rafmacalaba/data-use-mentions-extended huggingface dataset:
indicators are considered.

Compared to the CPI, the WGI CC, which is published by the World Bank Group, is a broader measure of public sector corruption. It measures both political and general bureaucratic corruption.<sup>8</sup> The WGI CC is updated yearly and it ranges from -2.5 (most corrupt/least effective) to 2.5 (least corrupt/most effective). It is based on 30 individual data sources, selected to include the views of citizens, business owners, academics, and experts drawn from the public, private, and NGO sectors from across the globe. Then, the WGI GE measures non-elected public sector corruption. To the extent that bureaucrats behave somewhat independently from elected officials, the WGI GE captures corruption in the bureaucracy, rather than political corruption.

Lastly, we consider one objective indicator of corruption: the index issued by ICRG. It refers to the financial corruption associated with conducting business (e.g., bribes) as well as other forms of political corruption (e.g., excessive patronage, nepotism, and close ties between politics and business).<sup>9</sup> As these four indicators use different scales, we have re-scaled them to range from 0 (least corrupt) to 10 (most corrupt). Summary statistics are presented in Table 1.<sup>10</sup> As shown in Figure 1, there is a substantial difference in corruption levels across European countries. The southeastern region of the European continent is characterized by more corruption levels (Bulgaria and Romania at 5.6, Greece at 5.4, and Italy at 5.1) when compared to the Nordic region (Finland at 0.7, Denmark at 0.8, and Sweden at 1.0).

# **2.2 Migration**

We focus on bilateral migration flow data across EU28 and EFTA countries between 2008 and 2018. These data are from Eurostat. Across the
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE This report presents the key AICD findings for Angola, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Given that Angola is a low-income resource-rich country, two sets of African benchmarks will be used to evaluate Angola’s situation: fragile low-income countries and resource-rich countries. Detailed comparisons will also be made with immediate regional neighbors in the Southern African Development Community (SADC).

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD for Angola runs from 2005 to 2009. But financial data for comparator countries typically cover an earlier period, 2001–06, and are averaged to smooth out fluctuations, while technical data are reported for 2006. Second, in order to make comparisons across countries, it was necessary to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

In recent years, Angola’s economy has been among the fastest growing in Africa. Looking ahead, the country’s GDP is projected to rise by 6.5 percent in 2011, with oil-sector growth of 3.8 percent and nonoil-sector growth of 8.1 percent (IMF 2011). A 27-year war that ended in 2002 ravaged the country and destroyed most of its economic infrastructure. Many roads, rails, and bridges were mined and obliterated; surviving infrastructure is dilapidated after years of neglect. Following the war’s end, the government turned its attention to the reconstruction of the country. And indeed, for the period 2003 to 2007, improvements in infrastructure added 1 percentage point to the per capita growth rate, which is substantial even if not as high as many countries in Africa (figure 1a). This boost to growth came predominately from the ICT
affected by insurgent attacks, but food insecurity also increases as a consequence of events perpetrated by criminals. Results are strongest in the North East, where conflict exposure with the ongoing Boko Haram insurgency has been the highest of the three regions. Furthermore, we find evidence of a relationship between victimization and low mental health, such that violence seems to matter more than property-related events do. Our results are robust to several different model specifications, different sample specifications using additional food insecurity data, different measures of the CSI, and accounting for assistance. We contribute to the literature by highlighting the importance of gaining a nuanced understanding of victimization: understanding the perpetrators of the attacks is as important as understanding whether households were exposed to violence or property crimes.

The results on poverty transition show that conflict prevents poor households from graduating out of poverty. This result is particularly striking given the income distribution in Nigeria and its development during the 2000s. The size of the middle class in Nigeria is small, estimated at 20 percent in 2013 (Corral Rodas, Molini, and Oseni 2019). Whereas the share of the middle class has been declining in the northern regions, it has increased in the southern areas. Overall, there has been a strong polarization of consumption between the wealthier south and the poorer north (Clementi et al. 2017). Our poverty transition results are indeed suggestive of some polarization. Given that a large, well-off middle class is often associated with better functioning civil society and social stability, more research is needed to investigate the link between the shrinking middle class and the conflict in the north and how it relates to the wealth distribution changes at the country level. Furthermore, because some states in the North East have been more affected than others, data that are representative at the state level could better reveal the regional heterogeneity in the effects of victimization across the most conflict-affected states.

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inward FDI by nonresidents and declining portfolio debt and other financial outflows by residents alike.

# **7. Correlates of Capital  Inflows**

To analyze the drivers of capital flows, we estimate regressions in the form of Equation (4), where the dependent variable ���� is capital flows of type i, in country c, in quarter t. As before, flows are normalized by subtracting from each observation the country-specific mean and dividing by the country-specific standard deviation.

# ���� ������ ���� ����� � �� ln������� ������� ��� ����               �4�

Capital flows are regressed on global factors: the Federal Funds Rate and the VIX (converted in log scale).<sup>8</sup> We also include a vector of country-specific variables, �����. Domestic variables include quarterly real GDP growth, capital account openness (the Chinn-Ito Index); financial sector depth (stock market capitalization or bank assets as percent of GDP); and proxies for the business environment (the ICRG rating on investment risk, which is an index ranging from 0 to 12, a score of 12 points equates to very low risk and a score of 0 points to very high risk).<sup>9</sup> We lag these by one quarter (or one year for the variables that are available at annual frequency).<sup>10</sup> Regressions are estimated with country-fixed effects and robust standard errors. Since some of the structural variables are slow-moving, the fixed effects estimates may not be very precise, hence we also estimate the same equations excluding the fixed effects. The results turn out to be very similar, hence we do not report them in order to save space.

The first four columns of Tables 11-14 suggest that FDI is driven mainly by pull factors, but that portfolio
We did not find statistically significant or substantial changes in housing, assets and food consumption of households in participant communities. We found suggestive evidence that the intervention reduced emigration and increased enrollment rates for secondary education in treated households. These results are similar to prior results on Mexico’s program (Alix-Garcia et al. 2015; Sims et al. 2017) but are not as positive, possibly due to the erosion in the real value of payments over time and increased mandates for the communities to spend the funds on land-management activities. Future research should consider whether these mandates translate into additional ecosystem services and whether they may effectively increase the participation or implementation costs of the program.

Our results also show that PES significantly increased community social capital (by 8-9% or 0.40 standard deviation) and had no statistically significant or large impacts on household-level measures of trust and participation. In addition, PES did not crowd out other freely contributed community work devoted by households.

## _Policy Lessons_

These findings suggest several policy lessons regarding the potential scale-up of PES programs. First, the finding of impact on land-cover management activities, one of the program’s primary goals, suggests that the program effectively generated behavioral changes at the community and household

> 19 The remote sensing data do not capture forest degradation or reforestation, do not measure impacts in areas with important types of cover that are not classified as forests (e.g. arid zones), and represent only one indicator of a broad range of ecosystem services supported by the program.

22
, further undermining the already small potential for distributive impact.

As an illustration, we present in table 8 a simulation using the 1994 data. The table shows how average tax rates on total income (that is, labor income plus capital income) of representative households respond to different tax schemes. Total income tax payments for each decile are given assuming that capital income is not taxed (column 4); that capital income is taxed at a flat-rate of 20% (column 5) and that capital income is taxed under the PIT schedule (comprehensive income 27 tax, in column 6). These results show that, compared with not taxing capital income at all, a flat27 By construction, every decile in table 8 contains the same number of taxpayers (namely 431,598) that is, the total number of tax filers divided by 10. The average income of every decile is easily calculated by dividing the total declared income of the decile (obtained by distributing total income reported in table 6 to the 10 deciles by using the pattern of table 7) by 431,598.
quantity index (physical infrastructure coverage and provision of social services<sup>18</sup> ) and a survey‐based quality index<sup>19</sup> respectively, as well as a hybrid of the two; while the input variable is public capital stock per capita.<sup>20</sup> While individual country efficiency scores have not been published, group averages of the quantity indicator suggest that advanced economies are 70% efficient (infrastructure output could be increased by 30% for the same amount of public capital input), emerging markets are 60% efficient, while low‐income developing countries are at about 45% efficiency. Nevertheless, these are still _relative_ performance indicators rather than cardinal indicators of quality: a score of 70% does not mean that 30% of infrastructure stock is not productive but rather the economy is operating 30% below the best performer in its peer group.

Aside from the above measures of inefficiency, there is also the IMF Public Investment Management Index (PIMI) (see Dabla‐Norris et al. 2012), a purely qualitative indicator based on scores for individual country performance in terms of the investment process (project appraisal, selection, implementation and evaluation). While it provides information on relative performance across 71 developing countries and shows positive correlation with GDP per capita and indicators of governance quality, it is not a cardinal indicator of the proportion of public capital that is productive, and only

> 16 Qualitative indicators of corruption, red tape, judiciary efficiency, public infrastructure quality; and quantitative indicators ‐ shadow economy size, secondary school enrollment, education achievement, infant mortality, life expectancy, as well as broad macroeconomic outcomes – income distribution, growth performance and stability, inflation and unemployment.

> 17 Sinha (2017) uses the emerging market/new EU member state average efficiency as a reference point for public investment efficiency in Bangladesh.

> 18 Length of road network, electricity production, access to water, number of secondary teachers and number
A key finding is that chemical fertilizer, labor, and irrigation water are the crucial factors that affect yield. Fertilizer application has a positive impact on yield. Yield is low when insufficient irrigation water is received. Labor input is also a crucial input. On the other hand, mechanization does not have much impact on yield increases. This feature is also observed in Asia as machine power can be replaced by animal power or human labor to some extent (David and Otsuka, 1994).<sup>12</sup> The negative and significant coefficient of the size of cultivated area indicates that higher yield is achieved under a smaller scale operation, which is also consistent with the observation in Asia that small farmers contributed to the rice Green Revolution.

In Table 9, we show the estimation results of reduced-form yield functions in Doho. In model (1), we use the distance from the main channel to the intake of the strip and the distance from the intake of the strip to each plot as proxies for the availability of irrigation water. In model (2), we use water depth (cm) at the critically important stage of flowering, and treat it as an exogenous variable. The distance from the main channel to the intake of the strip has a negative and significant coefficient on yield. Water depth has a significant and positive impact on yield. Both results indicate the importance of irrigation water for rice productivity. According to model (2), a 1-cm increase in irrigation water raises paddy yield by 0.13 ton per hectare.

# **5. Concluding remarks**

This paper investigated the potential of and constraints to the rice Green Revolution in SSA‟s large-scale irrigation schemes, using data from Uganda, Mozambique, Burkina

> 12 Although tractor power can be replaced by human labor, not all kinds of human labor activities can be replaced by tractor power (for example, crop establishment and harvesting). Thus, a labor shortage can still be a constraint.

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divided by the number of employees, expressed in constant US dollars. We also experimented with using total factor productivity (TFP) as a measure of productivity, which is often viewed as a cleaner measure of efficiency. However, the Enterprise Survey does not have data on capital stock for a large share of firms (including all those in service sectors). To preserve sample size, and with the knowledge that the results based on TFP and labor productivity tend to be similar, we rely on labor productivity to measure firm productivity. The specification we use is as follows: Yi i = ESi α1 + DB α2 + GC α3 + X β1 + Wi β2 + θ + η + εi i . (1) Here, Yi i is firm performance. When the outcome is employment growth, the unit is in ii ii ii ii ii ii ii percentage points (that is, one means one percentage point). Subscript ii _i_ stands for firm; _c_ for city; _j_ for industry; _k_ for country; and _t_ for survey year. ESi is a vector for city-industry level Enterprise Survey indicators. DB and GC are vectors for country-level _Doing Business_ and Global Competitiveness indicators. All sets of BE indicators in the regressions are standardized, ii ii so the coefficient should be interpreted as the effect of a one-standard-deviation increase of the BE indicators on firm performance. Wi is the country-level control variable vector, including GDP per capita, population, and population density (all in logarithms). θ and η are industry and survey year fixed effects, respectively. X is a vector for firm-level control ii ii variables, including the ownership share of the largest owner; two dummy variables indicating ii firm size (being middle-sized, with the number of employees three years ago between 20 and 100; being large-sized, with the number of employees three years ago exceeding 100); two firm age indicators (firm age of 6–10 years; firm age of 11<sup>+</sup> years);
During the global financial crisis we observe an increase in the poverty headcount in a number of lowincome and lower-middle-income economies. For example, the average poverty headcount ratio at $1.90 in low-income countries increases 11 percentage points — from 48.6% in 2009 to 60% in 2013. Some middleincome countries, including Croatia, Georgia and Romania in Eastern Europe and Honduras and Panama in Central America, experienced a significant increase in the poverty headcount.

We use the World Bank’s data on new business formation to investigate the channel through which business regulation may affect poverty. These annual data are collected from 143 company registrars on the number of newly registered firms. New business entry is defined as the number of newly registered corporations per 1,000 working-age people (those ages 15–64). The units of measurement are private, formal sector companies with limited liability. Data extend from 2005 to 2015.

As proxy for the regulatory attitude of governments, we use the World Bank’s Doing Business data, first published in 2003. The methodology for data collection is described in Djankov et al. (2002) and updated in Djankov (2016). We use the Doing Business data in two ways. First, we employ the overall score, which combines 10 distinct groups of measures that constitute the Doing Business index. These relate to the legal protections and regulatory processes that an average private business needs to go through in order to operate (Table 1).

Second, we use measures in four areas – starting a business, acquiring licenses, getting credit and enforcing contracts – as specific measures of business regulation. All measures are positively correlated, with the correlation ranging between 0.1810 and 0.7430, with low correlation between contract enforcement and acquiring licenses and high correlation between getting credit and the total aggregate _Doing Business_ measure. Property registration has the highest correlation with contract enforcement (0.4947) and lowest with acquiring licenses (0.2240). All measures are negatively correlated with the
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One final note on the starting point of the methodology is worth making. Pre-fiscal income reflects income before any transfers (including public spending on health and education, and cash transfers) or taxes (including personal income taxes) have been added. In analyzing contributory pensions, we treat pension contributions as deferred income and pension income received as market income. Thus, the team’s pre-fiscal income includes both pensions and contributions (top box in Figure 2).

# **B. Data**

We use three main data sources: the _Encuesta Continua de Hogares_ (ECH) 2017, administrative data obtained from public programs, and other sources for aggregate data.

The ECH is Uruguay ́s household survey conducted by the country ́s National Institute for Statistics ( _Instituto Nacional de Estadistica_ - INE). Running since 1968, the survey is representative at national and departmental levels. In 2017, the ECH sample included over 118,200 individuals and 45,300 households. ECH reports information on several topics, including labor market participation,
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declines in output and productivity, increases in output tariffs have a statistically positive impact on output, with output increasing by 3.1 percent in five years. The impact on productivity is positive but not statistically significant.<sup>21</sup>

# **4. CONCLUSION**

Using impulse response functions from local projections on a panel of annual data spanning 151 countries over 1963-2014, we find that tariff increases are followed by adverse effects on output and productivity; these effects are economically and statistically significant. They are magnified when tariffs are used during expansions, for advanced economies, and for smaller economies. We also find that that tariff increases are followed by higher unemployment and inequality, further adding to deadweight losses. Tariffs have only small effects on the trade balance, in part because they are associated with offsetting real exchange rate appreciations. To the degree that tariffs are being deployed today to achieve macroeconomic objectives (boost output, jobs, and the trade balance), the evidence presented in this paper—which seems robust to a long list of perturbations of the methodology— suggests that the chosen policy will fail miserably to achieve its goal. One caveat is that this paper does not analyze country- or region-specific trade impacts and does not include all the dimensions of how trade could affect people. Recent studies have found trade costs related to factors like labor adjustment to be substantial, particularly for subgroups of highly exposed workers (e.g., Autor et al. (2014) and Pierce et al. (2016) on the US; Helpman et al. (2016) and Dix-Carneiro et al. (2017) on Brazil). Pavnik (2017) find that the effect of trade on poverty and inequality are context specific. For example, the effect of trade on earnings could be geographically concentrated, partly due to low and imperfect inter-regional worker 24
on infrastructure in more than 40 Sub-Saharan countries, including all parts of Sudan. The results have been presented in reports covering different areas of infrastructure—ICT, irrigation, power, transport, and water and sanitation—and different policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for South Sudan, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. South Sudan is a newly independent country, affected by conflict, endowed with oil, but poor in terms of infrastructure and economic development. Because of these factors, both low-income, fragile states and resource-rich benchmarks will be used to evaluate its performance. Detailed comparisons will also be made with immediate regional neighbors in the East African Community (EAC).

Several methodological issues should be borne in mind. First, because of the cross-country nature of the data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006–07 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, to make comparisons across countries, the indicators and analysis were standardized so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level. Third, in terms of nomenclature, _all parts of Sudan_ refers to Sudan prior to the split of the country, _Sudan_ refers to the northern part of Sudan, and _South Sudan_ refers to the newly independent country. Fourth, data for Sudan and South Sudan 2