rafmacalaba/datause-encoder-data huggingface dataset:
from savings and microfinance as small or large. First, I note that my estimates of Marshallian surplus are not significantly different from estimates of the impacts of microcredit loans to women in Bangladesh on household consumption from Pitt & Khandker (1998) ($18 per $100 loan amount) and from Khandker (2005) ($15 and $21 per $100 loan amount); while the internal and external validity of these estimates has been widely debated, the magnitudes themselves were interpreted as implying that microfinance cost-effectively increases household consumption (Morduch, 1999). Second, I note that it is often the case that households’ borrowing and saving is large as a share of household consumption. Household credit as a share of GDP averages roughly 10% in low income countries where data is available, and rises to over 40% in high income countries (International Monetary Fund, 2006). In low income countries, my estimates of Marshallian surplus from microfinance in Table 2 would therefore correspond to consumer surplus from household credit of 1% - 2% of GDP. Lastly, I note that the price elasticities of demand I estimate for microfinance and savings are large relative to conventional estimates of demand for consumer goods. As consumer surplus is inversely proportional to the compensated price elasticity of demand, these large elasticities correspond to relatively small consumer surplus. However, as noted above, household borrowing and saving is large as a share of household consumption. The consumer surplus per dollar lent or saved may be small, and therefore often empirically elusive, but the intense use of financial technologies corresponds to large aggregate welfare gains from financial inclusion.

**Interpretation of accuracy and precision of elasticities** To visualize the variation in my estimates of price elasticities of demand, I plot the absolute values of my estimates of the price elasticities of deposits and loan amounts _ε_ , along with bounds on compensated elasticities _φ_ + _ε_ , in Figure 4. With the exception of Duflo et al. (2006), statistical imprecision in the estimates is substantial, as they introduced an order of magnitude more variation in prices than Karlan & Zinman (2018) and Karlan & Zinman (2019). In all cases, bounds – on compensated elasticities are narrower than 95% confidence intervals improvements in the efficiency of estimates of consumer surplus from financial technologies are likely to come primarily from more precise estimates of price elasticities of demand, which will likely require the introduction of large variation in prices.

# **5 Conclusion**

In this paper, I estimate the partial equilibrium consumer welfare gains from savings and credit. I show how experimental estimates of the impacts of changes in interest rates on savings and borrowing can be used to construct welfare-relevant price elasticities of demand.

38
It should be noted that the choice of population density map is important for the purpose of this study. Smith et al. (2019) provide a sensitivity analysis for flood exposure assessments using different population density maps, including GHSL-POP 2015 (3-arc second, ~90 m resolution). They show that high-resolution population density maps perform best in capturing local exposure distribution, in particular the HighResolution Settlement Layer (HRSL) with 1 arc second, or ~30 m resolution, produced jointly by Facebook, Columbia University and the World Bank (2018). While HRSL is only available for a limited number of countries, GHSL-POP is shown to perform better than alternatives with global coverage, such as LandScan data (30-arc second, ~900 m resolution; Bright et al. 2016).

## **3.3. Administrative boundaries**

The definition of national administrative boundaries follows the standard World Bank global administrative map. However, national boundaries are further disaggregated into subnational units for all countries where World Bank household surveys are available with subnational representativeness. These subnational units are typically provinces or states (i.e. admin1) but can also include custom groupings of subnational regions determined by the sampling strategy of household surveys. Overall, this study covers 189 countries, which are disaggregated into 2,260 subnational units.

## **3.4. Poverty estimates**

For each of the 2,260 subnational units, the World Bank’s Global Monitoring Database offers several poverty estimates, which are all derived from the latest available Living Standards Measurement Survey (LSMS) for the respective country. For the purpose of this study, the standard World Bank definitions of poverty are used to determine the number of poor people in a given subnational administrative unit. Specifically, poverty is defined by the daily expenditure thresholds of $1.90, $3.20, and $5.50.

# **4. Methodology**

## **4.1. Concept of analysis**

To estimate the number of people who are exposed to intense flood risk, this study follows four main steps:

**1. Generate a combined flood hazard map:** For each country and each subnational administrative unit, a single flood hazard layer is created by combining different flood types. The resulting flood map has a 90-meters resolution, with each pixel showing estimated inundation depths in meters. For pixels where different flood types overlap the higher inundation depth estimate is used (e.g. coastal areas near rivers are exposed to both coastal and fluvial flooding). The flood hazard map is then resampled to ensure that pixels perfectly overlay the GHSL population density map.

**2. Define flood risk categories:** While the flood hazard map offers inundation depths along a continuous scale, the values are aggregated into the following risk categories:

|**Flood ri**|**sk classification**|**Inundation depth**|
|---|---|---|
|**Low risk**|No risk|0 meters|
||Low risk|0 – 0.15 meters|
||Moderate risk|0.15 – 0.5 meters|
|**High risk**|High risk|0.5 – 1.5 meters|
||Veryhigh risk|Over 1.5 meters|

7
6 do not withstand small alterations in the conditioning information set "fragile" and those results that do withstand there alterations "robust." Furthermore, LR show that past results typically rely on searching beyond standard regression specifications to find the "right" set of right-handside variables that produce "good" results. We use the LR approach to further investigate the robustness of partial correlations between growth rates and "policy" variables over the 1960-1989 period for broad crosesection of about 100 coun'ries.

# 2. Techniue

The EBA employs the linear, ordinary least squares regression framework:

> **_GPW aPI_** + **_PON+,Z U_**

(1) where GYP is the growth rate in GDP per capita averaged over the 19601989 period for a cross-section of up to 100 countries, I is a set of base variables always included in the regression, M is the variable of particular analytical interest, and Z is a set of variables chosen from a pool of variables that we believe represent appropriate conditioning information. The EBA involves varying the Z variables to determine whether the coefficient on the N variable is consistently signif_cant and of the same sign when the conditioning information set varies. If the coefficient on the N variable is consistently significant and of the same sign we call this result "robust." If the coefficient on the N variable changes sign or becomes insignificant, we call this result "fragile."
(Paxson and Schady), which show that the targeting of FONCODES investments to the poor improved over time.<sup>28</sup>

|**Table 6: The magnitude of political and poverty effects on the distribut**<br>FONCODES**expenditures on community-based projects, November 1**|**ion of per capita**<br>**993-March 1995**|
|---|---|
|(i)<br>(n)|(iii)|
|**Decrease in the pro-Fujimori**<br>**vote**<br>15.80<br>22.43|33.62|
|In (FONCODES index)<br>75.44<br>42.53||

# **6. Conclusion**

This paper uses formal econometric modeling and a rich, highly disaggregated data set to analyze the effect of elections on the timing and distribution of expenditures made by the Peruvian Social Fund between 1991 and 1995. One important advantage of this approach is that it allows for a parsing out of the political and technical influences on FONCODES expenditures. I argue that there is a large, significant effect of elections on the timing and distribution of FONCODES expenditures. Expenditures were boosted before national elections; community-based projects were channeled to provinces where the political returns where expected to be large.

The results in this paper show that FONCODES has clearly been responsive to political imperatives. Indeed, FONCODES was arguably very well suited for President Fujimori's electoral ambitions. Unlike most programs conducted by the line ministries in Peru, FONCODES expenditures are truly discretionary: since 1993, FONCODES has had a backlog of thousands of project proposals which could be funded at opportune times, in provinces deemed to be electorally important. Polls suggest that FONCODES projects are closely identified with the presidency and with President Fujimori himself (Moncada, p. 62).

But FONCODES also appears to have had an important redistributive function. My results show that FONCODES funds flowed disproportionately towards poor provinces. Household survey data indicate that the infrastructure constructed by FONCODES reaches a larger fraction of households, especially poor households, than other programs in the Ministry of the Presidency (Paxson and Schady, World Bank 1999). Such success reaching the poor can in part; be explained by the fact that FONCODES has been free of many of the restrictions, paperwork and inefficiencies that have given the public sector in Latin America a bad name.

Was FONCODES, then, an effective poverty alleviation program or a brazen attempt to buy votes?

**28** Paxson and Schady present nonparanetric regressions and "concentration curves" for FONCODES expenditures on education projects, by year. The curves show that the cumulative fraction of resources flowing to poor districts was unambiguously higher in 1994 and 1995 than in 1992 and 1993.

25
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smaller than $500 million. Firms that issue these smaller bonds held 0.41 (0.25) million dollars per million dollars issued in before (after) 2008. We do not observe a similar increase for developed market firms (whose estimates decline from 0.49 to 0.34).

If the relatively small emerging market firms issuing large bonds were the ones stretching to take advantage of the yield discount in $500 million bonds in the post-2008 period, then we should observe that these are the firms driving our results in the uses of funds, and specifically the accumulation of cash. In Figure 10, Panel B, we present the Kim and Weisbach (2008) analysis for the post-2008 period for emerging market firms, dividing companies that issued large bonds into high- and low-asset firms (above and below the country median of assets, respectively). During this period, relatively small firms issuing large bonds tended to hold much more cash than large firms issuing large bonds, consistent with our prediction.

# **7. Conclusions**

The GFC led to a persistent period of low interest rates throughout the developed world. This low interest rate environment produced a search for yield by institutional investors that favored some classes of global securities, such as emerging market corporate debt, that had not been as popular among developed countries’ institutional investors prior to the crisis. In this paper, we show that institutional investors searching for yield in emerging market corporate debt after 2008 favored corporate debt securities that were large enough to qualify for inclusion in market indexes.

Inclusion in market indexes provides a liquidity benefit to investors in these bonds because holding a portfolio of bonds included in the index improves the liquidity of investors’ positions. Mutual funds that track a market index also benefit from holding bonds in the index; doing so reduces the risk that their performance will deviate from the market benchmark. The benefits of index inclusion are especially attractive for cross-over 32
> _ 1.59 -

# Flgure 1.C.13 FARM INCOME UNDER POPULATION AND INCOME GROWTH

> (lilittfa of 1987 Codis)

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be the case that much of the debate about what is happening to inequality in the world is actually a debate about the meaning of “inequality” (Ravallion, 2004).

Thirdly, there are also signs that the growth processes seen in many reforming economies in the 1990s have been putting more systematic upward pressure on inequality. Lopez (2005) reports evidence to support this view (though based on a smaller, selected, sample of countries than will be studied in this paper). To re-examine the relationship between growth and changes in inequality, I created 290 “spells” defined by two household surveys for a given country with more than one observation for most countries; there are about 80 countries represented, spanning 1980-2000.<sup>3</sup> I then compared the changes in the Gini index with the changes in the survey mean (in real terms, using local CPIs). Figure 1 gives a scatter plot of changes in the log Gini index against changes in the log real survey mean between successive household surveys. The correlation coefficient is –0.13 and is not statistically significant (at the 10% level). Among growing economies, inequality increased about as often as it fell, and similarly among contracting economies. Figure 2 focuses on the period after 1992, dividing the sample in two. There is now a mild positive correlation coefficient of 0.26, which is significant at the 5% level.<sup>4</sup> Fourthly, it must be acknowledged that there is likely to be considerable measurement error in the changes in inequality and the survey means. The errors can come from a variety of sources, including sampling errors (probably a minor concern in most cases for the surveys used here), errors arising from selective compliance (whereby certain types of households participate in surveys with lower probability than others), under-reporting of incomes and comparability

> 3 The data are drawn from _PovcalNet_ and the _World Development Indicators_ . _PovcalNet_ is a new interactive tool for poverty analysis that provides the primary distributional data for about 500 surveys for 100 developing countries, drawing on the World Bank’s data base; see <u>http://iresearch.worldbank.org/povcalnet</u> .  The primary background paper is Chen and Ravallion (2004).

> 4 All significance tests in this paper are based on White standard errors (corrected for heteroscedasticity, which is clearly present).

4
The availability of the baseline survey allows us to estimate the impact of ATI based on the double-difference approach whereby the matching estimate of the ITE ex-post is netted out of any preexisting differences between the treatment and control municipalities. Since observed characteristics and outcomes were balanced across the treatment groups at baseline, inclusion of the baseline does not change the results, but leads to more precise estimates.

Given that we have repeated cross-sectional data, as opposed to longitudinal data, the estimator following Smith and Todd (2005) can be implemented as: where t and t’ indicate periods after and before the program start date, respectively. This procedure differs from the one above in that matching is performed now twice, before and after the program starts.<sup>10</sup> The second term in the ITE-DDM estimator differences out from the ITT-M estimator any pre-program differences in the outcomes between treatment and control municipalities, hence producing more robust estimates of program effects. We consider time invariant covariates, the ones we use to construct our control group. Given this and that the control municipality remained control after the program started, it is plausible to assume W(i, j, t)= W(i, j, t′), i.e. the matching procedure is performed only once, before the intervention.

The standard error for a DDM estimator does not have an analytical form. Therefore, we construct standard errors using bootstrapping.

# _d. Potential threats to the evaluation design_

> 10 The difference-in-differences matching estimator is defined in Heckman, Ichimura and Todd (1997) and Heckman, Ichimura, Smith and Todd (1998). Blundell and Costa-Dias (2008) also present a variant of the estimator by Smith and Todd (2005) applied to a similar context, with repeated cross section data.

14
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24
Estache, A. and M. Vagliasindi (2007): “Infrastructure for accelerated growth for Ghana: needs and challenges”, unpublished manuscript.

- Estache, A., 2003. "On Latin America’s Infrastructure Privatization and its Distributional Effects." Washington, DC: The World Bank, Mimeo.

- Estache, A., B. Speciale and D. Veredas (2005): “How much does infrastructure matter to growth in Sub-Saharan Africa?”, unpublished manuscript.

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- Gradstein, Milanovic and Ying.2001. "Democracy and Income Inequality: An Empirical Analysis" CESifo Working Paper Series 411; World Bank Research Working Paper No. 2561

- Herbst, Jeffrey (2000) States and Power in Africa: Comparative Lessons in Authority and Control. Princeton, NJ: Princeton University Press, 248 pp.

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- Kamara, I. (2006): “Economic growth and government infrastructure expenditure in Sub-Saharan Africa”, unpublished manuscript.

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33
non-budgetary resources is correlated with land values, which are in turn correlated with economic growth, a more comprehensive picture that includes budgetary and non-budgetary expenditures would likely show even greater disparities across counties.

Moreover, the inequalities between counties are large even within provinces, as well as across provinces (Table 12). These differences may be partly explained by the heterogeneity within provinces, especially in a huge province like Sichuan; they may also reflect the different emphases individual provinces place on fiscal equalization. Through the decade of 1995-2005, there was no clear trend of decline.

**Table 12.  Variation of County Budget Expenditures by Selected Provinces**

||**Sichuan**|**Henan**|**Zhejiang**|**Jiangsu**|**Shanxi**|**Jilin**|**Hebei**|
|---|---|---|---|---|---|---|---|
|1995|0.65|0.37|0.41|0.26|0.31|0.46|<br>0.33|
|2000|0.73|0.34|0.40|0.33|0.43|0.44|<br>0.32|
|2003|0.84|0.29|0.41|0.38|0.30|0.40|<br>0.30|
|2004|0.83|0.34|0.45|0.41|0.32|0.43|<br>0.38|
|2005|0.81|0.37|0.38|0.39|0.34|0.41|<br>0.35|

Note: Measured as Coefficient of Variation in Per Capita County Budget Expenditure. Source: China Prefectures, Cities, and Counties Fiscal Statistics, various years. Authors’ calculations.

# **3.2.  The Township**

Townships and towns are the lowest level of the five-tier government structure. In 2007 there were some 34,400 townships and towns. Due to a continued push to merge or eliminate townships, the number is decreasing rapidly (Table 13). The average population of a township is about 24,000. With the exception of a small portion of the population residing in the township seat, township residents have a rural _hukou_ (household registration). A typical township has 15 administrative villages, but this number differs widely from region to region.

**Table 13.  Declining Number of Townships**

||**1985**|**1990**|**1995**|**2000**|**2005**|**2007**|
|---|---|---|---|---|---|---|
|**Number of**<br>**township-level**<br>**jurisdictions**|72,153|55,838|47,136|43,735|35,509|34,379|
|**of which: towns**|-|-|-|-|18,888|-|

Source: China Statistical Yearbook 2007, China Statistical Abstract 2008.

25