rafmacalaba/datause-encoder-data huggingface dataset:
Procuring Entity Officials were asked about the number of days taken for completing different administrative processes as per their estimation. Their estimates/perception of these times have been compared with the actuals times and tabulated in the table below.

TABLE 53: COMPARISON OF TENDER PROCESSING TIMES BASED ON ADMINISTRATIVE DATA AND ESTIMATION OF PROCURING ENTITY OFFICIALS

|Efficiency<br>Indicator|Model (<br>Manual|4a): CEM<br>across ye<br>e-proc|within PEs<br>ars<br>Treatment<br>vs Control|Model<br>ye<br>Manual|(4b): CE<br>ars acros<br>e-proc|M within<br>s PEs<br><sup>Treatment</sup><br>vs Control|S<br>Manual|tudent t-<br>e-proc|tests<br><sup>Treatment</sup><br>vs Control|
|---|---|---|---|---|---|---|---|---|---|
|Lead Time|84.1|65.0|-19.2***|80.9|65.3|-15.6***|64.9|50.0|-14.9***|
|Evaluation Period|9.0|19.2|10.2***|9.3|21.3|12.0***|15.3|10.5|-4.8***|
|Approval Period|11.0|3.5|-7.5***|13.4|3.4|-10.0***|19.8|14.9|-4.9***|

Note 1: + p<0.10 * p<0.05 ** p<0.01 *** p<0.001 We observe that PE officials have an overwhelming positive perception of the impact of the e-procurement system. On average they reported a positive effect of the transition on evaluation periods whereas administrative data shows a negative impact. Moreover, 22% of PE officials mentioned the lower turnaround time required for processing tenders as one of the two main advantages of the e-procurement system. Similarly, 59% of bidders reported preparation times of tenders as one of the main drawbacks of the paperbased system that is alleviated by the transition to the electronic system.

# 2. Access Indicators

The analysis using administrative data of access indicators showed a positive impact of the introduction of e-procurement. We observed a significant increase in the number of non-local winners of contracts. This finding was corroborated by bidders in the firm survey. Bidders were asked about their estimation of the percentage of tenders they participated in and won outside their district. On average, bidders reported a 16.6% increase in the proportion of tenders they participated in and a 14.8% increase in the proportion of contracts won outside their district.

When bidders were asked about their how confident they were in bidding outside their district, 164 reported they had become more confident in bidding outside their districts while 58 said they had become less confident as result of the transition to the e-procurement system, with 361 bidders reporting no change in how confident they were bidding outside their district. It’s worthwhile to note 100 bidders (out of the 583) reported becoming equally confident in bidding in-and-out of their respective districts as opposed to being more confident in their own district previously. The table given below tabulates the frequency of the responses.

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# **Part 2. Corruption in Bank Lending and Transparency**

# **Descriptive Statistics and Estimation**

In this section of the paper, I use and extend the estimation procedure in BDL (2005) to investigate whether economic transparency reduces corruption in bank lending. BDL use both firm specific and country specific data to assess how corruption may hinder access to finance. Much of the firm specific data, including the data on corruption in bank lending comes from a WBES firm level survey that covers 81 countries.

The dependent variable, corruption in bank lending, comes from a questionnaire which attempts to identify if corruption is an obstacle to firms’ access to finance. Bank corruption, is the response to the question: “How problematic is corruption of bank officials for the operation and growth of your business?” Answers vary between 1 (no obstacle), 2 (minor obstacle), 3 (moderate obstacle), and 4 (major obstacle). 12% in the sample report 4, 12 % report 3, 19% report 2 and 57% report 1. The dependent variable for the next three IV probit models is a binary response model based on Bank Corruption – with 1(no obstacle) recoded as 0, and all the other values recoded as 1 (obstacle to various degrees). This grouping puts 57% in the first category and 43% in the second. WBES data are useful in that they provide direct information on obstacles to financing, broad country and firm level coverage, and allow one to correct for both firm specific and country specific factors.<sup>14</sup> The WBES survey includes data on firm type. The variable **government** takes on the value 1 if the government owns any percentage of the firm. **Foreign** takes on the value 1 if foreign entities take on any fraction of the firm. The sample includes 12%

> 14 BDL (2005) point out that several papers have shown that these subjective indicators are correlated with objective measures of performance.

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**Figure 3: Investor Expectations for Source of Fundraising Over the Next Year**

<!-- Start of picture text --> 2%<br>8 % 10% 8% 8% 17 % %<br>4% 23%<br>12% 11% 16% 8 %<br>13%<br>16% 12% 12% 1 5%<br>12%<br>21% 44%<br>40%<br>44% 74% 21%<br>42%<br>33% 31% 27%<br>20%<br>5%<br>2015 2016 2015 2016 2015 2016<br>East Africa West Africa Southern Africa<br>Private individuals Fund-of-funds Pe rsions/endowment s G overnments/DFIs<br>B anks C orporates Insurance<br><!-- End of picture text --> _Source: Deloitte (2016)_ High net worth individuals, also known as family offices, represent another important source of potential investment capital for private equity funds. Family offices have taken an increasingly active role in the global private equity sector, including in Africa, over the last decade and, as of 2015, HNWs represented approximately 8 percent of <mark>the US$4 trillion invested in private equity worldwide, up from 4 percent five years earlier.</mark><sup>11</sup> <mark>As noted, in Kenya, these family offices are typically foreign in nature and may include investors who are seeking to combine their investment activities with broader impact-oriented objectives, such as investing at the bottom of the pyramid. While they are outnumbered by foreign family offices, some Kenyan families invest in funds as well, although the minimum investment of US$1 million usually required by private equity funds serves to limit the addressable market of family offices in Kenya. Kenyan HNWs include the Chandaria family, which is the owner of a large industrial conglomerate, among other businesses.</mark> There is also some investment by sovereign wealth funds (SWF) and institutional investors, although their capital is largely directed toward Pan African rather than regional/national funds. Investors report that large SWFs that are active in private equity on a global basis such as the Abu Dhabi Investment Authority (ADIA) and Singapore’s GIC have included Africa in their investment strategies. Specifically, GIC invested in Actis’ Africa Real Estate Fund III in 2016. Meanwhile, institutional investors such as Santander Pension, GE Capital, and DGGF (Dutch Good Growth Fund), have also included asset allocations for African PE funds. In particular, the New York State Common Retirement Fund, announced in 2015 that it planned to invest up to 3 percent of its US$180 billion in assets into Africa in the coming years. To date, the Common Retirement Fund has already invested in pan-regional players such as Helios, while Novastar raised capital from JP Morgan Social Finance and Axa’s impact investment group.<sup>12</sup> Finally, some Kenyan private equity firms, such as Catalyst, report receiving investment from US-based funds of funds. Unlike large pension

> 11 Holman, Kelly (September 1, 2015).

> 12 Africa Business Central (May 1, 2015).

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# **1. Introduction**

Taxes, government spending, and public transfers play a crucial role in advancing child rights and welfare and in reducing poverty and inequality. While there is increasing empirical evidence of the distributional effects of public finance in low- and middle-income countries, data and insights on the impacts on children are very limited.

However, it is essential to understand the specific impacts of public finance decisions on children because children have different demands and consumption patterns compared to adults, hence fiscal interventions may impact them differently. For example, children could be disproportionately affected if taxes add to the cost of goods and services particularly relevant for children. This is besides the indirect effects of consumption taxes, such as value-added taxes (VAT), and excises through their parents. Direct income taxes paid by adults could also have indirect effects on children’s welfare. Direct public transfers could have both direct and indirect effects on children’s wellbeing. Similarly, in-kind public transfers (spending on health and education) affect children’s school enrollment and access to basic health services. Moreover, household-level analyses often do not provide a full picture of the distributional effect of fiscal policy, and children may fare poorly in intrahousehold allocation (Dunbar et al. 2013). Recent evidence also suggests that many poor individuals do not necessarily live in poor households (Brown, Ravallion, and Van de Walle 2017; Belete 2021).

Children experience poverty differently from adults, as their experience of poverty is determined by material deprivations in the realization of child rights (e.g., health, education) rather than financial means (Alkire and Santos 2013; Gordon et al. 2003). Therefore, a multidimensional assessment of poverty and wellbeing, in addition to monetary poverty, would be required. This also serves to highlight the essential role public spending on health and education could have on children’s schooling and health access. Finally, a comprehensive analysis of the distributional effects of current fiscal policies on children is essential to estimate the potential impact of policy changes. Simulations of new policy approaches or changes to existing tax or spending regimes provide a clearer understanding of the policy’s impact on poverty and inequality and provide policy makers with essential information to guide evidence-based decision-making on public finance.

The Ethiopia case study offers an opportunity to examine the fiscal space in an environment with high child poverty and high child undernutrition. In 2016, approximately 88% or 36.2 million children in Ethiopia were multidimensionally poor, meaning they were deprived of the fulfillment of multiple rights or needs for basic food or services (CSA and UNICEF Ethiopia 2018). And despite progress over the previous decades, the most recent Demographic and Health Survey 2
and statistically different from zero. Furthermore, the economic magnitude of the estimated Chinese demand-elasticities for imports from LAC countries is large. A straightforward calculation of the magnitude of the China-demand effect, namely the product of the demand-elasticities times the change in China’s GDP between 2000 and 2004, suggests that if LAC exports to China had fully exploited the increased demand from China between 2000 and 2004, they would have accounted for 8 percent of LAC exports in 2004. As mentioned, this calculation is based on a particular counterfactual analysis, namely the comparison of Chinese imports from LAC in 2000 and 2004 under the assumption that these trade flows would have remained at their 2000 level if China’s GDP had not grown. Of course, we could choose other counterfactuals. For example, we could assume a low-growth scenario for China as the base case, instead of zero growth, and the resulting estimate of the magnitude of China’s demand effect on LAC exports would be smaller than the 8 percent.<sup>12</sup> The point is that China’s LAC-imports-demand elasticities are large, whereas LAC’s export-supply elasticities with respect to the Chinese market are negligible. That is, even if LAC’s GDP growth had matched China’s during 2000-2004, there would have been Chinese demand for LAC exports that would have not be satisfied by increases in the quantities or varieties of exported products. Rather, this rise in demand seems to have been satisfied through increases in the unit prices and perhaps increases in the quantities of pre-existing export varieties. Hence we interpret this evidence as suggesting that LAC economies missed out on handsome export opportunities offered by the Chinese market.<sup>13</sup> Table 2 lists our estimates of India’s demand elasticities as well as LAC’s supply elasticities for the Indian market. Table 4 presents the corresponding estimated elasticities derived from the application of the empirical model to the commodity-trade data. As was the case for Chinese-LAC trade, the results presented in both tables suggest that India’s demand elasticities were positive, large, and statistically significant for all four LAC subregions. However, Table 2 also suggests that there were no significant differences in the

> 12 To be precise, it would be around 2 percent of 2004 exports, when the counterfactual is that China grows at the same rate as the rest of the world.

> 13 A similar conclusion is observed when comparing the predicted export growth associated with China’s GDP growth with the observed export growth during the period. Export growth of LAC to China could have been 20 percent larger had if followed the increase in Chinese demand for LAC exports.

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CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 16 Côte d’Ivoire’s pattern of capital investment in infrastructure differs from that of comparator countries**

Investment in infrastructure sectors as percentage of GDP <!-- Start of picture text --> fragile low-<br>Cote d'Ivoire income<br>countries<br>public ODA non-OECD PPI<br>% of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia and others 2009.

# How much more can be done within the existing resource envelope?

Inefficiencies throughout the infrastructure sectors cost Côte d’Ivoire as much as $477 million per year (table 20). Improving efficiency would therefore have a significant benefit. The largest potential source of efficiency gains is improving cost recovery, particular in the power sector. Under-charging for power services costs Côte d’Ivoire about $190 million each year.The estimated average total cost of power is $0.18 per kilowatt-hour, while the average effective tariff is $0.15 per kilowatt-hour, which covers only operating and maintenance costs. As a result, the utility covers only 83 percent of costs, leaving capital investments underfunded. The associated financial burden is approximately 0.8 percent of GDP.

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Further, we find that commodity price shocks reduce non-resource taxes, both direct and indirect. Our findings relate to the volatility of public budgets due to commodity price volatility (Robinson et al., 2017) and the resource curse in terms of public finances (Borge et al., 2015). James (2015) establishes a negative relationship between resource and non-resource revenues as the expression of a crowding out effect between these sources of revenue in US states. Our findings further contribute to this literature by documenting that certain institutional arrangements can help curb the rise in top incomes.

The remainder of the paper is structured as follows. Section 2 presents the data. Section 3 describes the estimation strategy. Section 4 presents the main results and robustness checks. Section 5 presents additional results. Section 6 concludes.

# **2. Data**

This section presents the data used in the empirical investigation.

# _2.1 Top Incomes_

Data on billionaire net worth (in USD) are used to proxy for top incomes. The data are obtained from Forbes magazine’s updated database of billionaires (2001 to 2018). Billionaires are identified based on their first name, last name, and their profile in Forbes magazine. Information from Wikipedia is used to fill in missing information on billionaire characteristics such as country of citizenship. The number of billionaires in the sample rose from approximately 565 in 2001 to 2,208 in 2018. Forbes magazine’s billionaire database has been used in the literature to study wealth distribution (Piketty, 2014; Bagchi et al., 2016), the international mobility of billionaires (Sanandaji, 2014), the emergence of Russian billionaires (Treisman, 2016), and statistical regularities at the top end of the wealth distribution (Klass et al., 2006) among others. Summary statistics for the sample of analysis are provided in Table A1.

# _2.2 Commodity Windfalls_

Data on commodity price shocks are obtained from the IMF (Gruss and Kebhaj, 2019). The commodity terms of trade index is based on international prices of up to 45 individual commodities, constituting broad categories of energy, metals, food and beverages, and agricultural raw materials. We calculate commodity price shocks by taking the first differences of the log of the price index as shown in equation (1) below.

Where is the natural log of the real price of commodity _j_ in year _t_ . Ω represents the commodityand country-specific time-varying weights, which are based on three-year rolling average trade flows over jj,tt cc,jj,tt the previous three calendar years. Similar measures of commodity windfalls have been used by Arezki PP and Brückner (2012).<sup>7</sup>

> 7 We employ a similar measure to calculate specific commodity sub-indices. There are marginal differences in terms of weights, but the methodology is largely the same.

5
# **I. Introduction**

To what extent do local communities benefit from commodity booms? The question of high policy relevance given rising commodity prices has been subject to wide but inconclusive investigations. This paper utilizes new data on mining activity and government transfers in Peru to investigate the effect of mining and resource windfalls on socioeconomic outcomes at the district level, the lowest administrative unit in the country.<sup>2</sup> Peru is entering its second decade of an impressive mining boom. After decades of relative stability, the value of mining exports more than doubled between 1993 to 2000, to 3.2 billion US dollars, and by 2010 exports rose again sevenfold to 21.7 billion US dollars, or 14 percent of the country’s GDP. Local governments are benefitting generously from mining activities: the central government transfers to local authorities in mining departments a large share of the taxes levied on mining companies, in the context of an agreement that is commonly referred to as the _Canon Minero_ . In 2007, the year of our analysis, the overall budget envelope of the _Canon_ amounted to 5.1 billion soles (approximately 1.6 billion US dollars).

Yet, despite these generous transfers, the dramatic expansion of mining activities has been accompanied by rising societal tensions, which have grown to become a major concern for policy makers. In 2009, the _Defensoría del Pueblo_ (Ombudsman’s office) reported 268 social conflicts in Peru, of which 38 percent were related to mining activities. Major confrontations involved violence and the use of firearms, leading to death and injuries among both protesters and police (Taylor, 2011). While many protesters cite environmental concerns, case studies suggest that the underlying reasons are often more complex, involving revenue sharing disputes between mining companies, local authorities, and local populations (Arellano-Yanguas, 2011). Poor management of the _Canon_ also appears to add to the discontent (Hinojosa, 2011).

In this paper we use variation in mining across Peruvian districts to investigate the impact of mining activity and government transfers on local socioeconomic outcomes. The analysis uses a unique, district level dataset that merges administrative data on local mining production, transfers from central to local government, and census and survey-based measures of households’ average consumption, poverty, and inequality.

Our empirical approach improves upon the existing literature in two related aspects. First, since we are able to identify the location where the mineral is extracted down to the lowest administrative

> 2 In Peru, sub-national administrative units are called departments, provinces, and districts, in decreasing order of size.

2
Verbeek, Marno, and Theo Nijman. 1992. “Can Cohort Data be Treated as Genuine Panel Data”. _Empirical Economics_ 17:9-23.

Verhoogen, Eric. (2008). “Trade, Quality Upgrading, and Wage Inequality in the Mexican Manufacturing Sector”, _Quarterly Journal of Economics_ 123(2):489-530.

Warunsiri, Sasiwimon and Robert McNown. 2009. “The Return to Education in Thailand: A Pseudo-Panel Approach”. _IBS Working Paper_ University of Colorado at Boulder.

Ziliak James, Beth Wilson, and Joe Stone. 1999. “Spatial Dynamics and Heterogeneity in the Cyclicality of Real Wages”. _Review of Economics and Statistics_ 81(2):227-236.

20
level of primary completion in the subgroups does not seem to determine the level of household income decades later at the subgroup level.

# Figure 7. Water supply and Median income

<!-- Start of picture text --> Argentina: census(1980) - survey(2014) Bolivia: census(1976) - survey(2015) Brazil: census(1980) - survey(2015)<br>40 15 25<br>30 10 20<br>15<br>20 5 10<br>10 0 5<br>.2 .4 .6 .8 1 0 .5 1 0 .5 1<br>Chile: census(1982) - survey(2015) Colombia: census(1985) - survey(2015) Costa Rica: census(1984) - survey(2015)<br>40 15 20<br>30<br>10 15<br>20<br>10 5 10<br>0 0 5<br>0 .5 1 0 .5 1 .4 .6 .8 1<br>Dominican Republic: census(1970) - survey(2015) Ecuador: census(1990) - survey(2015) Mexico: census(1970) - survey(2014)<br>20 15 12<br>10<br>15 10<br>8<br>10 5 6<br>4<br>5 0<br>0 .5 1 .2 .4 .6 .8 1<br>Panama: census(1980) - survey(2015) Peru: census(1993) - survey(2015) Uruguay: census(1985) - survey(2015)<br>25 20 30<br>20 15 25<br>15 20<br>10<br>10 15<br>5<br>5 10<br>.4 .6 .8 1 0 .5 1 .7 .8 .9 1<br>Share of population<br>Rural Urban 95% CI<br>Medican income 2011 PPP US$ a day<br><!-- End of picture text --> The relationship between access to water supply in the 1980s and household income level in 2015 is slightly different than the case of primary completion (Figure 7). First, for most of the countries, there is a difference in access to water supply between rural and urban areas. Though it would be expected to find that 30 years ago rural areas had less access to water supply than urban 24