rafmacalaba/datause-encoder-data huggingface dataset:
**Tables**

**Table 1:** Turnover, Public Goods, and Nepotism: Correlations (OLS)

|||**Public**|**Goods Ind**|**ex**|**Village**|**government**|**Village h**|**ead survey**|
|---|---|---|---|---|---|---|---|---|
||_Podes_|2021|_Podes_202|1-2014growth|% Ne|w appts|Village h|ead relative|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Incumbent runs|0.006|0.102|0.046|0.643|-0.035|0.031|0.031|-0.024|
||(0.064)|(0.077)|(0.377)|(0.511)|(0.029)|(0.034)|(0.040)|(0.047)|
|Incumbent wins||-0.181<sup>_∗∗_</sup>||-1.121<sup>_∗∗_</sup>||-0.123<sup>_∗∗∗_</sup>||0.102<sup>_∗∗_</sup>|
|||(0.080)||(0.492)||(0.033)||(0.046)|
|_P-value_, total effect||0.29||0.22||0.0055||0.091|
|Region FEs|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Sample mean|0.31|0.31|1.33|1.33|0.39|0.39|0.37|0.37|
|Observations|565|565|562|562|784|784|679|679|

_Notes_ : This table reports OLS estimates of each outcome on two binary variables: _incumbent runs_ , indicating whether the incumbent village head competed in the most recent election and _incumbent wins_ , indicating whether the incumbent won that election, respectively. Units of observation are villages in columns 1-6 and village heads in columns 7-8. The dependent variable is: in columns 1 and 2, a standardized index of local public service provision constructed using the 2021 Podes data. The index has the following 10 components: drinking water, sewage, garbage collection, street lighting, kindergartens, primary schools, village maternities (polindes), community health centers (puskesmas), paved roads, and public transit. In columns 3 and 4, the growth in service quality between the 2014 and 2021 Podes waves. In columns 5 and 6, the rate of bureaucratic turnover at the village level since the last election, defined as the fraction of new bureaucrats appointed to their current position since the last election. In columns 7 and 8, a dummy equal to 1 if relatives of the village head are employed in the village government. Regressions include region fixed effects. The main regions in our sample are Java, Sulawesi, Sumatra, Kalimantan, and NTB-Bali. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust standard errors in parentheses.
12 female enrollments. The third factor is more difficult to interpret. Essentially, decreasing density at the lower consumption levels (falling poverty) coupled with increasing enrollments associated with policies and/or taste changes results in a negative number for the interaction effect. Some of these negative correlations are quite high at higher education levels.

# 4. Utilization of Health **Services**

# _The Picture in 1987_

According to the 1987 SUSENAS, 65 percent of all those who reported being ill during the preceding week also reported seeking treatment outside the family. The numbers imply that on average Indonesians each visit some kind of health care facility 2.2 times a year.10 Of all treatments sought outside the family in 1987, the greatest numbers consulted a primary health center (43%), followed by paramedics (22%), private doctors (17%), hospitals (8%), traditional healers (6%), and lastly, polyclinics (4.5%). The 1978 SUSENAS implied that 23 percent of visits were to public health centers, 19 percent to hospitals and 14 percent to private doctors (Meesook, 1984).

For a variety of reasons, including both the availability and the demand for services, households from different expenditure groups are often found to exhibit different health facilities utilization behavior. Table Sa provides evidence, based on the 1987 SUSENAS survey, of how individuals ranked into deciles of per capita household expenditures (with decile 1 being the poorest and 10 the wealthiest) responded to a health complaint. This information is presented for the all Indonesia, all urban, and all rural distributions. It is clear that area of residence also has bearing on health utilization characteristics.

'° This average is extremely close to the 2.1 times reported for 1978 (Meesook, 1984). This is a surprising result as all other evidence indicates increased usage of health services. For both survey dates, these figures are based on the weekly recall variable rather than the 3 monthly recall. (For the 1987 data, the latter implies only .4 visits per person yearly.) The number of total illnesses and hence, total visits to health facilities, may be influenced by the different times of the year during which the surveys were held.
- Cho, Yoonyoung and Zaineb Majoka (2020). _Pakistan jobs diagnostic: Promoting access to quality jobs for all_ . World Bank.

- Coniglio, Nicola Daniele and Jan Brzozowski (2018). “Migration and development at home: Bitter or sweet return? Evidence from Poland”. In: _European Urban and Regional Studies_ 25.1, pp. 85–105.

- De Coulon, Augustin and Matloob Piracha (2005). “Self-selection and the Performance of Return Migrants: the Source Country Perspective”. In: _Journal of Population Economics_ 18.4, pp. 779–807.

- De Vreyer, Philippe, Flore Gubert, and Anne-Sophie Robilliard (2010). “Are there Returns to Migration Experience? An Empirical Analysis Using Data on Return Migrants and Nonmigrants in West Africa”. In: _Annals of Economics and Statistics/Annales d’ ́economie et de statistique_ 97, pp. 307–328.

- Docquier, Fr ́ed ́eric and Hillel Rapoport (2012). “Globalization, brain drain, and development”. In: _Journal of economic literature_ 50.3, pp. 681–730.

- Dustmann, Christian, Itzhak Fadlon, and Yoram Weiss (2011). “Return Migration, Human Capital Accumulation and the Brain Drain”. In: _Journal of Development Economics_ 95.1, pp. 58–67.

- Dustmann, Christian and Joseph-Simon G ̈orlach (2016). “The Economics of Temporary Migrations”. In: _Journal of Economic Literature_ 54.1, pp. 98–136.

- Dustmann, Christian and Oliver Kirchkamp (2002). “The Optimal Migration Duration and Activity Choice after Re-migration”. In: _Journal of Development Economics_ 67.2, pp. 351– 372.

- Dustmann, Christian and Yoram Weiss (2007). “Return Migration: Theory and Empirical Evidence from the UK”. In: _British Journal of Industrial Relations_ 45.2, pp. 236–256.

- Fargues, Philippe (2011). “Immigration without Inclusion: Non-nationals in Nation-building in the Gulf States”. In: _Asian and Pacific Migration Journal_ 20.3-4, pp. 273–292.

- Fargues, Philippe and Fran ̧coise De Bel-Air (2015). “Migration to the Gulf States: The Political Economy of Exceptionalism”. In: _Global Migration: Old Assumptions, New Dynamics_ , pp. 139–166.

- Farole, Thomas, Yoonyoung Cho, Laurent Bossavie, and Reyes Aterido (2017). _Bangladesh Jobs Diagnostic_ . World Bank.

21
tical at 170%.

Third, the trend parameter estimates,  _3_ , are spread over an even wider range compared to energy pass‐through and inflation parameter estimates. The non‐energy price index, for example, shows no trend at all. Yet, the metal price index exhibited an almost 2% positive annual trend while the agriculture index showed a 1% negative an‐ nual trend. Furthermore, the trend parameter estimates of the agriculture sub‐indices vary considerably both in magnitude and level of significance, from 0.08 ( _t‐value_ = 0.19) for raw materials to ‐3.12 ( _t‐value_ = 5.22) for beverages, a result consistent with Deaton’s (1999, p. 27) observation that what commodity prices lack in trend, they make up in va‐ riability. On the other hand, the large variation among the trend parameter estimates implies that the validity of the Prebisch‐Singer hypothesis, often discussed in the con‐ text of the secular decline of primary commodity prices, may require some rethinking (see Spraos 1980, among others).

# **IV. Concluding remarks**

Based on 1960‐2008 annual data and a simple econometric model, I estimated long‐run energy transmission elasticities for 11 non‐energy commodity groups. The elasticity for the non‐energy index was estimated at 0.28. At a disaggregated level, the fertilizer index exhibited the largest elasticity, followed by precious metals, food, and metals & miner‐ als.

The key implication of these findings is that, for as long as energy prices remain elevated, most non‐energy commodity prices are expected to remain high and thus any analysis of non‐energy commodity markets cannot be undertaken in isolation to devel‐ opments in energy markets.

On the methodological side, the fact that the estimates of the current study (which included the recent commodity boom) are larger than earlier ones (not account‐ ing for the recent boom) implies that, perhaps, time‐varying parameter or switching‐

**_— 9 —_**
emphasizing the importance of policies to support cross-border trade with Sudan (World Bank, 2014a). Since 2013, the border was re-opened leading to increased trade. However, absence of bilateral trade statistics with Sudan makes it impossible to estimate the volume.

Before 2012, exports exceeded imports considerably. Between 2008 and 2011, exports remained above 60 percent of GDP largely consisting of oil. Imports averaged 22 percent of GDP from 2008 to 2011. The oil shutdown in January 2012 led to a collapse of oil exports and, hence, a significant trade deficit. The import share of GDP increased from around 22 percent in 2011 to 39 percent in 2012 and the resulting trade deficit reached 36 percent of GDP. In absolute values, imports of goods remained stable in 2012 but GDP declined sharply (World Bank, 2014a).

South Sudan imports food and vegetables as well as transportation and machinery. In 2008, the main imports categories were food, with 26 percent of imports in the category for food stuff and 12 percent in the category for vegetables.

Nimule, on the border with Uganda, is the most important border crossing. In 2012, about 80 percent of customs revenue was collected at Nimule. Kaya, another customs station at the border with Uganda, and Juba airport each contributed a share of about 10 percent to customs revenue. Other border stations with respect to revenue generation are negligible.<sup>10</sup> The vast majority of these imports are headed to Juba, which effectively represents the only international trade node in South Sudan.

# **3. Price Differentials and Price Volatility**

The integration of spatially separated markets implies interconnectedness among them through trade or through the possibility of trade. Thus, the forces of arbitrage make the long-run price differences that remain among them reflect trade costs only.<sup>11</sup> Assessing integration involves examining carefully the evolution of prices of specific products across different cities in South Sudan, and testing their short and long-run co-movement. When doing so, the drivers of different degrees of co-movement can also be explored, which allows us to understand how policy actions can help achieve greater market integration.

## **3.1. The Data**

We use consumer price series for seven food products for the period 2009-13: broad beans, groundnuts, maize (white), millet, sesame, sorghum and wheat flour, and for two energy products: diesel and gasoline. Five food products were dropped due to absence of data. These are: cassava and cassava meal for which the data available only covered the capital city of Juba, and red beans, maize in grains, and cooking oil for which data available covered less than 12 observations per city. For the remaining products, the period of analysis is heterogeneous, but typically covering 2009-2013 (diesel and gasoline prices, for example, are only available for 2012-2013). Appendix

> 10 This might change with the construction of the all year passable road to Kenya.

> 11 Varela, G. et al (2012).

7
A large scholarship posits the critical role of judicial effectiveness in encouraging economic growth. The link between the proper functioning of courts and business activity is put forward as early as in the writings of Thomas Hobbes, who observed that courts ensure the smooth working of the economy because otherwise “he that performeth first has no assurance the other will perform after because the bonds of words are too weak to bridle men’s ambitions, avarice, anger, and other passions without the fear of some coercive power” ([1651] 1962:8). Extending this line of thinking, Max Weber’s (Weber 1905) analysis of China and Western Europe argues that effective courts are the primary means of ensuring the rule of law.

The link between the rule of law and economic growth is in turn well-documented. Adam Smith ([1755] 1980:322) observes that “a tolerable administration of justice,” along with peace and low taxes, is all that is necessary to “carry a state to the highest degree of opulence.” Nobel Prize laureate Douglass North (1990) argues that “the inability of societies to develop effective, low-cost enforcement of contracts is the most important source of both historical stagnation and contemporary underdevelopment”. Williamson (1995) posits that the high-performance economy features a multitude of long-term contracts—business relationships that thrive in the presence of a well-functioning judicial system. A large empirical literature follows on these seminal studies (for example, Acemoglu and Johnson 2006; Dhillon and Rigolini 2011; Spruk and Kovac 2019).

Other theorists suggest that the pace and character of judicial improvement can build on a country’s initial conditions. This evolutionary perspective dates back to Veblen (1899), who describes “the evolution of social structure has been a process of natural selection of institutions,” and is also associated with Hayek (1973), who argues that institutions “have evolved because the groups who practiced them were more successful and displaced others.” These theories have been the subject of intense debate in both the academic and development aid communities. The empirical evidence has proliferated in the past quarter century, and we survey this evidence using the framework developed in three previous surveys on the practice of judicial reform (Posner 1998; Messick 1999 and Botero et al 2003) published in the _World Bank Research Observer_ (WBRO). We focus on empirical studies in peer-reviewed economics and law journals since these three scholarly articles were published, to answer some 2
**Table 2: Descriptive statistics for the pooled sample and by gender of the household head (2016)**

||Pooled<br>sample|Female-<br>headed<br>households|Male-headed<br>households|
|---|---|---|---|
|Self-reported productivity (XOF/ha)|200,438|196,096|201,015|
|Self-reported productivity (XOF/ha)- Export crops|192,969|176,564|194,143|
|Self-reported productivity (XOF/ha)- Food crops|217,913|214,056|218,550|
|Total land area cultivated in ha***|5.48|2.79|5.84|
|Number of Fields Managed by the household***|2.60|2.19|2.65|
|Household head age (years)***|43.72|50.16|42.87|
|Married***|0.84|0.38|0.90|
|Divorced/Separated***|0.03|0.09|0.02|
|Widowed***|0.06|0.44|0.01|
|Single/Never married*|0.07|0.09|0.06|
|Household head has Primary education***|0.28|0.21|0.28|
|Household head has Secondary education***|0.13|0.03|0.14|
|Household head has Tertiary education|0.01|0.00|0.01|
|Household head disability***|0.15|0.23|0.14|
|Dependency ratio***|1.05|1.39|1.00|
|Household Male Labor Use (Number)***|2.26|0.62|2.47|
|Household Female Labor Use (Number)***|1.24|2.16|1.12|
|Household Child Labor Use (Number)***|0.08|0.14|0.07|
|Hired labor use (Number)**|3.03|2.86|3.05|
|Total number of crops produced***|2.77|2.48|2.80|
|Household used any purchased seed|0.30|0.32|0.29|
|Plots that Use Chemical Fertilizer (% of total)***|0.17|0.08|0.18|
|Plots that Use Pesticide (% of total)***|0.51|0.35|0.53|
|Export crops dummy (1 if Household produced any<br>export crop)***|0.83|0.50|0.88|
|Observations|3936|517|3419|

Dependency ratio: number of individuals in the household under 15 and over 65 over the total number of household members. ***/**/* indicate statistical significance of the difference between male-headed and female-headed households at the 1/5/10 percent level respectively.

7
check their current balance at any time. Jisomee Mita data contains water consumption, invoice amounts, payment history, current balance, and contact information of the property owner. When payments or balance checks are submitted, the Jisomee Mita data are updated automatically. However, monthly standing charges are applied to each account independent of whether a property owner made a payment or billing enquiry which means that each property owner’s balance is updated at least once a month.

The billing data is complemented with tenant and property owner survey data. A short baseline listing phone survey of property owners was conducted in August and September 2018. This captured ownership and water/sanitation connection status, property owner residency and number of paying tenants in the compound.

From August to October 2019 a follow up survey of both property owners and tenants included in the enforcement intervention captured data on rent, service-level satisfaction, political engagement, property owner-tenant interactions, water use and practices, mental well-being and general demographic measures of one randomly selected tenant and the corresponding property owner from each compound in the sample.

We use utility billing data to generate payment outcomes. This includes: (1) the proportion of property owners making a payment for water/sewer charges since the intervention, (2) the total amount paid by property owners’ post-intervention and (3) the proportion of outstanding service charges paid post-intervention. The data spans the entire period from when the first property owners were connected in 2014 up to nine months after the interventions were implemented (September 2019).

We rely on the follow up tenant and property owner survey to measure a range of outcomes that assess the possible welfare effects of the enforcement intervention. We collect measures related to water and sanitation infrastructure and use to assess how the intervention may have affected access: whether the compound had a pour-flush toilet and piped water connection at the time of the endline, whether the water connection was working, the main source of water used by compound residents, the amount of time spent fetching water in the last week, the amount spent on water in the last month, and the overall piped water consumption in the compound. We also capture a range of outcomes that may be affected by the intervention and changes in water and sanitation access. Here we combine like outcomes into weighted, standardized indices following Anderson (2008) to reduce the potential for false positives from multiple hypothesis testing. We generate the following indices (full list of sub-indicators is presented in the appendix Tables A1 and A2): _Tenant-Property owner relationship_ : For the property owner index this includes property owner perceptions on whether tenants complain about the water and sewer facilities or about the general conditions of the compound, and whether tenants keep the compound in 15
# **1. Introduction**

Youth employment is a challenging issue in many developing and transitional countries (Kolev and Saget 2005). The youth unemployment rate is usually two times to three times that of the adult unemployment rate. And this figure is probably an underestimate because it normally does not account for those who are “discouraged” in seeking work and remain “inactive” (neither in school nor in the labor market). Even most of the working youth in developing countries are in low-quality jobs without any security or social benefits, receiving low wages, and working in poor conditions. The ILO (2006) estimates that globally about 23 percent of the total young work force are “young working poor,” with earnings of less than $1 a day.

To increase the awareness of and stimulate more interventions around youth issues, the 2007 World Development Report (World Bank 2007) summarizes the challenges to youth and stresses the necessity of investing in youth in developing countries, especially smoothing the transition from school to work and creating more opportunities for youth employment. However, before formal intervention plans are developed, a diagnostic analysis of the overall labor market, and specifically the youth labor market, should be carried out.

In the case of Pakistan, there is a growing recognition of the political urgency to respond to the challenges of youth employment. The challenges are multi-faceted. The transition from school to the labor market is not smooth; the youth unemployment rate is higher than the adult unemployment rate; many young people work in the informal sector as unpaid family workers, own-account, or casual wage workers; and female youth are in worse shape than their male counterparts on various employment dimensions.

However, an analysis of youth employment cannot be separated from the overall labor market analysis. Pakistan has a unique labor market, and many characteristics of the youth labor 2
The coefficients in Table 1 provide clear, consistent evidence of a negative association between the stationary or detrended components of per capita GDP and the infant mortality rate.<sup>7</sup> The implied effects are substantial: a one-percent increase in per capita GDP is associated with a decrease in infant mortality of between 0.18 and 0.44 deaths per thousand children born. When the dependent variable is defined in logs, the coefficients imply an elasticity of between -0.31 and -0.79.<sup>8</sup> Note also that those methods which are most flexible in accounting for underlying secular trends result in larger (in absolute value) estimates of the association between per capita GDP and infant mortality. Previous studies have generally adjusted only for linear trends (as in Jamison et al. 2004) and hence may underestimate the contemporaneous relationship between detrended GDP and infant mortality.

- _B. Main results based on unit data_

Table 1 suggests that, no matter how we account for underlying trends, there is a robust association between infant mortality and per capita GDP when we look at country-year averages. We therefore now move to analysis of the unit data with the assurance that modeling deterministic trends with a polynomial in time does not yield results that are substantially different from those obtained with other methods. We begin by presenting the results from a onestep regression of the probability that child _i_ born to mother _m_ in country _c_ and year _t_ dies as a

> 7 These results are not particularly sensitive to the decision to adopt a 10 year recall period. For example, in the change on change specification in Table 1 we estimate that (based on the 10-year recall), the elasticity of infant mortality with respect to per capita GDP is -0.557 (with a standard error of 0.146); when we do the same analysis with a 15-year recall, the comparable value is -0.518 (with a standard error of 0.132); and when we conduct the analysis with a 5-year recall period, the point estimate is -0.514 (with a standard error of 0.239). A complete set of results with different recall periods is available from the authors upon request.

> 8 Deaton (2006) uses the WDI data to show that there is a strong negative relationship between the growth of GDP and the _proportional_ change in infant mortality, but no significant relationship between the growth of GDP and the _absolute_ reduction in infant mortality. We identify a negative relationship between GDP and infant mortality both when infant mortality enters in logs and when it enters in absolute levels.

11