rafmacalaba/data-use-mentions-extended huggingface dataset:
birth year in order to allow the division sample into two subsamples of similar size. On the one hand, the sample of mothers is divided between mothers born before 1972 (included) and mother’s born after 1972. On the other hand, the sample of fathers is divided between fathers born before 1966 (included) and those born after 1966.<sup>35</sup> Mothers of younger cohorts show a positive and statistically significant effect of fertility on _hours worked per week_ at the 1% level. Also, the IV estimates for younger fathers show a positive and statistically significant effect on father’s likelihood of having a _second occupation_ as a consequence of childbearing. On the contrary, the coefficients on fertility for mothers and fathers of older cohorts are lower in magnitude and statistically insignificant. Hence, I can confirm that there exists a degree of heterogeneity in the link between fertility and labor market outcomes by parent’s birth cohort, where the positive effect seems to be driven mostly by younger parents. This result also suggests that the positive effect of fertility on parental labor-supply is not driven by parents whose older children in the household are taking care of the younger siblings, which is reassuring in terms of the exclusion restriction.

Third, Table 12 reports the effect of fertility on parental labor supply conditional on the _family structure_ . Given the Albanian context, it could be argued that extended families in this country are a given characteristic of society since they are already established by strong traditional and social norms. To give more evidence on this assumption, I use the 2002 Albanian LSMS which contains detailed information on the fertility history of mothers from old birth cohorts in order to calculate the proportion of parents in extended families living with their sons by their son’s birth order. Indeed, Figure 5 shows that most of the parents in extended families live with their youngest married son (61.1 %).

However, there still remains the concern that the fertility decision and the co-residence decision might be jointly
in school enrollment. In other words, girls with a school in their settlement have practically the same enrollment rate as boys.

> 9Andrabi, et al. 2007, follow a related strategy of using information on village population in rural Pakistan to instrument the availability of schools at the village level. They assume that households do not migrate across villages to take advantage of schooling opportunities.

> 10 Specifically, we want to directly compare estimates derived from strategies 1 and 2. However, if we take the discrete choice route, we would have to use a fixed effect logit for strategy 1 (given the large number of settlements relative to children) and a probit IV for strategy 2 (noting that the former estimator does not produce marginal effects). Moreover, as we will see later, probit (or logit) estimation is only possible on a reduced sample, which compromises efficiency relative to a linear probability model.

12
the highvalue-added goods mentioned above); and there are three sectors that closely matched the industries for which we apply the NTB estimates directly (textiles, fertilizers, and iron and steel).<sup>9</sup> For the five non-tradable sectors NTB, tariff equivalents are not applicable. For other tradable sectors to which are applied zero NTB equivalents, no information is available and no significant NTB effects were assumed. Nevertheless, the ERPs of those sectors will also be indirectly affected by NTB through changes in the cost shares of inputs directly impacted by NTBs.

# **6. Results: Levels and Dispersion of ERPs across Selected Industries, Tariffs Only (without Energy Subsidies), Comparing 2009 with the Late 1990s**

Table 4a presents the ERP results for private and public industries, comparing ERPs using 2009 applied tariff data and the tariffs used in the Nathan study of the late 1990s (which did not include NTB adjustments). ERPs for 2009 that include estimated NTB effects are discussed below.

We first concentrate our discussion of the private sector results in Table 4a. On average, ERPs in the private sector declined from 86 percent to 45 percent (excluding sector 2102 – boxes and container, which as discussed below is a special case). And as can be seen in Annex Table 1, applied tariffs – that is, nominal rates of protection – also declined generally across the board. In addition, the dispersion of ERPs, as measured by the standard deviation, fell from 1999 to 2009 (from 192 to 57 percent, excluding sector 2102).

> 9 The sector “textiles” matches with ISIC #1711 (spinning, weaving and finishing), the sector “fertilizers” matches with ISIC #2412 (manufacture of fertilizers and pesticides), and the sector iron and steel matches with ISIC #2710 (manufacture of iron and steel basic industries).

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# **1 Introduction**<sup>1</sup>

The economy of Moldova is strongly linked to the outside world via worker remittances and trade. As a consequence, it was also strongly affected by the global economic crisis that erupted in 2008. Its future evolution will depend on domestic policies and the timing and extent of resumed growth in the outside world, most importantly Moldova’s neighbors in the EU and the CIS, which have absorbed the bulk of the country’s worker emigrants and also dominate its foreign trade.

In this paper, we explore how influences from the outside world and domestic policy together may condition the development of Moldova’s economy during the period up to 2020. The content of the analysis is informed by discussions of the consequences of changing Moldova’s development strategy from reliance on workers’ remittances toward reliance on export-oriented goods and service production. To this end, we have developed a Moldovan version of MAMS (Maquette for MDG Simulations), a CGE model for country development strategy analysis developed at the World Bank. More specifically, we will address the impact of alternative scenarios for worker migration and remittances, international prices for Moldova’s main exports (in the agro-industrial area) and fuel imports, as well as government transfers aimed at promoting growth and equity. Our analysis covers the impact of these scenarios on indicators related to the macro economy, major production sectors, as well as household welfare and its distribution. We will proceed as follows: Sections 2 and 3 provide background on Moldova’s economy and describe MAMS and its Moldovan database. The simulations are presented and their results analyzed in Section 4. Section 5 summarizes our findings. Appendix A describes the construction of the Moldovan database in greater detail while Appendix B provides more detailed simulation results.

# **2 Background**<sup>2</sup>

During the period 2000-2010, Moldova’s GDP growth was rapid but quite volatile (see Figure 1), reaching an average annual rate
16). Moreover, there are many instances in which the EM-DAT records no disaster impacts at all. This is true especially for droughts, where the microdata suggests that droughts are prevalent to some degree across every country-year combination covered, while EM-DAT records droughts affecting the population in only a third of cases. Estimates of the economic value of disaster impacts are mostly missing in the EMDAT data for the study countries, even in years when drought and flood events were recorded to affect the population in the study countries (Tables 15 and 17).

Large, salient drought and flood episodes have better coverage in the EM-DAT, such as the severe droughts in Niger in 2011<sup>31,32</sup> and Ethiopia in 2015-16,<sup>33,34</sup> or the droughts and floods Malawi in 201516,<sup>35,36</sup> which were widely covered in international media at the time. The events that go unreported in EM-DAT are smaller, on average, in terms of the population affected and the damages caused. However, we show that such smaller, under-covered events have substantial overall impacts. More than a fifth of the population suffered production and income losses in the droughts in Malawi in 2009-2010 and in Mali in 2074, according to our micro-data estimates, while there is no coverage of these events in the EM-DAT for the same years. Overall, we estimate the total number of people affected by droughts or floods in all instances covered by the microdata is between 145 million and 170 million, more than 4 times higher than what is reported in the EM-DAT for the same periods and the same shocks.

The micro-data analysis suggests that the aggregate value of the disaster impacts on crop production is substantial. For the drought in Ethiopia in 2015-2016, the micro-data crop loss estimates are much larger than the total economic damage reported in the EM-DAT. For the 2014 floods in Niger, the estimated value of crop losses exceeds the total damage reported in the EM-DAT data by
8|
|Median|0.3|0.8|0.4|0.9|0.3|1.4|0.4|1.5|

_Source_ : World Bank staff calculations based on household survey data. _Note_ : For footnotes a–c, see the previous table. n.a. = not available.

Corruption in the form of bribes extracted add to the cost of using electricity. Surveys in Malawi and Nigeria asked households connected to the grid if they had to pay an “informal fee” over and above the official connection charge to get the connection (free since August 2012 in Nigeria), while the survey in Senegal asked about the general perception of corruption in the government and specifically about the power sector.

The results for Malawi and Nigeria are shown in Figure 8. Bribery was more prevalent in Nigeria. In Malawi, there were too few households in the bottom quintiles for meaningful statistics, but in Nigeria every quintile had enough households sampled. The percentage of households reporting informal payments was lowest in the bottom quintile and highest for the top quintile—that is, those who are most able to pay bribes are also most likely to be asked and pay—although there was no consistent pattern with increasing quintile. Recognizing the pervasive nature of this problem, the Nigerian Electricity Regulatory Commission in August 2012 prohibited connection fees and required that new customers be given a list of materials to purchase and provide the utilities with the materials, so as to avoid a “direct exchange” of money between the customer and the utility. The only variable is the length of the connection wire. After the materials are presented and checked by the utility, the utility has 48 hours in which to connect the new customer (NERC
0.0283)|(0.0404)|
|**preneur**<br>**Model**|Ever been a business||0.405***|0.330***|0.366***|0.374**|
|**Entre**<br>**Role**|owner||(0.0975)|(0.119)|(0.0829)|(0.150)|
|**e skills**|Digitspan score|||0.0217<br>(0.0480)|-0.0172<br>(0.0571)|-0.0964<br>(0.0861)|
|**itiv**|Raven test score|||-0.0280|0.0175|-0.0328|
|**ogn**||||(0.0261)|(0.0297)|(0.0654)|
|**n-c**|Personal intitative|||-0.334|-0.731***|-0.252|
|**no**||||(0.277)|(0.187)|(0.495)|
|**and**|Error competence|||0.178|0.722***|0.916**|
|**ve**||||(0.218)|(0.218)|(0.340)|
|**Cogniti**|Prosocial orientation|||0.381<br>(0.257)|0.0263<br>(0.221)|0.0754<br>(0.565)|
||How satisfied are you||||0.189*
In the next section, we explain our methodology to examine the issues described above based on a case study of Ghana.

# **3. Methodology**

## **3.1. Ghana context and price data**

### _Ghana context_

The official poverty level in Ghana is measured based on the nationally representative household budget survey (Ghana Living Standard Survey [GLSS]). GLSS7 covered 14,009 households in 1,000 EAs and was conducted over a 12-month period between October 2016 and September 2017 (GSS 2018).<sup>17</sup> The sampling was designed so that the survey gives information representative of urban and rural areas and across the 10 regions. Household-level consumption aggregates include both food (including auto-consumption) and non-food (including imputed housing rents) consumption. Further, consumption aggregates are converted to per adult equivalent annual consumption for poverty measurement to reflect differences in household size and composition and differences in calorie requirements of household members.

Consumption aggregates are deflated both temporally and spatially. The temporal deflation adjusts the differences in the timing of data collection between October 2016 and September 2017. Monthly regional CPI indexes are used for the within-survey-months temporal deflation. The spatial deflation on the other hand adjusts for the differences in the cost of living across the 10 regions based on the official regional CPI values. The reference region of the spatial deflation is Greater Accra (January 2017).

Ghana measures poverty based on the single poverty line approach, that is, there is only one poverty line (GSS 2018). Spatial cost of living differentials are adjusted at the consumption side (as opposed to the multiple poverty line approach where poverty lines account for spatial price differences). The poverty line value is GH₵1,760.8 per adult equivalent per year, which was deflated by using a mixed deflator based on CPI raw price data and survey weights from the original poverty line of January 2013 (GH₵1,314). The official poverty headcount ratio in Ghana
is a binary variable that can only take two possible outcomes, zero ( _0_ ) and one ( _1_ ). We also have a vector _z_ of variables that is assumed to have an effect on the outcome _X_ . Hence, we assume that our probabilistic model ( _Probit_ ) takes the following form: Pr _ob_  _X_  1   _F_  _z_ , 

> 6 Dollarization data by Levy-Yeyati (2006) does not have enough coverage. Therefore, we use this data only for robustness purposes.

12
**Figure 3: Median Consumption per Capita in 2002/03 and 2008/09, Official and Revised Estimates**

<!-- Start of picture text --> Median consumption<br>Official and Revised (full sample & 1997 coeff.)<br>2003 2008<br>Official Revised<br>North Urban North RuralCentral UrbanCentral RuralSouth Urban South Rural North Urban North RuralCentral UrbanCentral RuralSouth Urban South Rural<br>30<br>20<br>10<br>0<br><!-- End of picture text -->

**Official:** 2008/09 PLs (MPD-DNEAP, 2010); **Simulated:** 1996/97 official PLs deflated by the consumer price index from World Development Indicators (2011)

The full-sample re-estimation for the 2002/03 and 2008/09 data confirms these findings. For 2008/09, and to a lesser extent in 2002/03, the official data are compromised by the underestimation of consumption in Southern provinces and urban areas and by the substantial overestimation of consumption in some Central and Northern provinces and in rural areas. In both cases the model indicates that general household characteristics would predict lower consumption per capita in the Central and Northern provinces and in rural areas and, keeping the official PLs unchanged, higher poverty rates in those regions.

Although it provides useful insights into the temporal dynamics of consumption and poverty, the full reestimation of the two surveys is clearly an extreme measure, as not all consumption data are affected by measurement error. In Equation 3, using the out-of-sample estimates and combining them with the observation sets described in Section 2.2, the fitted consumption is assigned only to observations in Sets A and B for both 2002/03 and 2008/09.

# **Equation 3**

A partial prediction using the 1996/97 estimates possesses a number of important advantages. First, the revised overall provincial poverty rates show a similar pattern to those obtained using other correction 10
(2016) conclusion<sup>7</sup> that a more “transactional” type of exchange, whereby local ties are mobilized in project implementation, can produce sustainable service delivery in the longer term. In comparison, in our negative deviance cases, local leaders were found to be driven by a more zero sum understanding of self-interest, which Ang (2016) labels “extortionist”, and which leads to unsustainable service delivery. Our findings suggest, therefore, that in contexts where local government and community ties are more synergistic and, though maybe driven by self-interest, are precisely not at the expense of communities, they play an important role in generating sustainable collective infrastructure management. Project managers should therefore pay more attention to local socio-political ties during project preparation and ensure that projects are implemented in a way that will generate reduced transaction costs and an innovative space for water user group leaders. In this manner, projects can attempt to induce a positive pathway of community management at best or avoid a negative pathway at worst. The lessons from the paper can be used for context-specific practical strategies outside the Indonesian context as well (Brixi, Lust, and Woolcock 2005).

# Data and Methodology

We measure _sustainability_ through the lens of _water point functionality over time_ . This definition is consistent with both policy and academic measures with an agreed-upon view that community water supplies are considered sustainable if they continue to work and be used _over time_ (Carter and Ross 2016).

We use panel data on water system functionality from the project’s monitoring database that contains information at the village level on every single water point constructed since the program’s inception—currently amounting to water systems in 16,000 villages. The status of these water systems is assessed by facilitators every three months through in-person field visits. Based on this

> 7 Ang (2016) argues that local officials in China were able to carry out innovative solutions and had decision space to use resources though under the general guidance of central agencies, a process she calls
Deign, Jason (2019). 8 Countries (Besides the US) With Solar Under $25 per Megawatt-Hour. GTM, October 31, 2019. <u>https://www.greentechmedia.com/articles/read/8-countriesbesides-the-us-with-solar-under-25-per-megawatt-hour.</u> Dobrotkova, Zuzana, Kavita Surana, Pierre Audinet (2018). The price of solar energy: Comparing competitive auctions for utility-scale solar PV in developing countries. Energy Policy, Vol. 118, pp. 133-148.

Energy Information Administration (EIA) (2020). Levelized Cost and Levelized Avoided Cost of New Generation Resources in the Annual Energy Outlook 2020. Available at https://www.eia.gov/outlooks/aeo/pdf/electricity_generation.pdf (accessed on April 18, 2020).

- International Energy Agency (IEA) (2015). Projected Costs of Generating Electricity; 2015 Edition. IEA, Paris.

International Renewable Energy Agency (IRENA) (2018), Renewable Power Generation Costs in 2018, IRENA, Abu Dhabi.

International Renewable Energy Agency (IRENA) (2020). Renewable Power Generation Costs in 2019. IRENA, Abu Dhabi.

Joskow, P.L. (2011). Comparing the costs of intermittent and dispatchable electricity generating technologies. American Economic Review 101 (3):238-241.

Kelly-Detwiler, Peter (2020). Record Low Solar Prices in Qatar. Forbes, Feb 3, 2020. <u>https://www.forbes.com/sites/peterdetwiler/2020/02/03/record-low-solar-prices-in-qatarand-four-other-energy-stories-that-matter-from-week-5-of-2020/#4c8063f12215.</u>

- Landis, Florian and Govinda R. Timilsina (2015). The Economics of Policy Instruments to Stimulate Wind Power in Brazil, World Bank Policy Research Working Paper, WPS 7376, World Bank, Washington, DC.

Lazard (2019). Lazard’s Levelized Cost of Energy Analysis - Version 13.0. <u>https://www.lazard.com/perspective/lcoe2019</u
Practices Index = Visited at least one of its competitor’s businesses to see what prices they are charging; Visited at least one of its competitor’s businesses to see what products he or she offers; Asked existing customers whether there are products they would like you to offer; Asked a supplier about which products are selling well in this business’ industry; looked for ways to improve your marketing and advertising strategies; Advertised in any form.

(4) Stock Control Index = Negotiated with a supplier for a lower price on raw material; Compared the prices or quality offered by your supplier’s product/service with other suppliers (5) Financial Planning Index = Analyzed if the sales of your most important product/services have increased, decreased or remained the same; looked for additional financial resources for your business; looked for new markets.
# 4 Measuring the Local Economic Structure

Since the seminal paper of Glaeser et al. (1992), the empirical literature has used employment based indeces to measure dynamic externalities<sup>21</sup> . I follow the literature and construct employment based indeces. Throughout the text the subscript r stands for the geographic unit of “concelho” and r for the two digit ISIC sectors. The concentration index used in this paper is the same as in Glaeser et al. (1992), i.e., the employment share of sector s in region r.

where Lrs and Lr are the total employment in the sector-region and in the region, respectively.

Following Henderson et al. (1995) and Combes (2000), I use a measure of diversity given by the inverse of a Hirschman-Herfindhal index of sector concentration based on the employment share of other sectors on the total manufacturing employment of the region.

The index is maximum if the regional employment in the remaining sectors is evenly distributed. It is possible that for a given region, a sector has both a high concentration and a high diversity. This happens if there is a high concentration of employment in that sector but there are several remaining sectors all with approximately the same size.

As in Combes (2000) sector competition in region r is measured by the inverse of a HirschmanHerfindhal index of productive concentration: where Lirs is the employment of plant i operating in sector s and located in region r and Lis is the employment of plant i operating in sector s and located in Portugal. Increases in the employment share of plant i in total employment in region r reflect less competition in the region and, therefore, a decrease

> 21 One exception in this is Gao (2003) that uses output based indeces.

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<!-- Start of picture text --> Figure 3: Median Survival Time based on Fixed Costs by Country<br>Ukraine<br>Kazakhs ...<br>Kenya<br>Greece<br>Portugal<br>Turkey<br>Jordan<br>Russia<br>Italy<br>Morocco<br>Colombia<br>Peru<br>0 4 8 12 16<br><!-- End of picture text -->

**Figure 3: Median Survival Time based on Fixed Costs by Country**

<!-- Start of picture text --> 20<br><!-- End of picture text --> <!-- Start of picture text --> Weeks<br>Note : Data is extracted from the World Bank Enterprise Surveys. Number of Observations: 6,345<br><!-- End of picture text --> The median survival time has significant variation across countries within a given sector (Table A3). The median Portuguese firm in the manufacturing of food and beverages, for example, has a survival time of 7.6 weeks, whereas the median firm in the same sector in Colombia can last 26.3 weeks. Variation is even larger in the manufacturing of metals and fabricated metal products. The median Ukrainian firm can survive for just a little over 8 weeks, while the median Turkish firm has sufficiency liquidity for more than 10 months (44.1 weeks). Great variation is also present across sectors within a given country. In Kenya, for example, the median firm in the manufacturing of chemical, plastic and mineral products cannot even last a week, while a firm in the manufacturing of food and beverages can last for 16.8.

The mean survival time is longer, suggesting heterogeneity among firms and the likelihood that some firms can persist even in extreme economic hardship. The construction sector, for example, can survive a total