rafmacalaba/datause-encoder-data huggingface dataset:
_Pit_ represents the probability that household _i_ participated in public works in year _t_ . The vector _X_ comprises of covariates controlling for characteristics of the household head, i.e. age, sex, and the level of education; the demographic composition of the household, i.e. the share of children, working-age adults and elderly, and the share of members with a disability; and household wealth and assets measured by the land holding and housing characteristics. We also control for the reported poverty status of the household in some specifications. However, there is a strong discrepancy between the reported poverty status and the poverty status recorded in different administrative databases at the sectoral and national level. Since the discrepancy between the different administrative sources cannot be resolved in the absence of a centralized system, we are putting less emphasis on this, even though it is one of the formal criteria for eligibility. The choice of covariates entering the specification is also informed and validated by the qualitative evidence we obtained from the life-history interviews. We use the qualitative data when discussing the results to illustrate and provide more nuanced insights into the estimation results.

In a second step, we estimate the effect of the income transfer, either through public works or direct support, of household _i_ on the outcome of interest, livestock holding, _yi_ . We use a difference-in-difference estimator to measure differences in beneficiaries and nonbeneficiaries between 2009, 2011 and 2014. Beneficiaries hereby constitute households that have benefited from the VUP at the time of the survey. We look at the overall effect irrespective of the type of support as well as the effect for direct support and public works beneficiaries specifically. The comparison group comprises of households that are formerly eligible but have not benefited from the VUP at any point in time. We estimate the average treatment effect on the treated (TOT) as within estimator based on the following model: Yit = αi + θ<sup>D</sup> ∙1(t = 2011, 2014) + μ<sup>D</sup> ∙treated × 1(t = 2011, 2014) + C<sup>`′`</sup> it<sup>δD + ε</sup> itD. (2) 14
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These estimates imply rainfall insurance demand would increase significantly (by approximately 25-50%) if insurance could be offered with the same mark-up as US insurance contracts.<sup>21</sup> However, even this increase would still imply that only a relatively small fraction of all households in our study areas purchase insurance. (In addition, most households only purchase a single policy, only covering a modest fraction of their exposure to rainfall risk). Most starkly, the results from Ahmedabad shown in Panel B of Table 6 suggest that more than half of households do not purchase rainfall insurance even when the policy price is set significantly below the actuarial value of the insurance policy. This suggests that non-price factors play an important role in shaping demand.

# _B. Liquidity constraints_

Results from Andhra Pradesh suggest that a positive liquidity shock has a large positive effect on household insurance demand. Providing households with enough cash to purchase a policy increases participation by 39 to 41 percentage points, or around 140% of the average insurance purchase probability. Based on our estimated price elasticity, this is several times larger than the demand response generated by cutting the price of the policy by half. Consistent with this result, we also find two types of non-experimental evidence that suggest liquidity constraints are associated with lower insurance demand (see Section V).

Our findings provide an explanation for why insurance demand may be low amongst the poorest households, which are likely to have the lowest access to financial services, and face more severe liquidity constraints. The simple intuition is that for such households, there are large benefits of hoarding scarce liquid assets, or using those liquid assets for agricultural investment, rather than insurance. One side effect of credit expansion (e.g. greater use of central credit registries, or other improvements in enforcement) could be to increase demand for insurance.

We note that reciprocity may provide an alternative interpretation for our experimental results. Since the cash is given to the farmer by the ICRISAT representative, the former may feel

> 21 To calculate these values, we multiply our price elasticity estimates by the percentage estimated difference in price between the Indian rainfall insurance contracts (from Table 1) and US insurance data (provided by David Cummins of Temple University). The US contracts provide an average payout-to-premia ratio of 70%, compared to 46% for the Indian rainfall insurance contracts; implying the price per unit of payout is 34% lower for the US contracts. Our average elasticity estimate of 0.79 implies that cutting the price of the Indian contracts by 34% would increase demand by 27% (or around 25%). The upper bound of 50% is calculated in a similar way, except comparing the price of the lowest-value Indian insurance contract to the highest-value US contract.

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- Horioka, C. Y. and A. Terada-Hagiwara (2016): “The Impact of Pre-marital Sex Ratios on Household Saving in Two Asian Countries: The Competitive Saving Motive Revisited,” _NBER Working Paper_ .

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- REDS (2006): “Rural Economic and Demographic Survey,” National Council of Applied Economic Research.

- Rose, E. (2000): “Gender Bias, Credit Constraints and Time Allocation in Rural India,” _Economic Journal_ , 110, 738–758.

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24
Table 6: Impacts on toilet usage and OD practice by subsidy eligibility status

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Toilet in use|OD|Toilet in use|OD|
|Treatment|0.125<sup>_∗∗∗_</sup>|-0.138<sup>_∗∗∗_</sup>|||
||(0.0418)|(0.0442)|||
|SL - Subsidy eligible|||0.0760|-0.0712|
||||(0.0478)|(0.0491)|
|SL - Subsidy ineligible|||0.210<sup>_∗∗∗_</sup>|-0.253<sup>_∗∗∗_</sup>|
||||(0.0527)|(0.0565)|
|Subsidy eligible|||0.0277|-0.0466|
||||(0.0405)|(0.0428)|
|Strata FE|Yes|Yes|Yes|Yes|
|Household covariates|Yes|Yes|Yes|Yes|
|~~F~~<br>~~i~~nteraction|||0.0270|0.00532|
|contro~~l~~<br>~~m~~ean|0.227|0.759|||
|controlmea~~n~~<br>~~n~~oteligible|||0.228|0.772|
|controlmea~~n~~<br>~~e~~ligible|||0.226|0.751|
|N|959|959|959|959|

_Notes_ : Sample restricted to matched households not having a toilet at survey baseline. The dependent variable in (1) and (3) is an indicator equal to one if the household reports to own a toilet that is in use at endline survey August-Sept 2017; The dependent variable in (2)-(4) is an indicator equal to one if the household respondent reported during endline survey that at least one household member defecates in the open; SL stands for sanitation loan treatment arm; Robust standard errors clustered at the village level are shown in parentheses; *, **, *** indicates significance at the 10, 5 and 1 percent level; F-interaction stats show p-value of F-test of equality of subsidy eligible/non-eligible interactions with SL; Covariates: Indicator for presence of a child aged 0-2 at baseline, ratio between number of sampled clients and village size, indicator for whether there is any uncertainty around the matching quality; Strata and interviewer included.

34
(Lebanon, Montenegro, and Mozambique). Second, a striking feature of the graph is that the links between this specific objective indicator of rules and the broad outcome of interest, citizen satisfaction with elections, is at best very weak indeed, with a correlation between the two measures that is in fact slightly negative.

Third, the graph also illustrates how outcome-based indicators explicitly focusing on the _de facto_ implementation of rules can be useful. As we have noted, a noteworthy feature of Global Integrity is its pairing of indicators of specific rules with assessments of their functioning in practice. The second series on the vertical axis (in squares, with countries labeled) reflects the assessment of Global Integrity's expert respondents as to the _de facto_ functioning of electoral institutions. This series is much more strongly correlated with the broad outcome measure of interest taken from the Voice of the People survey, at 0.46. Yet at the same time, this correlation is far from perfect, and this in turn reminds us of the importance of relying on a variety of different indicators, pairing both expert assessments as well as survey-based indicators of "de facto" outcomes..

# **4.  Whose Views Should We Rely On?**

In this section we discuss alternative types of respondents on whose views governance indicators are based. The primary distinction here is between governance indicators based on the views of experts, and indicators capturing the views of survey respondents of various types. There are many examples of expert assessments listed in Table 1. We have already noted how rules-based indicators of governance like Doing Business rely on the views of one or a few legal experts per country, typically located in the capital city, to interpret the regulatory framework across countries. A large variety of governance assessments are produced by experts on behalf of commercial risk rating agencies and non-governmental organizations. The Global Integrity Index and the Open Budget Index for example rely on a locally-recruited expert in each country to complete their detailed questionnaires about governance, subject to peer review. Commercial organizations like the Economist Intelligence Unit rely on a network of their local correspondents in a large set of countries to provide information underlying the ratings that they produce. Other advocacy organizations like Amnesty International, Freedom House, and Reporters Without Borders also rely on networks of respondents for the information underlying their assessments. Governments and multilateral organizations 15
_Beyond Unequal Development_

# **3.4 Empirical Evidence**

The Kuznets curve spurted a vast empirical effort devoted at testing its shape, determining its robustness to the selection of countries and time periods, and detecting turning points in which income distribution starts improving along the development process.

The empirical cross section work of Ahluwalia (1976), Lindert and Williamson (1985), Adelman and Robinson (1989), Bourgignon and Morrison (1990) and others tend to give a (qualified) support to the existence of the Kuznets curve. In addition, for cross country regressions, the inequality portion of the Kuznets curve tends to be more unstable than the portion of declining inequality (see figure la). Since the inequality part of the curve comprises countries in a range of lower to moderate income per capital levels, the relationship is more unstable for these countries'<sup>2</sup> . In contrast, it seems a more established fact that inequality tends to decline for countries at the intermediate and high levels of per-capita income<sup>13</sup> (see figure lb). However, studies for individual countries in Latin America (Colombia, Brazil, Argentina) and for Asian countries are reported to conform to the Kuznets curve pattern (see Fields and Jakubson, 1993) with a comparison of the impact of inequality on growth in Latin America and East Asia conducted in Birdsall and Sabot (1994).

Two recent World Bank studies (Deininger and Squire, 1995a, 1995b) have produced a new expanded data-base on income distribution of improved coverage and consistency to reevaluate existing studies of the Kuznets curve. These World Bank studies, pooling cross-

**12** A recent paper, Field and Jakubson (1993), finds a reversal of the Kuznets curve using a "fixed effects" model that allows for different countries to lie on Kuznets curves with the same shape but with different intercepts. However, in pooled models the standard Kuznets curve is maintained.

13 This does not rule out changes in the levels of inequality even in high per capita income countries as the result of changes in economic policies. This seems to be the case for the US under President Reagan and the UK under Prime Minister Thatcher, in which inequality went up, see Krugman (1993).

-16-
**Table A.3.3: Results of Simulation 3 by region, income group, and lending type. Effect on proficiency (share)**

||||Post-COVID-19<br>||
|---|---|---|---|---|
|Aggregate|Baseline|Optimistic|Intermediate|Pessimistic|
|EAP|36%|41%|43%|46%|
|ECA|31%|39%|42%|46%|
|LAC|53%|60%|64%|68%|
|MNA|55%|61%|65%|68%|
|NAC|17%|22%|25%|28%|
|SAR|||||
|SSF|||||
|Grand Total|40%|47%|50%|53%|
|HIC|25%|32%|35%|39%|
|UMC|51%|58%|61%|65%|
|LMC|69%|74%|75%|77%|
|LIC|-|-|-|-|
|Grand Total|40%|47%|50%|53%|
|Part I|23%|30%|33%|37%|
|IBRD|49%|57%|60%|63%|
|IDA/Blend|80%|82%|84%|86%|
|Grand Total|40%|47%|50%|53%|

Source: Authors’ calculation. Share Students Below Minimum Proficiency (BMP). Simulation 3 results based on the latest available PISA and PISA-D of 92 countries. Unweighted average. Student coverage as share of lower secondary enrollment: 100% NAC; 95% LAC; 94% EAP; 91% ECA; 76% SAR; 39% MNA; 3% SSA.

47
2001, while only 4% were able to rise to the top quintile. In contrast, fully two-thirds had exited. For the top quintile, 28% were able to remain in the top quintile, with only 2% falling to the bottom quintile, and 44% exiting.<sup>19</sup> The bottom panel divides firms by the quintile there are in in 2001 and sees where they came from, with the columns now adding to 100%.

Table 3a shows a large role for firm turnover in the productivity distribution, similar to Foster et al. (2002) who used U.S. retail firm data. For any quintile in 1986, the most likely outcome (row percentage) is death. For any quintile in 2001, the most likely origin is birth (column percentage). Like Foster et al. (2002), we find that deaths are concentrated in businesses with low productivity in 1986.<sup>20</sup> In contrast to Foster et al. (2002) who found births to be uniformly distributed, new entrants in Morocco tend to be more likely to be located in lower productivity quintiles, although the results are not as strong as for deaths. Regardless of this, the fact that approximately two-thirds of firms in the highest productivity quintile in 2001 come from new entrants suggests that many new firms rapidly improve their productivity levels post-entry.

The productivity trajectories and transition matrices strongly suggest that while our descriptive analysis shows that new entrants initially have much lower productivity than other firms upon entry, many of these firms experience substantial productivity improvements. As Tybout (2000) has described, disaggregating time intervals in constructing a productivity decomposition too finely is likely to underestimate the true

> 19 Baily et al. (1992) find evidence of a systematic plant vintage effect in their data whereby older plants move down the productivity distribution over time. There is some limited evidence of this plant vintage effect in the Moroccan data given the high percentage of firm exits from the 1986 cohort in addition to a general downwards movement of surviving firms in the productivity distribution.

> 20 The impact of productivity of firm exit is examined more rigorously in a companion paper, HallwardDriemeier and Thompson (2009).

13
settlement.<sup>26</sup> Thus, this selection concern mainly comes from the choice of the government on where to locate the refugee settlement sites. If this choice, which by default is correlated with refugee intensity, creates other factors that are correlated with the outcome variable(s), the analyses should deal with them. Equation (1) includes household-specific (EA-specific) fixed effects that effectively eliminate such factors that are time-invariant.

This leaves me with the potential concern of omitted variable bias from time-varying factors. Absent such factors, the outcome variables in places that experienced high and low temporal variations in refugee intensity during the study period should have followed parallel trends if there was no difference in temporal variations in refugee intensity between them. As the existence of such parallel trends cannot naturally be tested directly, ideally, I would want to give indirect (suggestive) evidence, such as showing its existence going back in time to when there was no refugee in the country. I am not aware of any panel data on household welfare during early times, but there is repeated cross-section information on agricultural income since the 1990s from the Agricultural Sample Survey of Ethiopia. Nevertheless, I cannot provide such suggestive evidence using these data from the 1990s since there was a temporally and spatially varying degree of refugee intensity in Ethiopia owing to its openness to refugees during these times and way before that.<sup>27</sup> However, I partially circumvent this concern by including many (12) household- or zone-level (plausibly predetermined) time-varying control variables ) in equation (1). I discuss and conduct tests to falsify and employ alternative empirical strategy to allay the concern hvv (XX from other potential time-varying variables in sections 7.4, 7.5, and 7.6.

# **5. Descriptive Statistics**

This section presents the summary statistics of the main variables, followed by a description of the association between the two household welfare metrics.

Table 1 presents aggregate summary statistics for all the sample observations and disaggregated statistics based on whether the observations are from round-specific major or minor refugee-hosting EAs, which is respectively defined as with above or below the average refugee

> 26Refugees may, however, influence their settlement site across the existing ones by altering when or where to cross the border. If this influence is based on their anticipation of future living conditions across the settlement sites, it leads to reverse causality and thereby a bias in the estimates. I present and discuss (results from) a falsification test (section 7.4) to rule out this possibility. 27Ethiopia has always maintained open door policy to refugees although this was legalized for the first time in 1969, when it adopted the 1951 UN Convention Relating to the Status of Refugees and its 1967 Protocol.

17