rafmacalaba/data-use-mentions-extended huggingface dataset:
While the existing literature on informal businesses’ exposure to bribery shows that business<sup>3</sup> characteristics often matter, a clear framework is lacking. A first contribution of this paper is such a framework, albeit a simple one. A second contribution is to provide evidence using a representative and business-level data set; these survey-based data use a novel sampling approach, affording indepth, micro-level information on informal businesses, which are often absent from such firm-level data sets. A third contribution is to provide empirical results of this exposure. Specifically, results show that informal-sector operators with a higher opportunity cost of formalizing are more exposed to an extortion of a bribe; a business’s ability to pay is found to have a limited effect. Two measures of the opportunity cost of formalizing are used. First is a measure of whether the informal business owner started the activity due to a lack of other income (NECESSITY); since this measure is selfreported and opinion-based, an alternative measure is used. A second measure is if any member of the owner’s household has employment under a contract (NOWAGE). Positive and significant results for NECESSITY and NOWAGE are largely robust to methods adjusting for the potential endogeneity of the regressors. These methods leverage instruments of whether the owner started in the sector of activity before or after the 2009 dollarization of Zimbabwe’s economy.

The remainder of the paper is organized as follows: Section II summarizes the literature on informality and corruption, section III discusses the background of these issues in Zimbabwe specifically. Section IV presents a stylized model. Section V describes the data used, and section VI gives the main econometric specification. Section VII includes the main results, and VIII concludes.

# II. Literature on Informality and Corruption

# _<u>Informality</u>_

A broad literature discusses the informal sector as a driving sector of economic activity and employment. While this stylized fact remains largely undisputed, the characterization of the sector itself is a matter of debate, particularly
ism, and accepts the suspension of some freedoms in order to guarantee security; and (5) prioritizes law and order. We classified parties that did not include at least three of the stances from either list in their statutes as neither nor policy left-wing right-wing.

3. For parties for which official statutes were not available, we checked the government plan that candidates submit to the electoral authority before elections and— when available—searched them for the same policy stances as in the second step.<sup>22</sup> The majority of candidates are classified using individual-level information, rather than party affiliation: 40% are classified based on their government plans, 20% using social media posts, and 14% based on coalition composition (See Table A.4). This approach reduces the risk of over-relying on party ideology and helps capture local variations in candidate positioning, addressing the concern regarding the high number of centrist candidates.

Table A.4. Distribution of the Type of Source Used to Classified Candidates

|**Type of Source**|**Candidates**|**Percent**|
|---|---|---|
|Government Programs|562|41.82|
|Social Media|275|20.46|
|Other|243|18.08|
|Coalition Composition|192|14.29|
|News/Media|44|3.27|
|Blogs/Opinions|11|0.82|
|Unclassifed|10|0.74|
|Academic|7|0.52|
|**Total**|1,344|100.00|

_Note:_ This table provides an overview of the primary sources used to determine candidates’ political ideology.

Finally, we used electoral roll information to calculate the municipality’s turnout and the

> 22For example: for the 2019 elections, Colombia’s electoral authority official website gathered information on all government plans https://
a|n/a|93|68|73|1995|80|
|Hungary|9.5|1990-2003|125|102|81|96|71|76|1995|84|
|Latvia|0.8|1994-2003|n/a|n/a|126|100|86|86|2001|87|
|Lithuania|3.1|1994-2003|n/a|n/a|122|89|75|80|2001|86|
|Poland|9.6|1990-2003|119|97|78|103|82|81|1993|84|
|Romania|15.9|1990-2003|139|102|76|104|84|82|2003|84|
|Slovakia|4.6|1993-2003|n/a|n/a|101|98|85|86|1999|87|
|Slovenia|10.9|1992-2003|n/a|104|101|98|82|84|2001|84|

Notes: Manufactures defined as SITC 5+6+7+8-68 in SITC. Revision 2. Source: Data reported to UN COMTRADE database.

Third, note also that geographical proximity to the EU-15, accounting for most of preferential imports of these economies, does not offer any clues as the share of preferential imports. In fact, dispersion in the shares of preferential partners in manufactured imports of pan-European transition economies is very low, with the coefficient of variation of 0.08 for 2003 and 0.05 for the average shares in 1996-2003.

Last but not least, over 2000-03, despite zeroing of the remaining preferential rates on industrial products, the share of pan-European imports in manufactured imports of CEEC-10 mostly contracted. This share only slightly increased in Poland’s imports and Romania’s imports. Except for Romania, the share of pan-European preferential partners in manufactured imports was in 2003 below the levels recorded in earlier years (Table 16)
# **Living Arrangements and the Well-Being of the Elderly**

In terms of understanding the well-being of the elderly in developing countries, much prior research has relied on indirect evidence suggested either by patterns of living arrangements or by labor supply and retirement decisions. This focus derives from the fact that large sample surveys typically lack direct measures related to the provision o support within the household, including financial support, in-kind transfers and instrumental care. Living arrangements, or co-residence, are thus used as a proxy for elderly access to these frequently unobserved sources of support. Observed declines in co-residence with adult children have been cited most frequently as reasons for concern that China’s rural elderly are losing support from their adult children. Benjamin et al (2000) note that in rural north China over 85 percent of the elderly lived in extended households in 1935, but that this figure had dropped to just over 60 percent by 1995.<sup>5</sup> The decline in coresidence with adult children is strikingly evident over the six rounds, from 1991 to 2006, of the China Health and Nutrition Survey (CHNS). Figure 1 shows that in the CHNS, nearly 70 percent of adults over 60 in rural areas lived with an adult child in 1991, but by 2006 this share had fallen to just over 40 percent.

The change in living arrangements over both the long-term (from the 1930s to the 1990s as noted in Benjamin et al (2000)) and the short-run (from 1991 to 2006 in the CHNS) does not necessarily reflect a drop in the provision of care to the elderly. In-kind transfers, such as supply of labor on extended family plots and provision of care, are difficult to pick up in surveys, yet such transfers often occur both within and across households. Changes in living arrangements may reflect, in part, the increasing wealth of families. With increasing resources, co-residence may not be necessary to care for the elderly. Within villages in rural areas, elders and adult

> 5Selden
countries. In a cross-sectional regression framework, Ram and Zhang (2002) find some evidence that FDI is boosting host economies’ income growth rates, based on data for the 1990s. Yet they note that the results are not robust to all their model specifications. UNCTAD (1999), on the other hand, fails to find a clear linkage between FDI and growth rates, as the sign of the coefficient for FDI is either positive or negative depending on the variables that enter the regressions. In a similar approach, Dutt (1997) also falls short of detecting any empirical linkage between foreign investment and per capita growth rates.

Nonetheless, using a panel of data for the 1970-1999 period for 84 countries, Li and Liu (2005) establish a clear linkage between FDI and growth rates. They confirm this outcome for different econometric techniques, including a simultaneous equation system. In contrast, Carkovic and Levine (2005) also use a panel setting and control for simultaneity bias, but do not find robust results for positive growth effects of FDI inflows in their sample of 72 countries for the 1960-1995 period. They note that this outcome (and the inconclusive evidence in the literature in general) might be due to the specific empirical approaches and the different time periods used.

Apart from data and methodological issues, a few studies have tried to find further reasons for the inconclusive evidence. Based on their results, Balasubramanyam et al. (1996) note that FDI might promote growth only in export-promoting rather than in import-substituting countries and that, thus, openness to trade is essential for the growth effects of foreign investment. Borensztein et al. (1998) find that certain characteristics in the host countries may play an important role. More specifically, they discover that countries need a particular educational attainment level to benefit from FDI. Borensztein et al. argue that to be able to benefit from positive (technological) spillover effects, host economies have to have the educational capacity to incorporate these effects.

More recently, two papers have been
(survey-based) estimate of the proportion of the population of the developing world living below $1.25 a day is 21 percent for 2010 (representing 1.2 billion people), down from 33 percent in 2000 and 43 percent in 1990. Figure 2 plots the full series over time.<sup>7</sup> The overall poverty rate (“headcount index”) has been falling at a robust 1 percentage point per year over 1981–2010, and this was maintained after 2005. The regression coefficient of the poverty rate on the year in figure 2 is −1.04 (standard error = 0.05; n = 11). The “$1.25-aday” poverty rate for the developing world as a whole in the year of writing (2012) is estimated to be 19 percent (standard error = 0.6 percent), representing 1.1 billion people.

> 7 The estimates of absolute poverty measures used here are from Chen and Ravallion (2012), who discuss data and methods. (The update for 2010 is 20.8 percent below $1.25 a day, representing 1.227 billion people.) A fuller discussion of the absolute measures (with various tests of robustness) can be found in Chen and Ravallion (2010).

8
the six parameters at annual frequency using data between 1981 - 2009. I set every parameter as a percentage of GDP, whenever applicable. The OECD advocates a prudential debt target of 70-90% for advanced economies, but only 50-70% for Eurozone countries since they do not control monetary policy. Hence, I set U = 0.6 for Eurozone countries and U = 0.8 otherwise. r is the sample average of annualized return on 10-year government bond. _θ_ is the inverse of the sample average of life expectancy at birth. _σ_ is the variance of government primary balance from IMF’s Historical Public Finance dataset.

T is calibrated with the narrative fiscal series in Devries et al. (2010). The dataset provided sizes of both tax and spending based austerity programs relative to GDP on an annual basis. I calculate T as the average of the size of austerity programs in the sample, not the average of annual size, since most such programs span through multiple years. In the model, T is the size of a one-time tax that constitutes the entire stabilization program, so its empirical counterpart should be an entire program. For instance, if a country reduced its budget deficit by 3% of GDP in each year between 1983 and 1986 as prescribed in a legislation passed in 1982 and by 4% of GDP in 1996 as prescribed in a legislation passed in 1995, then there are two austerity programs in the sample with respective sizes of 9% and 4%. Accordingly, T is set to 0.065, the average size of these programs. All parameter values used for each country are shown in Table 7 Appendix III.

Table 1 below displays the value of _B_<sup>_∗_</sup> for each country in the sample. A definition of a high-debt regime for country i at time t, _Dit_ , is available: _Dit_ = 1 if _Bi,t > B_<sup>_∗_</sup> , _Dit_ = 0 otherwise.

#
# Introduction

refugee situations, combined with a lack of disaggregation in donor and government reporting, make tracking refugee financing, particularly at scale, extremely challenging. As a result, efforts to quantify this information have been partial to date, leading to a lack of detailed analysis of financing for refugee situations.

comprehensive dataset and paper on financing for refugee education in low- and middleincome countries (LICs and MICs). By collating and analyzing publicly available data, surveying key refugee education donors, and working closely with host country governments and international organizations, all major sources of financing for refugee education in LICs and MICs are collated and quantified for the first time. This paper then analyzes this data to produce 10 key facts and findings on refugee education financing for LICs and MICs.

# Literature Review

<mark>Difculties in tracking donor and host government fnancing to refugee situations have</mark> led to a dearth of publications on refugee educatio <mark>n fnancing.</mark> As emphasized by Marcus et al. (2023), a “lack of disaggregation in global aid reporting systems by source, sector (subsectors), or type of crisis (e.g., internal displacement, refugee) limits analysis of funding on refugee inclusion [while] data on domestic expenditure in major refugee-hosting countries are often unavailable internationally” <mark>, hindering understanding and analysis (Marcus et al., 2023). These data challenges extend to the philanthropy sector, another signifcant source of refugee education fnancing, with many major foundations and funds electing not to publish their fnancing allocations, either by sector, country, or region (OECD, 2021; ECW, 2019).</mark> <mark>The lack of information on refugee education fnancing in LICs and MICs has created a scarcity of literature in this area. Yet data on fnancing for refugee situations has improved dramatically in recent years. Most notably, the Organisation for Economic Cooperation and Development (OECD) surveyed Development Assistance Committee members, participants,
10 The microeconomic evidence at the individual worker level is also consistent with the firm-level evidence. Survey work performed in the summer of 1994 indicates that about 70 percent of an urban sampling of current or ex-state enterprise employees were engaged in unofficial activities. The sample participants as a whole derived more than one-half of their total income from these nonofficial activities; formal wage income represented less than one-half of their total income. For those working at lower-than-average official wages, the share of unofficial incomes was about two-thirds.'<sup>7</sup> To complement the microevidence presented here, and to provide for a comparative methodology for FSU and CEE countries, we turn now to a top-down approach to measuring the dynamics of the unofficial economy.

# **The Macroelectric Approach to Measurement of the Unofficial Economy and Its Application to Ukraine**

In most of the FSU and CEE states, official GDP figures measure the official economy. Almost by definition, and due to the slow (and very partial at best) adaptation of statistical methodology to more market-based systems, the unofficial economy has been minimally captured in official statistics. Official GDP figures can therefore be considered a good approximation of the measurement of official activities. But contrary to the prevalent use by analysts and decision makers, they cannot provide a reliable measure of overall economic activity in an economy. For a valid measurement of overall economic activity, a different indicator is needed.

# _Unitary Elasticity of Electricity Consumption: Sensible Assumption?_

To measure overall economic activity in an economy, electric-power consumption is regarded as the single best physical indicator of economic activity. Overall economic activity and electricity consumption have been empirically observed throughout the world to move in lockstep--with an electricity/GDP elasticity usually close to one."<sup>8</sup> By having a proxy measurement for the overall economy and subtracting it from estimates of official GDP, an estimate of unofficial GDP can be derived. Before proceeding further, however, we need to address the question of likely biases
In this paper, we document that different institutional arrangements lead to a differentiated effect of (plausibly) exogenous commodity price fluctuations on top incomes.<sup>5</sup> To do so, we combine a global panel data set from Forbes magazine on billionaires’ net worth with an index of (country-specific) commodity terms of trade shocks. Commodity shocks are significant sources of macroeconomic variation but also have important sectoral implications that elucidate linkages with concentration of income at the top. Results show that commodity booms lead to top income concentration, and the effect is economically large. Figure 1(a) globally traces the patterns of commodity shocks and the log differences of billionaire net worth and shows that they co-move. Figure 1(b) replicates the same pattern for developed (left panel) and developing economies (right panel) and shows the positive relationship between commodity price shocks and top incomes stand, regardless of the level of development. This finding is robust to accounting for sector of activity as well as the individual characteristics of billionaires as captured by billionaire fixed effects. The evidence is also suggestive that competition policy weakens the relationship between commodity booms and top incomes, and tax policy has no effect. However, we do find that commodity booms tend to lower tax revenues in the economy, hence reducing the scope for income transfers and redistribution.

In addition to the literature on top incomes, this paper contributes to several strands of literature. Specifically, the paper also relates to the so-called “resource curse” literature. The latter has provided (mixed) empirical evidence that countries with large dependence on natural resources grow slower (see survey by Ross et al., 2015) and are also more unequal (Ross, 2001; Sokoloff and Engerman, 2000).<sup>6</sup> Importantly, Mehlum, et al (2006) provide evidence that the effect of natural resources on the economy depends on the quality of institutions. Furthermore, the type of natural resource matters, with hydrocarbon and mineral
>��<br>��<br>~~.~~<br>IMF data:http://www.imf.org/external/np/fad/publicinvestment/.|
|**Projected Paths**|||
|TFP growthg�|Std LTGM<br>LTGM‐PC|Individual country average value over 2001‐2010 based on PWT 8.1 (or<br>applying PWT methodology with GTAP labor shares). Missing TFP in PWT<br>calculated by Barrot (2016) using GTAP labor share. Negative TFP growth<br>adjusted upto zero.|
|Human capital growth rateg�|Std LTGM<br>LTGM‐PC|Individual country average value over 2001‐2010 from PWT 8.1, based on<br>years of schoolingand returns to education.|
|Growth in labor participation<br>rateg�|Std LTGM<br>LTGM‐PC|International Labor Organization (ILO) individual country data for 2014,<br>available from the World Development Indicators(WDI)Database.|
|Population growthg�|Std LTGM<br>LTGM‐PC|World Bank compilation of data sourced from the United Nations<br>Population Division and countrycensuses. Projections for 2016 to 2050.|
|Growth in the working age‐to‐<br>population rateg�|Std LTGM<br>LTGM‐PC|World Bank estimates based on United Nations Population Division data on<br>age and sex distribution ofpopulation. Projections for 2016 to 2050.|
|Investment‐to‐output ratio<br>�<br>�|Std LTGM|Individual country data is based on most recent data in WDI (Gross fixed<br>capital formation (constant 2010 US$)/GDP (constant 2010 US$)).|
|Public investment‐to‐output and<br>private investment‐to‐output<br>ratios<br>�
Boggild‐Jones, and Gardiner 2017). Ideally, a separate initiative would collect cost data following a standard set of guidelines such as those laid out in Dhaliwal et al. (2013).

This review finds that general interventions are often comparable in impact to girl‐targeted interventions in improving access to school and learning once at school. But if a policy maker’s primary concern is improving girls’ education, then perhaps investing in girl‐targeted interventions would allow similar gains at much lower cost – i.e., just paying for the girls rather than girls and boys. This argument plays out differently for access versus learning interventions. For access interventions such as cash transfers, the cost could indeed be potentially reduced by targeting only girls. Indeed, several of the most effective general interventions were cash transfer programs that happened to not target girls specifically. One could imagine replacing those programs with girl‐only cash transfer programs and potentially achieving similar gains. For learning interventions, such as structured pedagogy interventions, many are introduced at the level of the school, so that in mixed‐gender schools, there is no clear cost gain to trying to limit the impact to girls only.

# Program attributes

We gathered data on a number of program attributes with the aim to provide more information on the most effective programs. For example, the average program size of the most effective access interventions is 262 students and for learning interventions, is 556 students. With the exception of the cash transfer programs, all others among the ten most and least effective programs are pilot programs. This is a result of the fact that most interventions that are carefully evaluated tend to be pilots. Therefore, we cannot from this sample infer whether or not pilot programs are more effective than those that have been implemented at scale. Another attribute that we examined was the level of education that the top programs targeted. In terms of access interventions, 7 of the top 10 interventions targeted school‐aged children in general, often between age
2016 (Table 2). Furthermore, by 2019, Figure 7 shows that these high-wealth individuals continued to report significantly larger assets, with their declared holdings nearly tripling compared to 2015.

One concern in this analysis revolves around the possibility that disclosing assets might lead to a reshuffling of individuals across different percentile groups over time. To address this potential concern, we utilize data from Argentina’s income tax returns, where individuals must report assets held in both the current and previous tax years. This approach lets us compare asset changes while keeping individuals’ income rankings constant.<sup>7</sup> The results are depicted in Figure 8, highlighting that the most significant increases in reported assets are primarily observed among individuals in the top 0.1% of the income distribution. In this group, reported assets more than doubled, with these individuals reporting roughly one-third more assets in 2016 than in 2015. Interestingly, the scale of the asset increase reported by Argentina’s richest individuals aligns with what has been observed in other regions such as Colombia (Londoño-Vélez and Ávila-Mahecha, 2021), Scandinavia (Alstadsæter et al., 2019), and the Netherlands (Leenders et al., 2023).

A.13 individuals’ likelihood of a Lastly, Figure compares high-net-worth reporting foreign asset, and the share of foreign assets declared. This information is presented separately by the top fractile groups, decomposing the top 1% of the wealth distribution into bins of increasing assets.<sup>8</sup> High-net-worth Argentines held most of their wealth offshore: three-quarters of individuals in the top 0.01% reported a foreign asset in 2015, and their foreign assets comprised over two-thirds of all their assets. Moreover, after 2016, virtually all individuals in this group reported owning foreign assets, and foreign assets rose to represent four-fifths of all assets. Furthermore, a minority of individuals in the next 0.09% declared their foreign assets to Argentine authorities in 2015, so they experienced the largest increases