rafmacalaba/datause-encoder-data huggingface dataset:
ETHIOPIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 11. Potential gains from greater efficiency**

<u>US$ million per year</u>

||ICT|Power|Transport|Water|Irrigation|Total|
|---|---|---|---|---|---|---|
|Underrecovery of costs|--|42|37|23|--|102|
|Overstaffing|9|--|--|--|--|9|
|Distribution losses|--|24|--|9|--|33|
|Under collection|--|15|28|1|--|44|
|Under maintenance|0|0|263|0|0|263|
|Total|9|40|290|9|0|451|

_Source_ : Briceño-Garmendia and others 2008.

# Annual funding gap

Ethiopia’s infrastructure funding gap amounts to $3.5 billion per year (or about 26 percent of GDP) across various sectors (table 12). Over 77 percent ($2.9 billion) of the infrastructure funding gap is in the power sector, much of which is associated with the development of the generation capacity that Ethiopia would need to become a power exporter (figure 19). The rest of the gap is related to water and sanitation, where an additional $809 million per year is needed to meet the Millennium Development Goals. No financing gap was found for transport.

**Table 12. Funding gaps by sector**

<u>US$ millions per year</u>

||ICT|Power|Transport|WSS|Irrigation|Total|
|---|---|---|---|---|---|---|
|Needs|(211)|(3,380)|(398)|(1,201)|(6)|(5,196)|
|Spending|126|476|301|383|—|1,286|
|Potential efficiency gains|9|40|290|9|0|451|
|(GAP) or surplus|(76)|(2,864)|193|(809)|(6)|(3,459)|
|Reallocation potential|149|142|196|0|—|486|

— = Data not available.

Despite the overall shortfall, about $486 million a year is spent above the estimated requirements to meet the identified infrastructure needs in some sectors. First, Ethiopia’s large road investment program, which is an effort to catch up with the investment backlog in roads, goes beyond what the country might need to do in the steady state, once it has caught-up with its historic road rehabilitation and construction backlog. Second, Ethiopia is allocating a much higher level of public spending for ICT than would be strictly necessary to meet the associated development goals if private sector initiative were used. Finally, operations and maintenance spending looks quite high relative to the country’s long-term needs once its hydropower portfolio is further developed.

For purposes of calculating the funding gap, only the potential efficiency gains are netted out from the investment needs. The reason is that, although there is evidence of overspending in some areas of infrastructure, it cannot be assumed that if these resources were reallocated they would necessarily be allocated to the other infrastructure sectors, as in principle they could contribute to any part of the budget.

26
**Table 7:** Social tax rate estimates

|||La|bor supp|ly elastic|ity||
|---|---|---|---|---|---|---|
||0.32|0.50|0.75|1.00|**1.11**|1.25|
|**Panel A: Sample average (ITT)**|||||||
|Endline rate: 0%|29%|19%|13%|10%|9%|8%|
|Endline rate: 2.5%|30%|21%|16%|12%|12%|11%|
|Endline rate: 5%|32%|23%|18%|15%|14%|13%|
|**Panel B: Compliers (ToT)**|||||||
|Endline rate: 0%|51%|37%|26%|20%|19%|17%|
|Endline rate: 2.5%|52%|38%|28%|22%|21%|19%|
|Endline rate: 5%|53%|40%|30%|24%|23%|21%|

_Notes:_ This table presents the baseline social tax rate, _τ_ 1, faced by workers, estimated for various values of _τ_ 2 (the social tax rate on earnings increases for workers assigned to the Private arm) and of _ζ_ (the labor supply elasticity). Note that our experimental estimates imply a labor supply elasticity _ζ_<sup>_c_</sup> = 1 _._ 11. Equation (5) is used for the computation of _τ_ 1.

# **10 Conclusion**

Informal transfers among kin groups and social networks are important for coping with risk. Because they substitute for missing insurance markets, they are typically viewed as unequivocally positive. Our findings indicate that these important welfare benefits may come at a cost: social insurance can turn into social taxation, creating a disincentive to work. Because our intervention minimizes the scope for income effects, our results do not necessarily reflect the policy impact of reducing existing redistributive pressure on labor supply. However, the large magnitude of our treatment effects points to sizable distortions—raising the potential for the social tax to meaningfully lower aggregate earnings and productivity.

This has potential implications for understanding labor market malaise in developing countries. In many Sub-Saharan African countries, two major impediments to the growth and profitability of formal firms are difficulty in finding enough low-skilled workers to work regularly in formal jobs, as well as low labor productivity among those who do work (McMillan and Zeufack, 2022). For example, in Côte d’Ivoire, these two labor supply challenges, despite high wages, are cited as major obstacles to enabling domestic processing of cashews— considered an important policy priority for economic growth (World Bank, 2018, 2020). The presence of a social tax could contribute to both these labor supply challenges. For example, in our setting, 74% of workers state that taking a formal job would lead to increased transfer requests, despite the fact that such jobs are also readily available to those in workers’ networks (Figure 3, Panel C). Among those who do hold these jobs, our experimental findings suggest the potential for social taxation to substantively lower worker productivity.

More broadly, our results provide empirical grounding for long-held views expressing concerns about the distortionary effects of informal redistributive arrangements (Lewis, 1955; 39
compared to UCTs.<sup>5</sup> Another argument against making transfers conditional is that because the conditions need to be verified, CCTs are more expensive per child to implement and administrative capacity to conduct them may not be sufficient in less-developed countries.

Conditionality is often justified by the observed low investment in human capital. This low investment may be due to parents not internalizing positive social externalities of education (de Janvry and Sadoulet, 2005), to parental agency problems whereby parents make education and child labor decisions but do not adequately consider the child’s future welfare (Edmonds, 2008), to parental irrationality, impatience, or lack of self-control (Das, Do, and Özler, 2005), to borrowing constraints or the absence of negative bequests across generations (Martinelli and Parker, 2003), or to underestimates of returns to education (Jensen, 2010). A further justification for conditionality invokes political economy arguments claiming that non-poor individuals would only agree to transfer programs if conditions were in place (Gelbach and Pritchett, 2002).

The common approach in the literature when comparing CCTs and UCTs is to depict investment in human capital against another good (Das, Do and Özler, 2005).<sup>6</sup> This essentially assumes educational investments are homogenous across children, and households differ only in how much they invest in their children. We take a slightly different approach and introduce the idea of a marginal child to motivate our hypotheses. We define a marginal child as one who has a lower tendency to enroll in school absent an external intervention. In contrast, a non-marginal child is one the household would be more likely to enroll even without an external intervention. In the empirical section, we confirm that specific types of children, such as girls or low cognitive ability children, are less likely to be enrolled in the baseline prior to the transfer program.

> 5 This is similar to the textbook example of in-kind versus cash transfers (Cunha, 2010).

> 6 In this section, we use education in our discussion because that is the focus of the empirical analysis. However, our framework can accommodate other types of human capital as well.

6
**FIGURE** 1

# HUNGARY: NOMINAL EXCHANGE RATE AND PRICE INDICES

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- 11 -

In practice, data for human capital is not available for large cross-country samples. Nevertheless, there exist now several attempts to measure proxies for the human capital stock, such as in Barro and Lee (1992) and Kyriacous (1991). In this paper, we prefer to use the data base developed by Nehru, Swanson and Dubey (1993) which certainly improves the two other existing measures." Educational stocks are measured as mean school years of education of the labor force and they are built from enrollment data using the perpetual inventory method, adjusted for mortality. **12** This construction takes account of the variations across countries in the typical duration of primary, secondary, and tertiary education. We should stress, however, that the data do not take account of differences in the quality of schooling across countries or over time.

The basic model has been applied to a panel of 74 countries (the list is presented in the Appendix) over the period 1960-90. We used the Two-Stage Least Squares (TSLS) estimation technique to account for the simultaneity between growth and savings, while heteroskedasticity has been corrected by using the consistent estimate of the covariance matrix developed by White (1980). 13 Notice that the instruments of the growth equation are those defined by Barro and Lee including lagged GDP per capita growth, the dependency ratio, the lagged savings rate and the initial GDP level in 1960. Finally, the basic model incorporates fixed country-specific effects in the intercept term, which seems to be an appropriate choice due to the marked differences in the economic features of the countries used in our sample. The differences among countries can be explained not only by the variables used in the 11. Briefly, these authors argue that Barro and Lee series present three major caveats: (a) the average number of years of education received is arbitrary because there is no information on those who completed only part of each schooling stage; (b) the few number of observations in many countries of their sample (only 77 countries out of 129 have three or more observations); and (c) the estimates only refer to the population aged 25 and over. The last caveat may be the most serious because " this can lead to a serious downward bias in the estimates of the education stock because in most developing countries the segment of the population between the ages of 15 and 25 is usually large and growing over time" (p.<sup>3</sup> , Nehru and al.).

12. Estimates are corrected for grade repetition among school-goes and country-specific drop-out rates for primary and secondary students.

13. We prefer this method rather than the weighted least square in the absence of appropriate information on the variable which would be proportional to the residual variance.
century. The government of India made the further commitment that by 2000, 6% of the gross domestic product (GDP) would be spent on education, out of which half would be spent on primary education. Though they did not quite reach this level, by the end of 2000, 94% of India’s rural population had primary schools within one kilometer and 84% had upper primary schools within 3 km. Special efforts were made to enroll historically disadvantaged castes and girls.

The National Literacy Mission (NLM), launched in 1988 by the national government, adopted the Total Literacy Campaign (TLC) model as the primary policy instrument for eliminating adult illiteracy in the 1990s. By most accounts, the NLM was responsible for the large gains in literacy over the 1980s and 1990s in India.

The educational mission in India since the original NPE in 1986 has been to provide free and compulsory education to all, and much progress was made toward this goal during the 1980s. While the goal may not have been achieved everywhere, India had recognized the value of human development and the need for public provision of education services. Thus, to a large extent, the provision of education is a policy variable set from above rather than generated purely within the district economy.

# **3.      Empirical Analysis**

## **3.1 Data**

The district level Net Domestic Product (NDP) measurements for this study come from internal Reserve Bank of India records (districts are the sub-state level economies). The measures of credit and deposits of scheduled commercial banks come from the Reserve Bank of India. Credit, deposits, and NDP measures are all converted to real terms using 1992-3 prices. The other control variables, including literacy rate, road density, and population density come from the Indian census and www.indiastats.com. The data set includes NDP 11
previously wanting questions or to revisit the answers to questions posed by other eminent scholars.

The first question we revisit is when judicial reform happens. Posner (1998) and Messick (1999) argue that such reform emerges as a result of a society-wide consensus or as a precursor to the creation of such consensus. The second question we pose is what constitutes judicial effectiveness. Messick shows that reform to improve judicial effectiveness involves four groups of measures aimed at increasing 1. independence, 2. efficiency, 3. access and 4. quality. Botero et al (2003) show that the impact of judicial inefficiency is particularly severe in poor societies, where all four ingredients may be missing at once.

The third and final question we revisit is what elements constitute a sound judicial reform project. Here we look more specifically at some hypotheses that have populated the development debate but have insufficient basis in academic studies predating the three WBRO surveys. One example is the focus on pay and other monetary incentives in reform projects – a focus that many foreign aid projects have (Posner 1998). A second example is the increase in the number of judges or courts – a reform that finds little empirical support but is nonetheless widely practiced in development projects. A final view, propagated especially in Botero et al (2003), is that most cases of judicial stagnation require simplifying procedures and increasing their flexibility. We study the new evidence for clues on the ingredients of successful judicial reform.

The new surveyed evidence does not always settle adequately the question of the direction of causality between various types of reform and judicial effectiveness (this question is also raised in Ramos Maqueda and Chen 2021). First, both institutional building and judicial effectiveness may be a result of preexisting attitudes and beliefs in society (Ellickson 1991; La Porta et al 1999). Second, the same fundamental factors that contribute to building any government institution may also be responsible for improvements in the judiciary, for example the accumulation of human capital (Pistor 1995; Glaeser et al 2004; Angrist et al 2021). As countries become more prosperous, they can afford to invest additional public resources in improving judicial effectiveness (Posner 1998). Finally, it may be that richer societies spend more on all public institutions, including the judiciary, so the causality runs from institution building to judicial effectiveness (Murphy, Shleifer 3
17 (2.6%) at the top decile of the distribution in large urban areas (Brazil as a whole). The slope of the expenditure effect PIC is lower in rural areas, where a higher fraction of the population is poor.

The implications of these expenditure effects for summary measures of poverty and inequality are summarized in the third columns of Tables 4, 6 and 8, for large urban areas, rural areas and the whole country, respectively.<sup>11</sup> These tables report poverty incidence (headcount), depth (poverty gap) and severity (squared poverty gap), defined with respect to both an extreme and a moderate poverty line, for the original pre-crisis income distribution (denoted “baseline”, and based on the PNAD 2006), and for each of the impact scenarios. The extreme and moderate poverty lines are drawn from IBGE. They allow for differences in spatial cost of living, and are reported in Table 3.<sup>12</sup> Tables 4, 6, and 8 also report the Gini coefficient of income inequality, both at baseline and for each impact scenario. As noted, column 3 reports poverty and inequality in the income distribution obtained from the expenditure effect PIC only: .

The effects of those relatively large compensating variations on poverty are substantial, particularly in rural areas, where extreme poverty incidence rises by four percentage points, or nearly a fourth. Moderate poverty rises by about a sixth, or six percentage points, and the effect on higherorder FGT indices is proportionately (somewhat) larger. In large urban areas, where people are on average better-off, the effects are smaller: the extreme poverty headcount rises by just over one percentage point, or ten percent. Moderate poverty incidence rises by two and a half points, about 7.5 percent. In the country as a whole, poverty effects fall somewhere between those two extremes, and are certainly not negligible: on their own, the first-order expenditure effects contributed to an increase of 22% in the incidence of extreme poverty, from 11.0% to 13.5%.

# _The market income effect_

But Brazil is a large food producer, and net exporter. 16% of the country‟s population live in rural areas and, according to the PNAD 2006, 19% work in agriculture (including animal husbandry,

> 11 Tables 5, 7 and 9 differ from Tables 4, 6 and 8 in that they consider a different value for the pass-through parameter from product prices to agricultural wages (α). These tables therefore report different values for estimates that include the market income effect, but there are no differences with respect to the expenditure effect.

> 12 These region-specific poverty lines were constructed for the 2002/03 POF and we adjusted them for inflation between 2003 and 2006 using INPC. The unweighted averages of these regional lines in 2006 prices are R$187.90 for moderate poverty and R$100.56 for extreme poverty. These compare to average lines of R$187.50 for moderate poverty and R$93.75 for extreme poverty used by Barros et al. (2008).
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|Non-zero|No|Treated|If a sector was not in the negative list, and<br>the excise rate across India was non-zero,<br>then a differential tax rate existed across<br>the border.|
|---|---|---|---|

We take three steps to classify a sector as treated or untreated in terms of central excise taxes. First, we identify all 3-digit NIC 2008 codes that are contained in the CBEC’s negative list of excise exemptions in Uttarakhand. Next, we use CBEC’s publication on central excise tax rate published in 2005 to identify if a product had zero or non-zero excise rate across India.<sup>10</sup> CBEC’s publication use 8-digit Indian Tariff Code (ITC) classification to notify the tax rates. And the first five digits of the ITC code correspond to the international Harmonized System codes 2002 (HS 2002).<sup>11</sup> Finally, we create a concordance between the 5-digit HS 2002 product codes and the 3-digit NIC 2008 sector codes and identify sectors that are not in the negative list but have zero central excise taxes. We use a sequence of concordance tables, containing cross-walks from HS 2002 to CPC1.1, CPC2 and ISIC Rev.4. To classify whether a 3-digit NIC 2008 industry code had a zero or non-zero central excise tax in India, we calculate the share of all HS 2002 products with a non-zero tax rate mapped to a NIC 2008 industry code. A 3-digit NIC 2008 sector is considered to have a non-zero excise tax rate in India if this share is above 50%, and a zero rate otherwise.

The final list of sectors receiving positive effective central excise tax incentives covered 49 sectors at the 3-digit NIC 2008 level. Other sectors such as agricultural food processing industries received zero excise taxes. And all services sectors received zero excise taxes.

A short negative list was also issued for the central corporate income tax incentives. The list covers only 4 sectors at the 3-digit NIC level. Three of them overlap with the negative list for central excise tax, including manufacture of man-made fibers, bodies (coachwork) for motor vehicles and motor vehicles. And one sector does not, namely, manufacturing of refined petroleum products. We expect much smaller impact of this negative list on firm’s investment behavior and do not control for these sectors explicitly in the baseline analysis. But we explore the impact of this negative list in several robustness analyses.

# _5.3 Coagglomeration measures_

Following Ellison, Glaeser and Kerr (2010), we compute three measures on coagglomeration between sector pairs. The first measure captures the overall tendency for industries to locate within a geographical unit and is calculated for each pair of sectors in our sample, as follows: 1 − ∑ where and denote sectors, and indicates geographic areas. represents the share of sector employment in place and is the average employment share of the place across all sectors. mmii DD jj mm DD mm DD′DD mm xx

> 10 2005 is the year closest to the start of the New Industrial Policy for which such publication exists.

> 11 At the 5-digit HS 2002 level, CBEC’s publication comprises of 5,203 unique products. See Velayudhan (2019) for detailed explanation.

18
|||Bank databases (Azevedo et<br>al 2011)||
|---|---|---|---|
|_seed_kg_|Quantity of<br>seeds (in kg)|.,<br>NER: Seeds are not at the plot<br>level, they are at the<br>household level. We divide<br>seeds among plots based on<br>the distribution of plot areas.|Summed up to the plot level in the<br>plot dataset<br>TZA: Absent in wave 1 and wave 2<br>UGA: Absent in wave 1, wave 2,<br>wave 3|
|_seed_value_LCU_|Value of<br>seeds in LCU|Seeds valued using median<br>prices per in a defined<br>geographical area. The area in<br>question is the EA (or admin<br>1 in some surveys) under the<br>condition that there are more<br>than 10 observed sales in the<br>area. If there are less than 10<br>sales, prices are calculated at<br>a higher geographical level.<br>Prices are calculated<br>independently for each crop<br>type and separately for<br>improved and traditional<br>seeds, where possible<br>TZA: No question asking for<br>the quantity of seeds used, in<br>w1 and w2, only the value of<br>seeds bought. Median prices<br>could therefore not be<br>computed, and seed values are<br>taken as they are reported.<br>Seeds only taken into account<br>if they were bought.<br>MLI: all seeds are assumed to<br>be purchased (no question<br>asking for the quantity<br>purchased). This might lead to<br>an underestimation of seed<br>prices as a portion may be<br>obtained for free as gifts or<br>leftover from the previous<br>season.<br>UGA: all seeds are assumed<br>to be purchased (no question<br>asking for the quantity<br>purchased). This might lead to<br>an underestimation of seed<br>prices as a portion may be<br>obtained for free as gifts or<br>leftover from the previous<br>season. In w1 w2, w3 no<br>question asking for the<br>quantity of seeds used, only<br>value. Median prices could<br>therefore not be computed,<br>and seed values are taken as<br>they are reported. Seeds only|Summed up to the plot level in the<br>plot dataset|

27