rafmacalaba/datause-encoder-data huggingface dataset:
ultimately translate into higher household expenditure (both food and non-food), as well as human capital investment (in children, for example). The results show that the returns to road investment for household per capita expenditure are about 11 percent in the RDP and RRMIMP villages, a substantial gain in terms of higher consumption and income for rural households. This means that rural households in villages targeted by the road development project have on average an 11 percent higher consumption per capita per year.<sup>20</sup> In addition to household consumption, average school participation among boys is about 20 percent higher in RDP villages, not controlling for proximity to the project road; road development also has a positive but barely significant impact on girls’ schooling in the same sample. In the RRMIMP data, the project effect is fairly similar for boys and girls, leading to a 14 percent increase in schooling. Indeed, in our results only the project indicator seems to have had any significant impact on schooling in the RRMIMP sample. Higher school attendance of boys and girls are therefore some of social returns due to road investment; additional social returns to be assessed from road investment include returns to health and nutrition, which are not pursued in this paper.

Finally, as a robustness check, we estimate the non-price outcomes (HH per capita expenditure, Laspeyres quantity index, boys’ and girls’ schooling, as well as male and female labor supply) on the same set of explanatory variables as well as the price variables discussed above (men’s agricultural wage, fertilizer price, and the agricultural price index facing the household). The net returns are presented in table 3, and reflect the returns if we held the structural model (taking prices as exogenous) to be correct. Overall, the estimated effects are slightly higher than our original returns, except for the aggregate output index where the return is lower. The results are fairly robust and in almost all cases retain significance with our earlier estimates.<sup>21</sup>

# **5. Distribution of benefits of road investment**

While the above analysis allows for a broad range of controls in road project evaluation to generate average returns to road investment, it still imposes a single response coefficient γ on all households and communities conditional on other factors, such as the household distance to a paved road. The estimates of the earlier section are based on the assumption that distance does not matter in terms of gains from a road development project. This may not be the case, however. That is, households located in a roadside village may benefit more from road investment than households living away from a paved road. Testing this hypothesis requires differences in household distance from road, which is very minimal

> 20 The returns to consumption seem to be higher for rural electrification than rural road investment (as high as 23 percent for electrification as compared to 11 percent for rural road development). 21 The only exceptions are boys’ schooling and female labor in the RDP sample.

12
<!-- Start of picture text --> = Re}<br>fa)<br>=io}<br>&g<br>z<br>BE<br>Es<br>i<br>go<br>3a,<br>£E a<br>t-1 t t+1 +2 +3 t+4 t+5 +6 +7 t8 t+9<br>Horizon, h (years)<br><!-- End of picture text --> <!-- Start of picture text --> a<br>fa)=<br>=<br>g5<br>z<br>BE<br>Es<br>2<br>ao<br>3—a,<br>- ia<br>t-1 t t+1 +2 +3 te t+5 +6 +7 t8 t+9<br>Horizon, h (years)<br><!-- End of picture text -->
spending in a region between 2002 and 2006, the average urban child z-score would have increased by 19% of a standard deviation. In addition, the coefficient for urban areas is almost six times larger than the one estimated for the whole sample<sup>21</sup> . This magnitude should be understood in the context of a growth curve.

I use growth charts<sup>22</sup> to assess the extent to which increasing public expenditure in urban areas can improve a child’s nutritional level. A growth chart consists of a series of percentile curves that show the distribution of height-for-age for boys and girls since birth. Using these tables, a child’s height can be compared to the expected height parameters of children of the same age and sex to determine whether the child is growing appropriately. I find that more than 61% of the increase in malnutrition disparities between rural and urban areas can be explained by the increase in total public expenditure during the two rounds of the survey (63% for boys and 60% for girls).

As a robustness check, I estimate the impact of changes in public spending on changes in child height between the two periods<sup>23</sup> . The results are consistent with the ones obtained with z- scores of height for age. In rural areas, there is no statistically significant effect. In urban areas, every increase of 100 million soles in public expenditures increases the average child's height by 0.83 cm. Furthermore, for an increase equal to the average change in public expenditures between survey rounds, a child’s height will change by 15.9 cm.

21 I performed additional estimations to address the possibility of non-linear effects in spending. I used a logarithmic transformation of the expenditure variable as the independent variable. I found no statistically significant effect of spending for the total sample or for rural areas. As before, I found a statistically significant and positive effect of public expenditure on z-scores in urban areas. According to this estimation, a 1% increase in expenditures would increase z-scores by 0.007.

22 Source: World Health Organization.

23 I include as an additional control the lag value of height, which relaxes the assumption that the coefficient on the lag is equal to 1.

16
# **4.2 Estimation Strategy**

Here we proceed in two stages. First, we describe the methodology to assess the relationship between services sector reform and performance, and second, that used to assess the link between services sector reform and manufacturing productivity.

## **4.2.1. Reform and Perception-Based Performance**

For reforms in the services sector to have an effect on manufacturing productivity, a preliminary condition is that these reforms affect somehow the performance of the services sector, either by affecting the quality, availability or price of the services provided in the economy.

Unfortunately, data on services performance in Indonesia are scarce. For this reason, here we use a perception based indicator of services performance. To construct them, we combined data on perceptions from the World Bank Enterprise Survey and on input-output data from BPS, and proceeded in the following steps:

1. We looked at firms’ responses to questions on whether electricity, transport or communications were ‘no obstacle’ to doing business, a minor obstacle, a moderate obstacle, a major obstacle or a very severe obstacle in 2003 and in 2009. Across all three sectors, the proportion of firms reporting ‘no obstacle’ increased significantly (electricity: 22.3%, telecommunications: 20.8%, transport: 17.8%) between 2003 and 2009. This suggests improved service sector performance.

2. We looked at how the degree of restrictiveness on FDI evolved over this period, relying on the OECD restrictiveness indicator. We found moderate increases in openness in the transport and electricity sectors, while restrictiveness increased after 2007 for the telecommunications sector.

3. Using data from input-output tables we constructed for each manufacturing sub-sector an index of performance of the services sub-sectors from which they sourced inputs, and an index of restrictiveness toward FDI in those same service sub-sectors (see Figure 9, a formal description of the way in which the index is calculated is presented below).

4. We then explored the link between these two indicators, controlling for sector specific effects.

## **4.2.2. Services Reform and Manufacturing Productivity**

To assess the impact that restrictive policies towards foreign direct investment in the service sector may have on the performance of the manufacturing sector, we proceed in two stages. First, we use input-output tables to get a sense of the importance that each service sector has for the input costs of each manufacturing sector. The service restrictiveness (rest) that each sector in manufacturing faces is calculated as the weighted average of each service sector’s OECD restrictiveness index, where the weights are given by the share in the total input bill of a given manufacturing sector ‘s’ accounted for the service ‘j’. Formally, we proceed as in equation (1): 10
# **1. Introduction**

“Noting that soils constitute the foundation for agricultural development, essential ecosystem functions and food security and hence are key to sustaining life on Earth,” the UN General Assembly declared 2015 the International Year of Soils (A/RES/68/232).<sup>1</sup> The recent increased attention afforded to soil health is for naught, however, if soil health measurements are inaccurate or of inadequate resolution. This is especially critical in the face of increased variability in weather conditions brought on by climate change. Renewed interest in increasing agricultural productivity to meet food security needs and increasing resilience of agricultural systems in developing countries, especially in Sub-Saharan Africa, makes understanding soil fertility constraints and trends ever more important.

Much of the current analysis on agricultural productivity is hampered by the lack of consistent, high quality data on soil health and how it is changing under past and current management. This is beginning to change, however. As soil testing methods become increasingly rapid and affordable, data constraints lessen. In Ethiopia, for example, an innovative national-scale soil mapping operation is underway. The Ethiopia Soil Information Service (EthioSIS) project, supported by the World Bank-funded Agricultural Growth Program and implemented by the Ethiopian Agricultural Transformation Agency, has begun to reveal its value (World Bank, 2016).<sup>2</sup> Although the project has yet to be completed at full scale, knowledge acquired through EthioSIS and disseminated by extension agents has already led to the reformulation of critical inputs and substantial increases in wheat yields (Sawa, 2016). The early successes of EthioSIS illustrate the potential agricultural gains that can be unlocked by improving the detail and geographical scope of soil data.

With an ever-expanding population and finite land resources, soils will become more and more taxed as we strive to produce sufficient food to meet the needs of the world population. Not only will there be a need to increase food production to accommodate the growing population, but at present roughly 795 million people are estimated to be undernourished, 98 percent of whom are in developing regions (FAO et al., 2015). Land, although of finite quantity, can be used more productively, as evidenced by startling yield gaps observed across the world. The magnitude of yield gaps varies significantly across crops and context. For example, Lobell et al. (2009) clearly illustrate the variation in maize yield gaps, as average tropical lowland maize yields in Africa are less than 20 percent of yield potential, while tropical lowland maize yields reach approximately 40 percent of yield potential on average in East and Southeast Asia. Rice exhibits consistently smaller yield gaps, with average rice yields exceeding 80 percent of yield

> 1 A/RES/68/232: http://www.un.org/en/ga/search/view_doc.asp?symbol=A/RES/68/232&Lang=E. 2 To learn more about EthioSIS, please visit: http://www.ata.gov.et/highlighted-deliverables/ethiosis/.

2
6 contrast overall profitable.$ Whereas the state-enterprise sector was concentrated in industrial production, the private sector was more specialized to trade and services; and whereas **the** state enterprises were characteristically large, the private enterprises were in general small, with average employment of 2.2 employees at the end of 1993.9 These circumstances have implications for the tax base in transition, to which we shall return. First, however, we consider the tax bases inherited from the prior socialist system.

# 3. SOCIALIST TAXATTON

As was the case in socialist countries generally, the principal tax base in Bulgaria under the socialist regime consisted of state enterprise surpluses subject to turnover and profits taxes. In 1988, the last complete year of planned socialism, these two taxes together with excises provided approximately 60% of total government revenue, equal to some 34% of GDP (see Table 1). Numerous different rates of turnover tax reflected pricing decisions based on conceptions of social need, while the profits tax distributed surpluses from enterprises whose prices yielded a "profit" to enterprises whose prices resulted in "losses." These profits and losses did not have the S' In 1992 aggregate profits of the private sector, for firms encompassed by the survey of the National Statistical Instltute of Bulgaria and reporting, were 3.4 billion leva on revenue of 67 billion leva.

9' Source: National Statistical Institute of Bulgaria
<u>Table 13. Maximum Price Regulation in Africa, 2001-2005</u>

||Sampl|<br>e countries|All|<br>in Africa|
|---|---|---|---|---|
||Obs|Share(%)|Obs|Share(%)|
|Not regulated|0|0.0|2|4.5|
|Regulated by:|||||
|Regulatory authority|12|66.7|28|63.6|
|Of which, autonomous regulatory authority|11|61.1|22|50.0|
|Line ministry|3|16.7|9|20.5|
|Regulatory authority and line ministry|2|11.1|4|9.1|
|Regulatory authority and other ministry|1|5.6|1|2.3|
|Total|18|100.0|44|100.0|

Source: ITU Regulatory Knowledge Centre: Regulators Profile Database.

<u>Table 14. Interconnection Rate Regulation in Africa, 2002-2005</u>

||Sampl|<br>e countries|All|<br>in Africa|
|---|---|---|---|---|
||Obs|Share(%)|Obs|Share(%)|
|Not regulated|0|0.0|1|3.6|
|Regulated by:|||||
|Regulatory authority|12|85.7|24|85.7|
|Of which, autonomous regulatory authority|11|78.6|19|67.9|
|Line ministry|2|14.3|3|10.7|
|Total|14|100.0|28|100.0|

Source: ITU Regulatory Knowledge Centre: Regulators Profile Database.

However, it may be questionable how to define regulatory autonomy in Africa. Among my sample countries, Uganda has been praised as its well designed regulatory authority, Uganda Communications Commission (UCC) (Shirley _et al_ . 2002). However, it is not still fully independent of the ministry; the budget and commissioner appointment are approved by the line minister. In the case of Senegal, the telecommunications regulatory authority, Agence de Régulation des Télécommunications (ART), is more independent in financial and personnel terms, because ART generates its own resources absolutely from spectrum fees and the president appoints the head of the agency (ITU Regulators Profile database). The Botswana Telecommunications Authority, which is the first independent regulatory authority in the region, has also been applauded for its financial and operational independency (ITU, 2001). To the contrary, Ghana’s regulatory authority, National Communications Authority (NCA), is called autonomous but may be relatively weak and subservient to the Ministry of Communications, even though its financial resources are earmarked and the head is appointed by the president (Haggarty _et al_ . 2003).

Discriminatory pricing is more common when countries have autonomous regulators. Table 15 relates the price discrimination practices in my sample countries to the ITU Regulators Profile database. Although the indication of regulatory autonomy is not a perfect measure for the extent to which discriminatory pricing is allowed, the shares of 37
To explore if and which observable characteristics about an individual or their household are associated with the prevailing liberality (or conservativeness) of beliefs and social expectations, we run an OLS regression of each thematic index on household and individual-level characteristics of respondents, using the specification presented below.

Yi is the PNB, SNE, or Sanctions index score and b1 to b10 are various individual and household characteristics of individual i _._ We conduct heterogeneity analysis by estimating equation 5 for each index across the two components (PNB, SNE) and separately for each of the two themes.

The outcome variable, indices, and the set of controls being the same as in equation (3). Mi captures the dimension of heterogeneity: migration status of the male spouse in the household (migrant vs. non- migrant households) and ethnicity (Madheshi vs. Non-Madheshi). αi captures the estimate on the association between each index and outcome when Mi = 0, and αi + βi is the estimate when Mi = 1.

# SECTION 5: FINDINGS

# Labor market outcomes and constraints faced by Nepali men and women.

Among the sample interviewed for this study, we find low levels of human capital investments which are comparable to the national average.<sup>24</sup> Education levels are low, with a third of respondents having completed some basic education (started a grade between one and eight but did not complete it), and 26 percent reporting not being educated at all. Only 19 percent of respondents have completed grade eight or some secondary education. On average, women have less education than men, with 34 percent having no education (compared with 15 percent among men), and only 7 percent of women having completed basic education (compared to 13 percent among men). **Table 2** presents sample statistics pertaining to key labor market outcomes of Nepali men and women (further details provided in **<u>Table A.7, Annex A</u>** <u>).</u> Most male respondents report having worked in the past 7 and 30 days prior to the survey. On the other hand, over half of all women indicate they have not worked in the past 30 days or in the week prior to the survey (57 percent). Almost 40 percent of women are not working nor looking for work.

The main reasons for not working in the past 30 days among women include household chores, lack of trustworthy childcare options, and lack of suitable jobs, with the latter being the most common reason for not working among men. Among non-working respondents, while 59 percent of men report having worked at some point in the past (most of them in the construction sector), only 17 percent of women report the same (most of those worked in the agriculture, forestry, and fishing sector). Among non- working women

> 24 A third of all working age individuals (15 years and above) in Nepal are illiterate, and 27 percent have up to basic education (Nepal Labor Force Survey, 2017/2018).

12
Table D.2. Impacts of PEP Program on Electoral Outcomes Matching Diff-in-Diff

||Election|Sha|re of Votes|for|Electoral|
|---|---|---|---|---|---|
||Turnout<br>(1)|Left<br>(2)|Center<br>(3)|Right<br>(4)|Competition<br>(5)|
|_Panel A. Mayoral Elections_||||||
|I(PEPm)_×_I(Post2018)t|-0.001|-0.007|0.012|-0.014|0.006|
||(0.004)|(0.020)|(0.027)|(0.022)|(0.016)|
|q-values|[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.847|0.464|0.359|0.428|0.384|
|Observations|5,035|5,035|5,035|5,035|4,842|
|_Panel B. Presidential Elections_||||||
|I(PEPm)_×_I(Post2018)t|0.002|-0.011|0.008|-0.002|0.001|
||(0.004)|(0.007)|(0.008)|(0.007)|(0.013)|
|q-values|[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.868|0.901|0.843|0.869|0.679|
|Observations|5,439|5,439|5,439|5,439|5,426|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Department FE_×_Year|Yes|Yes|Yes|Yes|Yes|
|Municipal controls_×_Year|Yes|Yes|Yes|Yes|Yes|

_Notes:_ This table presents the results of our main specification in equation 1 using a matching differencein-difference. We predicted the probability of having PEP take-up by municipality using the municipal characteristics that were significant in Table D.1 and restricted the sample to the common support sample in the treatment and control groups where the pscores overlapped. Panel A shows the results for the mayoral elections, and panel B for the presidential elections. The variable PEPm is the standardized value of the share of PEP holders over the population in 2018. Electoral competition was estimated following Chac ́on et al. (2006):1 - (% winning candidate - % second-place candidate). The estimates include municipality fixed effects. Clustered standard errors at the municipal level are reported in parentheses and False Discovery Rate (FDR) q-value in brackets *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

73
hurricanes and sea level rise), yet expenditures from overseas visitors plays a large role in economic output and can hardly be realized without increasing risk.

In situations where there is a trade-off between exposure to natural hazards and productivity or economic growth, improved risk management and more resilient development can mitigate this trade-off and accelerate growth and improved productivity (Hallegatte 2014).

This issue relates to the opportunity costs of ex-ante natural risk management, both by households and lenders. Uninsured risk exposure as well subjective perception endogenously change behaviors, and thus the conditional expected wealth creation dynamics. Due to failures in financial markets and risk aversion, the risk of weather-related shocks, including disasters, influences household choices of livelihood strategies in order to minimize the consequences of a shock. Households trade off expected gains for the reduced risk of suffering catastrophic losses.

Such livelihood strategies often entail diversification of activities and less productive investments, constraining productivity and wealth accumulation: households undertake costly behaviors as a means of reducing their exposure to uninsured risk, resulting in forgone welfare gains. Taking into account the prospective consequences of shocks, poor households may manage risk exposure by selecting low-risk, low-return asset and activity portfolios that reduce the risk of greater suffering but limit growth potential and investment incentives (Carter and Barrett, 2006; Dercon, 2005; Rosenzweig and Stark, 1989). This for instance discourages adoption of new technologies and decreases incentives to invest in productive capital accumulation.

An illustration of this effect is provided in an agricultural context in Zimbabwe by (Elbers et al., 2007). They find that farmers exposed to risk exhibit a mean capital stock which is half as large as for farmers who are not exposed. Of this reduction in capital, ex-ante risk accounts for two-thirds of the difference. In that case, therefore, most the welfare impact of risk is through reduced investments and risk-taking, not through the damages and losses when the hazard does materialize into an actual event.

# **4.2. Lower risk-taking due to “background risk”**

The previous section highlights that investing in natural risk-prone areas, such as settling in urban floodprone areas, may be necessary to improve living conditions and access higher-paying jobs and better schools. But households and firms face a wide variety of potential shocks that they have to manage together. As an illustration, the 2014 World Development Report (World Bank, 2013) reports the frequency of occurrence of a variety shocks, from loss of job to health and floods, in a number of developing countries (Table 3). In most countries surveyed, a large proportion of rural households report being affected by two or more shocks, with drought and flood predominant across most countries and regions.

18