rafmacalaba/datause-encoder-data huggingface dataset:
Samuelson, L., 2004, “Information-Based Relative Consumption Effects,” _Econometrica,_ 72: 93-118.

Schmaus, G., A. Birch, K. Fisher, J. Frick, A. Haag, G. Schaber, B. Kuchler and A. Villeret, 2003, The Cher project. CHER Working paper 1, CEPS/INSTEAD.

- Silber,  J., 1995, “Horizontal  Inequity,  the  Gini  Index,  and  the  Measurement  of Distributional  Change”  in _Income  Distribution, Social  Welfare,  Inequality and Poverty, Research on Economic Inequality_ by C. Dagum and A Lemmi, Eds., **6** : 379-392.

- Silber, J. and M. Weber, 2008, "The Generalized Gini index and the measurement of income mobility," _Economics Bulletin_ 4(9): 1-7.

- Silber, J. and P. Verme, 2010, " Distributional Change, Reference Groups and the Measurement of Relative Deprivation," Chapter 9 in _Research on Economic Inequality_ , Volume 18, _Studies in Applied Welfare Analysis_ , J. Bishop editor, pp. 197-217.

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censuses of the poor were deemed eligible since too few households were surveyed in the first place (World Bank, 2012).

Previous studies provide evidence that inaccurate targeting of social protection programs was a major obstacle to effective poverty reduction policies in Indonesia. Jamkesmas and BLT were characterized by significant undercoverage of poor households and leakage to non-poor households. For example, according to World Bank (2012), only about half of the households below the poverty line received the BLT program in 2008. Moreover, fragmentation in targeting approaches induced higher program administration costs and efficiency losses. Due to the lack of a unified approach to beneficiary selection, these programs were implemented independently from one another, by different government agencies with a limited capacity to interact and to properly assess the degree of complementarity in benefits provided to specific target groups.

# **The Unified Database for Social Protection Programs**

The UDB was established following two steps: data collection (enumeration) and PMT modeling (ranking). Hereafter, references to the “poor” (“non-poor”) indicate those households in the bottom 40 (upper 60) percent of the consumption distribution and hence meant to be included in (excluded from) the UDB.

The data collection stage involved pre-identifying all _potentially_ eligible households that should be surveyed. Building on lessons from the implementation of previous censuses of the poor, the UDB was intended to cover a greater number of households and to avoid relying exclusively on subjective nominations from community leaders. The registration of households in the UDB followed a two-step approach. First, a ‘pre-listing’ of households to be surveyed was generated through a poverty mapping exercise. Second, suggestions from communities were incorporated in the field to amend and complete the pre-listing.<sup>9</sup>

> 9 Alternative approaches adopted in other countries include surveying households that request it or conducting a census in the poorest areas (e.g., Camacho and Conover, 2011; Karlan and Thuysbaert, forthcoming).

8
# **1 Introduction**

Nearly all low-income countries are susceptible to aggregate shocks of some sort, be they through commodity price fluctuations, conflict, epidemics or climate change. We study the economic impacts of one such aggregate shock in Sierra Leone. We are able to overlay this aggregate shock with a randomized control trial evaluation of a human capital intervention targeted to young women, designed to raise their reproductive knowledge and health, and improve their labor market prospects. By combining the two, we provide novel insights on the microeconomic mechanisms through which the severity of such an aggregate shock impacts the economic lives of young women in a low-income and fragile state.

The aggregate shock we study is the 2014-16 Ebola outbreak. This led to the, _“longest, largest, deadliest, and...most complex [Ebola outbreak] in history”_ [UNDG 2015]. The outbreak affected Sierra Leone, Guinea and Liberia, spreading quickly through urban areas and infecting 28 _,_ 652 individuals, with 11 _,_ 352 deaths [CDCP estimate, April 2016]. There were more cases and deaths in this outbreak than all earlier outbreaks combined. Sierra Leone was the country most affected, hosting half of all cases. Rapid contagion forced the government to implement three main policy responses: (i) village lock-downs and travel bans; (ii) all primary and secondary schools were closed through the 2014-15 academic year; (iii) health workers were mobilized to record door-todoor cases and track contagion, and some health facilities were transformed into Ebola holding centers.

The aggregate shock severely curtailed economic activities and opportunities for all. There are however additional aspects of each policy response that might have had greater consequences for young women. For example, school closures would mean the loss of one year of human capital accumulation. For young girls this loss is non-trivial given their low levels of human capital to begin with. Moreover, without the protection of time in school, young women might have become more exposed to early pregnancy and sexual abuse. Furthermore, just before schools were due to re-open in April 2015, the Ministry of Education surprisingly announced the continuation of a pre-Ebola policy; that ‘visibly pregnant girls’ would be unable to re-enrol. Taken together, the consequences of these schooling policy responses could have done much to further increase stigma and discriminatory attitudes towards young women. The near collapse of public health provision would further have impacted teenagers and young women requiring antenatal care.<sup>1</sup> Our study context represents not only a low-income fragile state, but is also one in which women face a range of social and economic disadvantages. As Panel A in Figure A1 shows, on the

> 1Sierra Leone is not unique in having such policies preventing pregnant girls from attending school: Tanzania recently announced a similar policy.

1
<!-- Start of picture text --> 80<br>60, = ——<br>ooO°40 —<br>20<br>0<br>1990 1995 2000 2005 2010 2015 2020<br>—Ghana SSA, average LMICs, average<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>60<br>o(3)° 40<br>20 : ——<br>0<br>1990 1995 2000 2005 2010 2015 2020<br>—Ghana SSA, average LMICs, average<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>60<br>5<br>25 40 a:<br>o_aoe<br>20<br>0<br>1990 1995 2000 2005 2010 2015 2020<br>—Ghana SSA, average LMICs, average<br><!-- End of picture text -->
estimated differences using relevant microeconomic data. The gaps between Macedonian and Slovak structural indicators show that the economic rigidities in Macedonia could be significantly diminished by appropriate structural reforms. While structural shocks by their nature cannot be generally reduced through government policies, reducing fiscal shocks, i.e. the magnitude and frequency of changes in the fiscal policy stance, is in control of government policy. More specifically, we find that for Macedonia vis-à-vis the Slovak Republic, the consumption habit formation and cost of adjusting capital stock are higher, the credit channel of monetary policy is weaker, the net exports elasticity to exchange rate changes is weaker, the portion of non-Ricardian households is higher, the effect of increasing capacity utilization on inflation is higher, and the exchange rate passthrough is lower. On the other hand, we find that Macedonia does better in terms of containing fiscal policy shocks than the Slovak Republic. Overall, it appears that the Slovak Republic is enjoying a lower inflation/output volatility tradeoff compared with Macedonia, for which our gap analysis provides some directions for improvements in the structural reforms. In addition, to our knowledge, this paper makes a first attempt to fit a structural model with rational expectations to the data on the Macedonian and Slovak economies.

The remainder of the paper is organized as follows. Section two discusses the main features of the employed structural model. Section three describes the data and the estimation methodology. Section four discusses the estimation results. Section five offers microeconomic explanations of the estimated macroeconomic characteristics of the Macedonian and Slovak economies. Section six analyzes impulse responses of selected economic variables to identified structural shocks. And, section seven concludes.

# **2. Model Description**

This section describes theoretical underpinnings of the New Keynesian policy model that we estimate to capture some fundamental characteristics of the Macedonian and Slovak economies. Let _Et_<sup>_X_</sup> _t_ + 1 denote the rational expectation forecast of _X t_ + 1<sup>co</sup> nditional on the 3
<!-- Start of picture text --> 44<br>ad 38<br>_ 40<br>E<br>33 32<br>2 31<br>2 28<br>E 30 96<br>We)<br>o><br>& 20<br>i<br>oO<br>Oo<br>{<>}<br>al<br>10<br>a)<br>Not new New Total<br>Newness of firm<br>1998-2006 [NNN 2006-2012 ME 2012-2018<br><!-- End of picture text -->
- Ferreira, F. and N. Schady. 2009. Aggregate economic shocks, child schooling, and child health. _World Bank Research Observer_ , 24(2): 147–81.

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Table 2: Geographic concentration of economic activity by degree of formalisation

||**Nairobi**<br>**Mombasa**|**Rank r**|**egression**|
|---|---|---|---|
||in %|_α_|SE|
|Population overall|9<br>3|1.29|0.18|
|Population of cities & towns|31<br>9|0.85|0.01|
|Migration infows|29<br>6|0.59|0.04|
|Migration outfows|5<br>2|0.77|0.07|
|GDP|25<br>5|1.00|0.07|
|GDP w/o agriculture|33<br>7|0.97|0.05|
|GDP w/o non-market services|25<br>5|0.91|0.08|
|No. MSMEs|14<br>3|0.86|0.17|
|Employment in MSMEs|19<br>3|0.78|0.13|
|No. licensed MSMEs|18<br>3|0.73|0.09|
|Employment in licensed MSMEs|28<br>3|0.67|0.07|
|No. SMEs|37<br>3|0.58|0.06|
|Employment in SMEs|36<br>3|0.60|0.05|
|No. census establishments|36<br>4|1.10|0.12|
|No. frms census of industrial production|48<br>6|0.54|0.02|
|Sales census of industrial production|61<br>7|0.32|0.03|
|No. VAT frms|64<br>9|0.63|0.03|
|Employment in VAT frms|62<br>9|0.36|0.03|
|Value added of VAT frms|72<br>10|0.38|0.03|
|VAT network sales|68<br>13|0.35|0.02|
|VAT network outlinks|69<br>11|0.35|0.02|
|VAT network purchases|60<br>9|0.43|0.02|
|VAT network inlinks|65<br>10|0.48|0.02|

The columns for Nairobi and Mombasa report their share of the respective national aggregate figures (e.g., Nairobi’s contribution to Kenya’s GDP). The Pareto exponent _α_ is the estimated coefficient from a county-level regression of each county’s rank (log) on the respective measure _x_ (log): log rank = log _A − α_ log _x_ . All measures reported in the final section of the table are derived from the VAT data. All other measures are based on data sources summarised in Table 1.

without multi-establishments. We find that the excess spatial concentration introduced by multi-establishments does not explain the aggregate concentration patterns of formal private sector activity.

# **Formal firms predominately source from and sell to firms in urban hubs.**

Consistent with the previous section, a plot of county-by-county trade flows in Figure 3 highlights the primacy of Nairobi and Mombasa. The width of each segment on the left reflects the county’s total sales within the network, while segments on the right are proportional to total purchases. The color of the trade flows aligns with the county of origin.

Importantly, visualizing trade flows also reveals that Nairobi not only has more outlinks to other counties but also is the most important buyer of goods and services by firms from other counties. Given the high concentration of manufacturing firms and their upstream position in the network, 12
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

**Figure 21: Payment of accrued rights and contributions for teachers – Reform I.1A (% of GDP)**

<!-- Start of picture text --> 0.7%<br>0.6%<br>0.5%<br>0.4%<br>0.3%<br>0.2%<br>0.1%<br>0.0%<br>2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072<br>Year<br>accrued rights contributions<br>Accrued rights (% GDP)<br><!-- End of picture text -->

**Source** : WB calculations based on PROST

In the reformed system, benefits will be paid out of two sources: from the old defined benefit scheme in the form of accrued pension rights and from the new scheme as phased withdrawal based on the individual’s accumulated balance. In the shorter run, while the value of benefits is dominated by the defined benefit component, replacement rates are higher. In the longer run, with the prevailing defined contribution component, the expected benefit becomes more modest compared to what is promised by the current system.

For example, under a quite optimistic assumption of 5% rate of return, by the time the new system matures, the flow of retirees can expect replacement rates of about 60% for civil servants and 50% for teachers. These are substantially lower compared to around 82%-85% that the same retirees would have obtained in the unreformed scheme. Pensions for the stock of retirees in the reformed scheme will decrease even more compared to the baseline due to the fact that price indexation increases the value of pensions by less than average wages. Replacement rates for the stock of old age pensioners projected for variant Reform I.1A (with 5% real interest rate) are compared with the baseline (no-reform) scenario in Figure 22 and Figure 23.

- 54 -
27 Figure 1: Nominal rate of assistance, OECD countries, 1960 to 2007 (percent)

# (a) EU countries

<!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0<br>1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-07<br>-20<br><!-- End of picture text --> <!-- Start of picture text --> All covered Exportables Import-competing<br><!-- End of picture text -->

# (b) All OECD sample countries

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-07<br>All covered Exportables Import-competing<br><!-- End of picture text --> Sources: Authors’ calculations based on data in Anderson and Valenzuela (2008).
# **VI. Conclusion**

Achieving universal access to electricity is one of the most important energy policy goals set by governments in the developing world. Recent empirical literature on the development effects of electrification however reported vastly different findings. They range from negative net benefits to significant positive gains from electrification. In this paper, we explain the conflicting evidence reported in the literature by exploring two underlying determinants of the magnitude of electrification benefits: the quality of electricity supply and the time span of being connected to the grid.

Using a panel data set of 7,018 households in Bangladesh for 2005 and 2010, we examine a wide range of household and individual socioeconomic outcomes after gaining access to the electric grid, including kerosene consumption, per capita income and expenditure, time spent on studies by boys and girls, labor force participation among men and women, and women’s empowerment. We use both an instrumental variable approach and a two-stage propensity-score-weighted fixed-effects model to address potential endogeneity associated with the nonrandom adoption of electricity.

We find that gains from electrification are significantly positively correlated with the reliability of electricity service and the time span of being connected to the grid. Taking the example of the impact electrification has on income growth, every one hour increase in daily power outages is associated with a 0.3 percent reduction in annual income, while every one additional year of grid connection is associated with a one percent increase in income. The average treatment effect of electrification on per capita annual income growth is estimated to be 17 percent. Like in many other developing countries, power shortages severely constrain business and households’ use of electricity in Bangladesh, where average duration of power outages is 9.8 hours a day in 2005. Reducing power outages is estimated to generate income gains of US$ 486.3 million per year. Accounting for income growth alone, the total potential benefits from improving reliability and achieving universal access to electricity could reach US$2.3 billion a year in Bangladesh.

Using a semiparametric quantile regression approach, we also estimate the distributional treatment effects of electrification. We find that the benefits of grid connection monotonically increase with households’ initial income status. For example, the expenditure gain from gaining access to grid electricity is about 10.6 percent for the households in the 80th expenditure quantile whereas it is only about 3.8 percent for the households in the 20th expenditure quantile. Although rich and poor households are 16
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