rafmacalaba/datause-encoder-data huggingface dataset:
14 poverty analysis seek to capture these interactions, and revealed preference seems to suggest that they are of interest to policymakers in many developing countries.

Despite this widespread agreement on the essential fact that poverty is multidimensional, there has been a lively debate on whether that implies that one should construct scalar indices that summarize information on those various dimensions in a single number, or provide multiple indices, one for each dimension, in a “dashboard approach”. Drawing entirely on the existing literature, we have argued here that such a dichotomous view misses the point. The most interesting aspects of the multidimensionality of poverty arise from the _interdependence_ among dimensions. The joint distribution of dimensions over the population contains more information than the corresponding marginal distributions, and the correlation patterns in that joint distribution may change how we compare poverty across two countries or time periods. This dependency structure is missed entirely by the dashboard approach, and often obscured by scalar indices.

Drawing on examples from both developed and developing countries, we illustrated three alternative approaches that allow researchers and policy analysts to focus on the dependency structure of a joint distribution. The first was stochastic dominance analysis, which permits partial orderings across joint distributions that are robust not only to poverty lines and welfare weights (as in the unidimensional case) but also to dimension weights. The second was a representation of the overlap of deprivations over the population, by means of simple tabulations or Venn diagrams. Given agreement on the identification criterion along each dimension, this extremely simple tool can complement the “dashboard” statistics on the marginal distributions in informative ways. And the third was the use of copula functions to study the multivariate association among different components of well-being across two or more joint distributions.

Finally we have argued that multidimensional poverty indices, like most other tools, can be accommodated in the economist’s toolkit, and that the risk of serious injury decreases with reliance on relative prices and the focus on a few core, truly irreducible dimensions. If such indices are used, plenty of fodder will remain for future controversies on the choice of weights, poverty lines, and functional forms. But they would largely be a distraction from what really matters to policymakers, which is the pattern of associations and overlaps across the core dimensions of well-being.
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>2</sup> Second, it can explain why we observe a sharp and immediate increase in deposits in the disbursement quarter with no increases in subsequent quarters: to the extent political elites divert aid to foreign accounts, either directly or through kickbacks from private sector cronies, aid inflows and capital outflows

> 1Alstadsæter et al. (2019) find that around 50% of the assets hidden in havens by Scandinavians belong to the 0.01% wealthiest households and 80% belong to the wealthiest 0.1%. Londono-Velez and Avila (2018) find similar results for Colombia.

> 2Anecdotally, havens are often associated with the laundering of proceeds from high-level corruption. For instance, a report by the Financial Action Task Force describes 32 cases of grand corruption of which 21 involved bank accounts in havens (FATF, 2011).

2
29 Table 2: DifferentIl. Between Projected **and** Actual Government **Revenue** for _2992_

|||Percen**Differenial**from|**the**Proieclioa||
|---|---|---|---|---|
||**Ql,**1992|Q2,**1992**|Q3, 1992|Q4, 1992|
|**TOrAL**REVENUE|+ 1.3|-9.9|-15.1|-1.0|
|TAX**REVENUE**|+ 6.0|-2.9|**.10.5**|-7.0|
|**Profita lax.**|-20.0|+7.2|-50.5|-37.0|
|Nonfinsacia|-31.6|-24.3|**.38.3**|-42.0|
|Financial|+ 3.7|+61.2|.69.1|-30.0|
|Tumover**ax**|+16.0|-12.2|-4.2|-5.0|
|Incomeux|+60.6|+71.0|+102.0|+180.0|
|Customs duties|+64.3|+ 16.5|+30.7|+32.0|
|Excise**taxes**|-74.3|-55.9|46.3|-.<br>0|
|Social**scurity**|-|- 6.1|+ 7.7||
|**NON-TAX RVENUFJ**|**+136**6|+36.6|+ 1034|**4-20**|

Sourc: Minity of **Finance and** IMF.

of Bulgaria, see the World Bank, 1993). The conditionality specifies macroeconomic targets and fiscal performance objectives, and is also a discipline on government, either directly, or by providing a force-majeure response to claimants for government transfers and subsidies. For the tax-base related reasons that we have set out, compliance with the budgetary conditionality requirements has in practice been difficult. If (or when) a program breaks down because of failure of compliance, the tax-base problems are exacerbated, since the government is also left without the foreign financing which is withdrawn as the penalty for non-compliance.

Table 2 compares budget projections and realized tax revenues in the four quarters of 1992. The differences are a reflection of the extent of uncertainty, or indeed, optimism in
# **Does the Village Fund Matter in Thailand?**

**Jirawan Boonperm** National Statistics Office of Thailand, Bangkok

## **Jonathan Haughton**

Suffolk University, Boston

## **Shahidur R. Khandker**

World Bank, Washington DC JEL codes: O16 G21 I38 Note: The authors listed above are in alphabetical order. They would like to thank Chalermkwun Chiemprachanarakorn and her colleagues at the Thai National Statistics Office for helping us work with the Thailand Socioeconomic Survey data, Sheila Buenafe for excellent research assistance and Xiaofei Xu for research support. We are deeply indebted to Kaspar Richter, who was instrumental in adding a special module on the Thailand Village Fund to the 2004 Socioeconomic Survey and arranging for a panel component for that survey. We benefited from comments received from Dean Karlan and Darlene Chisholm, and from participants at a seminar at Suffolk University at a presentation at NEUDC 2007. The views expressed in this paper are those of the authors, and not necessarily of the institutions with which they are affiliated.
to small and medium firms (Rozkru, 2015). This counterintuitive result may be explained by the fact that micro firms may be more likely than larger firms to report improvements as innovation.

In the following sections we attempt to contribute to the literature on the impact of survey methodology on innovation measurement by shedding some light on the importance of problems typical of subjective data when measuring innovation in developing countries.

# **3. Data**

This paper draws on Innovation Enterprise Surveys data collected between 2012 and 2014 on a representative sample of the formal (registered) non‐agricultural, non‐mining private sector in Sub‐ Saharan Africa and South Asia. Fifteen countries are covered by the surveys: 11 in Sub‐Saharan Africa (DRC, Ghana, Kenya, Namibia, Nigeria, Sudan, South Sudan, Tanzania, Uganda, Malawi and Zambia) and four in South Asia (Bangladesh, India, Pakistan, and Nepal) (see Table A2 in the Appendix for more details). The data were collected by the Enterprise Analysis Unit of the World Bank Group as a part of a DFID‐ funded project, which aimed to increase the coverage of innovation surveys in developing countries while assessing at the same time the robustness of the Oslo Manual framework.

The project followed a two stage approach to data collection. In the first stage, a short innovation section (short questionnaire) consisting of 9 questions based on the CIS‐type of survey was incorporated into the Enterprise Surveys (ES). The ES is a multi‐topic firm‐level survey that collects data on firms’ characteristics, balance‐sheet, and firms’ experience of the business environment.<sup>3</sup> In the second stage, an in‐depth innovation module (long questionnaire) was administered to randomly selected ES respondents. The questionnaire used in the innovation module is also based on the Oslo manual but expanded to capture more details on the innovation introduced and to integrate other important questions on organization and management practices, use of ICT, and innovation inputs.<sup>4</sup> The follow up survey was submitted to firms independently from their answers to the innovation section in the ES. Whenever possible the same respondent was interviewed in the two rounds of data collection.<sup>5</sup>

> 3 More information on the Enterprise Surveys are available on the website http://www.enterprisesurveys.org/ 4 Methodological tests and pilot field work were conducted in preparation to the in‐depth innovation module. The methodological tests was conducted in Ethiopia, Rwanda and Zimbabwe and included preliminary innovation surveys complemented by focus groups and semi‐qualitative interviews. The in‐depth questionnaire also benefited from inputs from experts on innovation in the National Science Foundation (NSF), WB and IDB.

> 5 The World Bank Group Enterprise Analysis Unit also collected data on innovation in Europe and Central Asia (ECA) and Middle East and North Africa (MENA) for a total number of 49 countries with innovation data. Innovation Enterprise Surveys in ECA and MENA are also collected following a two stages approach. The methodology for the

9
**Table 17: Variance within versus across McEwan’s (2014) intervention categories**

||**In cat**|**egory**|**Not in c**|**ategory**|**Tot**|**al**||
|---|---|---|---|---|---|---|---|
||||||||**More within category**|
|**Category**|**Mean**|**S.D.**|**Mean**|**S.D.**|**Mean**|**S.D.**|**variation?**|
|Teacher training|0.171|0.225|0.067|0.156|0.097|0.184|Yes|
|Computers or technology|0.200|0.309|0.082|0.154|0.097|0.184|Yes|
|Instructional materials|0.107|0.160|0.093|0.192|0.097|0.184|No|
|Deworming drugs|0.044|0.188|0.102|0.183|0.097|0.184|Yes|
|Food, beverages, and/or micronutrients|0.066|0.188|0.102|0.183|0.097|0.184|Yes|
|Contract or volunteer teachers|0.117|0.093|0.093|0.196|0.097|0.184|No|
|Monetary grants|-0.005|0.098|0.103|0.186|0.097|0.184|No|
|Class size or composition|0.132|0.083|0.092|0.194|0.097|0.184|No|
|School management or supervision|0.118|0.188|0.094|0.184|0.097|0.184|Yes|
|Student/teacher performance incentives|0.102|0.106|0.096|0.191|0.097|0.184|No|
|Informational treatments|0.058|0.095|0.102|0.192|0.097|0.184|No|

38
the effect of ‘positive’ health information on a worker’s physical activity.<sup>20</sup> Similar to the above, this is a valid TmUT estimate if those workers assessed negative on day _t+7_ are also malaria free over the interval from day _t_ to day _t+7_ .

# **7. Results**

The paper presents two sets of results. First, we present the conditional correlations of labor outcomes and physical activity to validate the use of physical activity data as a proxy measure of productivity. Then we present the results for the estimated effects of a malaria treatment and testing program on worker physical activity.

# _Relationship between physical activity and worker individual labor supply and output_

Table 4 presents summary statistics for the tracked subsample including the physical activity outcome variables sedentary, light intensity, medium intensity, and high intensity minutes of activity (labelled ‘lightly’, ‘fairly’ and ‘very active’, respectively). As described earlier, applications of the accelerometer technology can result in a high degree of respondent noncompliance, and this study is no exception. In order to distinguish valid observation days from invalid days in the analysis, the three following trimming criteria were adopted: (1) We delete an observation day if there was a reporting error in the accelerometer data identified because it did not sum to 24 hours in that day. (2) We stipulate a cut-off for a valid day of data by establishing thresholds on total time devoted to activity (of any level). A day is deemed invalid if the activity tracker measures active time of less than three hours on work days or less than 1.5 hours on nonwork days. Figure 2 presents the histograms of sedentary time per day for both (plantation) work days and non-work days. The degree of non-adherence is readily apparent in the right tails of both histograms, and especially that for non-work days.<sup>21</sup> (3) The remaining days are trimmed at the 1<sup>st</sup>

> 20 One key difference with previous seasons is that the plantation did not allow cutters to switch between different cutting and scrabbling in the current season. This study therefore does not investigate task or occupational switching.

> 21 Any classification threshold for a valid day will include some proportion of false positives and false negatives. We believe the chosen thresholds are conservative in that they likely include a relatively larger number of invalid days. However, the presented results are robust to other cut-offs in either direction at 30 minute increments.

19
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In addition to investing different percentages of the grants, did males and females invest in different types of assets? For the half of the treatments given in kind, we have very detailed information on the purchases. For the half given as cash, we know from follow-up surveys whether the initial investments were made in inputs or equipment. Males invested a larger share of their grants in inventories and working capital (58 percent vs. 45 percent for female, p=.03). Among the equipment purchases, women appear to have spent more on items which have uses both in the business and in the home—sewing machines, ovens, cookers, furniture, and the like. This largely reflects the fact that they are more likely to work in food processing and garments. But our profit data will not reflect the consumption value to the household of the purchases. For both males and females, we find weak evidence that the return to investments in equipment is lower than the return to investments in working capital, with the gap slightly larger for females. Columns 4 and 5 of Table 3 show the increase in profits as a function of the amount spent on working capital and the amount spend on capital equipment. For males, working capital investments have returns of 8.5 percent per month, while equipment investments return 5.3 percent per month. For females, the comparable estimates are 1.4 percent for working capital and -5.8 percent for equipment. The somewhat larger gap for females may reflect the greater value in home consumption of the equipment purchased by females.

In sum, we find that females invest little of the smaller treatment amount, but more of the larger treatment amount than do males. Females who do invest the grants in the enterprise show no increase in profits as a result. We next develop a simple framework for thinking about investment decisions in household enterprises.

# **4. A Simple Model to Explain Why Returns May Vary by Gender**

With perfect markets and diminishing returns to capital, we would predict that in steady state all firms will chose their capital stock such that the marginal return to capital equals the market interest rate. In practice, however, both credit and insurance markets are imperfect, which can lead marginal returns to vary across firms and owners. A simple model can be used to identify the main potential sources of heterogeneity in returns, which can then be used to investigate gender differences in returns.

14

-  -
Figure 8: Diversification patterns in resource-rich and resource-poor countries <!-- Start of picture text --> Lowess curve for Gini of employment, value added and exports against GDP per capita (PPP) at 2-digit level<br>Employment Value-added Exports<br>-]-]-|<br>||<br>l<br>l ] |<br>a|A|||<br>|| |] \ \ | Pa<br>| | \ | 4”<br>0 | © | ; ea; \ xo<br>= / ‘ ,<br>a | = | / = S=f<br>| Lo] 1 ] f a |<br>TM | | /<br>| y = | / TM I<br>| y; i ] |<br>| a \ |<br>©:|4\|I<br>y | é 2 \, | é ee} |<br>\ ‘ /<br>Sop 7 || |I<br>|<br>wn w wn<br>xSEESOS P Oo OSE 5FEISSoS © OP Oo oFESS OG S$O OH<br>GDP per capita, PPP (constant 1995) GDP per capita, PPP (constant 1935) GDP per capita, PPP (constant 1955)<br><!-- End of picture text -->

Source: Authors’ calculations In resource-poor countries, the Gini index follows a similar pattern to Figure 2. Countries first diversify then they start specializing at a turning point around PPP GDP per capita of 20,000 (constant 1985 US dollars). Instead, resource-rich countries reach a plateau at around PPP GDP per capita of 10,000 (constant 1985 US dollars) and start specializing again later at a higher-turning point. Resource-rich countries have a higher Gini index on average than resource-poor countries and therefore look more concentrated. This observation is most noticeable in the third panel of <!-- Start of picture text --> observa-<br><!-- End of picture text --> <!-- Start of picture text --> tion is most noticeable in the third panel of<br><!-- End of picture text --> Figure 8. After the turning point, re-specialization in resource-poor countries is higher in employment and value-added than in resource-rich countries. While re-concentration is higher for resource-rich countries’ exports.” <!-- Start of picture text --> re-concentration is<br><!-- End of picture text --> <!-- Start of picture text --> higher for<br><!-- End of picture text --> <!-- Start of picture text --> countries’ exports.”<br><!-- End of picture text --> The upward bending part of the lowess curve in the 3 panels is steeper for resource-poor countries than for resource-rich countries, suggesting that richer resource-poor countries <!-- Start of picture text --> countries than for<br><!-- End of picture text --> <!-- Start of picture text --> suggesting<br><!-- End of picture text --> <!-- Start of picture text --> that richer<br><!-- End of picture text --> <!-- Start of picture text --> countries<br><!-- End of picture text --> <!-- Start of picture text --> Figures using<br><!-- End of picture text --> <!-- Start of picture text --> and exports display similar results and can be available upon request.<br><!-- End of picture text --> 3 Figures using value-added and exports display similar results and can be available upon request. 22
# **1. Introduction**

Small and informal firms are the source of employment for large sectors of the labor force in many developing countries. A key question of interest is whether these firms hold the potential for income growth for their owners, or whether they merely represent subsistence sources of incomes for low-productivity individuals unable to find alternative sources of work. Evidence that _some_ firms have high marginal returns is seen in the very high interest rates paid by some individuals to moneylenders, and by literature which identifies the effect of credit shocks on those who apply for credit (see Banerjee and Duflo, 2005 for an excellent recent summary). However, the sample of firms who apply for credit or who belong to microfinance organizations may not represent the full universe of firms for any number of reasons.<sup>1</sup> In this paper we use a randomized experiment to identify the marginal return to capital for all firms, irrespective of whether or not they choose to apply for credit at market interest rates. We then examine the heterogeneity of returns in order to test which theories can explain why firms may have marginal returns well above the market interest rate.

We accomplish this by surveying microenterprises in Sri Lanka and providing small grants to a randomly selected subset of the sampled firms. The grants were either 10,000 Sri Lankan rupees (about $100), or 20,000 rupees (about $200). Half the grants were given in the form of cash, and the other half in the form of equipment or inventories selected by the entrepreneurs. We purposely restricted our survey to small firms with less than 100,000 rupees (about $1000) in capital other than land and buildings. The median level of capital investment among firms in the sample is about 18,000 rupees. The capital shocks provided were therefore large relative to the size of the firms, with the larger shock equal to more than 100 percent of the median level of invested capital.

An accurate estimate of returns to capital in small enterprises is important for understanding both the limitations and the potential of microenterprises to play a role in increasing income levels in developing countries. If returns are very low at low levels of capital stock, then this would indicate that capital is an important barrier to opening a

> 1 These reasons include both selection among entrepreneurs as to whether or not to apply for credit, determined by factors such as their attitudes to risk, access to alternative sources of finance, perceptions of the returns on investment, and expectations of getting a loan; as well as selection on the part of the lender as to which firms to accept as clients.

2