a 30 to 40 year period they would break even.

The pricing of catastrophe reinsurance is based on the notion of spreading risk over time. The reinsurance company promises to indemnify the primary company for losses above a retention level. For example, a primary insurance company might purchase reinsurance for catastrophe losses between $20 million and $30 million. In pricing this $10 million layer of insurance, the reinsurer will first estimate the probability of loss in this range. Let us assume that this probability is 10 percent. In other words, the reinsurer believes that once every ten years the primary insurer will need to pay losses of
suppliers that do not engage in exports and thus are absent from the customs transaction data. Second, it miss intermediate that are but not this may products produced domestically imported. Lastly, approach assumes that products classified under the same HS 6-digit code are substitutes, implying that imported goods can be replaced with their domestically produced counterparts, which may not always hold true in practice.

Based on the definition of potential domestic suppliers, the CI for a single imported product ( _j_ ) of each exporting firm ( _i_ ) in each year ( _t_ ) is calculated as follows: where _Njt_ is the total number of potential suppliers of product _j_ in year _t_ , which equals the total number of exporters of product _j_ in year _t_ . The indicator variable, _Mjit_ , takes on the value 1 if firm _i_ imports product _j_ in year _t_ , and 0 otherwise; Likewise, the indicator variable, _dnit_ , takes on the value 1 if the potential supplier _n_ is connected to firm _i_ via the HSR network in year _t_ . So the denominator is the total number of potential suppliers of a given imported product in a given year, and the numerator is the total number of potential suppliers that are connected via HSR to the exporting firm in a given year. In short, for each imported product _j_ , the variable, _CIjit_ , is the share of potential suppliers that are connected to firm _i_ via HSR. The variable _CIjit_ takes a value of 0 if firm _i_ does not import product _j_ in year _t_ or if none of the potential suppliers are connected to firm _i_ through the HSR network. Conversely, if all potential suppliers of product _j_ are connected via HSR, _CIjit_ equals 1. A higher value of _CIjit_ indicates a greater likelihood that firm _i_ can source material _j_ domestically through HSR-enabled connections.

For firms importing multiple products, the variable _CIit_ represents the weighted average share of potential suppliers connected to the firm via HSR. The weights correspond to the share of each product _j_ in firm _i_ ’s total imports for
# **1. Introduction**

Do higher food prices help or undermine efforts to reduce poverty in developing countries? This remains a critical issue in guiding policy, since most the world’s poor live in rural areas and work in agriculture. At the same time, expenditures on food account for the largest proportion of the budget of poor households. Global integration and international trade play a key role in determining food price developments that face poor farmers. The recent increases in food prices across the globe have led a number of governments to limit trade in agricultural products and intervene in agricultural markets, more often in the name of protecting the poor (Jacoby, 2013).

Most studies that have sought to assess the impact of higher food prices on the welfare of poor households have focused on whether the household is a net consumer or net producer of food. Since most poor households have been found to be net consumers of food, analyses using this approach typically conclude that higher food prices are likely to increase poverty in the short run (see, for example, Ivanic and Martin ((2008), (2014)).

However, there are some concerns with this approach. First, there is the issue of the reliability of the data on the net food position of households from household surveys. Substantial errors can arise from recall biases that lead to the underestimation of food production and from annualization of short recall responses that overstate consumption (see Headey (2016)). Second, there may be second-round effects beyond those on household income that can lead to households adjusting to higher food prices in ways that lead to lower poverty in the medium term. For example, food producing households could increase their use of fertilizers and/or invest in higher yielding seeds, that contribute to higher productivity and greater output. An increase in the demand for unskilled labor in rural areas, where poverty is concentrated, could result in higher wage income for the household. Finally, intra-household distributional decisions will influence the outcome of rising
WPS 3489

# **Environmental Factors and Children’s Malnutrition in Ethiopia**

# **Patricia Silva World Bank, Environment Department**

## **Abstract**

Ethiopia has one of the highest child malnutrition rates in the world. A considerable effort to monitor child malnutrition rates over the last two decades shows that, despite some improvements, approximately half of the children under five are still malnourished. Much of the burden of deaths resulting from malnutrition, estimated to be over half of childhood deaths in developing countries, can be attributed to just mild and moderate malnutrition. Several biological and social economic factors contribute to malnutrition. Using the 2000 Ethiopia Demographic and Health Survey data, this study examines the impact of access to basic environmental services, such as water and sanitation, on the probability children are stunted and underweight. The focus is on the impact of externalities associated with access to these services. We find that biological factors, such as child’s age and mother’s height, and social economic factors, such as household wealth and mother’s education, are important determinants of a child’s nutritional status. This is consistent with the findings of most studies in this literature. With respect to the environmental factors, we find that there are indeed significant externalities associated with access to water and sanitation at the community level. The external impacts of community level of access to these services are an important determinant of the probability a child is underweight. Our results also show that the external impact of access to water is larger for children living in rural areas.

## World Bank Policy Research Working Paper 3489, January 2005

_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
) show that when using a more accurate dataset on average years of schooling across countries, increases over time in this measure of human capital are in fact positively related to GDP growth.

Other authors attempted to solve the identification problem through instrumental variables. This approach consists of finding variables (instruments) that are correlated with the variables of interest, but not with the unobservable confounders that bias the regression results to start with. By focusing on variation “induced” by these instruments, in principle one can separate the effect of the variables of interest from the one of unobservable factors.

Unfortunately, finding suitable instruments in the context of cross-country regression is a challenging task. A seminal contribution in this direction is Acemoglu et al. (2001). The paper focuses on political institutions, and proposes to identify the importance of institutional quality for economic development by using the mortality rate among European colonizers as an instrumental variable. The logic is that colonizers created extractive institutions in colonies where they could not easily settle, whereas they attempted to recreate European institutions in environments more favourable to their inhabitancy. The main result of the paper 6
consider having collaboration with IT providers to attempt building antidotes to such act with consideration on relative marginal cost and marginal benefit.

# **7 Conclusion**

This paper examines the impact of POS distribution on tax payment among hotels and restaurants in the Indonesian districts of West Manggarai and Gorontalo. Using Allingham and Sandmo’s tax compliance framework as cited in Naritomi (2019), we find that the POS distribution has various impacts on tax compliance. While some POS machines have proven to improve tax compliance in both districts, RPOS has consistently showed insignificant impact on tax compliance.

We have identified several key takeaways from this varying impact of POS devices. First is the capacity of the receiver matter. POS receivers must sacrifice their resources to accommodate the machines. Without any personal benefit gained from the device, it is hard to imagine that taxpayers with low resources will voluntarily utilize the POS machine given to them. Second, interventions that require minimum adaptation are likely to succeed. Minimum adaptation means less resources needed to operate the POS machines. Again, without personal benefit, taxpayers tend to be more open to devices that require minimum resource allocation.

Finally, third-party data and informality play a vital role in increasing perceived probability of detection. This explains why big hotels and restaurants, which are TMD and WS users, were more compliant than small hotels and restaurants, which are RPOS receivers. We suggest that national scalability should be considered based on several factors.

First, district governments should not solely rely on POS machines to increase compliance. Without a strong and effective tax enforcement system, POS distribution will not be effective. Given their limited resources, district governments should manage their enforcement efficiently and effectively. This calls for the second, that is utilization of a risk-based enforcement system.

Next, district governments ought to have a clear legal framework on POS distribution. This can help district governments to enforce POS usage and oblige receivers to maintain the devices properly. Fourth, district governments must conduct a thorough cost-benefit analysis to determine the actual benefits
|57.90|64|45.56|
|income<br>countries||(42.32)||(42.79)||(34.73)||(24.64)|
|Lower middle|558|56.93|567|72.38|239|49.86|148|49.73|
|income<br>countries||(35.03)||(36.12)||(23.97)||(34.69)|
|Low<br>income|572|68.96|577|82.61|31|68.71|223|59.67|
|countries||(37.37)||(40.45)||(23.79)||(28.06)|
|Oil exporters|452|45.25<br>(39.32)|460|56.60<br>(41.45)|170|51.04<br>(35.58)|149|33.31<br>(24.51)|

- _World Bank country classification based on 2010_

- _n = number of (country-year) observations; sd = standard deviation;_

_Source: Deutsche Gesellschaft für technische Zusammenarbeit (GTZ) for diesel-, premium gasoline- and kerosene prices. Regular gasoline prices originate from International Energy Agency._ The data on fuel prices were merged with data on independent socio-economic variables from the World Bank, IMF and United Nations, governance indicators from the World Bank Governance Indicators (WGI)<sup>10</sup> and with institutional and political variables originating from the 2010 release of the _Database on Political Institutions_ (DPI).<sup>11</sup> The resulting panel dataset consists of an unbalanced panel of 201 countries for the period 1991 – 2009.

# **5. Fossil Fuel Energy