rafmacalaba/data-use-mentions-extended huggingface dataset:
some data come in grouped form (notably China), we use ten decile groups for every countryyear.<sup>1</sup> That is, every individual is assigned the average income of her decile (in the within-country distribution). Second, because income is difficult to measure in an economy where ownconsumption is widespread, developing countries tend to use consumption expenditure instead of income (Deaton and Zaidi, 2002).<sup>2</sup> While this is a serious issue, as Anand and Segal (2015) write, “one simply has to live with the noncomparability [because] [t]here is no reliable way to infer an income distribution from an expenditure distribution”.

An important caveat, however, is that we are likely to underestimate top incomes for a number of distinct reasons. First, we use household surveys, which do a poor job at capturing the richest households (e.g. Atkinson et al., 2011; Alvaredo and Londoño Vélez, 2013). Second, even if the survey manages to interview the richest households, their income may be understated. In particular, household surveys in developing countries often fail to capture entrepreneurial and capital incomes, which remain the main sources of incomes of the rich in developing countries according to the few available data sources (Alvaredo and Gasparini, 2015). Third, consumption surveys – which are used in most developing countries outside Latin America – understate true living standards at the top because the rich save a higher proportion of their income than the poor. Actual consumption expenditures at the top may also be underestimated because expenditure surveys capture spending on durables poorly (Aguiar and Bils, 2015).

This suggests that household surveys are likely to underestimate the _level_ of inequality. However, if top incomes increase faster than the rest, the surveys would also underestimate the _trend_ in inequality. There is some suggestive evidence pointing in that direction. First, while the evidence from administrative data remains very limited, in some of the
The second set of data, the Targeting data set _,_ is also a census-like data set for refugees living in Chad. The main aims of this census are to fill knowledge gaps on refugee livelihoods and the levels and differences of vulnerability in refugee households, and to categorize refugees into wealth groups for cash, food, and livelihood assistance. In addition to categorizing refugees, the Targeting data set aims to identify factors that can enable self-reliance. This data set is based on a mixed methods approach, including qualitative and quantitative methods. The first step is the use of focus groups with refugee leaders, women’s organizations, and youth associations, to identify the wealth characteristics and key challenges specific to age and gender. Next, a sample survey is carried out across camps to confirm the wealth characteristics that were identified by refugees in the first step. Based on the outcomes of the first two steps, a quantitative survey that is designed to capture wealth characteristics is administered to all the refugee households. <mark>The T</mark> argeting data set <mark>includes all Sudanese, Central African, and Nigerian refugees living in Chad. The data were collected between June 17, 2017 and July 15, 2017 and cover 19 refugee sites and refugees living in nine host villages</mark> **<mark>.</mark>** After the data collection, a statistical model, which takes into account household welfare, is used to classify households into four socioeconomic groups (very poor, poor, average, and better off). For the variables that are relevant for this study, this data set contains demographic variables (household size, gender, age, country of origin, and region of residence), variables for asset and animal ownership, and variables reflecting coping strategies. As for the ProGres data set, the Targeting data set does not contain information on consumption or expenditure. However, it does contain information on wealth.

The last set of data is the Post-Distribution
**_<u>________________________________________________________</u>_**

||**TRANSP**<br>|**ORT INFRAST**|**RUCTURE**<br>|
|---|---|---|---|
|International Road<br>Transport Union|178||Road and railway networks|
|UN Economic<br>Commission of Latin<br>America and Caribbean<br>(ECLAC)|11 LA<br>countries.|2000|Foreign trade and transport statistics|
|World Bank : Railways<br>database|90|1980-99|Scale, output and performance|
|International Road<br>Federation|189|1997-2001|Road and vehicle statistics|
|Dutz, Mark (2005).<br>Road freight logistics,<br>competition, and<br>innovation: downstream<br>benefits and policy<br>implications|165<br>logistics<br>service<br>providers<br>and 493<br>user<br>enterprises|2004|the impact of innovative road freight<br>services on downstream business users|
|International Air<br>Transport Association<br>||**PORT Efficien**|World Air Transport Statistics (data on<br>IATA member airlines), Air Cargo<br>Annual(market trendsinaircargo)<br>**cy**<br>|
|US Department of<br>Transportation|US and<br>trading<br>partners||Maritime Transport Costs|
|MaerskSealand||current|MaritimeTransport Costs|
|International Monetary<br>Fund (IMF) :<br>International Finance<br>Statistics|||CIF/FOB|
|UNCTAD Review of<br>MaritimeTransport|World|Since 1968|Freight Costs as a percentage of Import<br>Value|
|Clark, Micco, Dollar<br
They document the process of initial agricultural intensification and then economic diversification out of agriculture, which has been associated with rising per-capita incomes and declining poverty. In Palanpur, this process has also been associated with a rather sharp increase in inequality, particularly during the period of accelerating non-farm diversification in the 1980s and thereafter. Income mobility increased over time, and in Palanpur a striking observation has been the relative gains achieved in recent years by a particularly disadvantaged sub-caste in the village, offset to some extent by the relative decline of a sub-caste that historically concentrated on cultivation for its livelihood. Although the patterns of intragenerational mobility in Palanpur point to an evening out of lifetime income inequality compared with inequality estimates for a given year, Himanshu et al. (2018) also note that the observed rise in annual inequality seems to be accompanied by declining income mobility across generations. They find that the association between income levels of fathers and sons has become stronger across the two generations spanned by their data.

Finally, Dang and Lanjouw (2018) examine intragenerational poverty dynamics in India based on the NSS consumption surveys combined with a methodology to construct synthetic panels. The authors look at patterns of poverty mobility during the 2000s, examining the 2004/05, 2009/10, and 2011/12 NSS consumption surveys. Dang and Lanjouw (2018) indicate that there was considerable poverty mobility in India during the 2000s, and that this increased over time: their results point to faster poverty reduction and more transition out of poverty in the period 2009/10–2011/12 than in the period 2004/05–2009/10. Factors including more educational achievement, urban residence, wage work, and belonging to forward castes were positively associated with a relatively high likelihood of movements out of poverty and a relatively low likelihood of movements into poverty.

11
Sources**<br>Trust parliament / national assembly<br>Satisfaction with democracy<br>Freeness and fairness of the last national election<br>Political participation (SI)<br>Stability of democratic institutions (SI)<br>Political and social integration (SI)<br> Civil liberties<br>Accountability and public voice<br>Confidence in Parliament<br>Elections are Not Free and Fair<br>Independent media (NIT)<br>Civil society (NIT)<br>Electoral Process (NIT)<br>Elections<br>Public management<br>Access to information and openness<br>Rights<br>Right to Opinion and Expression<br>Right to Participate in Government<br>Right to Assembly and Association<br>Policies and framework for rural development and rural poverty alleviation<br>Legal frameworks for and autonomy of rural people's organizations<br>Electoral index<br> Satisfaction with democracy<br>Trust in parliament<br>People have rights to create, share, and consume information; people have adequate access to channels of information; there are<br>appropriate channels for government information; there are diverse channels for information flow; and information channels are independent.<br>Open budget index<br>Trust in parliament<br>Satisfaction with democracy<br> Transparency of government policy is satisfactory<br> Factor 1: Limited government powers<br>Factor 4: Fundamental rights<br>Factor 3: Opengovernment|

45
indication of risk during the year of 2020, but the financial system is measured as safer now than before the COVID-19 crisis.

Finally, some research suggests strong connections between public sentiment about banks and the general stock market in the U.S. John and Li (2021) find that an index of banking sentiment derived from Google search data reduces jump volatility in the S&P 500 index during the COVID19 pandemic.

# **4.3. Suggestions for Additional Research on Banks and Financial Markets in the Remainder of the World**

We close this U.S.-oriented data section with suggestions for more research on banking and financial conditions in the rest of the world. We first note that while we remain unaware of visible banking crises outside of the U.S. during the COVID-19 crisis, we are less confident in the conditions of banks in other developed or developing countries. For most countries, there are fewer accessible comparable quality datasets, hindering similar investigations as in this paper. However, a key exception is Europe, which has a number of datasets available of equal or higher quality as the U.S. We recommend using these in future research.

Second, we have reasons to suspect that the favorable “surprises” observed in the U.S. may not be replicated in many other nations, which may be more likely to have unfavorable outcomes instead. Our reasons are that the causes of the U.S. “surprises” that we review in Sections 5 and 6 below likely do not hold to the same degrees elsewhere. While governments of many nations around the world also introduced stimulus programs, these were not as large or introduced as quickly as the U.S. did, as discussed in Section 5 (e.g., Feyen, Gispert, Kliatskova, and Mare, 2021). Additionally, the extent to which the safety of different banking systems were fortified and 26
## _3.3 A Note on Aggregation_

For comparability with the previous literature, and to serve as a foundation for the village-panel estimation, we also show results for the village analog of (1) (setting aside the jackknife dimension): In Appendix 1 we show in detail how this equation can be aggregated from a household version (like (1)), and thus how the coefficients can be compared across household and village-level specifications. A few key points are worth highlighting. First, given our chosen functional form, it is easy to aggregate from the household-level regression by simple averaging, which is equivalent to using either village means, or village dummies, as instruments for the appropriate household specification. The estimated effect of inequality will be numerically identical, whether estimated at the household or village level. Second, our specification allows us to better highlight the distinction between the village and household-level analysis: Important differences in key parameter estimates emerge in the details of empirical implementation, especially the loss of household-level controls, and the use of an internally consistent sample that properly aggregates (i.e., only the non-attritted, balanced panel). Third, following the insights of Devereux (2007), we show that our jackknifed specification (1) can be derived as the reduced form growth equation for Deaton’s (1985) measurement-error-correcting estimator of the village-level specification (2). This provides “bonus” justification for using Equation (1) as our base specification. We are thus able to also implement the Deaton (1985) estimator for the village-level Equation (2).

# 4.0 Empirical Results

## _4.1 Data_

The data come from annual household surveys conducted by the Survey Department of the Research Center on the Rural Economy (RCRE). The survey collected detailed household-level information on income and other household characteristics. The starting point is a sample from 1987 of 4,847 households drawn from 82 continuously observed villages in nine provinces.<sup>16</sup> Originally planned
16 Anderson, K. and C.P. Nielsen (2004), ‘Economic Effects of Agricultural Biotechnology Research in the Presence of Price-Distorting Policies’, _Journal of Economic Integration_ 19(2): 374-94, June.

Baffes, J. (2005), “The ‘Cotton Problem’”, _World Bank Research Observer_ 20(1): 10943, Spring.

Beintema, N.M. and G.J. Stads (2004), ‘Sub-Saharan African Agricultural Research: Recent Investment Trends’, _Outlook on Agriculture_ 33(4): 239-46, December.

Bennett, R., U. Kambhampati, S. Morse and Y. Ismael (2006), ‚Farm-Level Economic Performance of Genetically Modified Cotton in Maharashtra, India’, _Review of Agricultural Economics_ 28(1): 59-71, Spring.

Cohen, J.I. (2005), ‘Poorer Nations Turn to Publicly Developed GM Crops’, _Nature Biotechnology_ 23(1): 27-33, January.

Dimaranan, B.V. and McDougall, R.A. (eds.) (2005), _Global Trade, Assistance, and Protection: The GTAP 6 Data Base,_ Center for Global Trade Analysis, Purdue University, West Lafayette.

FAO (2004), _The State of Food and Agriculture 2004/05,_ Rome: UN Food and Agriculture Organization.

Fedoroff, N.V. and Brown, N.M. (2004), _Mendel in the Kitchen: A Scientist’s View of Genetically Modified Foods_ , Washington DC: Joseph Henry Press.

Hertel, T.W. (ed.) (1997), _Global Trade Analysis: Modeling and Applications_ , New York: Cambridge University Press.
27 (Gainesville, FL: lJniversity Press of Florida, 1997), p. 89.

° The 1991 F'ersian Gulf crisis did much to reduce Egypt's exlernal debt. In May 1991 a Paris Club agreement agreed to write off about US $28 billion of Egypt's civilian and military debt. This debt relief was linked to the successful Egyptian completion of a IMF package of economic reforms.

1' Interview with Dr. Hlamed Latif el-Sayeh, New York Times, February 26,1977, p. 7.

12 There are three basic types of bread produced in Egypt: a coarse brown loaf called baladi (82 percent extraction rate); a whiter loaf called shami (76 percent extractiDn rate); and a longer loaf resembling French bread, called fino (72 percent extraction). At present, the only type of bread which is subsidized in Egypt is the coarse, baladi bread.

13 In 1980 the full list of subsidized food items included: breal (baladi, shami and fino), flour, sugar, rice, tea, edible oil., beans, lentils, macaroni, coffee, sesame, shortening, imported cheese, frozen meat, fish, eggs and chicken.

14 Ali and Adarns, (note (3) above), Table 2.

15 1 feddan eqiuals 1.038 acres.

16 Harold Alcderman and Joachim von Braun, The Effects of the Egyptian Food Ration and Subsidy System on Income Distribution and Consumption, Research Report 4f; (Washington, DC: International Food Policy Research Institiute, 1984), Table 14.

17 It has been estimated that 80 percent of the wheat flour consumed in Egypt comes from subsidized baladi wheat flour . See Adel Mostafa, "Leakage in the Food Subsidy System" (draft report prepared for Food Security Research Project in Egypt, Cairo, January 1997, p. 28).

18 Ali and Adtams, (note (3) above).

19The 1990/91 HI1ES Survey included 14,232 households; 8
of asset sales is added to net monthly earnings. The corresponding figure in the groups receiving UCTs is $0.60, smaller because of reduced consumption from liquidation of group assets. These numbers correspond with the evidence from the household data, an insignificant average increase in consumption of 72 cents per person per month over a baseline mean of $19. Hence the combination of preserved (not consumed) group asset values and low profits limit the scope for increases in household income.

These somewhat bleak results help to inform three distinct literatures in the debate over how to build effective economic inclusion programs. First, they add a data point to the literature on group-based business investment programs in Sub-Saharan Africa. In many ways TASAF is similar to the NUSAF Youth Opportunities Program evaluated in neighboring Uganda by Blattman et al. (2014, 2020), which saw impressive short-term gains followed by long-term catch-up by the control.<sup>4</sup> However, as we discuss in more detail in the next section, key differences exist; NUSAF selected youth at the beginning of their working careers, invested mostly in training for skilled trades, allowed more scope for individuals to own enterprises, and avoided low-skilled sectors such as livestock that make up the majority of TASAF investment.<sup>5</sup> These findings echo those of Haushofer et al. (2022), who find that cash transfers targeted based on deprivation (rather than, say, potential in the form of high expected conditional average treatment effects) may not be attractive for increasing social welfare – even when the social planner cares about redistribution. In Appendix B, we provide a more detailed comparison to related social protection programs.

> 4 A similar increase in earnings in the short run followed by convergence a few years later is observed in an entrepreneurship program in Ethiopia that provided a cash grant and business training to young people (Blattman and Dercon 2018a & 2018b).

> 5 In a more individualized context, Argent, Augsburg, and Rasul (
# **Accompanying Notes to Table 5**

# A. Range of Observed Values of financial risk indicators:

For Category A MFIs the indicators used are those for Fundacion Integral Campesina (FINCA) in Costa Rica, La Asociacion Dominicana para el Desarrollo de la Mujer (ADOPEM) and Asociacion para el Desarrollo de Microempresa Inc. (ADEMI) in the Dominican Republic, Kenya Rural Enterprise Programme (K-REP) in Kenya, and Alexandra Business Association (ABA) in Egypt as reported in Christen et al. (1995), supplemented by data from Benjamin and Ledgerwood (1998, draft), and The Microbanking Bulletin, v. no. 1 (1997) and no. 2 (1998).

For Category B MFIs the indicators used are those for Union Popular Credit Union (Tiquisate), UPA Credit Union (Amatitlan) and 20 core credit union members in the National Credit Union Federation (FENACOAC) in Guatemala, as reported in Almeyda and Branch (1997, draft). For Category C MFIs the indicators used are those for the Lembagan Perkreditan Desa (LPD) community-owned village banks and the Bank Rakyat Unit Desas in Indonesia, and Banco Solidario S.A. (BancoSol) in Bolivia, as reported in Christen et al. (1995).

- B. Suggested values of financial risk indicator thresholds for internal governance and external supervision (Columns 6 and 7, Category B) for credit unions and savings and credit cooperative societies (Category B) are taken from Richardson (WOCCU, 1997).

- C. Suggested threshold values of financial risk indicators for external regulation (Column 4, Category A) apply only to NGO MFIs availing of an exemption provision for limited deposit-taking under the banking law and/or receiving authorization for mobilizing wholesale deposits through commercial paper issues, large-value certificates of deposit or investment certificates from the bank supervisory authority.

- D. Suggested threshold values of financial risk indicators for external regulation (Column 10, Category C) apply primarily to MFIs obtaining authorization and license from the
# **1. Introduction**

Private sector growth remains one of the main challenges facing developing countries in their quest for development and poverty reduction. Extensive evidence shows that a favorable business environment helps promote the growth of firms. As shown in recent research, however, firms in developing countries face a tougher business environment than their counterparts in the developed world.

Our aim in this paper is twofold. First, we seek to go beyond the traditional menu of constraints on firm growth to find out which of these constraints is the most binding. As the growth diagnostics approach points out, developing countries have scarce resources and therefore need to focus on removing the most binding constraint. Second, we examine the effects of the most binding constraint on firm growth not only across countries but also by firm characteristics.

We first explore the relationship between the business environment and firm growth as measured by employment growth. Among 15 components of the business environment, we identify the most binding constraint using both subjective and objective measures. Our focus is on the most binding constraints for existing firms and, more specifically, the binding constraint that matters the most for firm growth. The methodology follows two steps. The first is to find out which constraints are statistically significant among all regressions after controlling for firm characteristics and country fixed effects. The second is to identify the most binding constraint. We find that besides informal sector competition, access to finance is the obstacle that matters the most for growth. This result is robust for all regions and all sectors.

Our analysis contributes to the existing literature in several ways. First, using a large sample, containing more than 39,000 firms across 98 countries, we identify the most binding constraint on firms using subjective measures, then evaluate the importance of this constraint to firm growth using objective measures and controlling for firm characteristics. The sample comes from World Bank Enterprise Surveys conducted in 2006–2010 in mostly emerging and developing countries. The surveys provide both subjective data on perceived obstacles and objective measures of
this region is Rs 40 and that all the days lost due to bad weather are likewise valued. Then, at this opportunity cost, the total annual savings for the whole village amount to Rs 0.20 million, whereby the limit _e_ ̄ will not reached until 20 years will have passed. The Orissa Survey data suggest that while, in the short-run at least, the road will have little effect on morbidity, there is substantial willingness to pay for treatment. Putting a value on the latter is difficult, however, so we leave it aside for the moment. Turning to mortality, the assumption that the road will reduce the rate among primeage adults by 10 percent (see Section 4.3), while conservative in Orissa, may be on the optimistic side for Himachal Pradesh. Suppose, therefore, that a reduction of only half as much will result. Since the changes are quite small, the components making up the EV do so approximately linearly; so that, in the first 20-year period, the two-to-one ratio will shrink to four-to-one – in the extreme case that the estimated Rs 0.20 million associated with school attendance is also halved. The aggregate ‘measurable’ annual benefits at this stage are, therefore, Rs 2.69 (= 1 _._ 25 _×_ (2 _._ 05 + 0 _._ 10)) million, leaving us still Rs 0.59 million short.

One way of finding this missing sum is, first, to list what has not been measured and valued: in the commercial sphere, trade in commodities other than the main crop and substitution into more valuable crops when there is land to spare; in that of education, the fact that producing better educated citizens arguably also yields a public good; and in that of health, any reductions in pain and suffering or in infant and child mortality. The second step involves an assertion of belief, namely, that the sum of these benefits for the community in question is at