rafmacalaba/datause-encoder-data huggingface dataset:
design is how can a transfer program increase such investments. One issue that is common to both CCTs and UCTs is whether the recipient’s gender matters. This was noted by Duflo (2003), who found that pensions received by women in South Africa had a significant impact on the nutritional status of their grandchildren, while this was not observed for male pensioners. The practical implications of this observation for child development, however, are less than often claimed since only 46% of pensioners – either male or female - lived with their grandchildren and the impact was only observed if the woman’s grandchild was a girl. On the other hand, this result does support the view that women and men have different patterns of investment. Akresh, de Walque, and Kazianga (2012a) also find small gender differences in expenditures in randomized trial of CCTs and UTCs in Burkina Faso but caution that there is not a clear case that father’s under invest in children.

The amount of a transfer that is spent on human capital investments can also be enhanced by what is variously termed labeling (Kooreman, 2000 ) or social marketing. For example, Schady and Rosero (2008) found that household assigned randomly to an UCT, termed a social program, spent more on food than indicated in the expenditure patterns of similar households. It is, however, often difficult to distinguish the social marketing aspect of a transfer from the possibility that women have greater control of such labeled expenditures; Attanasio, Battistin and Mesnard (2012) note that both factors can contribute to the shift in the Engle functions for food they document in Colombia. Similarly, Macours, Schady and Vakis (2012) claim that one or both of these effects contributed to the outcomes in a cash transfer in Nicaragua. In particular, the role of social marketing likely accounts for the fact that impacts on child development did not fade out two years after the transfers ended. Similarly, the fact that households that were not eligible for a CCT in Indonesia also increased their health seeking 6
- 29 -

# Table (3.1) Key Indicators of the Agricultural Sector Role in the Sudan Economy

|INDICATOR|YEAR|(%)|
|---|---|---|
|Share of agriculture in GDP|1988/89|36|
|Contribution to foreign exchange earnings|1986/87|93|
|Share to GDP of Govt. investment in agriculture|1984/85|8|
|Share of total invesment in GDP|1984/85|17|
|Share of total employment|1982/83|44|

Source: An updated version of Table 1, Elbadawi (1988)
On The Governance Of Public Pension Fund Management Factual evidence also links this poor performance to undue political interference in the investment decision of public funds. Governments have often imposed on financial intermediaries explicit social and developmental objectives that undermine their financial viability. When explicit mandates are absent, governments have often relied on their powers of coercion to ensure sufficient demand for their debt to finance public expenditure or demand for other securities to finance well-connected entrepreneurs and public enterprises. Sometimes regulations require commercial banks to meet reserve and liquidity requirements by holding government paper. Other times, and both in developing and in developed countries, commercial banks, insurance companies, pension funds, as well as social security funds are required to invest in government bonds, sometimes in specially issued nonmarketable instruments with substantially below-market yields.

**Figure 1. Correlation between average real return on public pension assets and a governance index**

<!-- Start of picture text --> 5.4 - Korea<br>IndiMaMocco Malaysia Swedgbnaapor<br>Ph iiippl it ao<br>Sri Lank<br>s etemral JamalcJonIa Cosa RI<br>Egypt Equador<br>Venetuel<br>Zambia<br>-33.1 Uganda<br>4.2 10<br>gov<br><!-- End of picture text --> <!-- Start of picture text --> Source: Iglesias and Palacios (2000).<br><!-- End of picture text --> Among public pension funds, many countries impose a minimum quantitative floor for assets to be invested in government paper, and politicians are often allowed to have a say in the investrnent policy of social security funds.<sup>5</sup> Obviously, these policies can greatly _5_ In the Philippines, the Social Security System (SSS) and the Governmnent Service Insurance System (GSIS) are "offered" tax-free government bonds that remain tax free to the extent that they are not subsequently traded, effectively forcing the two social security institutions to hold these assets until maturity. In Singapore, the Central Provident Fund is required to invest a large share of its assets in nonmarketable government securities; and only until recently this share was 100 percent. In the Philippines, SSS and GSIS were often instructed to provide direct lending to industries to which commercial banks refused to lend. In Mexico as well as in the Philippines, a large share of pension contributions have to be invested in housing, INFONAVIT and Pag-IBIG, respectively. The real returns on the assets of these "specialized" institutions have been rather disappointing (Impavido, Musalem, and Vittas 2001). Also, as discussed in Mitchell (2001), the Malaysian Employee Provident Fund was used to keep insurers solvent, and the Korean Pension Fund was forced to lend two-thirds of its assets to the Ministry of Finance for "social" purposes. The Korean investment companies (mutual funds) have received loans from the Central Bank with the mandate that they must support the stock market. African social security plans are required

- 4 -
# _Spectrums_

Sexual orientation, gender identity, and expression are commonly understood not to be binary, but rather exist in a series of spectrums.<sup>12</sup>

# **Biological sex (anatomy, chromosomes, and hormones)**

|male<br>intersex<br>female<br>**Gender Identity (internal sense of self)**|
|---|
|man<br>transgender<br>woman|
|**Gender Expression (presentation and communication of gender)**<br>masculine<br>androgynous<br>feminine|
|**Sexual Orientation (enduring emotional, romantic, or sexual attraction)**|
|lesbian<br>bisexual/asexual<br>gay|
|**Sexual Behavior**|
|sex with women<br>sex with men and women<br>sex with men|

**Source: World Bank modeled after Center for Gender Santiy 2009**

Where a person finds themselves on the various spectra can change over time and only taken together do they compose a person’s sexual orientation, gender identity, and expression.<sup>13</sup> Some surveys have asked respondents to place themselves on one or more of these spectrums, while others simply offer a binary set of options for each e.g. male/female or transgender yes/no. Sexual orientation, gender identity and expression, and sex characteristics also interact with each other. For instance, a transman, a transgender individual who identifies as a man<sup>14</sup> , can be attracted to men, women or both and thus might be straight, homosexual or bi (or might adopt some other term).

# _Being out_

In addition, people often hide their sexual orientation, gender identity and expression, and/or sex characteristics wholly or in part (i.e., from certain people and/or in certain

> 12 The following diagram is adopted from: Center for Gender Santiy. 2009. „Sex and Gender diagram”.

> 13 There might be other dimensions which are relevant to a person’s sexual orientation, gender identity and/or expression based on cultural difference or other circumstances that have not been taken into consideration for this paper.

> 14 National Center for Transgender Equality. 2014. Transgender Terminology.

5
correlated with household poverty status. Furthermore, we control for several household and household-head characteristics in the regression.<sup>9</sup>

**Table 1** Variables’ definition and basic statistics (Total observations = 25,145)

|**Variable**|**Definition**|**Mean**|**Std. Dev. **|**Min **|**Max**|
|---|---|---|---|---|---|
|**Dependent variable**||||||
|MP dummy<br>**Alternative dependent**<br>**variables**|Multidimensional<br>poverty<br>dummy<br>taking<br>value<br>1<br>if<br>household deprivation score is<br>1/3 or more and 0 otherwise|0.059|0.235|0|1|
|Consumption per capita<br>dummy|A dummy taking value 1 if<br>household’spatially deflated<br>monthly consumption per capita<br>is lower than the poverty line<br>which is 105,500.4 dinars<sup>10</sup>and 0<br>otherwise.|0.678|0.467|0|1|
|World Bank MP dummy|Multidimensional<br>poverty<br>dummy<br>taking<br>value<br>1<br>if<br>household deprivation score is<br>1/3 or more and 0 otherwise|0.104|0.305|0|1|
|**Explanatory variables**||||||
|IDH|Internally displaced household; a<br>dummy taking value 1 if at least<br>one household member is an IDP<br>and 0 otherwise.|0.081|0.272|0|1|
|Pre03IDH|Internally<br>displaced<br>household pre-US-led invasion:<br>taking value 1 if at least one<br>household member is an IDP who<br>migrated before 2003 and 0<br>otherwise.|0.046|0.210|0|1|

9 The control variables include age, gender, education, and marital status of the household head, household dependency ratio, shares of working-age males and females in employment, location (rural vs. urban), and access to the public distribution system. We also include household’s spatially deflated monthly consumption per capita to control for whether internal displacement drives multidimensional poverty independently of monetary poverty.

> 10 See Vishwanath et al. (2015) for a detailed description on the poverty line calculation.

13
- 14 -

about 0.8, which means that the government’s engineering cost estimates are reasonable but tend to slightly overestimate the true costs. The question is why some districts are spending twice as high normalized costs as others. This paper will explore reasons.

|**Table 1. Average rural road c**|**ontract a**|**nd unit c**|**osts in Nepa**|**l**||
|---|---|---|---|---|---|
||Obs|Mean|Std. Dev.|Min|Max|
|Actual payment per contract (million rupee)|156|7.76|<br>7.95|0.14|39.89|
|Actualpaymentper km(million rupee)|156|1.63|<br>2.32|0.02|21.97|

**Figure 10. Average normalized bid by district**

<!-- Start of picture text --> Average normalized bid<br>0.55 - 0.61<br>0.62 - 0.75<br>0.76 - 0.83<br>0.83 - 0.88<br>0.89 - 1.05<br>1.2<br>1.1 1.05<br>0.99 1.00<br>1.0 0.95<br>0.9<br>0.80<br>0.8 0.75 0.750.780.800.820.830.830.840.860.870.870.88<br>0.71<br>0.7<br>0.62<br>0.6 0.55<br>0.5<br>Bid / Cost estimate<br>All Dhanusa Makwanpur Siraha Udayapur Palpa Rupandehi Mahottari Nawalparasi Kapilvastu Banke Salyan Sarlahi Bardiya Nuwakot Dhading Kaski Rautahat Kailali Syangja<br><!-- End of picture text --> Lack of competition may be one of the alleged concerns. The average number of bidders that participated in competitive bidding is about 6. This appears consistent with the existing literature. For highway construction auctions in Florida, the number of bidders ranged from 2 to 19, with an average of about 5 (Gupta, 2002). In Oklahoma, the average number of bidders is 3.3 (De Silva, Dunne, and Kosmopoulou, 2003). In Africa, road auctions have an average of 5 firms with a range of between 1 and 15. Most have 3 or 4 bidders (Alexeeva, Padam, and Queiroz, 2008).
# **Appendix I.1: Definition of Industries**

|______________________<br>NACE 2 letters code<br>______________________|___________________<br>ISIC code<br>___________________|_______________________________________________________________________________________<br>Industry<br>_______________________________________________________________________________________|
|---|---|---|
|DA|15, 16|Manufacture of food products, beverage and tobacco|
|DB|17, 18|Manufacture of textile and textile products|
|DC|19|Manufacture of leather and leather products|
|DD|20|Manufacture of wood and wood products|
|DE|21, 22|Manufacture of pulp, paper and paper products, printing and publishing|
|DF|23|Manufacture of coke, refined petroleum products and nuclear fuel|
|DG|24|Manufacture of chemicals, chemical products and man-made fibres|
|DH|25|Manufacture of rubber and plastic products|
|DI|26|Manufacture of other non-metallic products|
|DJ|27, 28|Manufacture of basic metal and fabricated metal products|
|DK|29|Manufacture of machinery and equipment n. e. c.|
|DL|30, 31, 32, 33|Manufacture of electrical and optical equipment|
|DM|34, 35|Manufacture of transport equipment|
|DN<br>______________________|36<br>___________________|Manufacture n. e . c. excluding recycling<br>_______________________________________________________________________________________|

61
forthcoming).<sup>12</sup> Rather than indicating policymakers are doing a bad job, it could show that nature makes their job harder than elsewhere.

Furthermore, various composite indicators of the trade environment are closely correlated with the level of development. This is most clearly reflected in Figure 5 which plots the 2009 World Economic Forum Enabling Trade Index (ETI) against log GDP per capita – a proxy for development. The fitted (quadratic) line is also included. Similar plots based on logistics quality and time delays are presented in Appendix 2.

Analogous to our benchmarking of trade flows earlier in this paper, we benchmark SADC‟s trade environment against other countries with a similar level of development. For example, Figure 5 reveals high ETI values in many SADC countries relative to their GDP per capita. This suggests that these countries are not unusually constrained by their environment relative to their peers.

**Figure 5: Overall Enabling Trade Index against log GDP per capita**

<!-- Start of picture text --> Overall Enabling Trade Index<br>Mauritius<br>Botswana<br>Na mi bia South Africa<br>MalawiMadagascar<br>Zambia<br>Moza mbiq Tanz Le sotu a e nia ho<br>Zimbabwe<br>4 6 8 10 12<br>ln(GDP per Capita), 2005 PPP<br>6<br>5<br>4<br>3<br><!-- End of picture text --> Source: Team calculations using the World Economic Forum Enabling Trade Indices (ETI) obtained from the WEF Global Enabling Trade Report (2009) and GDP data from the World Bank World Development indicators. Notes: ETI indicators range from 1 (worst outcome) to 7 (best outcome). Values for Botswana are obtained from the 2010 edition Table 8 presents the results of a benchmarking exercise to identify the relative performance of SADC in terms of its institutional and other obstacles to trade. Composite indicators of the trade environment are regressed on GDP per capita and its square as well as population, area and a dummy for whether the country is landlocked.<sup>13</sup> A dummy for the SADC region is included to estimate the extent to

> 12 The gravity model estimates presented in column 4 of Table 9 in the appendix reveal that SADC countries are disproportionately disadvantaged by their distance from markets. 13 Note that this equation does not account for the endogeneity of trade-related institutional environment and level of development, although lagged GDP levels (2005 values) are used in the regression. High trade costs, for example, may contribute towards low levels of development. The equation is therefore not to be interpreted as a causal relationship. Rather, it is used a simple benchmarking exercise.

18
the differences in practices when projects scale up, 2) demonstrating how incentives ‘misalign,’ and 3) explaining the variation in implementation between two phases of a project.

# **METHODOLOGY**

This qualitative analysis draws on a data set of almost 800 in-depth interviews and focus group discussions conducted from 2011 to 2015 in four villages in Bihar. The villages form two matched pairs—one each in Madhubani and Muzaffarpur districts— representing two Phase I villages (where JEEViKA has been operating since 2006) and two Phase II villages (where JEEViKA has been operating since 2011).

The Phase I villages were selected randomly from a larger quantitative sample in Muzaffarpur and Madhubani districts. These two villages were matched with a set of Phase II villages using propensity score matching methods (Imbens and Rubin 2015) on the basis of village-level data on literacy, caste composition, landlessness, levels of outmigration, and availability of infrastructure from the 2001 government census.<sup>2</sup> In order to improve the quality of the match, field investigators visited all the possible Phase II villages for a week, studying their geography and their economic, social and political structures to select the two best matches for each Phase I village (see Table 1).

**Table 1: Sampling**

||**Madhubani**|**Muzaffarpur**|
|---|---|---|
|**Phase I (project from 2006)**|Ramganj<sup>3</sup>|Saifpur|
|**Phase II (project from 2011)**|Nauganj|Raipur|

The quality of the match was good. All four villages are divided into segregated and caste-homogenous habitations or _tolas_ , and are very similar in caste composition. In Madhubani district, Brahmins and Bhumiars are a majority in both villages, and their tolas are located close to the main resources of the village—the temple, pond and school—while all the other tolas extend southwards in decreasing order of status in the

> 2 We used 2001 census data because the treatment villages were from Phase I, which was initiated in 2006 by selecting poorer villages on the basis of that census.

> 3 For the purpose of keeping their identities anonymous, we have re-named the villages and informants.

6
# **_Box: The fuel subsidy and Indonesia’s poor_**

Domestic fuel prices (and, implicitly, subsidies) have direct and indirect effects on household real income. The direct effect of the subsidies’ is seen in the gain in disposable income due to lower prices paid by households for consumption of fuel products. The indirect effect is seen in the lower prices paid by households for other goods and services led by the lower costs for fuel-based inputs of production, for example, the food cooked by street vendors using kerosene stoves.

Precisely quantifying these effects is difficult. We cannot directly observe which socio-economic groups consume which products and at what price. The official energy statistics handbook in Indonesia reports only aggregate fuel consumption by sectors, while the disaggregation by refined products (e.g. premium, diesel, kerosene) is not available by the sector of the economy consuming the fuel.<sup>7</sup> This is probably because retail sales of refined products do not record whether the purchased product will be used for domestic or commercial transport purposes. A 2004 household survey found that households consume 5.2 million liters of gasoline or 30 percent out of total retail sales (17.5 million liters); 291,000 liters of diesel, or 1 percent of the total retail sales (27.1 million liters); and 10.1 million liters of kerosene or 89 percent of the total retail sales (11.4 million liters). If the same consumption shares are assumed for 2007, then 51 percent of the total amount budgeted for fuel subsidies would be absorbed by households.

Indonesia’s fuel subsidies are regressive. Benefit incidence results can be drawn based on the 2007 National Household Survey. Defining the lowest income quintile of the population as poor,<sup>8</sup> more than 90 percent of fuel subsidies benefit the non-poor. Assuming that consumers pay the same price regardless of their income, we can see that the pattern of fuel products consumption directly determines the distribution of the subsidy. It is commonly argued, for example, that the significant subsidies for kerosene are justified on grounds that kerosene is largely the only fuel product consumed by the lower income population. Yet, while the poor consume more kerosene than any other fuel, national survey data reveal that kerosene consumption increases with income level, with the only exception of the highest 10 percent of the income distribution, for whom kerosene appears to be an inferior good (Annex 9).

**Figure 14. Incidence of fuel subsidies, 2007**

<!-- Start of picture text --> Subsidies go mostly to the richest<br>Share of fuel subsidy received by households in each<br>consumption decile<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>Poor Household consumption decile Rich<br>Souce: Susenas 2007 and preliminary World Bank calculations<br>Source: World Bank calculations from Susenas 2007<br><!-- End of picture text -->

> 7 Annex 9 describes the distribution of refined fuel products by overall consumption decile, based on the 2004 National Household Survey.

> 8 About 15 percent of the population was defined as poor in 2008.

17
# **References**

- **[1]** Barro, Robert and Jong-Wha Lee, “International comparisons of educational attainment,” _Journal of Monetary Economics_ , 32(3), 363-94 (1993).

- **[2]** Barro, Robert and Jong-Wha Lee, “International data on educational attainment: updates and implications,” _Oxford Economic Papers_ , 53(3), 541-63 (2001).

- **[3]** De Soto, Hernando, _The Other Path: The Invisible Revolution in the Third World_ , HarperCollins (1989).

- **[4]** Fields, Gary. 1990. “Labour Market Modelling and the Urban Informal Sector: Theory and Evidence.” In David Turnham, Bernard Salomé and Antoine Schwarz, eds, _The Informal Sector Revisited_ , 49-69. Paris: OECD.

- **[5]** Gerxhani, Klarita, “The Informal Sector in Developed and Less Developed Countries: A Literature Survey,” _Public Choice_ , 120(3/4), 267-300 (2004).

- **[6]** Gwartney, James, Robert Lawson, Russell Sobel and Peter Leeson, _Economic Freedom of the World: 2007 Annual Report_ , The Fraser Institute (2007). www.freetheworld.com.

- **[7]** International Labour Organization (ILO), Bureau of Statistics, _LABORSTA Internet_ . laborsta.ilo.org.

- **[8]** International Labour Organization (ILO), _Global Employment Trends for Youth,_ Geneva (2004).

- **[9]** Loayza, Norman, “The Economics of the Informal Sector: A Simple Model and Some Empirical Evidence from Latin America,” _Carnegie-Rochester Conference Series on Public Policy_ , 45, 129-62 (1996).

- **[10]** Loayza, Norman and Claudio Raddatz, “The Composition of Growth Matters for Poverty Alleviation,” World Bank Policy Research Working Paper No. 4077 (2006).

- **[11]** Loayza, Norman and Jamele Rigolini, “Informality Trends and Cycles,” World Bank Policy Research Working Paper No. 4078 (2006).

- **[12]** Maloney, William, “Informality Revisited,” _World Development_ , 32(7), 1159-78 (2004).

- **[13]** Miles, Marc, Edwin Feulner and Mary O’Grady, _2005 Index of Economic Freedom_ , The Heritage Foundation and The Wall Street Journal (2005).

- **[14]** Perry, Guillermo, William Maloney, Omar Arias, Pablo Fajnzylber, Andrew Mason and Jaime Saavedra-Chanduvi, _Informality: Exit and Exclusion_ , The World Bank (2007).

- **[15]** The PRS Group, _International Country Risk Guide_ (ICRG). www.icrgonline.com.

- **[16]** Schneider, Friedrich, “The Size of the Shadow Economies of 145 Countries all over the World: First Results over the Period 1999 to 2003,” IZA DP No. 1431 (2004).

- **[17]** Schneider, Friedrich and Dominik Enste, “Shadow Economies: Size, Causes, and Consequences,” _Journal of Economic Literature,_ 38(1), 77-114 (2000).

15