rafmacalaba/datause-encoder-data huggingface dataset:
25 FUNDING AND FINANCING INFRASTRUCTURE very high, as is indeed the case in most developing countries, the feasibility constraints may simply be impossible to satisfy, precluding access to private finance. Clearly, providing more efficient mechanisms to resolve bankrupcy cases tops the list of desirable policy reforms in that respect.

In addition, even if this feasibility constraint is satisfied, the implications in term of higher prices and lower access are likely to restrict the social acceptability of private arrangements and fragilize the social contract between the state and the population, including non-poor segments.<sup>24</sup> The other important aspect highlighted by our model is that other institutional and efficiency improvements, notably those regarding the cost of raising taxation and the competitiveness of the financial sector, have the opposite effect of enhancing the comparative advantage of public finance and should therefore reduce the likelihood to rely on private debt. Again, this explains why countries do not switch to extensive private financing as their public sectors become more efficient.

Overall, these effects paint a complex picture, in which reforms and institutional improvements have heterogenous effects that may increase the feasibility of both private and public finance along the development path. We conjecture that this may lead to a non-linear relationship between the prevalence of private finance for infrastructure and the level of development, as different effects dominate at different level of per capita GDP.

Next, we use a sample of infrastructure projects from the World Bank Private Participation in Infrastructure (PPI) database to illustrate these different effects.

Starting 2015, the PPI team collected, whenever possible, information on the financing structure of the projects. The PPI database includes any infrastructure project (energy, telecommunications, transport, water and sanitation) in low- and middle-income countries for which information is available in publicly available sources, which have at least 20% private participation in the project contract, and in which private parties assume operating risk, by either operating the project alone or jointly with a public counterpart, or owning an equity share in the project. As such, this data base covers a large variety of projects and is relevant to illustrate our model.

The PPI data base contains detailed financial information for 361 projects, across four infrastructure sectors (energy, transport, water and sanitation, and ICT backbone) and three years (2015-2017; see Table I). The projects span 55 countries across 6 regions of the world.<sup>25</sup> For each of these projects, the data reports the total financing amount, disagregated between public and private financing. These two categories are further disaggregated in the following way: (4.1) _PublicFinancing_ = _PublicDebt_ + _PublicEquity_ + _DirectGovernmentSupport_

> 24See Martimort and Straub (2009), and Bonnet et al. (2012) for such an argument in the context of infrastructure privatization.

> 25See the list of countries in the Appendix.
Put Your Money Where Your Butt Is: A Commitment Contract for Smoking Cessation<sup>*</sup> Xavier Giné World Bank Dean Karlan Innovations for Poverty Action Jonathan Zinman Innovations for Poverty Action Keywords: commitment contract; commitment device; public health; addictive consumption; intertemporal choice; behavioral economics; field experiments JEL codes: D12, I12

* Thanks to the management and staff of Green Bank for their cooperation, and to Paulette Cha, Kareem Haggag, Scott Nelson, and especially Tomoko Harigaya for research and project management assistance. Thanks to Satish Chand, Stefano DellaVigna, Esther Duflo, Meredith Rosenthal, and conference and seminar participants at the American Society of Health Economists biennial meeting, Australian National University, Case Western, and NBER Summer Institute for comments. Thanks to the World Bank and Innovations for Poverty Action for funding. Disclosures: Karlan is President of stickK.com, which offers online commitment contracts. Zinman is on the Research Advisory Board of stickK.com.
# **ABSTRACT**

This project constructs a composite-leading indicator (CLI) to forecast economic activity in Lithuania. This cycle is the result of deviations of the economy from its longterm trend. Here, a contractionary phase means a decline in the rate of growth of the economy, and not necessarily an absolute decline in economic activity.

For this analysis it is necessary to select an indicator of economic activity (usually the Index of Industrial Production, IIP), as well as a group of variables that, when filtered/adjusted construct the CLI that forecasts the reference series. Selection of the components of the leading indicator is based on the forecast efficiency of the series and on their economic significance. Once selected, the relevant variables are aggregated into a single CLI that forecasts the detrended IIP. For detrending the series, the HodrickPrescott (HP) Filter method is applied. This method is a smoothing technique that decomposes seasonal adjusted series into cyclical and trend components. One of the advantages of the HP Filter is that provides a reasonable estimate of the long-term trend of a series.

# **INTRODUCTION: LEADING INDICATORS AND FORECASTING OF ECONOMIC ACTIVITY**

This manual presents a forecasting method to predict growth cycles in economic activity, measured by the total industrial production.' The analysis of series that having a leading relationship to the reference series forms the basis of the prediction. These series are aggregated in a single composite-leading indicator (CLI) that predicts the path of the economy. A. Burns and W. Mitchell at the National Bureau of Economic Research developed this technique during the 1930s and 1940s. The composite index presented in the following sections is a variant of the one elaborated by the OECD, Statistics Division. The modifications incorporate statistical techniques that facilitate the estimation process.

To understand the interpretation of this index it is important to identify what is being predicted. The _growth cycle_ is understood to be the result of deviations of the economy from its long-term trend.<sup>2</sup> Here a contractionary phase means a decline in the

> i The forecast variable is referred to as the "reference series."

> **2** Strictly speaking it is hard to decompose the trend-cycle component in a time series. Nevertheless, here it is assumed that this decomposition is possible by estimating a long-run trend. See annex on moving average decompositions.

2
cohorts. Both average years of education and, even more dramatically, the proportion of individuals who have at least some secondary education are much lower for individuals who were teenagers in 1975. This is especially marked for males (the differences are only statistically significant for males and for both gender together, not for females). Since, among the young adult males in 1975, the educated have been especially targeted by the violence and the mortality, as shown in figure 5, it is also likely that the difference between the teenagers in 1975 and the preceding birth cohorts would have been even larger in the absence of selective mortality of the highly educated.

As a consequence of the collapse of the school system, a large number of children were not allowed to attend secondary school, even though they might have been willing to. Once the reconstruction of the country and the school system started, they were older and had probably many other priorities. This constitutes an exogenous shock to educational attainment. Initially, the aim of this paper was to attempt to use this exogenous variation to identify the labor and non labor market returns to schooling. The preceding analysis has shown that, if the exogenous variation in education is only defined at the birth cohort level, the exclusion restriction, i.e. the condition that the birth cohort only affects the outcomes of interest, like earnings or health, through education, is not likely to be satisfied. Indeed, mortality, violent death, fertility, marriage and disability rates are all strongly affected by birth cohort effects. If in addition to the cohort related variation, I could rely on some exogenous geographical variation in schooling, this approach might be more appealing<sup>15</sup> . I will try to find and use data about school reopenings or reconstruction after the Khmer Rouge period to make progress on this strategy. However, this approach would only be valid if the geographical variation in education proves not to be correlated with geographical variations in mortality or other outcomes.

# **VI.  Conclusions and Further Research**

This paper studies the long-term impact of genocide during the Khmer Rouge regime (1975-79) in Cambodia. This study uses the siblings’ mortality data from the Cambodia Demographic and Health Survey in 2000 for quantitative analysis. It shows that excess mortality was extremely high and heavily concentrated during the 1974-1980 period. Adult males were the most likely to die, indicating that violent death played a major role. The paper also establishes that individuals from a family with an urban or an

> 15 A similar approach is developed by Giles, Park and Zhang (2004) for disruptions to education during the Chinese Cultural Revolution. Approaches based solely on birth cohort effects are Meng and Gregory (2002 (b)) for China during the Cultural Revolution and Ichino and Winter-Ebmer (1998) for Austria and Germany after World War II.

12
- Sahn, D., Alderman, H., 1988. The Effect of Human Capital on Wages, and the Determinants of Labor Supply in a Developing Country. Journal of Development Economics 29, 157–83.

- Schultz, T. P., 1990. Testing the Neoclassical Model of Family Labor Supply and Fertility. Journal of Human Resources 25 (4), 599–634.

- Sen, A. K., 1984. Family and Food: Sex Bias in Poverty. In: Sen, A. K. (Ed.), Resources, Values, and Development. Basil Blackwell, Oxford.

- Sen, A. K., Sengupta, S., 1983. Malnutrition of Rural Children and the Sex Bias. Economic and Political Weekly Annual Number.

- Skoufias, E., McClafferty, B., 2003. Is PROGRESA Working? Summary of the Results of an Evaluation by IFPRI. In: Quisumbing, A. R. (Ed.), Household Decisions, Gender, and Development. International Food Policy Research Institute, Washington, D.C.

- Smith, L. C., Ramakrishnan, U., Ndiaye, A., Haddad, L., Martorell, R., 2003. The Importance of Women’s Status for Child Nutrition in Developing Countries. In: Quisumbing, A. R. (Ed.), Household Decisions, Gender, and Development. International Food Policy Research Institute, Washington, D.C.

- Standing Committee on Nutrition, 2000. Fourth Report on the World Nutrition Situation. United Nations System, Geneva.

- Standing Committee on Nutrition, 2004. Nutrition and the Millennium Development Goals. SCN News, July, United Nations System.

- Stiglitz, J. E., 1976. The Efficiency Wage Hypothesis, Surplus Labour, and the Distribution of Income in LDCs. Oxford Economic Papers 28, 185–207.

- Strauss, J., 1986. ”Does Better Nutrition Raise Farm Productivity”. Journal of Political Economy 94, 297–320.

- Strauss, J., Thomas, D., 1995. Human Resources: Empirical Modelling of Household and Family Decisions. In: Srinivasan, T. N., Behrman, J. (Eds.), Handbook of Development Economics. Elsevier Science, Amsterdam.

- Thomas, D., 1990. Intra-household Resource Allocation: An Inferential Approach. Journal of Human Resources 25 (4), 635–664.

- Thomas, D., 1994. Like Father, Like Son; Like Mother, Like Daughter: Parental Resources and Child Height. Journal of Human Resources 29 (4), 950–988.

31
Surveys ask the main owners of informal businesses for how long they have lived in the current city. We consider as migrants all owners who lived in the city less than their entire life. Using this definition, 28.3% of businesses are owned by migrants. Curiously, there are no differences between migrant and non-migrant-owned informal businesses in terms of the average age of businesses, or of their owners, or the number of employees, or sectoral distribution.

# **3.2 Characteristics of Informal Businesses**

The uniqueness of the Informal Sector Enterprise Survey data used in this paper is the richness of information that they provide about informal businesses. The next sub-sections provide a descriptive overview of some of the key characteristics of informal businesses.

## **Demography**

Figure 3 presents one of the most well-documented stylized facts about informal businesses: that they are very small (Kanbur, 2017; La Porta and Shleifer, 2014). The median informal business engages just two persons (including the owner/manager). Across the seven countries, 49% of the businesses are one-person operated, while another 31% engage two individuals.<sup>13</sup> Only 4.6% of informal businesses have five or more people working in the business. Importantly, these businesses generally have even fewer paid employees, presumably because the workers are often the owners and their family members, hence the residual beneficiary of the profits. The average number of paid employees (including the owner/manager) is one, with some variation on the right tail as about 67% of the businesses have at least one paid employee. Informal businesses in the manufacturing sector tend to engage slightly more individuals than resellers and businesses in other service sectors. This difference is not statistically significant and is of a lesser magnitude than might be anticipated given the economies of scale inherent in the manufacturing sector (see Panel A of Figure 3). Close to half of those in reselling businesses, which include hawkers, those in open market areas, and small shops, are single-person businesses. A similar pattern is observed across countries, albeit with some minor variation (see Panel B of Figure 3). In all countries, an overwhelming majority of informal businesses have only one or two employees. Iraq is in the only country in the sample where 2-person informal businesses are more common than those run by a single person (35% and 31% respectively). In India and Mozambique, over

> 13 This is based on information about the number of people working in the business at the end of the month just prior to the interview, and includes paid or unpaid, full-time or part-time workers as well as owners and family members if they contribute to business operations.

14
sample (not reported), then we find almost identical and insignificant coefficients for the trade intensity variable. Hence, we may conclude that the omission of proxies for horizontal and vertical occupational segregation does not lead to an important omitted variable bias.

We also investigated whether the gender wage gap is related to overall wage inequality. Blau and Kahn (2001) have shown that a large part of the cross-country differences in the gender wage gap can be explained by differences in wage inequality.<sup>31</sup> This is because the pay of women tend to be at the bottom of the wage distribution so countries with a compressed wage structure will tend to have a lower gender wage gap.

If women tend to be at the bottom of the wage distribution _within_ occupations, then we may also expect countries with a greater wage inequality to have a larger gender gap. However if the occupations are sufficiently narrowly defined, no such effect can be expected.

We have estimated whether empirically there is a relationship between wage inequality and gender wage gap. Wage inequality has been measured as occupational wage inequality, reflecting the wage structure. We have used the ILO October Inquiry to estimate the occupational wage inequality as the standard deviation of the log male occupational wage.<sup>32</sup> Columns 4 and 8 of Table 8 show the regression results if this measure of wage inequality is included. Unlike Blau and Kahn, we do not find a significant (positive) relationship between wage inequality and the gender gap, suggesting that the occupations are sufficiently narrowly defined. Also the coefficients for the trade intensity variable are virtually unaffected implying the unimportance of omitted variable bias in this instance.

> 31 They also show that another large part can be explained by cross-country differences in female net supply. We have also calculated their measure of female net supply and found a weakly significant _negative_ effect on the gender wage gap in richer countries (p-value 0.09). This may reflect a simultaneity bias, if higher gaps lead to less net supply because of low female wages. Importantly, the coefficient for the trade variable was not significantly affected.

> 32 We have estimated the occupational wage inequality for all country/year pairs for which at least 30 occupational wage observations were available (see Freeman and Oostendorp, 2002).

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# **Impact Assessment**

_Take‐Up and Lighting Usage_ Among the 131 households that still have a Pico‐PV kit when interviewed in the follow‐up survey, usage rates are very high (see Table 4). In sum, 86 percent use the kit at least once per day, primarily for lighting. Radio and especially cell phone charging usage rates are rather low. Most households report that both the radio and the cell phone charger were very difficult to use with the kit, which was confirmed by technical inspectors involved in testing the kit for Lighting Africa. The major reason for this seems to be the low capacity of the panel, which only allows for charging all devices completely within one day if the daily sunlight is exploited at a maximum. In practice, households used the charging capacities mainly for the lighting device. Given this preference for lighting, too little capacity is left for the other two services. For cell phone charging, noncompatibility of the solar charger with some of the widely used cell phone types in rural Rwanda posed additional problems. In line with these technical deficiencies and the households’ expressed priorities for lighting, charging patterns are dominated by the lamp: most of the time, the kit is used to charge the lamp (26 hours per week), followed by operating the radio (20 hours). It is hardly used to charge a cell phone (only two hours<sup>8</sup> ).

Due to the technical drawbacks of the Pico‐PV kit, we will concentrate in the following on effects related to the usage of improved lighting service. Virtually all kit owning

> 8 The share of households using the kit for cell phone charging is very low at less than ten percent. Those households that do charge their phone with the kit charge it 19 hours per week.
represents the proportion of households in the full estimation sample considered to be adequate. Pairwise correlations of the seven adequacy dimensions are reported in Annex 3.

**Table 1: Summary Statistics of the Seven Adequacy Dimensions in 64 Economies**

|**Adequacy Dimension**|**Number**<br>**of**<br>**Observations**|**Mean**|**Std. dev**|
|---|---|---|---|
|Access to Water|2,461,605|.941|.235|
|Access to Sanitation|2,460,289|.575|.499|
|Adequate Living Space|2,395,203|.657|.475|
|Durable Material Good Structural Quality|2,145,454|.520|.519|
|Security of Tenure|2,453,341|.782|.109|
|Access to Electricity|2,463,409|.876|.330|
|Access to Clean Cooking|2,249,519|.443|.497|

**Note** :  Summary statistics of adequacy dimensions across 64 emerging economies. Missing data within countries are imputed as presented in section 3.4. Country specific statistics are presented in Annex 3.

Across the board, access to safe cooking fuel is most commonly absent in emerging economies’ HHs. This is particularly acute in Sub-Saharan African economies, where the overarching majority of HHs does not have access to clean cooking and where HH air pollution stemming mostly from cooking smoke is a serious health hazard. This avoidable first-order public health crisis disproportionately harms women and children (cf. World Bank et al. 2014). Moreover, cooking with inadequate cooking fuels such as wood, charcoal, crop waste, dung, coal, or kerosene, also has a wide range of negative environmental and climate change effects.

Durable material is the second most deprived dimension in emerging economies. Only about 66 percent of housing units are built with durable material for roof, wall, or floor. The lowest are Botswana and the Democratic Republic of Congo, where mostly rudimentary or natural material are used.

For tenure security, about three-quarters of HHs feel insecure with the lowest perception of tenure security reported in Mali with only 28 percent of HHs feeling secure. It is key to point out that our operationalization of tenure security (by relying on HCES and Prindex data sources) measure perceptions of tenure security which might differ from _de facto_ tenure security. Yet, perceptions influence behavior and investment decisions and bear key insights into the rather elusive concept of tenure security. Further, this standardized way of measurement allows for comparison across different tenure systems including those where customary titles or traditional forms of ownership may be more meaningful to HHs than legal documentation of rights. Further, this is hitherto the best possible data source on tenure security for a large set of countries (Prindex 2020).
<!-- Start of picture text --> Figure 3: Social assistance spending Figure 4: Cash transfer spending<br>(EAP non-HIC) (EAP non-HIC)<br>6 2<br>5<br>4<br>3 1<br>2<br>1<br>0 0<br>Percent of GDP Percent of GDP<br>Timor-Leste Mongolia Vietnam Kiribati China Thailand Cambodia Malaysia Fiji Philippines Indonesia Samoa Lao PDR Myanmar Papua New... Mongolia Malaysia Philippines Vietnam Timor-Leste Fiji China Thailand Indonesia Myanmar Cambodia Samoa Kiribati Lao PDR Papua New...<br><!-- End of picture text --> Source: CEQ and World Bank databases and World Bank calculations. Sosa and Wai-Poi (forthcoming). Note: Total SA spending excluding health fee waivers. Cash transfers include CCT and UCT. HIC = High-income country, CCT = conditional cash transfers, UCT = Universal cash transfers.

# **3. Methodology, Data, and Assumptions**

## **CEQ framework**

This paper applies an internationally recognized methodology developed by the Commitment to Equity (CEQ) Institute to assess how taxes and government spending affect poverty and inequality in Cambodia. The CEQ framework has in recent years emerged as the gold standard to answer questions concerning the implications of fiscal interventions across the income distribution.<sup>12</sup> The approach has been applied in over 60 countries, allowing comparisons of Cambodia’s performance with peer lower middle-income countries.

The CEQ approach combines detailed household survey data with tax and benefit regulations to estimate what each household paid in taxes and received in benefits. The CEQ approach then describes the distributional effect of the fiscal system sequentially through four income measures. The baseline concept is _market income_ , which is the income that an individual (or a household) earns before any taxes are subtracted and any transfers are included. Three “post-fiscal” income measures— _disposable income_ , _consumable income_ , and _final income_ —are sequentially built by subtracting and adding different forms of fiscal interventions to the pre-fiscal income. Comparisons of the four income aggregates provide information on how fiscal interventions affect the income distribution sequentially from the pre-fiscal income to the final income. Poverty and inequality measures derived from the pre-fiscal and post-fiscal income measures are then compared. A comprehensive incidence analysis is therefore built by

> 12 See http://commitmentoequity.org/.

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