rafmacalaba/datause-encoder-data huggingface dataset:
62

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# **3. Empirical Strategy**

Our evaluation was designed to test three propositions: First, we were tasked with determining whether the program increased savings and access to credit. Second, we were asked to evaluate whether greater access to credit and LEAP’s programs to better link poor villagers to markets produced income and livelihood enhancements. Finally, we were asked to ascertain whether the program increased civic engagement and social capital among the poor. The program’s interest in social capital was motivated by an interest in encouraging the poor to take collective social action to address issues of importance to them. Any evidence that we could find that LEAP produced social capital, especially among the poor, would be taken as an extremely important side effect of the program.

# **_3.1 Measurement_**

We measure savings, incomes and sources of incomes with a standard survey. Measuring social capital poses particular challenges, especially when evaluating a program like LEAP. Program staff stressed the importance of participation and civic engagement in the program communities, but did not operate at all in the control communities. Often researchers measure social capital with responses to survey questions like “Do you think people are generally trustworthy?” “Would you be willing to contribute to public good X?” and so on). The obvious problem with this measurement approach is that program staff teach villagers the “right” answer to these questions as part of the program and, of course, they do not operate in the control communities at all. Thus respondents in treated communities may, consciously or sub-consciously, give the “right” answer to these questions, when in fact there was no change in their community or their behavior. Control-community members, by contrast, may not even know what the “right” answer is, since they have not received this training from the program (Mansuri and Rao, 2013).

Observational measures of social capital are on the surface a better measurement strategy, but they possess their own different set of problems. First, they are confounded. Observational measures of pro-social action like voter turnout, participation in public meetings and participation in public works projects are potentially manipulable by elites, including through corruption and coercion. In extreme cases high levels of these activities 7
# **Using Satellite Imagery to Assess Impacts of Soil and Water Conservation Measures: Evidence from Ethiopia’s Tana-Beles Watershed**<sup>**¶**</sup>

Daniel Ayalew Ali Klaus Deininger Daniel Monchuk The World Bank, Washington DC <u>dali1@worldbank.org, kdeininger@worldbank.org, dmonchuk@worldbank.org</u>

> ¶ We gratefully acknowledge funding support from the German Ministry for Economic Cooperation and Development through GIZ and the United Kingdom Department for International Development. We also thank the Amhara Bureau of Agriculture and the Tana Beles Integrated Water Resources Development team led by Mitiku Kebede for providing project data; Shewakena Abab, Anupam Anand, Nick Clinton, John Bryant Collier, Steve Danyo, Marguerite Duponchel, Ran Goldblatt, Nagaraja Rao Harshadeep, Thea Hilhorst, Paul Martin, Sisay Nune, and Rahel Wogayehu for support and insightful discussions; and participants in the 18<sup>th</sup> Annual World Bank Land and Poverty conference for helpful comments and Anne Grant for editing.
- 20 -

Table 8. Estimated Hydro Equation: Thac Mo, Vietnam

||W/o t|rend|With tr|end|
|---|---|---|---|---|
||Unrestricted|Restricted|Unrestricted|Restricted|
|ln_HYDRO_(t-1)|0.532 <sup>***</sup>|0.657 <sup>***</sup>|0.481 <sup>***</sup>|0.642 <sup>***</sup>|
||(0.056)|(0.030)|(0.060)|(0.030)|
|ln_TEMP_(t-1)|2.145||0.849||
||(1.520)||(1.607)||
|ln_PREC_(t-1)|0.097||0.111||
||(0.145)||(0.144)||
|_c February_|-0.158||-0.207||
||(0.175)||(0.174)||
|_c March_|-0.124||-0.172||
||(0.241)||(0.239)||
|_c April_|0.057||-0.096||
||(0.232)||(0.239)||
|_c May_|0.713 <sup>***</sup>|1.041 <sup>***</sup>|0.728 <sup>***</sup>|1.022 <sup>***</sup>|
||(0.220)|(0.093)|(0.218)|(0.093)|
|_c June_|1.231 <sup>***</sup>|1.581 <sup>***</sup>|1.300 <sup>***</sup>|1.575 <sup>***</sup>|
||(0.178)|(0.086)|(0.179)|(0.085)|
|_c July_|1.517 <sup>***</sup>|1.762 <sup>***</sup>|1.648 <sup>***</sup>|1.773 <sup>***</sup>|
||(0.189)|(0.088)|(0.196)|(0.088)|
|_c August_|1.820 <sup>***</sup>|1.958 <sup>***</sup>|1.996 <sup>***</sup>|1.981 <sup>***</sup>|
||(0.206)|(0.099)|(0.217)|(0.099)|
|_c September_|1.525 <sup>***</sup>|1.567 <sup>***</sup>|1.747 <sup>***</sup>|1.602 <sup>***</sup>|
||(0.231)|(0.113)|(0.248)|(0.113)|
|_c October_|1.237 <sup>***</sup>|1.271 <sup>***</sup>|1.424 <sup>***</sup>|1.307 <sup>***</sup>|
||(0.242)|(0.116)|(0.253)|(0.116)|
|_c November_|0.478 <sup>**</sup>|0.518 <sup>***</sup>|0.630 <sup>***</sup>|0.551 <sup>***</sup>|
||(0.238)|(0.112)|(0.245)|(0.112)|
|_c December_|0.095||0.163||
||(0.188)||(0.188)||
|_t_|||0.0008 <sup>**</sup>|0.0007 <sup>**</sup>|
||||(0.0003)|(0.0003)|
|constant|-8.273|0.779 <sup>***</sup>|-2.804|0.634 <sup>***</sup>|
||(6.550)|(0.122)|(6.907)|(0.146)|
|Obs.|239|239|239|239|
|R-squared|0.947|0.943|0.948|0.944|
|Normality test|||||
|<br>Skewness statistics|31.928 <sup>***</sup>|21.081 <sup>***</sup>|28.797 <sup>***</sup>|19.477 <sup>***</sup>|
|Eigenvalue stability con|dition||||
|Max modulus|0.536|0.657|0.471|0.642|
|Autocorrelation test|||||
|LMtest statistics|7.404|16.901 <sup>*</sup>|4.209|12.995|

Note that the dependent variable is ln _HYDRO_ . The standard errors are shown in parentheses. *** 1 % significance level, ** 5 % significance level, and * 10% significance level.

# **_Long-term time trends_**

From the long-term perspective, it is of particular interest whether the system involves a time trend component. Table 9 summarizes the trend coefficients in each unrestricted model. As touched upon in the above, only the hydrological series at the Thac Mo Reservoir has a positive and significant time trend among our sample projects.
16 forecasts since our framework is much too basic for that. The objective is rather to illustrate trade-off and policy choices. Given the differentiated impact of growth on inequality in rural and urban areas, yielding higher elasticities of poverty to growth in rural areas, rural growth should reduce poverty more than urban growth, at least for the poverty gap and even more so for the distribution sensitive squared poverty gap. The question is: just how much difference do sectoral growth patterns make for poverty reduction?

To promote GDP growth, various policies are needed, including steps to increase investments. Investments may be financed nationally or internationally. If financing come from national resources, the share of GDP allocated to consumption must decrease, so that the short term impact of growth on poverty will be reduced. Nationals would essentially give up current consumption (and poverty reduction) for future benefits. By contrast, if investments are financed intemationally, national consumption as a share of GDP need not decrease, and the immediate impact of GDP growth on poverty will be larger (but debts will have to be repaid at a latter stage). Since at least part of the investments necessary for higher national growth will need to be financed through private national savings, the analysis must include a lower propensity to consume in the two higher growth scenarios, so that part of the benefits of growth for poverty reduction will be lost in the short run. At the extreme, higher growth may not imply any gain in poverty reduction over the planning horizon (but of course, in the long run, all benefits would be reaped). For the three scenarios, the World Bank's RMSM-X consistency macroeconomic model for Bangladesh was used to estimate how much investment will be needed to achieve various levels of growth, and to allocate the necessary investment levels to private nationals, the government, and the rest of the world. The RMSM-X model will not be not discussed in details here: only basic assumptions will be outlined<sup>8</sup> .

> 8 Using a Leontief-type production function in which labor is abundant and capital is rationed, RMSM-X assumes a relatively stable relationship between current investments and future GDP growth. The model also includes detailed monetary, budgetary, trade, pricing, and debt information. The assumptions and economic reasoning behind the RMSM-X model are outlined in Easterly (1989) and Khan, Montiel, and Haque (1990).
10

# **III. Evidence from the Literature**

The empirical evidence on the crime-inequality relationship, mainly based on comparisons across countries, or states and cities in the U.S., generally shows a positive correlation between inequality and crime. A meta-analysis of 34 aggregate data studies (Hsieh & Pugh, 1993) shows that 97% of bivariate correlation coefficients for violent crime with either poverty or inequality were positive, with 80% of the coefficients above 0.25. Ehrlich (1973) finds a positive relationship between relative poverty (percent of population under half the median income in the state) and crime, with higher elasticities for property crime than violent crime. Ehrlich also finds median income in a state to be positively associated with property crime. Machin and Meghir (2000) argue that higher wages at the bottom end of the wage distribution reduce crime, while criminal activity is positively correlated with returns from crime in the United Kingdom. Gould, Weinberg, and Mustard (2002) also find mean wages of non-college educated men and unemployment rate to be significantly related to crime, but find no consistent relationship between mean income and crime rates across U.S. counties. Kelly (2000) finds a strong relationship between income inequality and violent crime across US counties, but finds no such relationship for property crime. Kelly does not control for mean or median income. Blau and Blau (1982) find that both between-race and within-race economic inequality are associated with criminal violence across U.S. states. Lederman et al. (2001) argue that increases in income inequality and lower growth rates lead to increases in violent crime across countries. Using panel data for approximately 39 countries, Fajnzylber et al. (2001) report similar findings for homicide and robbery rates across
stadiums, sport clubs, art galleries, theatres—were closed most of the time during COVID-19 before vaccination became available.

We estimate excess deaths using a simple regression of the number of deaths in each province on dummy variables for province and time (quarter of observation) and the population size for four years (16 quarters in total) before the arrival of the pandemic (2015-2019). The linear regression with quarter and province fixed effects offers a good fit (R<sup>2</sup> = 0.99), resulting in good estimates for the predicted number of deaths in each province for the remaining 12 post-pandemic quarters, 2020 through 2022, had COVID-19 not arrived. The results are presented in Table 2 and Figure 7.

For the most part, excess death estimates confirm the timing and severity of the COVID-19 pandemic as reported in the news. Most provinces experienced unusually high death rates in 2020 and 2021, but not in the same quarter (see Figure 7). For example, in the Gilan province, where the pandemic disrupted life early in the pandemic (Salehi-Isfahani, 2020a), government vital registrations data report a one-third increase in the number of deaths winter 2020 (the first quarter of the pandemic) over winters 2019 and 2018, one quarter ahead of other provinces. The variation in the arrival dates of the pandemic across provinces adds to the variation that we need to identify the causal impacts of the pandemic on women’s market work because other shocks, like sanctions, are unlikely to have impacted provinces in the same way.<sup>6</sup> Excess deaths and official lockdowns are different indications of disruptions in normal life and the labor market that affect individual decision to participate in market work. Each has its advantages and drawbacks as markers of labor market shocks. While excess deaths are less precise than lockdowns as markers of labor market disruptions, they are considered the gold standard for

> 6 Ghafari _et al._ (2021) use excess deaths in the Islamic Republic of Iran to show that the official counts of COVID-19 deaths underestimate actual deaths by a factor of 2 to 3. Note that the data in Figure 7 are based on official counts and appear to be under-estimates when compared to the actual number of registered deaths.

12
|Panel B: Chainalysi|s Top 20<br>|Global Crypto-ass<br>|ets Adoption Ind<br>**Ranking for indi**<br>**into Global Cryp**|ex (2021)<br>**vidual weighted**<br>**to Adoption Inde**|**metrics feeding**<br>**x**|
|---|---|---|---|---|---|
|**Country**|**Index**<br>**score**|**Overall index**<br>**ranking**|**On-chain value**<br>**received**|**On-chain retail**<br>**value received**|**P2P exchange**<br>**trade volume**|
|Vietnam|1.00|1|2|4|3|
|India|0.37|2|3|2|72|
|Pakistan|0.36|3|12|11|8|
|Ukraine|0.29|4|5|6|40|
|Kenya|0.28|5|28|41|1|
|Nigeria|0.26|6|10|15|18|
|Venezuela|0.25|7|22|29|6|
|United States|0.22|8|4|3|109|
|Togo|0.19|9|42|47|2|
|Argentina|0.19|10|17|14|33|
|Colombia|0.19|11|23|27|12|
|Thailand|0.17|12|11|7|76|
|China|0.16|13|1|1|155|
|Brazil|0.16|14|7|5|113|
|Philippines|0.16|15|9|10|80|
|South Africa|0.14|16|16|18|62|
|Ghana|0.14|17|37|32|10|
|Russian Federation|0.14|18|6|8|122|
|Tanzania|0.13|19|45|60|4|
|Afghanistan|0.13|20|38|53|7|

Sources: Statista; Chainalysis.

Note: The panel shows the 2021 crypto adoption index calculated and published by Chainalysis. These figures should be interpreted with caution as data gaps remain significant.

# 2 Literature

## _Drivers of crypto-assets usage_

Szabo (2017) observes that economic transactions and exchanges require trust between participants. However, trust does not scale well as the number of participants in a network grows, thereby increasing transaction costs. As a result, historically, trust has been delegated to a central authority which may be susceptible to failure, fraud or rent seeking. He then posits that the technology which underpins crypto-assets can lower social transaction costs in low-trust environments without the need to delegate trust to a central third party. As such, the technology may enable more and more efficient economic interactions between large numbers of agents as 10
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basis and the program for reducing forest losses would remain in force only as long as payments are made. Any protection plan would go into force only when supported by a population majority in a majority of contributing countries.

Our Delphi exercise had two rounds. After an initial round 1, mean and median values across the respective surveyed experts in each country or region were calculated and reported back to the experts,<sup>11</sup> who then got the opportunity to adjust their value estimates in round 2. National values were reported to the experts except for Europe and Asia, where regional values were reported. The information provided to experts after round 1 may have influenced their round 2 answers, typically by drawing round 2 responses toward the round 1 summary statistics. Such effects are however in the “spirit” of the Delphi method as an implicit objective of a Delphi exercise is to reduce the variance of participants’ answers, thus achieving more of a group consensus, while at the same time not pressuring those with tightly held positions to change.

To induce maximum participation among experts, we introduced a (limited) incentive scheme which took slightly different forms to different groups. For completing both rounds of the survey, European experts received a small cash award; experts from North America and Oceania received a gift card on Amazon.com; and Asian experts received a personally signed copy of Partha Dasgupta’s book: “Economics: A Very Short Introduction”. In total 216 out of 308 contacted experts completed both survey rounds.<sup>12</sup> In each round of the Delphi exercise, each expert was asked to provide four numbers: their estimate of the mean and median annual household WTP in their own country, to implement each of Plan A and Plan B, given that a valuation survey would be undertaken in their country.<sup>13</sup> Tables 3-4 in Section 3 below provide an overview of the main summary results from these two rounds.

> when compared to our protection plans, namely the establishment of specific, protected, areas, comprising respectively 5% and 20% of the total Amazon forest, and where no further deforestation is permitted. 11 In the Asian survey, the distribution of responses by WTP ranges ($0, $0-1, etc.) was provided instead of the mean and median.

> 12 Completion rates were 63% in North America, 83% in Europe, 78% in Asia, and 53% in Oceania.

> 13 European experts were asked to provide 4 additional estimates, of equivalent values for all of Europe. These are reported in Navrud and Strand (2013).

8