rafmacalaba/data-use-mentions-extended huggingface dataset:
the impacts of temporary cash transfers on household consumption. In this literature, a large body of work has leveraged random and quasi-random income shocks to estimate short run impacts in richer countries (Jappelli & Pistaferri, 2010) and developing countries (Haushofer & Shapiro, 2016). More recent work in Norway has taken advantage of high frequency household balance sheet data to estimate dynamic impacts leveraging natural experimental variation in cash transfers (Fagereng et al., 2019). Aggregating across experimental UCT studies meets the data requirements to produce comparable dynamic estimates for developing countries. These impacts can also be interpreted as intertemporal marginal propensities to consume, key parameters for disciplining models of intertemporal optimization subject to constraints (Auclert et al., 2018). Second, we contribute to a deep literature that investigates the role of poverty traps and implements empirical tests (Carter & Barrett, 2006; Kraay & McKenzie, 2014; Balboni et al., 2020). Complementarily, recent work leveraging experimental variation has consistently found large UCT and TUP programs increase household consumption 4 or more years after initial transfers, suggesting these programs enable households to escape from poverty (Bandiera et al., 2017; Blattman et al., 2020). Through a metaänalytic lens, we provide a relatively powered test of the relationship between intervention size and the impacts of cash transfers on household consumption over time. In addition, we theoretically link this relationship to the existence of poverty traps. Third, we contribute to a growing literature in economics that uses metaänalysis to aggregate estimated program impacts across studies (Baird et al., 2014; Burke et al., 2015; Meager, 2019; Vivalt, 2020). We do so by providing a template for future “metabenchmarking” of the cost-effectiveness of one intervention (here, the cost-effectiveness of either large UCT or TUP at increasing household consumption) against UCT.

In summary, we present a nuanced view of the impacts of intervention size on cost effective sustained poverty reduction in the context of temporary cash transfers and their theoretical
estimates of the impact of CIT rate increase on employment, wage bill and gross revenue of the offshore sector are all null. If anything, we find small _increases_ in aggregate economic activity among manufacturing firms in the offshore sector, but the estimates are imprecise.

To explain the apparently inconsistent findings of a large fall in the number of entrants and no changes in aggregate activity, we turn to more granular data. First, we find that entrant firms are typically very small and that economic activity in the offshore sector is highly concentrated in a few, large incumbents remaining largely active in the postreform period. Second, the differences-in-differences analysis on a balanced firm-level panel shows that the CIT reform has no significant effects on the employment, wage bill and revenue of offshore incumbents.

Taken together, our results strongly reject the hypothesis that the increase in corporate income taxes led to a decrease in aggregate economic activity, or that they hampered the performance of firms subject to the CIT rate increase.

Like our paper, several other empirical contributions document a limited economic effect of CIT reform. Buba and Wong (2017) review the literature on SEZ and conclude that despite their ability to attract foreign investment and boost exports, their impacts on employment and spillovers to other local firms are unclear. Interviewing management from firms in SEZ across countries, Frick and Rodríguez-Pose (2023) document that factors such as infrastructure, labor costs and political stability are more important for investment than tax benefits. We add to this evidence by focusing on the specific role – of corporate income taxes in developing economies which is crucial given their typical – fiscal needs independently of other policy measures.

Beyond developing economies, survey evidence from small and medium enterprises (SMEs) often points to other barriers such as financial constraints and regulatory burden, as much more relevant to business decisions (Ravšelj et al., 2019; Wang, 2016). Gordon and Li (2009) conclude that the most important tax provision to encourage risk-taking by corporations is beneficial treatment of losses (e.g
to a typical public sector pay scheme (flat salary) or a more high-powered scheme (piece rate). We then observe the motivation and ability profile of workers choosing the variable pay contract.

The overall structure of the experiment is displayed in figure 7. After health workers are given instructions and introduced to the two schools, we obtain incentivized measures of pro-poor preferences, and an incentivized measure of ability in the task. These are explained below. Then, health workers enter the “virtual clinic” with the video vignettes. Finally, an exit survey records health worker demographics and other preferences.

The sessions were conducted in February-March 2014. Each day health workers were invited to participate throughout the day (beginning at 11am). Health workers could participate in the experiment at any time during the day. Hence, our results reported errors clustered by day rather than by session, as sessions were not relevant for our setup.
# 1  Before beginning the fieldwork

The objectives of the pre-field stage are to:

- Define the scope of the exercise in terms of the country-specific institutions to be analyzed;

- Understand the institutional setting governing infrastructure;

- Identify (primary and secondary) sources of data and information;

- Answer as many qualitative (and quantitative) questions as possible based on secondary sources.

The information collected in this preparatory, or pre-field, phase might in some cases be referential but in all cases will be completed and cross-checked in the course of the fieldwork, making the whole process more efficient.

One important item on the pre-field agenda is a thorough literature review to determine the extent of the information and data readily available from public sources (both primary and secondary). A starting point for the literature and data review is offered by the specialized Web sites of development institutions (World Bank, African Development Bank [AfDB], Organisation for Economic Co-operation and Development [OECD], International Monetary Fund [IMF], and donors) and of relevant institutions in the country (such as the ministry of finance, statistical offices, central bank, line ministries, parastatals, monitoring units for parastatals, and so on). This search should include, but must not be limited to:

- Recent sectoral analysis material and publications;

- Recent fiscal analysis material such as the Country Financial Accountability Assessment (CFAA), Public Financial Management (PFM) diagnostic studies, and IMF‘s Recent Economic Developments;

- Organizational charts, annual reports, budget statements, budget speeches, budget laws, Poverty Reduction Strategy Papers (PRSPs), and the Medium-Term Expenditure Framework (MTEF);

- Budget books;

- Tariff schedule publications and their indexation;

- Financial accounts of infrastructure operators and funds;

- Annual reports of infrastructure operators and funds.

The pre-fieldwork should be organized so as to:

- Produce a blueprint of the institutions involved in infrastructure provision.

- Mine existing readily and publicly available data.

- Account for country-specific circumstances (and concerns)
Using small-scale economic data, the analysis shows that Landsat imagery can act as a strong predictor for counts of enterprises and employment as well as expenditure in the cross-section in the context of Vietnamese communes. Simple combinations of Landsat bands that were developed to detect urban areas and vegetation already have reasonable predictive power, while flexible combinations of band means and standard deviations can explain a large share of the differences in production and consumption measures between communes. Cross-validation exercises and LASSO regressions indicate strong parameter stability and do not suggest overfitting due to the large number of potential parameters.

Comparing the Landsat measures to the often-used nighttime light approach, the results indicate that Landsat outperforms nightlights at predicting economic outcomes in the crosssection aggregate. This is especially true in very small areas and in communes with a high share of formal employment. These units tend to be urban areas and this confirms concerns that the DMSP-OLS nightlight data is too coarse and saturated to be applied on a very small geographic scale. However, nightlights perform reasonably well at larger commune sizes. This suggests that Landsat and nightlights are complements rather than substitutes for applied researchers interested in capturing the spatial variation of economic activity. Given their very different nature of data collection, including both satellite measures into prediction models can improve their precision. This is especially attractive as both nightlights and Landsat data are freely accessible.

When looking at changes over time, neither nightlights nor Landsat have much prediction power in the simple models. Although some coefficients of Landsat bands are significant in the regression, they can only explain a small share of the variation over time and the R- squared of the linear models is close to zero. Likewise, the results show large differences between the nightlight coefficients in the cross-section and time-series regressions. This casts doubt on the usefulness of simple Landsat or nightlight models for predicting economic 24
# Appendix 2: Parallel trends

The difference-in-difference approach relies on the assumption that in the absence of the PBF intervention, changes over time in the PBF and control districts would be similar. We used the Tajikistan Demographic and Health Survey (DHS) 2012 to test whether the parallel trends assumption could be rejected for the years prior to the launch of the PBF pilot. For the years 2008-2012, we constructed annual indicators for timely antenatal care, receiving at least four antenatal consultations and for vaccination coverage of children.

The results presented in the table below are from a difference-in-difference specification in which we define the period 2010-2012 to be a ‘post intervention’ period. As can be seen, for none of the indicators were we able to reject the parallel trends assumption. We clustered standard errors at the sampling cluster level, which provides a more conservative test in comparison to clustering at the district level as we do in our analysis. The results are robust to changing the pre- and post-intervention periods.

|||Diff-in-diff co<br>|efficient<br>|
|---|---|---|---|
|SelectedIndicators|N|β|SE|
|ANC Visits within 12 weeks|1163|-0.05|0.07|
|4 or more ANC visits|1463|-0.05|0.04|
|BCG vaccination date|2532|0.02|0.06|
|DPT 1 vaccination|2532|-0.01|0.06|
|Polio 1 vaccination|2532|0.01|0.08|
|DPT 2 vaccination|2532|-0.03|0.08|
|Polio 2 vaccination|2532|-0.02|0.06|
|DPT 3 vaccination|2532|-0.02|0.05|
|Polio 3 vaccination|2532|-0.01|0.05|
|Measles vaccination|2532|-0.03
from local or national authorities, and if they have not, they are asked about the reasons why.<sup>5</sup> The data set then offers a unique window into the private sector to assess both the need for policy and the availability of public support during the COVID-19 shock.

Given the variation in country samples and timing of the surveys, we follow Apedo-Amah et al. (2020) and introduce different controls in the analysis. Unless stated otherwise, we usually include in the analysis dummies for size, sector (i.e. 10 sectors), country, and the timing of the survey in terms of weeks relative to the peak of the COVID-19 shock.<sup>6</sup> To control for differences in the number of observations in each country sample, we weight our results using the inverse of the number of observations in each country, that is, in the spirit of traditional cross-country analysis, each country has the same weight in our analysis.

# **3. The Supply of and access to policy support**

To respond to the economic downturn caused by the COVID-19 pandemic, countries around the world have enacted a large suite of stabilization and recovery measures. According to the World Bank, governments across the world have implemented 1,607 measures directly aimed at supporting firms in 135 countries. More than three-quarters of these measures are concentrated in three categories: debt finance support, employment cost support, and tax support.<sup>7</sup> Debt finance support has been the most common, accounting for more than a third of all policy measures adopted. Within this category, new lending under concessional terms accounts for 43%, followed by the deferral, restructuring or rescheduling of payments (26%) and credit guarantees (13%). Employment support constitutes roughly one-quarter of all measures that have been announced, with wage subsidies (38%) and support to self-employed individuals (26%) being the most common within this group.
such as the WFP also often employ asset indexes to target food assistance programs for refugees; one such recent application was for the Malian refugees in Niger (Beltramo et al., 2019).

# _Asset index_

We consider a variant of Equation (1) where the left-hand side variable, household consumption y� is now missing but we have data on household assets a�, which is a subset of x�. Still, we want to generate a wealth index w� which offers the best combination of (the elements of the different) household assets a�. Suppressing the household index to make the notation less cluttered, this can be expressed as follows where α are the (vector of) weights we place on the a� to generate the wealth index w�. A common way to derive α is through Principal Component Analysis (PCA), another way is just to sum up all the assets available in a�.

We briefly describe here a couple of reasons that make asset indexes more likely to result in biased estimates of poverty. First, the wealth index w� does not include the non-asset components, which is equivalent to the well-known issue of omitted variable bias. Second, β� and α are generally different from each other, since the estimator for α maximizes the variance in a�, while the estimator for β maximizes the variance in y�.<sup>17</sup> Finally, in a refugee context, the temporary nature of displacement likely affects refugees’ behaviors in terms of accumulation and use of assets. For example, refugees may choose not to invest as much in high-quality durables as a

> 17 See Rencher (2002, pp. 389) for a graphical illustration of the general difference between principal component analysis and OLS methods, and Dang _et al._ (2019) and Dang (forthcoming) for further discussion on asset indexes.

20
# **5. Results**

# **5.1 Relative deprivation in the income space**

In this section, we look at the spatial divide between urban and rural areas in Morocco using standard measures of inequality and relative deprivation. The purpose of this section is to assess the urban-rural divide in Morocco and to illustrate the use of the Yitzhaki (1979) relative deprivation measure and its relation with the Gini index.

The sharp inequality in living standards across urban and rural areas is a very apparent feature of the Moroccan economy. According to a recent World Bank study (2010a), the poverty rate in rural areas is almost three times as high as in urban areas (14.5 percent and 4.8 percent respectively). This is likely the largest urban-rural gap in the Middle East and North Africa region and one of the largest in the world (World Bank 2010b).

A decomposition of economic inequality using the MHYS 2009-2010 data illustrated the extent to which the rural-urban gap is a substantial contributor to Morocco’s overall economic inequality. We decompose economic inequality into its between-group and within-group components following Bourguignon (1979) and Shorrocks (1984). Between-group inequality reflects the mean differences across the rural and urban populations; within-group inequality reflects the inequality within the rural and urban sub-groups. Table 3 shows only slightly higher inequality within urban areas as compared to within rural areas. However, the between-group inequality across the rural-urban areas explains roughly a third of the national inequality. Using slightly older datasets from Morocco, the World Bank (2010b) arrives at similar conclusions for expenditures. The sharpness of Morocco’s rural-urban divide is particularly evident when put in the context of other countries. For example, Shorrocks and Wan (2008) show the range of expenditure based between-group (rural-urban) inequality, corrected for spatial price differences, to typically range from 5 to 15 percent of overall inequality. The rural-urban sharp divide in Morocco is thus very high by international standards.

**Table 3: Inequality (
# **3 Theoretical Motivation**

Gordon Allport’s (1954) book The Nature of Prejudice outlined ‘contact theory’, which has – subsequently become one of the most influential theories in the social sciences applied across sociology, political science, and economics. It suggests that, under certain conditions, inter-group contact can reduce prejudice between majority and minority groups. The conditions he identified include socio-economic equality, shared norms, cooperation, and institutional support. The ideas have been widely applied to examine attitudes and prejudice relating to, for example, race, sexual orientation, religion, and immigration.

The evidence on the impact of inter-group contact on receiving community attitudes to immigrants is mixed (Paluck et al. 2019). On the one hand, for example, Quillian (1996)’s finding of a positive relationship between inter-group interaction and white racial attitudes towards African-Americans has been widely cited across the immigration literature. On the other hand, for example, Hopkins (2010) examines panel data on US public attitudes to immigration to explore a ‘politicized places hypothesis’, finding that when faced with a large influx, local communities adopt negative attitudes, albeit only when the influx is combined with destabilising local demographics and salient political rhetoric at the national level.

Relatedly, there is a broader literature that examines the correlates of ‘native’ attitudes to immigration. This literature tries to explain attitudes, policy preferences, and voting preferences, usually focusing on European or North-American contexts. Its main methodological challenge has generally been to identify causal relationships in the absence of panel data sets or experimental methods. This literature identifies two main sets of explanations underlying ‘native’– i.e. host community – attitudes (Hainmueller and Hopkins 2014). First, economic explanations. From this perspective, host communities are concerned with the effects of immigration on labour market competition i.e. increased supply of low-skilled workers leads to a decrease in wages (Mayda 2006) or the fiscal implications i.e. immigrants pay less taxes
rule (Persson, Roland, and Tabellini 2007).

Early econometric studies find mixed and often only weak evidence of the effect of democracy on agricultural protection (Lindert 1991; Beghin and Kherallah 1994; Swinnen et al. 2000; Olper 2001, 2007).<sup>23</sup> These studies predominantly rely on cross-section variation in the data and are subject to problems of reverse causality (policies may also influence governance structures) and omitted-variables bias. Studies using long-run historical data allow more careful measurement of the impact of shifts from one set of political institutions to another. Swinnen, Banerjee, and de Gorter (2001) show that changes in electoral rules that 19 Electoral systems can be classified across several dimensions, such as the electoral formula (how votes translate into seats) and the magnitude of the electoral district (the number of legislators elected in an average district). Because these dimensions are closely related across electoral systems, it is common to contrast majoritarian election (with plurality rule and smaller districts) with proportional election (where the seats are attributed in proportion to votes in larger districts). Regarding forms of government, the classical distinction is between presidential and parliamentary forms of government. In the former, citizens elect the chief executive directly. In the latter, the executive is appointed indirectly, through a vote of confidence from an elected parliament.

20 Persson and Tabellini 2000; Austen-Smith 2000; Iversen and Soskice 2006; Lizzeri and Persico 2001; MilesiFerretti, Perotti, and Rostagno 2002; Persson, Roland, and Tabellini 2007; Ticchi and Vindigni 2010. 21 Persson, Roland, and Tabellini 1997; 2000; Persson and Tabellini 2000.

22 In terms of trade policy, Roelfsema (2004) identifies a positive effect of majoritarian elections on trade protection. Grossman and Helpman (2005) predict that tariffs will be higher under a majoritarian regime, because in a proportional system, all regional interests will receive equal support.

23 Empirical evidence on trade policy yields mixed results. Some studies suggest that democracy
# **Activities carried out by female and male respondents classified under ICLS 19 Forms of Work framework**

Even when they answered negatively to either one of the survey questions on employment, both paid and unpaid, the study found that women engage in a wide range of activities that qualify as ‘employment’ under the ICLS 13 definition and as ‘work’ or ‘employment’ following the ICLS 19 Forms of Work framework. Female respondents reported tasks that intertwine with their household responsibilities, ranging from agricultural activities, to non-agriculture related economic activities, to volunteer work. Women’s reported tasks fall under three categories of the ICLS 19 Forms of Work framework, namely: 1) Own-use production of goods; 2) Own-use production of services; and 3) Employment (See Table 5 and Annex 1 for description of each category).

In terms of _own-use production of goods_ , many women who responded “no” to the household survey questions, reported activities that involved producing goods intended for final use by their household/ family (see Table 5, row a). Such activities include collecting firewood, livestock/poultry rearing (for own consumption), growing food in their own vegetable gardens, providing farm support (planting, weeding, harvesting, cleaning crops; collecting water for crops; irrigation; fumigation). These are activities very similar to those reported by men in the same communities.

_Own-use production of services_ are activities that were not considered ‘employment’ under either the ICLS 13 or ICLS 19 definition, but are considered ‘work’ under the ICLS 19 definition. These encompass many of the tasks typically thought of as domestic work. Among this type of activities, women reported engaging in preparing and bringing breakfast to men in fields, cleaning the house, washing/ironing clothes, cooking for the family, preparing tortillas for own consumption, taking care of children, and maintaining dwelling with adobe, among others (Table 5, row a)<sup>16</sup> These activities