rafmacalaba/data-use-mentions-extended huggingface dataset:
simple record keeping, many of the technologies described in this section can be adopted at reasonable costs by local governments.<sup>6</sup> It is important to pilot and test software to be sure it meets the specific needs in a given context. Knebelmann (2022) provides detailed illustrations from the experiences from over 15 property tax digitalization projects in different countries to show how satellite imagery tools and georeferenced data systems can help in detecting, registering, characterizing and valuing properties.

# **4 Technology and Trade Taxes**

Trade and customs taxes account for a notable share of total tax revenues in Africa where they account for 14 percent of tax revenues, relative to 4 percent in other regions (Figure 3b). Also, the majority of VAT revenues are collected at the border in many countries (World Customs Organization, 2017). For this reason, national customs departments play a crucial role in revenue mobilization, in addition to being instrumental in facilitating trade and exchange with foreign investors. At the same time, customs departments are often characterized by high levels of corruption – mostly due to the fact that tax officials enjoy strong discretionary powers in their profession, and risk-based systems of control and accountability are often missing or easily manipulated.

Technological innovations can significantly improve the administration of trade taxes. Similar to VAT, automated customs systems produce high frequency transaction level data that can be used for tax monitoring. It is not surprising that, particularly in developing countries, the initial, experimental attempts to digitalize and automate tax administration took off from customs offices, usually in parallel with broader reorganization reforms of customs bureaucracies. According to the World Bank Global e-Government Systems database, by 2014 low-income countries have invested much more in automating customs (97%), than tax management (57%), filing of taxes (32%), or online service portals (11%). For instance,

> 6Relatedly, geolocalized address information and a more accurate taxpayer register can improve enforcement on other tax types as well
into mid-class values, real terms, USD and Purchasing Power Parity (PPP). In the World and European Values surveys, each country uses a ten steps income scale where each step is reported in local currency. For each country/year, we first calculated the midclass values in local currencies. For the lower class, we used the average between zero and the lower bound of the second class. For the upper class, we used the lower bound inflated by 20%. This is evidently a normative choice based on the notion that the distribution of incomes in the top decile is typically right-skewed, with most of the observations concentrated near the lower bound. The top class has a relatively small number of observations and changing the inflation factor from 20% to, say, 30% has a very marginal impact on results. However, the upper class contains outliers and if we had used higher inflation factors it is as if we were trying to better represent these outliers rather than the median value of the top class.

Mid-class values were then transformed into constant, USD and PPP values using the IMF GDP and PPP data published in the IMF economic outlook report. The IMF GDP data are reported in nominal values (local currency and USD), constant values with base 2000 (local currency) and PPP values (constant USD equivalent) providing all ingredients

> 10This variable is the only income variable present in the database.

8
To measure industrial pollution intensity we use the Industrial Pollution Projection System (IPPS) developed by the World Bank. Numerous studies use the results from IPPS for studies on countries where data is insufficient<sup>12</sup> . We use the assumption that global technological constraints make some industries more polluting than others. Limitations to this assumption is discussed in Gamper-Rabindran (2001), Laplante and Meisner (2001) and Ederington and others (2004)..

To calculate the pollution load for industries in Vietnam, we first mapped the VSIC categories to the ISIC (Rev.3) codes. Using purchasing power parity between Vietnam and the U.S., we converted IPPS pollution intensities to Vietnam dollars. We deflated the output data from the General Statistical Office (GSO) and the pollution loads from IPPS to 1987-88 Vietnam prices using CPI. The steps involved in the calculation of the pollution load are detailed in Annex 2. We applied the deflated pollution load (in kg per thousand Vietnamese dollar) to output (per thousand Vietnam Dollars) to obtain the pollution intensity for each manufacturing sub-group. In the absence of actual pollution intensities from Vietnam, we are unable to verify the correspondence between the IPPS estimates and the actual pollution load of Vietnamese industries.

> 12 See for instance Gamper-Rabindran (2001); Laplante and Meisner (2001).

undergone many revisions from time to time and the latest version is Rev. 3 (1990) with ISIC Rev 3.1 in draft form. See http://esa.un.org/unsd/cr/registry/ for details.

17
Aggregate non-tradable production is estimated through a minimal opportunity-cost framework in which the annual output of an individual is proxied by the income the worker would have earned if she had been allocated to the traditional sector of the labor market. The cumulative value of the non-tradable production is given by the yearly number of hours devoted to the domestic chores and housework activities by gender. The approach applies the average traditional-sector wage, as not doing so would transfer the gender wage inequities from the market to the valuated output (Chadeau, 1985). The model does not predict or impute the wages respective to earning-related characteristics such as the employment status or age-group of the different shares of the population partaking in non-tradable activities as this is deemed problematic for an aggregate standpoint (Hill, 2009). Instead, the alternative that is adopted ascribes the traditional-sector wage as the single and minimum remuneration with which the non-tradable production is valued by the economy.

# **4 Parameterization**

## **4.1 Data Sources**

A wide range of data are used to calibrate the model. In following the CKW 2017 approach, the demographic module is comprised of total fertility rates (TFR) and projected quinquennial population by five-year age group that are gathered from the World Population Prospects of the United Nations (2010). The module of two-sector production is divided between modern sector and the traditional sector. The data on sectoral share of labor is taken by the Liberian Labor Force Survey (LFS) of 2010 and the sectoral share of GDP are taken from the 2007-2013 estimated average of Abid (2016). The amount of arable land and the value rendered by the natural resources are likewise inputted into the production module and taken from the World Development Indicators (WDI) database. The capital stock estimates and the Gross Domestic Product (GDP) statistics are drawn from the Penn World Tables (Feenstra, Inklaar, & Timmer, 2015).

Baseline data on labor force participation rates (LFPR) by sex and five-years age groups are obtained from the ILOSTAT
al., 2021); and the recent Georgia Survey of Agricultural Holdings (50x2030 Initiative, 2020; FAO, 2018). Appendix I includes a brief description of these surveys.

We also draw on experiences from phone surveys used in regions affected by conflicts or natural disasters (Hoogeveen and Pape, 2020) and during the 2014-2016 Ebola epidemic in West Africa (Etang and Himelein, 2020; Himelein et al., 2015; Maffioli, 2020; World Bank, 2014; Zafar et al., 2016). Several survey experiments involving the use of phone surveys aimed at measuring agricultural labor and crop production (Arthi et al., 2018; Gaddis et al., 2021; Kilic et al., 2021) also provide valuable insights for the administration of agricultural surveys via Computer Assisted Telephone Interviewing (CATI). Finally, we draw lessons from several review articles on phone surveys conducted in low- and middle-income countries, including Dabalen et al., 2016; Dillon, 2012; Etang and Himelein, 2020; Glazerman et al., 2020; Gourlay et al., 2021; Henderson and Rosenbaum, 2020.

# 3. Issues of coverage and nonresponse

## 3.1. Sampling and representativeness

In phone surveys focusing on agriculture, the survey design problem when it comes to sampling and representativeness is reaching a representative sample of the target population of agricultural households, holdings, or individual farmers over the phone. Many of the challenges are common to any survey, but a few are specific to the phone survey mode (phone ownership or access, the existence or ability to generate a list of phone numbers, the difficulties of securing respondents’ consent at a distance) and to the fact that agricultural subjects (and in particular a non-random portion of them) are often less reachable by phone than the general population. In this section we illustrate how some of these issues play out in practice and the way and extent to which recent survey efforts have been able to overcome these issues to produce representative national
2009; Eaton et al., 2016) and early evidence for the pandemic indicates that depressed demand was the most frequently reported firm concern initially (Dai et al., 2021; Balleer et al., 2020). Nonetheless, financial channels are also at play. Evidence from past financial crises shows that worsened access to credit and trade finance play a role in reducing trade (Ahn et al., 2011; Amiti and Weinstein, 2011; Chor and Manova, 2012; Crozet et al., 2022). In particular, protecting international transactions from uncertainty and financial risks, through letters of credit for instance, is more difficult during recessions. However, the impact of a crisis on firm exports depends on the main channel of transmission. The GFC had a particularly adverse impact on exports of products heavily reliant on letters of credit which were in short supply by financially distressed banks (Crozet et al., 2022). In contrast, such exports were more resilient in the early stages of the pandemic because banks were not financially distressed and such financing was not in short supply. Securing letters of credit helped exporters cope with the heightened uncertainty by reducing their exposure to risk (Demir and Javorcik, 2020; Crozet et al., 2022).

In the context of the current pandemic, emerging evidence using customs data shows a significant decline in firms’ exports, particularly to destination countries with strict initial lockdown measures. The negative impacts on exports were strong in the first semester of 2020 but followed by partial recovery, indicating some adaptation by globally engaged firms (see Bricongne et al. (2021) and Lafrogne-Roussier et al. (2021) for France, Pimenta et al. (2021) for Portugal, and de Lucio et al. (2020) for Spain). The body of evidence from the ‘Great Trade Collapse’ during the GFC suggests 5
trend assumption. This implies that in the absence of the quota intervention when accounting for the fixed effects, the outcome trends for the treated and untreated groups would follow a similar pattern as the trends between the two groups in the pre-treatment period. I found evidence from an event study analysis that the parallel trends assumption holds.

I use the universe of matriculation results of Kankor test-takers from the academic years 2013–2018. This dataset contains detailed applicant characteristics for 1.51 million participants, such as gender, test score, high school name, and the unit of study to which they were admitted. For this study, I focus _exclusively_ on a subset of 336,221 applicants admitted to public universities over six-years. I employ district-level monthly average luminosity data from 2014–2015 to indicate economic development<sup>3</sup> and identify applicants from areas of low socioeconomic status (SES). This categorization enables me to assess the impact of the quota intervention on the participation and score-related outcomes of female applicants with high and low<sup>4</sup> statuses.

The quota led to a nine-percentage point increase in the matriculation rate of female applicants in the treated concentrations, a 32% increase over the control group mean. Specifically, the quota raised the matriculation rate of female applicants from low-SES districts by three percentage points, a 30% increase compared to the control group mean. This expansion in women’s admissions to public IHEs has lessened the gender gap. However, it has also resulted in a reduced score threshold for female students’ admissions to treated units. This is revealed by a significant decrease of 0.04 units in the average score ratio of female-to-male applicants in the treated concentrations, a 4% reduction compared to the control group mean. This indicates that the preferential treatment enrolled female students with lower Kankor scores. This favored treatment might raise concerns about applicants’ academic performance post-admission,

> 3I acknowledge the coarse nature of this classification. However, given the lack of granular metrics
Bilinsky. 2007. Household Food Insecurity Access Scale (HFIAS) for Measurement of Food Access: Indicator Guide (V3). Food and Nutrition Technical Assistance Project, Academy for Education: Washington DC.

- Cox Edwards, A. and M. Ureta. 2003. International migration, remittances and schooling: Evidence from El Salvador. _Journal of Development Economics_ , 72(2): 429–61.

- de Onis, M., A.W. Onyango, E. Borghi, A. Siyam, C. Nishida and J. Siekmann. 2007. Development of a WHO growth reference for school-aged children and adolescents. _Bulletin of the World Health Organization_ , 85: 660–7.

- Dewey, K.G. and S. Adu-Afarwuah. 2008. Systematic review of the efficacy and effectiveness of complementary feeding interventions in developing countries. _Maternal and child Nutrition_ , 4: 24–85.

- Duflo, E. 2003. Grandmothers and granddaughters: Old-age pensions and intrahousehold allocation in South Africa. _World Bank Economic Review_ , 17(1): 1–25.

- Duryea, S. And M. Morales. 2011. Effects of the global financial crisis on children’s school and employment outcomes in El Salvador. _Development Policy Review_ , 29(5): 527–46.
and King 2001; Maluccio et al 2005; Martorell 1999; and Strauss and Thomas, 1998.

> 2Studies using the Indonesian Family Life Survey (IFLS) and rural Central Java survey data have found that there was no significant decline in the health and nutritional status of Indonesia’s children in the wake of the crisis (see Frankenberg et al, 1999; and Strauss et al, 2004; and Block et al, 2004).

2
Croke et al. (2016) additional data or contacting the original authors to obtain additional information.<sup>3</sup> Additionally, in three cases, the updated analysis includes improved estimates, for example, by obtaining information on intra-cluster correlation directly from the original study authors rather than by imputing data from other studies.

With the full data set, the hypothesis of a common zero effect of multiple-dose deworming on weight is rejected with p<0.001. With the TMSDG sample, this hypothesis is rejected at the 10% level, but applying either a study classification approach used in the previous 2012 Cochrane Review or a study classification approach used in earlier Cochrane Reviews leads to rejection with p<0.01.<sup>4</sup> Any one of five other individual updates to the TMSDG data leads to rejection of the null hypothesis of a common zero effect at the 5% level.

Using our updated sample, and following TMSDG in including studies from lowprevalence environments where the WHO does not recommend MDA, mass deworming is estimated to increase child weight by 0.134 kg (95% CI: 0.031,0.236; p=0.01; random effects estimation). This effect remains robust (at p<0.05) when any individual trial estimate is dropped from the meta-analysis. The result is also nearly unchanged even when simultaneously dropping any two of the 22 estimates: among the 231 possible combinations of two studies that could be dropped simultaneously, in 96% of cases the estimated effect remains statistically significant at p<0.05, and the largest p-value is just 0.067. In areas with prevalence above 20%, where the WHO recommends MDA, the average estimated impact on child weight is 0.148 kg (CI: 0.039,0.258; p=0.008; random

> 3 In several cases we received data from the Campbell Collaboration deworming team, who had themselves directly contacted the original
Section 5 presents the main findings, while Section 6 tests robustness to allowing for a wider range of covariates. Section 7 concludes.

# **2. Insights from the literature**

Several strands of the literature have bearing on how effective household poverty data can be expected to be in revealing the presence of poor individuals. Here we summarize relevant arguments and evidence.

A body of research on the economics of the household has focused on the wealth effect on nutritional status, i.e., how much nutrition improves as a household’s economic welfare—income, consumption or wealth—rises. One strand of this literature has estimated income elasticities of demand for food and (hence) nutrition; an influential early example is Behrman and Deolalikar (1987). Rather than focus on food consumption, as in consumer demand studies (such as Pitt, 1983), other work has instead studied the income effect on nutritional adequacy, taking account of requirements for good health and normal activities in society. A low income elasticity of demand for food can be consistent with a high responsiveness of nutritional adequacy to income gains, since even small gains in nutritional intakes can make a big difference at low levels (Ravallion 1990, 1992).<sup>10</sup> New evidence on this topic has emerged from analyses of the many micro data sets (including the DHS) that have become available to researchers over the last 20 years or so. A limitation of the DHS is that the surveys have not included the questions needed to measure consumption or income. (At the same time, most surveys of the LSMS-type have not included anthropometrics.) The DHS wealth index was developed to help address this deficiency (Filmer and Pritchett 2001). Some studies have argued that the DHS wealth index is a good predictor of various human capital and other outcomes (Filmer and Pritchett 1999, 2001; Filmer and Scott 2012; Sahn and Stifel 2003; Petrou and Kupek 2010). For example, on comparing DHS wealth
not working or studying; i.e. the ―jobless‖. We can define the ―jobless rate‖ as the share of girls age 15-24 who are unemployed, not working in labor market activities, and not in school. Thus, the difference between the inactivity rate and the jobless rate is that the former includes students while the latter excludes students.

Unfortunately, no data source provides ―joblessness‖ rates so we estimate them for the purposes of this paper. To purge students from the ILO’s KILM inactivity rate, we use EDStats secondary school attendance rates to roughly estimate the percentage of girls 15-24 who are in school.<sup>11</sup> We generate two joblessness rates, under different assumptions. First, we assume that girls age 15 and older are only in upper secondary. We use gross upper secondary school enrollment rates to calculate the number of girls aged 15-24 who are in secondary school and divide by the number by the female population age 15-24. We call this the ―lower bound‖ because it does not account for girls over age 15 who are delayed in school (i.e., in primary or lower secondary). Second, we assume that all girls in upper secondary are 15 and older and that half of the girls in lower secondary are over age 15. We divide this number by the female population, age 15-24 to estimate an ―upper bound‖ since we are likely to be overestimating the number of girls in lower secondary who are over-age. We then subtract the lower bound and the upper bound from the KILM activity rate to get two measures for girls’ joblessness rates.<sup>12</sup> We present the opportunity cost both of girls’ inactivity and of girls’ joblessness relative to women’s and boys’ inactivity and joblessness. We use the more precisely measured but less conceptually correct inactivity rate as our base case. Then we present – and compare – the costs when using the less precisely measured but more conceptually
results are “mixed and difficult to generalise” (p. 79). The OECD’s own analysis, based on quantitative data from 18 countries, finds that parental leave policies are associated with higher productivity levels. An additional week of unpaid leave results in a very small (between 0.005 and 0.01 percentage points) but statistically significant increase in both labor and total factor productivity levels. The effect is larger in the case of paid leave, but it is only statistically significant in the case of TFP. For example, if a country without paid parental leave introduced a policy of 15 weeks of paid leave (the OECD average), the study estimates that the long-run effect would be an increase in the total factor productivity level of 1.1% (OECD 2007; Bassanini and Venn 2007). It is not clear, however, whether the productivity gains from leave programs fully compensate for higher associated costs. An analysis in the U.K. also finds positive productivity effects associated with paid leave programs; however, the authors cannot determine whether paid leave policies are a cause or consequence of superior performance (Riley _et al._ 2008).

# **_6.3  Impacts on social cohesion_**

Family-related leave policies can exert some effect on various dimensions of social cohesion. The most obvious concerns the ability to balance work and family formation. Parental leave programs, especially those that provide for some wage replacement, are associated with higher employment rates for women of child-bearing years. At the same time, Gornick and Hegewisch (2010) conclude that fertility rates are higher where women are able to combine employment and
customs office for clearance. The customs office may be at the border crossing point or it may be inland, depending on the particular route that is taken. For example, there is a terminal in the capital city of Belgrade (which is inland), and most shipments bound for Belgrade would go through clearance procedures at the Belgrade terminal. Congestion or other complications at the terminal may increase clearance times or cause them to vary in ways that are problematic for the importing firm. By clearing shipments at the warehouse rather than at the terminal, IHC firms can avoid these difficulties. In principle, shorter and less variable clearance times may allow firms adopting the IHC program to increase their imports if the program reduces these implicit trade costs.

# **2.2. Take-Up and Use**

The Serbian customs agency implemented the IHC program in 2011, with the first firm to adopt it doing so in July 2011. The program is open to any firm that meets the qualification criteria that are reported in Table 1. In the import customs data we have for the period 2010-2013, 34 importing firms used the program. The first use of the IHC program to clear imports occurred in July 2011. Our data end in December 2013, and evaluation is not possible without a period of time that follows take-up of the program. We therefore limit the sample to 21 importing firms that adopted the program before August 2013 and that import in every month of the 2010-2013 period. These firms are the early adopters, and they tend to be substantially larger than the typical Serbian importing firm, as will be documented in Section 4.<sup>13</sup> Discussions with those familiar with the operation of the Serbian IHC program indicate that many of the initial participants were large multinational firms. Information from World Bank staffers in Belgrade suggest that smaller firms do not find the benefits of the IHC program to be sufficient to cover the costs associated with participation. Anecdotally, the main constraint that limits firm participation is the requirement that