rafmacalaba/data-use-mentions-extended huggingface dataset:
_4_ They find that hotter years are associated with lower or higher output per capita ranging between 3 and 4 percent for hot or cold climate zones, respectively. In most cases, labor productivity may be the key link between climate shocks and economic outcomes at the macro level (Heal and Park, 2013). Yet, there are few studies on the direct links between labor productivity and temperature changes. Tol (2009) labels the labor productivity impacts of climate change as _unknown unknowns_ in a review of studies on the economic effects of climate change, noting the wide gap in the literature, although there has been significant progress since then.

More recent studies have examined the impacts of rising temperatures on various aspects of labor productivity at the micro level (see Lai et al. (2023) for a review). However, many such studies provide average estimates of the impact of climate change or temperature deviations from the average, without accounting for the potentially uneven impacts across climate zones, regions, or countries. Neglecting the uneven impacts will have significant implications for understanding how climate change shapes the future distribution of economic outcomes across these categories. Using detailed production data from half a million manufacturing firms in China, Zou and Zhong (2022) find a relatively large negative impact of excess temperatures—a day with average temperature above 90 degrees Fahrenheit (<sup>0</sup> F) is associated with a total factor productivity (TFP) loss of 0.56 percent, relative to a day with average temperature between 50<sup>0</sup> F and 60<sup>0</sup> F. A study of a census of manufacturing firms in India shows that annual plant output falls by about 2 percent per 1<sup>0</sup> C increase in temperature (Somanathan et al., 2021). In a U.S. study, Deryugina and Hsiang (2014) show that productivity on an individual day declines by 1.7 percent for each
of these assets was chosen to be a good proxy. Charity and tanker water were assumed to be protected, but not directly drinkable water sources. Drinking water source, sanitation and shelter data was in waves 1 and 2 only available in the post-harvest surveys and in wave 3 and 4 only in the post-planting survey. 17 Information concerning Education and current school enrollment deprivations is taken from the household roster and supplemented by information provided in the men and women surveys.

10
is located in the tropics. We also find some modest evidence of diminishing returns to aid: in countries receiving large volumes of aid it is harder to have successful projects.

The third section of the paper disaggregates project success rate in different ways. First, policy-based adjustment loans are separated from investment loans. An interesting result here is that democratic political institutions facilitate successful policy-based loans, whereas property rights/rule of law is more important for investment loans. Given the well-established relationship between property rights and growth, this latter finding makes sense: it is difficult to have a high-return public investment if the institutional framework is not conducive to economic growth. This may appear self-evident with “hard” investments such as transport infrastructure. But it is interesting that there is a similarly strong relationship between property rights and “soft” investments in education and health. It is difficult to have successful projects addressing these important social needs in a weak institutional environment.

The fourth section concludes by making a link between these project-level results and the larger macroeconomic literature on aid effectiveness.

# **_2. Overall Project Success Rate and Institutional Quality_**

We argued in the previous section that cross-country growth regressions alone are unlikely to settle the debate about aid effectiveness and institutional quality because different data-sets, specifications, and methodologies yield different results. We take the view that the cross-country correlations are informative, but need to be complemented by other kinds of information. Here we are going to draw on the extensive database of World Bank-financed projects carried out in nearly 100 different developing countries during the 1990s and examine the factors that influence the success or failure of aidfinanced projects.

4
defined as age minus years of schooling minus 5. The benefit value is calculated based on the number of months worked in the previous calendar year considering all formal jobs. Given that this information is not available in PNAD-C data, we use the number of months worked in the current job (capped at 12 months).

- _Bolsa Família._ PNAD-C has a specific question about Bolsa Família, which allows us to identify those who declared to be beneficiaries of such program. However, the total number of beneficiaries measured by the PNAD-C question is substantially smaller than those from administrative records. Given this study’s objective of providing relative comparisons of fiscal policies, it is therefore important to rely on an estimate of the number of Bolsa Família beneficiaries closer to official accounts. To do this, we calculate individual’s per capita household income – under certain assumptions – to determine elegibility to the Bolsa Família program. The main assumption is that individuals under-report their total income, and that the level of under-reporting is higher the lower their formal income is. In particular, we assume that a pre-determined share of households’ declared informal income is considered for the Bolsa Família elegibility (World Bank, 2020).

- _Benefício de Prestação Continuada (BPC)._ There are two different groups targeted by BPC: the elderly poor and the poor with disabilities. In principle, PNAD-C captures information about respondents’ BPC beneficiary status. For individuals in the first group, we verify individuals’ age to confirm the BPC beneficiary status. For the latter, we use the same criteria used to identify persons with disabilities in the case of Salário Família. An important stylized fact of PNAD-C is that the number of individuals reporting to have received BPC is smaller than the number of actual BPC beneficiaries according to administrative data. To represent the BPC population in this fiscal incidence analysis we also note that

> 23 The minimal insurance was the minimum wage in 2019 (BRL 998.00), while the maximum could not surpass 1.74 minimum wages
# 5.2 Beyond FAFH: Unintended impacts

Although the FAFH experiment focused on a single module within a larger consumption survey, the changes in protocols could potentially affect the responses in other sections of the questionnaire. We can directly test such knock-on effects for at-home consumption because that module and its protocols were the same across all treatment arms. Unintended changes in at-home reporting are also of substantial interest since it is also a critical component for many measures of household welfare (Kilic & Sohnesen, 2017).

Columns 2-4 of Table 6 show results from an estimation of Equation 1 using at-home food spending, total food spending, and total consumption expenditure as the dependent variables.<sup>16</sup> An evident – and puzzling – message from column 2 is that the report of food consumed at home differs between the individual recall and the food diary.<sup>17</sup> Reports across all other arms are, as expected, statistically equivalent.

A closer look at the distribution of AH consumption across treatment arms reveals that part of the difference is driven by the large number of T3 households with high at-home consumption. Fifty percent of the top 1 percent of at-home consumption households come from treatment arm 3. When we trim the top 1 percent of the at-home consumption distribution based on the full sample, the difference disappears. However, this results in significantly higher at-home food expenditures for the one-line recall arm relative to the diary.

> 16 The log results are not shown, but they mirror the results in Table 6.

> 17 This result is robust to many alternative specifications, including the inclusion of household-level covariates. However, household characteristics do have some impact as this result does not hold when we use alternative methods to individualize household consumption (instead of using per-capita consumption).

23
In this paper, we seek to unpack the economic effects of EVD, and epidemics more broadly, in agrarian societies. We focus on the availability of agricultural labor for critical and time-sensitive tasks such as planting and harvesting, which normally require a degree of worker coordination and mobilization. Liberia, for example, has a labor recruitment system known as _kuu_ for assembling agricultural workers for seasonal tasks, especially in the production of rice, the principal crop. We consider the hypothesis that fear of congregating in groups disrupted worker mobilization and led to severe labor shortages, thus depressing rice production and, ultimately, rural welfare. To explore this mechanism, we link a geographically disaggregated indicator of Ebola disease mortality with nationally representative household survey data on agricultural production and household welfare.

To preview, we find that higher Ebola prevalence (as measured by local mortality) led to greater disruption of group-labor mobilization for planting and harvest and reduced rice area planted as well as rice yields. Consistent with this evidence, household welfare, measured by per capita expenditures, fell by more in Ebola prevalent areas with more intensive rice-farming, i.e., in precisely those areas differentially affected by agricultural labor shortages.

Other research, produced during and in the immediate aftermath of the EVD crisis in Liberia, also suggests that serious agricultural labor shortages occurred due to contagion fears. However, these findings are based largely on impressionistic or subjective evidence from non-representative samples.<sup>6</sup> Using a simulation model, FAO (2015) estimates that,

> 6 Among respondents to a World Bank cellphone survey who had already harvested, over 80 percent reported lower production in 2014 than in the previous year, the main reason being the labor disruption due to Ebola (World Bank 2015). This was also the main reason cited by those with crops still in the field as to why the harvest had not been completed. UNDP (2014) finds a decline in the supply of labor due to deaths, the migration of workers to escape the disease
displaying public procurement data and gradually evolve into a comprehensive anti-corruption data system through linking further datasets. The implementation of a comprehensive data analytics framework needs to rely on supportive regulations, organizational setup, and sufficient resources, as well as advances and investments in technological solutions.

Recommendations:

1. A flexible and easy-to-use dashboard for data-driven corruption risk assessment should be developed and gradually the underlying data scope should be extended by linking it to additional datasets.

2. The data scope could further be expanded by linking it to high-value datasets, allowing for a more comprehensive and detailed risk assessment. Such datasets could include, among others, beneficiary ownership data or data on sanctions and convictions.

3. High-quality data on investment projects and performance as well as its easy access and disclosure can facilitate oversight by relevant public institutions, enable monitoring by civil society, and promote confidence and consultation of both domestic and foreign investors.

4. Depending on the desired scope of the dashboard, Bulgaria will need to prioritize different kinds of data and conduct an in-depth assessment of the institutional, organizational, technological, and resource changes that may be required to move toward a more comprehensive corruption risk dashboard.

5. To ensure the continuous improvement and fine-tuning of the corruption risk framework developed in this paper, it will be important to establish a feedback loop in which results of the analysis are incorporated into the design of future analytics.

# 5.3. Improve public procurement and budget policies

Based on the analysis of individual red flags in Bulgaria and the available evidence around the world, a number of specific improvements can be made to the current public procurement regulatory framework. We review each of the key risk factors and propose specific reform pathways below.

**1. Fostering competition** is essential for the reversal of the decline in corruption control in Bulgaria in the last few years. Single bidding is one of the strongest determinants of high prices and belowoptimum quality in public procurement. While a high number of bidders should not be pursued at all costs, lowering the
US$1.90-a-day poverty lines. Moderate poverty showed a smaller decline, from about 44 to 34 percentage points in 1992–2006 and has remained stagnant since then. Nonetheless, these declines are relatively small compared with those in other countries typically mentioned as examples of successful export-led growth. For example, while the share of exports in GDP in Vietnam rose from 54.7 to 70.3 percentage points in 2002–08, the share of people living on less than US$3.20-a-day declined from about 70.8 to 46.8 percentage points. Measured according to the US$1.90-a-day poverty line, the decline was also large, from about 38.0 to 14.8 percentage points.<sup>7</sup> Table 1 shows descriptive statistics on the sample used in the estimations. The focus is on urban and semiurban areas (1,982 of 2,457 municipalities), given that these areas represent more than 99 percent of total exports over 2004–14 and two-thirds of rural municipalities show zero or close to zero exports. Municipalities with a higher ratio of exports per worker exhibit a lower incidence of poverty and higher incomes (figure 4). Income inequality is similar across these two groups of municipalities, with a Gini coefficient of about 0.38 or 0.39. Moreover, a simple inequality decomposition of the Theil index shows that within-municipality inequality accounts for about three-quarters of total inequality in Mexico. Understanding the role of trade in shaping inequality at the local level in Mexico is thus relevant because trade explains most of the inequality at the national level.

# **4. Results**

# **4.1.Poverty and Income**

Table 2 shows the estimates of equation 1 using the share of the extreme poor as the dependent variable. The preferred specification is in column (3), which controls for the share of the rural population, the literacy rate, total population, and the shares of workers by sector as
matters: most studies find significant effects using “rule of law” as a proxy for governance, however, robustness of the results is weaker when using corruption or democracy as proxies.

The role of governance in handicapping MENA’s economic growth is not a new thesis (see World Bank, 2003a). Weak governance has been estimated to have slowed MENA’s GDP growth by 1 to 1.5 percentage points (World Bank, 2003a). Poor governance also discourages foreign investment that can be important for job creation, innovation and trade. Unlike other regions, trade has not been a driver of job creation in MENA. Political instability is a primary source of risk and uncertainty for the region, and FDI has not only decreased but has also skewed towards sectors with low job creation and that do not support exporting (World Bank, 2013).

The low quality of governance in MENA is readily apparent in the World Governance Indicators (WGI).<sup>15</sup> World Governance Indicator scores for MENA countries are much lower relative to other countries. In five out of the six areas, MENA countries have significantly lower scores than both upper middle and high-income countries. Two governance values identified to be very relevant for MENA are inclusion and accountability (World Bank, 2003a). The WGI Voice and Accountability indicator is very closely aligned to these two values. Indeed, the MENA region ranks lower than any other in this indicator. Moreover, among all size WGI indicators, the MENA region has the lowest scores in Voice and Accountability.

Figure 6 (in the Appendix) illustrates WGI rankings given GDP per capita, averaged from 19952009. In the instance of Voice and Accountability, MENA countries perform the worst in this ranking given their level of development.

# **3. ESTIMATION**

The literature offers many studies on complementarities between country-level characteristics and policies (or country-country level interactions) towards growth and economic outcomes.<sup>16</sup> For example in the MENA
The household survey is from 2009, while the rents derived from the real estate data refer to late 2013 - early 2015. There is no real need however to express the values in prices from the same year, i.e. to inflate the 2009 expenditures to 2014 prices or to deflate the house prices to 2009 prices. Instead we will be assuming that the Pareto tail index associated with the top tail of the income distribution is stable over the 2009-2014 period.

# **3.3 Does the household survey indeed omit the rich?**

One way of illustrating whether the household data under-represent the top part of the distribution is to compare some of the characteristics of the top 1 percent the household survey with those of senior Egyptian executives. For the purpose of this exercise, household income is imputed from household expenditures in the survey using the average savings rate in Egypt for 2009.<sup>14</sup> The data on executive pay come from Payscale, an online information company providing current information on salary, benefits, and compensation by type of job, location, and other characteristics. The numbers are presented in Table 1.<sup>15</sup>

||% surveyed population|Minimum|Median|Maximum|
|---|---|---|---|---|
|Household income|Top 1%|11,995|14,666|98,080|
|CEO total pay|Top 1.2%|23,723|68,970|168,545|
|CFO total pay|Top 0.8%|22,551|54,563|212,393|

Table 1: Annual income of top earners in Egypt (USD, nominal, 2009 prices) We focus on the total compensation of senior executives, who represent 2 percent of survey participants and have the highest reported median compensation among survey participants.<sup>16</sup> Therefore, in
modest increase in productivity growth. Key factors cited in various studies to explain why Mexico has not grown as fast as other countries (see, for example, OECD, 2009) include relative weaknesses in education, infrastructure, financial development, and the rule of law, as well as anticompetitive and restrictive regulation of product and labor markets (Loayza and Palacios, 1997; RodríguezOreggia, 2010).

The Mexican economy was hard hit by the financial crisis and the increase in international food prices in 2008. The inflation rate has been on declining trajectory since the end of 2008 while the exchange rate has experienced a slight increase (figure 2.1). The collapse of external demand, particularly in durable consumer goods, in the last quarter of 2008 and the first half of 2009 led to an almost immediate and severe downturn in economic activity. The loss of employment and the high level of uncertainty and risk brought by the economic crisis contributed to a fall in private consumption and investment, further reducing aggregate demand and inflationary pressure.

**Figure 2.1: Annual Inflation and Exchange Rate**

<!-- Start of picture text --> Inflation and Exchange Rate<br>16.00 7<br>14.00<br>6<br>12.00<br>5<br>10.00<br>4<br>8.00<br>3<br>6.00<br>2<br>4.00<br>2.00 1<br>0.00 0<br>I-07 II-07 III-07IV-07 I-08 II-08 III-08IV-08 I-09 II-09 III-09IV-09 I-10 II-10 III-10IV-10<br>Quarter<br>Exchange Rate Inflation<br>Inflation %<br>Exchange Rate (dolars per peso)<br><!-- End of picture text --> _Source:_ Data from Central Bank of Mexico.

A subsequent rebound in external demand as of the second half of 2009 gave rise to a recovery,
the WTP for a principal SWM service ranges between 1-3% of household income, while the WTP for an SWM service improvement ranges between 0.1-0.9% of household income.

# **3. The Context**

# 3.1 SWM Service in China

Along with fast economic development, the quantity of municipal solid waste generation is increasing rapidly in China. With an annual average increase rate of 3.7%, the per capital municipal solid waste generation<sup>8</sup> in China has reached 1 kilogram per day per person in 2002 (Huang et al., 2006). World Bank (2005) indicates that China has surpassed the U.S. in 2004 and became the world’s largest municipal solid waste (MSW) generator. In 2004, the urban areas of China generated about 190 million tons of MSW. Considering China’s relatively low level of per capita GDP and that over 56% of population still live in rural areas, the projected future MSW generation in 2030 for this country will be up to 480 million tons (World Bank, 2005). No country has ever experienced such a large and rapid increase in waste generation.

Along with the rapid increase in municipal solid waste generation is significant improvement in the waste management sector in China. The treatment and disposal of municipal solid waste was only started in the 1980s. By 1990, the total disposal rate of municipal solid waste was lower than 2%, but the quantity of disposed municipal solid waste has increased continuously since then. In 2007, the ratio of the disposed municipal solid waste reached 62% of the total quantity collected and transported.<sup>9</sup> Landfill is the main disposal method for municipal solid waste in China; in 2007, over 80% of the disposed municipal solid waste was land filled, about 15% was incinerated, and about 2.6% was composted (China Statistic Yearbook, 2008).

Even though the pace of China
as well as “capture” of the state by elites and private interests.

We control for internal armed conflict using the UCDP/PRIO Armed Conflict Database updated by Pettersson and Öberg (2020). We follow a definition, similar to Abu Bader and Ianchovichina (2019) where armed internal conflict is a contested incompatibility where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths per year. For the 18
|+|||+||||||<sup>d</sup>||||
|_MINORITY_|+<sup>c,d</sup>|||||||||||||||
|_IMMIGR_|+<sup>a,b</sup>||+|0|0|||||||||||

_Notes:_ For the coverage, the following notation is used: E, S, W, N –Eastern, Southern, Western and Northern Europe, respectively; EUR – Europe (countries as in Table 1, excl. Cyprus, Israel, and Switzerland); EUR17 – 11 „old Europe‖ countries and 6 NMS; EUR25 – same as EUR, excl. Russia and Ukraine; DC –developing countries. Explanatory variables: _REG_QUAL_ and _CORR_CONTR_ – indicators of Regulatory Quality and Control of Corruption, respectively, from the Worldwide Governance Indicators (see Kaufmann et al. 2010; World Bank 2010); _FIS_FREE_ (Fiscal Freedom) _-_ the Heritage Foundation (2010) measure of the tax burden, which is inversely related to top tax rates and to tax revenue. Other variables – see Table A1. Loayza et al. and Loayza/Rigolini use other versions of _RULE_LAW_ and _BUS_FREE_ ; Loayza et al. control also for average years of 49