rafmacalaba/data-use-mentions-extended huggingface dataset:
for access to services. It is not the aggregate index that we need for this purpose but its components.

Once we recognize that not even the Alkire-Santos MIP can be considered a sufficient statistic for poverty assessments, and that it will need to be complemented by other measures for those things left out, we are essentially back to the standard approach of using multiple measures. Whether that still leaves a role for the MIP depends on whether we think that it captures an important subset of the relevant dimensions of poverty. Here the ambition to be “multidimensional” is in such marked tension with the need to obtain all dimensions for each surveyed household that one must question what role such an index can usefully play. Arguably it would be better to derive the best measure possible for each of a logically defensible set of grouped dimensions—such as “income poverty,” “health poverty” and “education poverty”.

Clearly that task will still require some degree of aggregation. How should that be done? Here too there is an important, but poorly understood, distinction between the approach taken by recent MIPs and standard approaches to measuring poverty.

# **3. How should we aggregate?**

One can distinguish two approaches to forming an aggregate poverty index. The first is to use prices (actual or imputed) to form a composite index for aggregate consumption, to be compared to a poverty line defined in the same space.<sup>9</sup> Ideally this is not just consumption of market goods and services, but should include imputed values for non-market commodities. For market goods, either their market prices or appropriate shadow prices can be used. For nonmarket goods the missing “prices” will need to be assigned on _a priori_ grounds or estimated. In practice, most poverty measures require imputations for missing prices, so this approach is a natural extension of prevailing practices. In principle we can broaden this approach to allow for non-commodity dimensions of welfare. The space defined by all primary dimensions of
- [29] Laeven, Luc and Fabi ́an Valencia. 2012. “Systemic Banking Crises Database: An Update”. IMF Working Paper WP/12/163, International Monetary Fund.

- [30] Lane, Philip R. and Gian Maria Milesi-Ferretti. 2007. “The External Wealth of Nations Mark II: Revised and Extended Estimates of Foreign Assets and Liabilities, 1970–2004”. –

- _Journal of International Economics_ 73, 223 250.

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- [32] Menkhoff, Lukas and Jakob Miethe, 2019. “Tax evasion in new disguise? Examining tax havens’ international bank deposits.” _Journal of Public Economics_ forthcoming.

- [33] OECD, 2019. “Using bank deposit data to assess the impact of exchange of information.”

- [34] Olken, Benjamin A. and Rohini Pande. 2012. “Corruption in Developing Countries”. _An-_ –

- _nual Review of Economics_ (4), 479 509.

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- [36] Powell, Jonathan M. and Clayton L. Thyne. 2011. “Global Instances of Coups from 1950 –

- to 2010: A New Dataset”. _Journal of Peace Research_ 48(2), 249 259.

- [37] Sachs, Jeffrey. 2005. “The End of Poverty: Economic Possibilities for Our Time”. New York: Penguin Press.

- [38] Svensson, Jakob. 2000.
NTPs receive substantially more aid than those that have not done so. The substantial difference between the amount of additional aid allocated for civil and human rightsrelated projects to countries that sign non-binding NTPs and those that make binding commitments in this area is not a function of the type of NTP but a reflection of the fact that the EU and the US do not include enforceable NTPs dealing with civil and human rights in their PTAs.<sup>21</sup> Nonetheless, the difference between the pattern of aid and NTPs for environment and civil rights is noteworthy. Finally, in the case of labor rights, there is little difference in the average amount of ODA received by countries that have agreed to NTPs in this area and those that have not, nor does the type of NTP appear to matter much in terms of average dollar values disbursed.

To assess whether and to what extent signing a PTA affects official development assistance flows, we again use the SDID estimator (see Eq. 1). We are interested in determining whether signing an agreement affects the amount of development assistance received, and whether there are differences in this respect between signing binding provisions vs. non-binding ones.

Table 4 presents the SDID estimates for this second exercise. We report the ATT estimates of signing a PTA that includes a NTP of interest (covering environmental, labor, or civil rights protection) on aid flows targeting the associated policy domain, as well as on total aggregate ODA received. Given the focus on ODA, we restrict our sample to the set of countries that were eligible for receiving financial support during the period 1990-2015. To improve matching, we include per capita GDP, population, total trade, and share of trade with the US and the EU.<sup>22</sup> The results for binding (non-binding) provisions are reported in the upper (lower) panel of Table 4. The first three rows of each panel focus on the relationship between the amount of issue specific ODA received and acceptance of a
be reflected in the DFO‘s flood magnitude measure, which includes the area affected as one of its three components.

Regarding governance, our results indicate that it helps explain flood mortality only in developing countries. In addition, they indicate that its effect is more complicated than suggested in previous papers, which have examined only a linear effect, with higher levels of democratic accountability and lower levels of corruption being associated with reduced disaster mortality (Kahn 2005, Keefer et al. 2010). We find that improvements in our governance index reduce flood frequency and flood mortality in developing countries only up to a point, which roughly corresponds to the mean/median. Beyond that threshold, further improvements in governance are associated with increases in both flood frequency and flood mortality. We find no effects of improvements in governance in developed countries, although that might be due to our governance index varying less within those countries than developing countries.

One possible explanation for the reversal of the effect of governance in developing countries is a combination of what has been called the ―local government paradox‖ (Burby 2006) in the flood control and government disaster policy literature, and the emphasis on decentralization by institutional strengthening programs promoted by international development organizations. The local government paradox is the proposition that local governments take natural hazards less seriously than central governments do. Although elected officials at the national level may face distorted incentives to invest in disaster preparedness—Healy and Malhotra (2009) show that voters reward incumbents for delivering disaster relief but not for investing in disaster prevention—Burby (2006) argues that this distortion is more marked at the local level. In the U.S., there are examples of local 26
1998).

As a share of FOB, producer prices in Cote d'Ivoire have been low compared to those in other major producing countries. Data from 1995 (Figure 2) show that the share of the producer price in Cote d'Ivoire was smaller than any other major grower, with the exception of Ghana. Similar comparisons for 1997 showed that the producer share of the FOB price in Cote d'Ivoire and Ghana remained between 48-50 percent, while producer shares of the FOB price in other major cocoa producing countries such as Brazil, Dominican Republic, Ecuador, Indonesia, Malaysia and Nigeria were between 82-92 percent.
Malaysian ringgit, NZD - New Zealand dollar, AUD - Australian dollar.

Note: CZK - Czech koruna, SKK - Slovak koruna, EUR - euro. Source: Thomson Datastream. Note: January 2001 = 100. Decline means appreciation.

The explanation for the success depicted above is not a straightforward one. Basically, central bankers in small and open economies have been much more willing to accept that credit booms and asset-price bubbles can emerge without signs of inflationary pressures and that inflation measured in terms of consumer prices has not always signaled a buildup of imbalances in the economy. In some countries they felt—partly as a result of their own experience—that strong credit expansion and increasing asset prices preceded almost all banking crises and the majority of deep recessions. Some of them could also easily imagine a realistic scenario in which a bubble builds up without visible signs of inflationary pressures. Such a scenario can arise when higher economic growth creates excessively optimistic expectations about the future of the economy that lead to nominal appreciation of its currency. In such a situation, very low inflation can prevail for a long time even under rapid credit growth and asset price acceleration. When agents recognize that the real situation is not so rosy, and open inflation pressures subsequently appear, it may be too late for monetary policy to react. In addition, excessive demand, especially in small economies, may for a long time be reflected in rising current account deficits rather than in inflationary pressures.

central banks maintained much higher rates. In these countries, however, the maintaining of higher domestic interest rates resulted in households and corporations switching to foreign currency loans, which further increased the risks to financial stability. See also Čihák and Mitra (2009).

13
for the average plot of that size. Lastly, these two mechanisms are by no means mutually exclusive such that the observed impacts reflect a combination of the two effects.

Next, we turn to economic outcomes as measured in the household income and expenditure surveys. Panel C of table 5 reports the 2SLS estimates of the effect of insurance payment on log expenditures per capita and log income per capita. In column 1, we see the point estimate for log expenditures per capita using the optimal bandwidth. The point estimate is large at 0.613, although it is somewhat noisy and only significant at the the 10% level. Column 3 reports a point estimate for log income per capita of 0.852, which is significant at the 5% level. Thus, going from a municipality that receives no payment to the average treatment municipality (i.e., 45% treated) we would expect increases in expenditure and income per capita of approximately 27% and 38%, respectively. Comparing these point estimates to alternative specifications suggests that they are 8
the sum of 10,699 instances where a grid cell is impacted by a specific flood event (before resilience investments), only in 97 of these do we observe unusual behaviors where a bigger flood-depth is not contained within the area of a smaller flood-depth for any given return periods. We also find that out of the 961 grid cells that get flooded in part or in whole by at least one flood event (before flood mitigation), in only 31 of these does the flood data show bigger flood depth areas that are larger than smaller flood depths. These localized discrepancies can possibly be explained by some small errors in the hydrological modeling or the transcription of these results into shapefiles. We deal with these outliers by assuming that a concentric ring for a smaller flood-depth cannot have a negative value: . jj,ττ,dd,ww jj,ττ,dd+1,ww ss 14 ≥ss
subjective well-being from several survey rounds of the Gallup World Poll (GWP) and Latinobarómetro, we first compute the average subjective well-being scores for different subnational (level-1) administrative regions in each LAC country.<sup>11</sup> We then estimate a set of adjusted fundamental amenities and migration frictions in locations across Latin America. In the next section, we provide details on the quantification methodology for the baseline period of 2000.

> 9 The Cantril’s Ladder is an ordinal scale indicating how an individual rates their own life. The scores vary from 0 for their worst possible life to 10 for their best possible life. Although the benchmarks for worst and/or best possible life might vary across individuals, regions, and countries, Desmet et al. (2018) abstract from such potential differences in the benchmarks based on the finding by Deaton and Stone (2013) and Stevenson and Wolfers (2013) of a relationship between subjective well-being (SWB) and the log of real income that is similar within the U.S. as well as across countries.

> 10 The inclusion of a location fixed effect in the estimation of the linear relationship between SWB and log of real income addresses endogeneity concerns that a location with a higher utility attracts more people and affects the amenity levels in the location.

> 11 The details of this procedure, as well as its geographical coverage, are provided in the Data Appendix.

11
- 11 -

supported these expectations. Estimates of formal sector employment impacts are sometimes insignificant or positive, either because of monopsonistic employer behavior or limited compliance. And, several studies have found that minimum wage increases resulted in wage gains in the informal sector, most probably because of what is known as the “lighthouse” effect.

The proliferation of research on the minimum wage has contributed to a deeper understanding of how labor markets operate, but many questions remain about the impacts of the instrument itself. As we have already noted, and will further detail below, there is no clearer consensus on its employment impacts. Moreover, analysts continue to focus on employment and wage effects, with little research on other impacts that should be of interest to policy-makers.

# **_3.1 Impacts on living standards_**

The minimum wage has consequences for living standards through its effects on the availability of employment, especially good jobs, and on the level and structure of earnings. The proliferation of studies on employment and wage impacts means that living standards is the area where knowledge has expanded the most.

# _Employment_

Research done up to the 1980s was primarily based on U.S. times-series data and typically found negative employment impacts. In a comprehensive review of the analysis up to that time, Brown, Gilroy, and Kohen (1983) estimated that the elasticity of teenage employment with respect to the minimum wage was in the -0.1 to -0.3 range (i.e., a 10% increase in the minimum wage reduced teenage employment by 1-3%). According to Neumark and Wascher (2007), this became the consensus view among economists.

Beginning in the early 1990s, a new wave of studies significantly expanded the minimum wage research in a number of ways (Neumark and Wascher 2007). First, new econometric techniques were applied to the (continually longer) time-series data. Second, new research designs were introduced based on variations in within-country minimum wages (e.g., U
found in the annex, but each scenario was costed as between US$1.89 and US$3.76 per case reduced for every 10,000 people.

# **A4 DISCUSSION**

The main contribution of this paper is the demonstration of how geospatial methods can be employed to target water supply and sanitation investments to attain desired outcomes – reduction of cholera risk in the city of Lusaka in Zambia. This study has shown how a wide variety of very detailed geospatial data can be brought together in a formal spatial statistical analysis to explore the geographic patterns and potential drivers of cholera risk during an urban epidemic. The high-resolution risk map provides a highly granular information source for decision makers considering how to reduce risk of future outbreaks, clearly delineating neighborhoods with elevated underlying risk from those where, even in the midst of the epidemic, risk was low. The analysis has also demonstrated how cholera risk arises from a complex set of interacting factors. Unsurprisingly, water and sanitation access play a key role, but this is mediated by environmental factors such as poor drainage and variations in the vulnerability to contamination of the underlying groundwater. The approach presented here has also enabled estimation of the possible
# _(c) Heterogeneity dimensions_

We perform heterogeneity analysis along the dimensions of gender, social capital, family socioeconomic background and skill level at baseline to understand whether program impacts vary between subgroups of beneficiaries. Overall, we find little robust evidence of heterogeneity. There are no dimensions of heterogeneity that consistently or strongly show a specific sub-group benefitting more in the short-term and in the medium-term. A handful of coefficients suggest significant effects for some sub-groups in the medium-term, but they do not consistently highlight significant heterogeneity.

One of the most robust heterogeneity results based on the short-term 2011 survey indicates that students with a stronger set of skills at baseline (proxied by higher grade in the previous year at university) were more likely to succeed in becoming self-employed and in setting up active projects at midline (Table 9, Panel A). However, by the endline, these differences in program impacts are not significant anymore between the two groups, indicating that academically stronger students at baseline were not more likely than their less skilled peers to be selfemployed and have active projects by endline.

On the other hand, there is some indication of a positive medium-term impacts on project ownership for students from families of better socio-economic background (Table 9, Panel B). This effect only comes through in the medium-term, with no significant heterogeneity by family wealth in the short-term. One explanation could be that graduates from wealthier backgrounds have more resources allowing them to stay without a job and taking the time to prepare a more sustainable project. Still, these patterns of heterogeneity remain weak so that they should not be overinterpreted.

We do not find robust heterogeneity by gender and results on gender are not entirely conclusive. In the short-term, impacts on entry into self-employment (independent or seasonal activity during the last 7 days) are mostly observed among men, but with no significant heterogeneity between men and women (Table 9, panel C). On the other hand, positive effects are found for women for self-employment in the 12 months prior to the 2011 survey,
- 2) _Measuring impacts over longer-time frames:_ the returns to these programs will differ substantially if they merely speed up the process of gaining employment versus having lasting impacts. Yet most studies measure impacts over at most 1 to 2 years, leaving them to speculate about cost-benefit on the basis of assumptions about how impacts vary over time. Tracking impacts over longer-time periods is therefore needed. Studies which link participants to administrative records (such as Attanasio et al, 2015) offer one promising way to do this.

- 3) _Limiting attrition:_ When the likely impact on employment is only 2 to 3 percentage points, and attrition rates are 10, 20, or even more than 30 percent, any treatment effects are dwarfed by attrition, and bounds that incorporate this attrition will be completely uninformative. Limiting attrition is particularly difficult given that so many ALMPs focus on youth, who tend to be more mobile and difficult to track over time. Serious investment in limiting attrition, combined with the use of administrative data is needed.

- 4) _Continued and improved careful measurement of costs:_ I was pleasantly surprised by the number of studies which did report the costs of the intervention, although a number still lack this key information. More work is needed to make clear average versus marginal costs in understanding the cost structure as pilot programs expand.

- 5) _Pre-specifying outcomes and heterogeneity:_ a number of studies fail to find a significant impact on either employment or earnings, but then emphasize impacts on a particular subgroup (such as one gender, or one skill level) or for one outcome (such as formal employment). Pre-specification of the primary outcomes and key heterogeneity of interest lessens concerns about multiple hypothesis testing.

- 6) _Testing placebo effects:_ Several studies find impacts despite almost no direct hires through the program they study (e.g. Beam (2016), Galasso et al. (2004), Levinsohn et al. (2014)). These studies raise the possibility that simply doing
Table A1: Types of contracts, in percentage, by year

|||In acreage term|s|
|---|---|---|---|
|year|1992|2002|2012|
|sharecropping|51|42|31|
|fxed terms|36|51|57|
|fxed amount of money|17|31|42|
|fxed amount of crop out|19|19|15|
|from relatives|9|3|6|
|duration more than two years|48|43|59|
|written contract|14|9|12|
|||In contract ter|ms|
|year|1992|2002|2012|
|sharecropping|53|49|38|
|fxed terms|32|42|49|
|fxed amount of money|13|23|32|
|fxed amount of crop output|19|20|17|
|from relatives|11|3|7|
|duration more than two years|48|38|62|
|written contract|17|9|10|

Note: Rabi (dry) season. India NSS Land and Livestock Survey Rounds 48, 59 and 70. The top panel presents the percentages in acreage terms (as a percentage of the total acreage rented in). The bottom panel presents the percentages in contract terms (as a percentage of the total number of rented in fields (plots)). Statistics are generated using NSS population weights to generate nationally representative statistics.

68
explored the determinants of CO2 emissions growth. Most of the attention has focused on drivers of CO2 emissions from fossil fuel combustion and cement production (e.g. Raupach et al., 2007; Jotzo et al., 2012). Land-use change also produces large CO2 emissions, which have been estimated more precisely by recent research (Gasser et al. 2020; Winkler et al. 2021). However, emissions drivers in this sector have received less attention than work on industrial determinants (Sanchez and Stern 2016). For both industrial and non-industrial sectors, previous studies have relied almost exclusively on estimates from CO2 emissions inventories that apply engineering parameters to measures of activity in industry, transport, land-clearing, residential heating and other sectors. Standard engineering estimates are particularly suspect for developing countries, because many of the parameters have been calibrated using databases and models developed for high-income economies.

This study takes a completely different approach, employing only direct CO2 observations from satellites. The dependent variable in our model is the atmospheric CO2 concentration above an urban area. For climate change analysis, the dominant concentration component is the global cumulative stock of long-duration CO2 molecules that have been emitted by human activity since the Industrial Revolution. Another global component is seasonal, reflecting differential CO2 absorption and respiration by vegetation over the annual cycle. The seasonal CO2 component is more pronounced in the Northern Hemispheric because it has more plant life than the Southern 4