rafmacalaba/data-use-mentions-extended huggingface dataset:
the baseline model with leads and lags of the aid variable. Reassuringly, we find no differential trends in deposits during the quarters prior to aid disbursements. Second, we instrument disbursements with pre-determined aid commitments, which are plausibly exogenous to contemporaneous shocks (Kraay, 2012, 2014). The IV estimates are qualitatively similar, but somewhat smaller than the OLS estimates. Third, we exclude observations where specific events such as wars, natural disasters and financial crises might cause both inflows of aid and outflows of domestic capital and introduce controls for potential confounders such as oil prices and exchange rates. We also estimate specifications with country-year fixed effects where identification comes exclusively from variation in the timing of disbursements within the year. The main results are robust to all these tests.

While our results document cleanly and robustly that aid disbursements are associated with wealth accumulation in offshore accounts, the macro nature of our deposit information represents an important limitation: since we do not observe _who_ stores wealth in havens in periods with large aid disbursements, we cannot directly identify the economic mechanism underlying this correlation.

Despite this inherent limitation, it is almost certain that the beneficiaries of the money flowing to havens at the time of aid disbursements belong to economic elites. Recent research using micro-data from data leaks and tax amnesties documents that offshore bank accounts are overwhelmingly concentrated at the very top of the wealth distribution.<sup>1</sup> By contrast, the poorest segments in developing countries often do not even have a domestic bank account (World Bank, 2017) and it is entirely implausible that they should control the money flows to havens.

While it is more difficult to identify the precise mechanism by which aid inflows cause capital outflows to havens, aid capture by ruling politicians and bureaucrats is a salient and plausible one. First, it can explain why the trail leads to havens rather than non-havens: if the money derives from corruption and embezzlement, we should not be surprised to see it flowing to jurisdictions with legal institutions emphasizing secrecy.<sup>
lxxxv Jordan Interview with CCD.

> lxxxvi _MENA Report_ , Chapter 14 Saudi Arabia

> lxxxvii _MENA Report_ , Chapter 16 Yemen. The fact that cultural issues or Shari‟a considerations inhibit out-of-court restructuring is in contrast to the rest of the MENA countries in the _MENA Report,_ most of which report that there are no cultural or Shari‟a barriers and that consensual agreements are supported by the business environment.

> lxxxviii Landon Thomas, “Dubai World Reaches Deal to Restructure Debt”, _New York Times,_ May 20, 2010. http://www.nytimes.com/2010/05/21/business/global/21dubai.html?ref=world

lxxxix“Dubai World Agrees Headline Economic Terms in Principle with Coordinating Committee”, article dated May 20, 2010, posted at Dubai World online media/press centre on Sunday June 6, 2010, http://www.dubaiworldmedia.net/2010/05/82971502.html

> xc _MENA Report,_ Chapter 6 DIFC

xci The Zakat (a form of tithe) is paid annually by Saudi individuals and companies within the provisions of Islamic law as laid down by Royal Decree No. 17/2/28/8634 dated 29/6/1370 H. (1950). The Zakat is an annual flat rate of 2.5 percent of the assessable amount. http://www.the-saudi.net/business-center/regulation-tax.htm. xcii <u>Uncitral Legislative Guide on Insolvency Law, Part One: II. B. Voluntary Restructuring Negotiations 1.</u> Necessary preconditions, pages 22-3. xciii _MENA Report_ , Ch. 1 The MENA Region‟s First Regional Survey on Insolvency Systems, Chapter 5 The Need for Insolvency System Reform.

> xciv _MENA Report,_ Chapter 5 The Need for Insolvency in the MENA Region and Beyond, Chapter 6 DIFC,Chapter 7 Egypt, Chapter 9 Kuwait, Chapter 11 Oman, Chapter 12 Palestine, Chapter 14 Saudi Arabia.

> xcv
., college loans. Economic sanctions may also affect children’s education through changes in government spending. While the direct benefits of public spending on education are widely agreed upon, the effect of sanctions on public spending is unclear. Economic sanctions target government revenues by imposing trade and financial restrictions. However, the effect of a government revenue shock on sub-categories of government expenditures (e.g., expenditure on education) is not clear and depends on fiscal and political institutions.

The key empirical challenge of measuring the effects of sanctions on children’s education is one of identification. Sanctions that are not confounded with other factors, that also affected children’s education, are difficult to come by. Farjo (2011) finds a reduction in primary school enrollment during 1990-2003 when the UN imposed economic sanctions on Iraq. However, its causal implications are limited because this study does not distinguish the effects of sanctions from the effects of several other relevant factors such as war and political instability.<sup>4</sup> Credible estimates of sanction effects on children’s education require a solution to the identification problem. The second challenge is a dearth of reliable data. In most cases, the presence of conflicts poses a substantial obstacle to the collection of survey data especially on the displaced populations and people in conflict areas (Barakat et al. (2002)). Even if data are collected, their accuracy is an open question.

> 3On average, changes in household income or liquidity cause significant changes in household spending among households with low liquid wealth or low income, even when the shock is predictable (Johnson et al. (2006); Stephens Jr (2008); Jappelli and Pistaferri (2014)). Moreover, adjustment costs vary across households depends on their consumption commitments. For example, an adjustment is more costly for homeowners who have to pay the mortgage, especially in the short run. Consumption of many other durable goods (e.g., vehicles and furniture) and services
which increases significantly under both treatment arms. In other words, the psychosocial value to employment appears to be driven largely by the non-pecuniary dimensions of the employment experience.

# **5.4 Impacts of employment on reported physical health, cognitive function, and economic decision-making**

The positive effects of employment extend to other measures beyond psychosocial health. Table 3 presents results on reported physical health, cognitive function, and incentivized measures of risk and time preference. We observe a significant increase in the days reported healthy. This effect may be due to ‘real’ health improvements from increased exercise (which has also been documented to translate to improved mental health (Herbert et al. (2020))) from the employment task or ‘perceived’ health improvements in which improved psychosocial well-being translates into feeling less physically ill. Should the channel be exercise, we may expect health improvements to grow over time. Our weekly data on days healthy suggests this is not the case: we observe the treatment effect on health from the first week of working, and the gap remains steady throughout the following two months (Appendix Figure A3).

The employment arm also significantly improves cognitive function as measured through an index of memory and basic arithmetic tests, a finding consistent with a large psychology literature documenting the relationship between cognitive processes and depression (Semkovska et al., 2019). As with physical health, improvements to cognitive function are unlikely to be a direct product of the employment task itself, which was specifically designed to require no literacy or mathematical skill. Rather, these results are suggestive of a downstream impact to reducing depression through the experience of employment.

Finally, we find no change in time preferences: treated individuals are no more or less likely to discount the future relative to control counterparts, although results may have differed had we engaged participants in an effort or consumption-based time preference game rather than a financial one. However, we find a substantial increase in risk tolerance among the employed. A greater preference for risk-taking may be
080)|(0.048)|(0.027)|(0.049)|(0.027)|(0.048)|(0.026)|
|Share of T2 effect|||||||||||||
|explained by|28.0%|**16.4%**|12.3%|**6.9%**|32.8%|**17.5%**|24.9%|**16.9%**|11.2%|**6.4%**|32.1%|**23.3%**|
|knowledge|||||||||||||
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|Observations|2,334|2,334|2,334|2,334|2,334|2,334|2,116|2,116|2,116|2,116|2,116|2,116|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note_ : Causal mediation estimates for overall, principles, and how-to knowledge variables on number of practices adopted (OLS specification) and integrated adoption of full ISFM package (probit specification, AME shown). ACME stands for average causal mediation effect, ADE for average direct effect. Additional controls identical to those listed in notes of Tables 2 and 5. Robust standard errors in parentheses, clustered at the mws level. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.

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exogenous to the survey used for the life satisfaction estimations, others use Gini calculated from within the surveys used. For example, Alesina at al. (2004) use the Gini taken from the Deninger and Squire database<sup>5</sup> and Helliwell (2003) uses the Gini taken from a World Bank database whereas Senik (2004) and Clark (2003) calculate the Gini from within their own surveys.

This choice is mostly dictated by the data. The first two studies are cross-country studies that make use of values surveys. Values surveys such as the World Values Surveys, the European Values Surveys and the US Social Survey do not hold information on individual incomes in continuous form. Income is typically reported in terms of income classes. When these surveys are used, researchers either transform income classes into comparable monetary values or they draw on external sources for measures of inequality. This explains the choice of ‘exogenous’ inequality variables. The second set of studies uses instead longitudinal data on single countries such as Russia, the UK or Germany where individual income is typically available in continuous form. The shortcoming here is that only a few panel surveys have questions on life satisfaction and one also needs many years or split the sample into sub-groups to make some inference on the role of inequality.

Combining longitudinal and cross-country data can also lead to different conclusions. Suppose that we could use an ‘endogenous’ and an ‘exogenous’ income Gini simultaneously. Suppose also that both samples on which the Ginis are estimated are representative of the population under study. The two Gini may, in fact, be different in value either because the income distribution cannot be identical in the two samples or because the welfare measure is different (such as income as opposed to consumption). Moreover, when the two Gini are compared across countries and time, the cross-section and longitudinal distributions of such Gini may also be very different affecting the covariance between income inequality and subjective well-being.

Another factor may relate to _different tastes for inequality_ across
, the age-specific female labor force participation rates are endogenized with a factor that reflects the impact of fertility on the labor supply of women, as has been calculated by Ashraf, Weil and Wilde (2013).

Fertility and demographic change influence the accumulation of human capital, captured by the educational attainment and health of the age-specific cohort. These are respectively calculated by the

> 7 A low variant, a medium variant and a high variant are available from the World Population Prospects of the UN (2010). Since the comparison of the projected output is undertaken at the next available period from the baseline, the difference between the variants becomes negligible. Here, the low variant is adopted.

> 8 The original CKW model was a fully supply-side model, meaning there is an implicit assumption of full employment. In the context of many LMICs, this full employment assumption likely does not hold in the short run. However, there are a number of theoretical and practical reasons to not independently model labor demand. Most importantly among these, modeling labor demand in a traditional manner (by deriving labor demand curves from the production function) is inappropriate, as this also assumes full utilization and costless movement of factors -- the same underlying rationale behind a full employment assumption. As a result, any modeling of labor demand outside this traditional method would be necessarily ad hoc, and likely introduce even more untenable assumptions into the analysis, and add to the complexity of the model without guaranteeing more accurate results. In addition, the model already considers several labor market inefficiencies by introducing employment sectors with different wage regimes (average product wages in the traditional sector versus marginal product wages in the modern sector), further blunting the necessity of independently modeling labor demand.

> 9 While the lower-bound for the labor force is commonly 16 or 18, we use age 20 to align with the five-year cohorts that we are using for population projections.

> 10 Labor force participation rates are obtained from the International Labour Office’s ILOSTAT database (International Labour Office
# **I. Introduction**

Monetary policy transmission in emerging market economies (EMEs) is confounded by the puzzling behavior of prices and exchange rates in response to changes in policy interest rates. Evidence that monetary policy tightening leads to an increase in prices has been characterized as a “price puzzle.” In a survey of 70 studies spanning 31 countries, Rusnák et al. (2013) find that half of the studies find evidence of the price puzzle. Country-specific studies have confirmed the existence of this puzzle. Another curious empirical feature in many EMEs is that monetary policy tightening leads to domestic currency depreciation ( _foreign exchange rate puzzle or FX puzzle_ ) or gradual appreciation over a prolonged period ( _delayed overshooting)_ .<sup>2</sup> These empirical puzzles may partly reflect the complications EME central banks face in stabilizing business and financial cycles. The channels through which monetary policy influences economic activity and financial markets—including interest rates, exchange rates, asset prices, and credit (and bank lending)—appear not to operate properly in EMEs due to the incompleteness of and imperfections in financial markets or due to the procyclical nature of monetary policy.<sup>3</sup> Alternatively, standard empirical models may fail to capture the true effects of monetary policy shocks because macroeconomic and financial variables in EMEs are highly volatile.

In the case of advanced economies, an appealing explanation for the price puzzle is that their central banks have more information about future inflation than can be captured by a simple VAR model (Sims 1992; Grilli and Roubini 1996). This omission implies that a policy tightening in anticipation of a jump in future inflation would be misinterpreted in conventional models as a policy shock. If monetary policy only partially offsets inflationary pressures, such models would deliver a spurious positive correlation between policy rates and inflation.<sup>4</sup> Prior resolutions of the price puzzle have focused largely on advanced economies (e.g., Barth and Ramey 2002; Blanchard 2004
rent-seeking incentives of lower level officials. This paper argues that the latter is an important source of regulatory uncertainty and that the greater is the variation in firms’ experiences with intraregional corruption, the less certainty over enforcement of regulations (or the “correct” bribe to elicit enforcement) and the fewer incentives to invest. Thus, we explicitly attempt to tie together literatures on administrative corruption and uncertainty, testing these hypotheses using data on intra-regional variation on corruption gleaned from aggregating firms’ responses on corruption questions in the Russia’s Regions Business Environment and Enterprise Performance Survey (RR BEEPS).

Second, the paper also examines _in-country inconsistency in property rights enforcement_ as another source of underexplored regulatory uncertainty tied to informal practice. Drawing on emerging literatures on violence as a non-market business tactic, we argue that the willingness of the state to selectively enforce property rights to achieve its own ends opens up the possibility of using non-market, extra-legal strategies to acquire firms and assets. In the contemporary Russian context, such strategies are carried out hand-in-glove with law enforcement officials, who often perpetrate violence against business owners in ways that enable rivals to strip them of assets (Gerber and Mendelson 2008; Firestone 2010; Gans-Morse 2014; Belokurova 2012; Rochlitz 2014). Although in theory formal legal protections prevent such practices, informal practices by state organs largely define the actual “rules-of-the-game” and govern expectations about the willingness of the state to participate in expropriation. By allowing some to make use of extra-legal strategies to strip others of property, the state therefore creates uncertainty about how and when fundamental protections will be enforced and depresses investment (North, Wallace, and Weingast 2009). Unlike administrative corruption, however, inconsistent property rights enforcement is a fundamental, existential threat to businesses and, in the Russian context, to the life and liberty of owners. To test this hypothesis, the paper draws on a measure that captures private “raiders” attacks on firms – hostile, often violent takeovers of firms by outsiders aided and abetted by law enforcement agencies.<
# Data and descriptive statistics

Primary data used in this brief comes from the Uganda Refugee and Host Communities Household Survey 2018 (RHCS). The RHCS is one of the few surveys in the region providing a representative picture of the socio-economic status of the refugee and host community population. The survey is representative of the refugee and host population in the West Nile and Southwest regions, and the city of Kampala. The host population is defined as the native population in districts where refugee settlements are situated. One important advantage of the RHCS is that it uses an identical survey instrument as the Uganda National Household Survey (UNHS). The UNHS is the key survey conducted by the Uganda Bureau of Statistics to measure poverty. Using the same instrument allows measuring poverty in the RHCS in a way comparable to how the UNHS measures official national poverty rates.

According to the World Bank (2019), despite all economic inclusion efforts and basic service integration, refugees in Uganda were much more likely to be poor than Ugandans in 2018, 46 versus 18 percent using the national poverty line. This implies that almost half of refugees did not have enough resources to satisfy the minimum daily caloric requirements and basic non-food needs – an issue that has become more acute recently as WFP’s general food assistance has had to be prioritized and reduced for many refugees due to financing shortfalls. Using a different daily poverty line, the international poverty line of US$2.15 expressed in 2017 purchasing power parities (PPP), one finds even higher poverty rates: 70 versus 38 percent accordingly. The highest poverty rate among refugees is observed in the West Nile region reaching 58 percent using the official national poverty line and almost 80 percent using the international poverty line. Having a better educated household head is associated with lower poverty among refugees. There is little difference in poverty incidence among refugees who depend on income from economic activities or aid. However, the poverty rate for refugees who depend on remittances is lower.<sup>3</sup>
# **1. Introduction**

Recent estimates indicate a rapid decline in poverty in India. The Government of India’s Planning Commission (2013) estimates the annual average decline in poverty to be 2.2 percent between 2004/05 and 2011/12—from a poverty headcount ratio of 37.2 percent to 21.9 percent. While there were hints of acceleration in poverty reduction between 1993/94 and 2004/05, the next seven years significantly stepped up in pace. The poverty headcount rate fell at an average annual rate of 1.5 percentage points between 2004/05 and 2009/10 and at nearly 4 percentage points per year between 2009/10 and 2011/12.<sup>2</sup> There are many possible drivers of falling poverty over this period. There is evidence of a stronger feedback effect from urban economic growth to rural poverty reduction (Datt, Ravallion, and Murgai, 2015), consistent with growing linkages—through trade, migration, and transfers—between rural and urban areas (see also United Nations, 2011). Deininger and Liu (2013) find evidence that implementation of the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) since 2006 has significantly increased consumption and accumulation of financial assets. Sen and Himanshu (2013) point to the role of the Public Distribution System (PDS) as a major contributor to poverty decline. And Dang and Lanjouw (2015) find that more education, salaried work and social capital contribute to upward mobility during this period.

In this paper, we examine the relative contributions of different sources—at the household level—to poverty reduction in India during the latter part of the decade of the 2000s. We use panel data from the nationally representative India Human Development Survey (IHDS) for 2005 and 2012. Poverty rates estimated from this survey using a similar welfare standard (consumption levels below official poverty lines) as in the NSS (National Sample Survey, on which official poverty estimates are
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Notes: This table reports panel regressions with individual firm fixed effects for the El Salvador COV-ES data. Sector information used as the interaction terms is taken from the baseline ES. Columns 3 through 8 report coefficients from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 6 for more details.

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