rafmacalaba/datause-encoder-data huggingface dataset:
offers more precision than model-based measures relying on national-level food supply (Coates, 2013; Smith _et al._ , 2017). The FIES is constructed based on respondents’ responses to eight questions about experience in various domains of food insecurity:

- FS1: “Household members have been worried that they will not have enough to eat?”

- FS2: “Household members have been worried that they cannot eat nutritious foods?”

- FS3: “Household members had to eat always the same thing?”

- FS4: “Household members had to skip a meal?”

- FS5: “Household members had to eat less than they should?”

- FS6: “Household members found nothing to eat at home?”

- FS7: “Household members have been hungry but did not eat?”

- FS8: “Household members have not eaten all day?”

Both the EHCVM sample and the COVID-19 phone survey include each of the eight FIES questions, allowing us to get a measure of food insecurity for both periods. As long as the data generated via these questions align with the Rasch measurement model’s assumptions (Rasch, 1960), food security status can be measured by summing the number of affirmative responses to the FIES questions to produce the raw FIES score. We use the raw FIES score as a primary outcome as well as the food insecurity severity classifications developed by Smith _et al._ (2017) as follows: (a) We define “mild food insecurity” with a raw FIES score greater than zero. This definition aligns closely with the FAO’s definition of food security as having “physical, social, and economic access to sufficient, safe, and nutritious food [...] at all times” (FAO, 2009). (b) We define “moderate food insecurity” with a raw FIES score greater than three. Finally, (c) we define “severe food insecurity” with a raw FIES score greater than seven—or answering affirmatively to each of the eight FIES questions.

# **4 Estimation Strategy**

Our estimation relies on a difference-in-differences empirical framework based on the presence of subnational variation in the intensity of pandemic-related disruptions. Consistent with existing predictions highlighting the primarily post-farm disruption of the coronavirus pandemic on food systems (Reardon _et al._ , 2020), the fact that the pandemic peaked prior to the beginning of the agricultural season in Mali, and the biological dynamics in which COVID-19 spreads via close human contact, we claim that urban areas experienced more intense pandemic-related disruptions than rural areas in Mali. This assumption is consistent with recent findings by Aggarwal _et al._ (2020) on the effects of COVID 19 disruptions on food security in rural Malawi and Liberia. Using panel data monthly phone surveys that started before and 9
Macedonia, Czech Republic, Ecuador, El Salvador, Belarus, Burkina Faso and Azerbaijan- that are matched to the registry reform countries based on their region and their per capita GDP. Table 1 shows the countries and surveys used in our analysis and identifies (a) countries that introduced collateral registries for movable assets, (b) countries that introduced other collateral reforms, (c) countries that did not introduce any reforms and are not part of the match sample, and (d) countries matched based on availability of surveys, region and GDP per capita to our sample of countries that introduced a collateral registry for movable assets.<sup>11</sup> Table 2 lists the treatment countries (i.e., those that introduced movable collateral registries) along with their respective matched countries, including details on the survey years, region and GDP per capita (our three matching criteria).

For each country that introduced a registry for movable collateral, Figure 1 shows the share of firms that reported having access to a line of credit, loan or overdraft in the period before and after the registry for movable collateral was introduced. With the exception of Guatemala, all countries that introduced a registry for movable collateral seemed to have experienced a significant increase in access.<sup>12</sup> Figure 2 compares the share of firms with access to a bank loan, line of credit or overdraft in countries that introduced collateral registries for movable assets vis-a-vis the matched sample of countries that did not introduce such registries. This graph shows that the share of firms with access is higher among reform countries. Both Figure 1 and Figure 2 offer prima facie evidence that introducing a collateral registry for movable assets improves firm access to finance. However, more rigorous evidence that controls

> of collateral (e.g., Armenia, Georgia, Mauritius, Romania) and introducing a law that allows a business to grant a nonpossessory security right in a single category of movable assets (such as accounts receivable or inventory), without requiring a specific description of the collateral (e.g., Kyrgyzstan, Vietnam):

> 11 The matched countries may or may not have a pre-existing registry, but have not had a registry reform (i.e. new registry introduction) within our sample frame.

> 12 Alvarez de la Campa et al. (2012) conduct a survey of movable registries in 35 jurisdictions including Guatemala and show that relative to other registries the Guatemalan  registry charged higher fees and consequently was not used frequently by firms.

8
_14_ wage work were more likely to be crossovers. The direction of causality is unclear for the revealed preference variable because it may be argued that the wage a person would accept to leave their business is correlated with the profits they make in that business. Crossovers were also significantly less likely than noncrossovers to have started the business themselves, which adds weight to the suggestion that crossovers had easier market entry opportunity.

# 5. Role Models and Early Influences

In the Uganda study, Campos and colleagues (2011) highlight the “importance of male role models and ‘gate openers,’ and the active engagement of the role models in engaging a female entrepreneur in the activities of the crossover sector and in breaking traditional norms.” We assessed whether having a role model influenced the sector of operation in Ethiopia.

Table 7—Early Influences Table 7 shows the early influences in the women’s lives to assess whether there is any predictability in crossing over based on childhood influences; no conclusive evidence of this was found in Ethiopia. Women were asked explicitly in the additional survey module whether they had a male or female role model while growing up. To further analyze potential early influences, we coded the outcomes to highlight the occupational choice of the parents and grandparents. The father having a government wage job was positively correlated with being a crossover,
pure profit shifting in the sense of wholly artificial arrangements intended to reduce the multinational group’s total tax liability. To the extent that the amount of profit shifted through such arrangements is directly affected by real production decisions, however, their impact can be captured in that framework. Suppose for instance that the affiliate in _h_ produces an intermediate input used by that in _j_ , so that _Rj_ = _r_ ( _Kj, Fh_ ) _− c.Fh_ , for some function _r_ ( _._ ), where _c_ denotes the internal transfer price. Then _∂Rj/∂Fh_ will differ from zero to the extent that the transfer price differs from the marginal product of the intermediate in its use by _j_ ; if the transfer price is set higher than that, for instance, then _∂Rj/∂Fh <_ 0.

Many other forms of profit shifting, however, are not directly related to production: debt shifting, for example, and routing through tax-favored conduit countries. The latter, as stressed above, is particularly relevant here as being a key driver of the distinction between FAI and FDI data. While FAI data looks though pure conduits to identify the ultimate parent of any affiliate in some host country, the real investment decisions of the multinational can of course nonetheless be expected to reflect its awareness of any tax savings that it can enjoy by the use of such conduits. This means that the potential use of conduits as means of tax avoidance cannot be ignored in understanding FAI data. Importantly, however, if the use of conduits is not connected to real investment decisions, then it reduces _average effective rates_ but has no effect on the _marginal_ effective rates that guide real investment decisions.

It may be, nonetheless, that real production can itself facilitate transactions-based tax avoidance, for example by meeting ‘substance’ tests required to draw on advantageous treaty provisions. To allow for this, it has become standard practice in the literature to suppose there to be some real cost associated with deploying devices of pure profit shifting. Suppose, for example, that shifting profits of _Sj_ into country _j_ requires costs of the form ( _Sj_ )<sup>2</sup> _/_ (2 _φρKj_ ) (with cost parameter _φ >_ 0), capturing the idea that these costs are lower to the extent that there is real production activity in _j_ .<sup>42</sup> Adding the possibility of such explicit profit shifting to the setting above, it is shown in Appendix A that (3)-(7) of Proposition 1 continue to hold, but now with

> 42This approach dates back to Hines and Rice (1994), with recent applications including Su ́arez Serrato (2019) and Beer et al. (2023).

24
|**Table 4. OLS Regressions across countries for decisionmaking o**<br>**respondent**|**ver large purchases whe**<br>**Wife says she is**<br>**the main**<br>**decision maker**|**n wife is the main**<br>**Wife says decision**<br>**making is joint**|
|---|---|---|
||(1)|(2)|
|**_Women's Status & Empowerment Proxies_**|||
|Difference in years of schooling: husband-wife|-0.002***<br>[0.00]<br>|-0.002***<br>[0.00]<br>|
|Wife not working|-0.031***<br>|-0.080***<br>|
||[0.00]<br>***|[0.01]<br>***|
|Wif k ff f|0.027|0.018|
|e wors o arm|[0.01]<br>|[0.01]<br>|
|Wife says she earns more than her husband|0.254***<br>[0.01]|0.074***<br>[0.01]|
|Wife says she owns land|0.157***<br>|-0.002<br>|
||[0.01]|[0.01]|
|W d hbd bi hi if|<br>-0.023***|<br>-0.066***|
|oman conones a usan eatng s we|[0.00]<br>|[0.00]<br>|
|<sup>b</sup>|-0.043***|-0.110***|
|Wife is aged 15-19<sup>()</sup>|[0.01]|[0.01]|
|Wife is aged 20-34<sup>(b)</sup>|-0.023***<br>|-0.048***<br>|
||[0.01]|[0.01]|
||<br>-0.002***|<br>-0.002***|
|Age difference: husband-wife|[0.00]<br>|[0.00]<br>|
|Wife was married before age 20|0<br>|0.016***<br>|
||[0.00]|[0.00]|
|Years of marriae|0.002***|0.001***|
|g|[0.00]<br>|[0.00]<br>|
|Total number of children woman has|-0.005***<br>[0.00]|-0.005***<br>[0.00]|
|Total number of children who died|0<br>|-0.010***<br>|
||[0.00]|[0.00]|
|Cl i l i|-0.006|-0.106***|
|oupe n a poygamous marrage|[0.00]|[0.00]|
|**_Individual/Household-level characteristics_**|||
|Hsband orks in aricltre|0.004|0.008|
|u w guu|[0.01]|[0.01]|
|Rural area|-0.043***<br>|-0.008<br>|
||[0.01]|[0.01]|
|L ti (i) t th t t|-0.002***|0|
|og me mns o e neares waer source|[0.00]|[0.00]|
||<br>0.012**|<br>0.020***|
|Household has electricity|001|001|
||[.]|[.]|
|Wealth quintile: bottom 20%|-0.005<br>|-0.057***<br>|
||[0.01]|[0.01]|
|lh iil b 20|0|-0.030***|
|Weat qunte: next-to-ottom %|[0.01]|[0.01]|
||<br>0.011*|<br>0.017***|
|Wealth quintile: second highest 20%|<br>[0.01]|<br>[0.01]|
||0056***|0083***|
|Wealth quintile: top 20%|.<br>[0.01]|.<br>[0.01]|
|Observations|34,495|56,625|
|Adjusted R-squared|0.189|0.201|
|(a) Robust t-statistics in brackets. *** p<0.01 ** p<0.05 * p<0.1 .|||
|, ,<br>(b) Country fixed effects are included.|||

36
that the period of the most dramatic relative numbers of youth were experienced several decades ago in most countries. Most countries in the first three panels of Figure 12 experienced the largest peak of the youth population relative to the working age population in the 1970s or 1980s. As was the case with the growth rate of the youth population, in many cases we see similar patterns in the timing and magnitude of the peak in the relative size of youth cohorts for countries that look quite different in terms of the timing of the peak in the total size of the youth population. Thailand and the Philippines both reached a peak around 1975, when there were about 0.9 12-24 year-olds for every 25-59 year-old. The difference in the countries is that this ratio had fallen to about 0.5 in Thailand in 2005, while in the Philippines it was still over 0.7.

Even in the countries in Panel D, where the youth population will continue to grow for the next several decades, the ratio of the youth population to the working-age population has in most cases already begun to decline. To the extent that this ratio is a critical measure of the pressure the youth population places on resources or labor markets, it is important to recognize that the pressure has already begun to diminish in most countries.

# **6. Family Size versus Cohort Size**

One of the important, if somewhat paradoxical, features of population dynamics during the demographic transition is that the size of birth cohorts can move in the opposite direction from the size of families. This is another consequence of population momentum, which can cause a gap in the timing between the decline in fertility and the decline in the size of birth cohorts. Lam and Marteleto (2005) analyze the case of Brazil, using the detailed census and survey data back to 1960. As can be seen by comparing Figure 8 and Figure 9 above, there is roughly a fifteen year delay in Brazil between the onset of fertility decline, which occurs in the late 1960s, and the birth of the largest birth cohort in 1982. This means that there is a period in which family size is 14
# **1 Introduction**

Offshore tax evasion poses a severe challenge for tax policy (EU Tax Observatory, 2024; Slemrod, 2019; Tørsløv et al., 2022). Approximately 8% of households’ financial wealth is held in tax havens (Zucman, 2013), with the wealthiest 1% of the population owning the majority of these offshore assets (Alstadsæter et al., 2019; Guyton et al., 2021; Johannesen et al., 2023; Londoño-Vélez and Ávila-Mahecha, 2021). A significant portion of them goes undisclosed and untaxed by their respective home authorities, eroding tax revenue and hampering tax progressivity. To encourage tax evaders to reveal their foreign incomes and assets, governments worldwide have introduced voluntary disclosure programs that offer reduced penalties in exchange for tax compliance (OECD, 2015). In addition to these proauthorities have various measures to combat cross-border tax eva- grams, implemented sion and promote transparency in the global financial system, including automatic tax information exchange agreements (TIEAs), the US Foreign Account Tax Compliance Act (FATCA), and the global multilateral network for the automatic exchange of financial account data known as the Common Reporting Standard (CRS).

how these can work to boost tax com- Understanding policies effectively together pliance, revenue, and progressivity is crucial for the conduct of tax policy. However, extracting meaningful insights from the diverse experiences of various countries has proven challenging due to differences in enforcement capabilities across jurisdictions, baseline evasion rates, and program design. Furthermore, most voluntary disclosure programs researchers have examined, such as Alstadsæter et al. (2019, 2022) and Johannesen et al. (2020), took place before recent advancements in international tax coordination. These earlier programs witnessed limited participation and disclosures of income and assets that represented only a negligible fraction of countries’ gross domestic product (GDP). Consequently, we know little about evaders’ willingness to comply with tax regulations in the current landscape of global tax coordination.

This paper overcomes these challenges by studying how tax enforcement initiatives impact individuals’ tax behavior, tax progressivity, and revenue collection, focusing on Argentina. The country is particularly well suited to examining these issues for several reasons. First, there is a lot at stake: the equivalent of 36.5% of the country’s GDP was held offshore before these initiatives (Figure A.1), nearly four times the world average (Alstadsæter et al., 2018). Second, Argentina’s rich policy variation makes it the world’s largest natural experiment for enforcement policies. Over the past few decades, both leftand right-wing governments have implemented voluntary disclosure programs, adapttheir and features. unsuccessful a ing scope Despite previous attempts, policy package 1
Policy Research Working Paper 8343

# **Abstract**

This paper investigates whether social structure helps or hinders factor allocation using unusually rich data from The Gambia. Evidence indicates that land available for cultivation is allocated unequally across households; and that factor transfers are more common between neighbors, co-ethnics, and kinship related households. Does this lead to the conclusion that land inequality is due to flows of land between households being impeded by social divisions? To answer this question, a novel methodology that approaches exhaustive data on dyadic flows from an aggregate point of view is introduced. Land transfers lead to a more equal distribution of land and to more comparable factor ratios across households in general. But equalizing transfers of land are not more likely within ethnic or kinship groups. In conclusion, ethnic and kinship divisions do not hinder land and labor transfers in a way that contributes to aggregate factor inequality. Labor transfers do not equilibrate factor ratios across households. But it cannot be ruled out that they serve a beneficial role, e.g., to deal with unanticipated health shocks.

This paper is a product of the Strategy and Operations Team, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at urb@dors.dk.

_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 are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Are Irrigation Rehabilitation Projects Good for Poor Farmers in Peru?** _A Spatial Regression Discontinuity Approach_

# **I. Introduction**

In many developing countries, improving agricultural performance is seen as a critical component of sustained poverty reduction. This view is supported by the fact that most impoverished people still live in rural areas and derive their livelihoods from agricultural activities (World Bank 2008). This paper measures agricultural and economic impacts of the first phase of the Peruvian Irrigation Subsector Project (PSI) – a large irrigation infrastructure rehabilitation project implemented in the coastal regions of Peru – on farmers below the poverty line<sup>1</sup> . A key objective of the project was to raise agricultural production and productivity by enhancing the sustainability and efficiency of existing public irrigation systems (World Bank 1996), and thus reduce poverty.

An important policy question for which little evidence exists is whether public projects such as irrigation infrastructure can be specifically designed so that gains are redistributive among all farmers and progressive for the poor. Pro-poor interventions have either been defined as those that impact the poor to a more positive extent (or a less negative extent) than they impact the non-poor, or as interventions that reduce aggregate poverty. Thus, the first definition takes distributional effects into account, requiring that pro-poor interventions reduce inequality and differentially impact those above and below the poverty line, while the second takes positivity into account, requiring that pro-poor interventions lead to positive growth (Ravallion, 2004). This

> 1 The project has two other components – plot level irrigation support (technology and capacity building) and training for Water Users’ Associations – that are accounted for in the analysis.

2
# **Table 24: Do wealthier district contribute more to funding the schools?**

||Marginal effect of 2004<br>District Level SES|
|---|---|
|Gave books to school|-0.01|
||(0.04)|
|Cash contribution|0.04|
||(0.04)|
|Building supply|-0.03*|
||(0.02)|
|Furniture contribution|0.00|
||(0.01)|
|Food contribution|-0.03|
||(0.04)|
|Observations|3659|

Notes: Robust Standard errors in parentheses. *** 1% Significance Level, **5% Significance Level, *10% Significance Level. District 2004 SES is the district level composite measure of the socio-economic background as measured in 2004 – Prior to the interventions. The variables included in the factor analysis are the quality of the housing (floor, roof, walls, electricity), the assets (phone, motorcycle, fridge, car, TV, fan, generator, livestock), and the expenditure on educator the past 12 months – Higher values of the factor associate with higher economic status of the district. The coefficients are the marginal effect of the District’s 2004 SES on the dependent variable.

40