rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 7599

# **Abstract**

This article examines how a policy oriented toward a specific group within the population can have collateral effects on the economic decisions of other groups. In 1996, the Chilean government approved the extension of the school day from half- to full-day school. This article exploits the quasi-experimental nature of the reform’s implementation by time, municipality, and age targeting of the program in order to examine how the maternal labor supply is affected by the childcare subsidy implicit in the lengthening of the school day. Using data from the Chilean socioeconomic household survey and administrative data from the Ministry of Education for 1990–2011, the authors estimate that, on average, there is a 5 percent increase in labor participation and employment rates of single mothers with eligible children (between 8 and 13 years old) with no younger children, who are the group that would be mainly affected by the policy. No significant labor supply responses are detected among others mothers with eligible children.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dcontrer@fen.uchile.cl and pausepulv@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the
of the countries.

While the level of trade participation of firms in the MENA region is comparable to other lower-middle income (LMI) and upper-middle-income (UMI) economies, we find that innovation rates are particularly low (Figure 3). This lack of innovation, which has even been deteriorating in recent years, is also reflected in the low level of investment in intangible assets, except Morocco (Figure 4). According to the Global Innovation Index, an annual global ranking based on countries’ characteristics and performance in innovation, innovative activity in MENA economies lag behind Southeast Asia and East Asia (Morrar, 2019). The low and deteriorating investment levels, both in fixed and intangible assets, may be explained by worsening external financing conditions of private firms and crowding out of private sector investment (Akbas et al., 2021). This is worrisome as this could have negative consequences for the medium- to long-term economic prospects in MENA. Our findings are in line with previous literature documenting that developing countries do not invest enough in innovation despite its central role in closing the gap between developed and developing countries (Cirera and Maloney, 2017). During the COVID-19 crisis, innovation proved to be key for firms to be more resilient and to adapt better to the economic shock.

5
of grievance process at local council|0.22|0.23|0.16|0.16|0.01|-0.00|
||_(0.02)_|_(0.02)_|_(0.02)_|_(0.02)_|_(0.61)_|_(0.87)_|
|Any Mtaa/village meeting|0.41|0.41|0.51|0.47|-0.00|-0.04|
||_(0.05)_|_(0.05)_|_(0.04)_|_(0.03)_|_(0.92)_|_(0.34)_|
|Any Ward meeting|0.29|0.25|0.32|0.27|-0.03|-0.05|
||_(0.04)_|_(0.03)_|_(0.04)_|_(0.03)_|_(0.50)_|_(0.40)_|

_Note:_ This table presents group averages and differences in means for households matched across the two waves of the panel data. Columns (1)-(4) present average and s.d. for each variable described for ULGSP LGAs and comparator, for each of the survey waves. Columns (5)-(6) present differences in means for the specific groups and the p-value of a t-test for equality of means (clustering at LGA-level) 47
and have stronger GVC participation of natural-resource intensive sectors. Importantly, countries with stronger institutional quality have a comparative advantage in exports and in GVC participation of contract-intensive sectors. Importantly, conditioning on countries’ factor endowments, trade policies play an important role for GVC participation and exports. Sectors that face lower tari¤s in their destination markets or lower tari¤s on intermediate inputs exhibit stronger GVC participation and exports.

To compare the strength of di¤erent determinants, Figure 2 shows the standardized beta coe¢ cients corresponding to the estimates in Table 8. Hypothetical examples are useful to provide an economic interpretation to the coe¢ cients. If Ghana increased its skilled labor share (7.5 percent in 2012) to the cross-country median (20 percent), its backward GVC participation level and exports would grow by 42 percent, and its forward GVC participation level would grow by 39 percent at the sample mean of sectoral skill intensity. If Mozambique improved its rule of law index to the cross-country median, its forward GVC participation and export levels would grow by 32 percent, and its backward GVC participation would grow by 29 percent at the sample mean of sectoral contractual intensity.

Two robustness checks are performed on the cross-country cross-sector analysis. First, we add country-sector level FDI to vector Z. Given that, to the best of our knowledge, country-sector-year level FDI in‡ows data are not available for a wide range of countries, we use FDI announcement data from the fDi Markets Database for the period 2003-2015 as a proxy for FDI in‡ows, following the approach of Hallward-Driemeier and Nayyar (2019). Speci...cally, we cumulate the number of cross-border green...eld FDI projects announced from 2003 onwards for each country-sector. The estimates show that FDI has a positive and signi...cant marginal impact on country-sector GVC participation levels and on gross exports. Second, we show that countries with a higher internet penetration exhibit signi...cantly higher GVC participation and gross exports in sectors that
other way around by imputing from Sample 2 to Sample 1 and then compare with the true poverty rate in Sample 1.

We consider three model specifications based on different sets of regressors for further comparison. Specification 1 employs the variables that are only available in the PG data set (PGspecific variables), which include case (household) size and the PA’s demographic and employment characteristics (age, gender, different levels of education achievement, occupation group, marital status, religion, and the governorate or city of original residence in the Syrian Arab Republic).<sup>9</sup> Specification 1 also includes variables related to the PA’s immigration status such as the type of border crossing point and the legal status of entry. It is the main model specification. Specification 2 adds to Specification 1 several variables that are only available in the HV data and that are related to household assets, utilities, and the physical characteristics of the house. These variables include the quality status of the kitchen, electricity access, and the ventilation system, the living area of the house (as measured by the number of square meters per person), whether the house is made of concrete, and the availability of tap water and piped sewerage system. Specification 3 further adds to Specification 2 HV-specific variables related to the household’s shock-coping strategies (i.e., whether receiving humanitarian assistance, help from the host family, or from the host community), whether the household has a valid certificate of asylum, and whether the household receives UNHCR financial assistance.

We are particularly interested in examining whether adding HV-specific variables to the main specification in Specification 1 can improve the accuracy of the estimates. If we find that some key predictors of household expenditure—that are not available in the PG data—can improve the accuracy of the poverty predictions significantly, this provides a strong argument for collecting

> 9 We consider the following five levels of education achievement: 1) below six years of schooling, 2)
_et al.,_ 2009b). Therefore, to mitigate these likely productivity-depressing long-lasting effects, the Government must resume structural reform policies that support real cost reduction.

# **A. PROSPECTS: IMPACT ON POTENTIAL GDP IN THE LONG RUN**

To estimate potential GDP in the period 2010-2020, we adopt a production function approach. First we construct a potential GDP baseline based on assumed trajectories for human capital, physical capital, and productivity growth had there been no crisis. Second, we examine deviations of the medium-term (defined as ten years after the crisis) level of output from the baseline (or the level it would have reached without the crisis). We assume that the impact on potential GDP will be mostly through the effect of the crisis on capital accumulation, as has been the case in most OECD revisions of potential growth (OECD, 2009). We also assume lower TFP growth as a result of increased public spending (Kheir El-Din and Moursi, 2007) and reduced savings rates (Bernanke and Gurkaynak, 2000).

> 21 The 2008 _Doing Business_ indicators show that Egypt tops the list of countries on ending-employment costs: the cost of firing, in terms of weeks of salary, is 132 weeks, while in India it is 56, in Tunisia 17, in Morocco 85, and in Brazil 37 weeks.

24
>0% 5%<br>0%<br>1870 1890 1910 1930 1950 1970 1990<br>Japan India South Korea India South Korea Singapore<br>Singapore Taiwan, China China Taiwan, China China Japan<br>employment (%)<br>AShare of agricultural employment in total Share of Manufacturing employment in total employment<br><!-- End of picture text --> Note: After 2002. The employment statistics for China is not comparable with earlier years because the NBS stopped publishing data for rural employment. Source: ILO,UNIDO and UNCTAD In general, the factors that powered structural transformation in the Western, East Asian and emerging market countries have been elusive in Sub-Saharan Africa (excluding South Africa), and suggest that there are no shortcuts to catching up (Figure 1.6). As an example, with the 9
9 corresponding counterfactual income distributions, we report our estimates of the effects on poverty and inequality measures.

# **3. Data**

Three different data sources are used in this paper: individual price data from the National Consumer Price Index (INPC); the Household Budget Survey (POF); and the National Household Income Survey (PNAD). The INPC records percentage changes in consumer prices on a monthly basis. In total, information is collected for 472 consumption items, which are then classified into increasingly more aggregated commodity groupings. Ultimately, the information feeds into Brazil‟s overall consumer price index. Individual prices are recorded for a total of 156 food items in nine metropolitan regions (Belém, Fortaleza, Recife, Salvador, Belo Horizonte, Rio de Janeiro, São Paulo, Curitiba, and Porto Alegre), and for two municipalities (Brasilia and Goiânia). In what follows we refer to these eleven urban centers as Brazil‟s “large urban areas”. Monthly prices are available beginning in April 1979, and we use the series from January 2007 to December 2008.

To facilitate the concordance between food item classifications in the INPC and in the household budget survey, our analysis is conducted at the level of 16 food groups, which are listed in Table 1. The table also presents the average price increase for each food group during 2007-2008 and the maximum price increase (12 months to peak) observed during the two-year period. (The last column indicates the peak month for each price series.) This maximum 12-month price increase is the proportional price change variable we use throughout the analysis.

Results are presented in Section 5 for the country as a whole, as well as separately for Brazil‟s large urban areas (which accounted for approximately 30 percent of the national population in 2008), and for the rural areas (16% of the population). Since the IBGE only collects prices in the 11 large urban areas, price changes were imputed to other localities on the basis of
less vulnerable to liquidity constraints, fared much better during the crisis than comparable domestically owned firms.<sup>2</sup> Discriminating between the competing predictions of the cleansing and scarring paradigms requires firm-level analysis on the impact of crises on resource allocation, as it requires one to analyze the link between productivity, exit and firm growth. Although such analysis is scant, there are a few studies that shed light on the debate. Liu and Tybout (1996) compare the performance of continuing plants and exiting plants in Chile and Colombia from 1980-1985 but find no evidence for systematic covariance of the efficiency gap between these two groups of firms over the business cycle in either country, even though Chile suffered a recession in 1982. Exit rates of Chilean firms only increased modestly during the recession. Casacuberta and Gandelsman (2009) examine the impact of the 2002 banking crises in Uruguay on resource allocation. Even during the crisis, productivity was negatively correlated with exit, although the evidence suggests the crisis may have attenuated the link between productivity and exit somewhat. Nishimura, et al. (2005) use a cohort analysis comparing productivity of entrants and survivors to examine the impact of the Japanese recession on productivity growth and find that the 1996/97 banking crisis induced the exit of some relatively efficient firms among the youngest cohorts. Similarly, Eslava et al. (2010) demonstrate that Colombian firms dependent on external credit were more likely to exit during the 1998-2001 recessions, even if they were highly productive. On the other hand, using manufacturing data from Russia, Brown and Earle (2002) show that the recession induced by the transition from communism to a market based economy coincided with an improvement in the reallocative process.

In short, theoretical models yield competing predictions regarding the impact of crises on resource allocation and the empirical evidence on the impact of crises on firm dynamics is limited and ambiguous.

> 2 Similarly Oh et al. (2009) find that the Korean credit guarantee scheme for SMEs implemented in response to the Asian crisis
0.013|11306|Senegal|0.810|0.033|2157|
|Jordan|0.662|0.025|7316|Sierra Leone|0.764|0.049|1029|
|||||South Africa|0.836|0.136|343|
|South Asia||||Tanzania|0.672|0.032|1952|
|Afghanistan|0.725|0.030|2727|Togo|0.839|0.070|430|
|Bangladesh|0.861|0.017|5228|Uganda|0.755|0.029|1489|
|India|0.759|0.005|53132|Zambia|0.777|0.029|1325|
|Nepal|0.684|0.023|2171|Zimbabwe|0.687|0.034|1481|
|Pakistan|0.814|0.007|27858|||||

_Notes_ : This table presents the estimated share of the intergenerational component using the Bingley and Cappellari (2019) method for each of the 53 developing countries in the Demographic and Health Surveys (DHS) using the 1980s birth cohort sample.

44
0.658|0.000|0.000|0.566|0.732|0.812|0.336|0.000|0.000|
|**(IV) impact of Formalization:**|||||||||||
|Formalization instrumented by 1st stage|‐0.078|‐0.150|679,408*|52,453|‐15.20|‐0.055|‐0.000|0.243|0.352|0.323|
|treatment variables|(0.062)|(0.117)|(362,873)|(478,538)|(10.78)|(0.070)|(0.099)|(0.196)|(0.356)|(0.286)|
|P‐values|0.213|0.200|0.061|0.913|0.158|0.437|0.999|0.214|0.323|0.259|
|Sharpened two‐stageq‐values<sup>μ</sup>|0.749|0.749|0.749|0.76|0.749|0.749|0.76|0.749|0.749|0.749|

Note : Panel data from midline and endline surveys in 2015 and 2016. All regressions are controlling for strata dummies (dummies for each triplet). Standard errors (in parentheses) are clustered at the firm level. **α:** top‐coded at the 99th percentile. **Β:** controling for baseline value. **η:** the standardized summary index includes the following questions: "Is the business premise generally well organized?", "Is the premise generally clean and in good shape?", Are there
investment climate in Africa are simply not good enough. Foreign firms deciding to outsource part of their production face a trade off between lower costs of production and increasing time costs, transport costs, and coordination costs generally. With poor infrastructures, cumbersome bureaucratic practices, poor supporting services or even corruption, outsourcing to Africa will be associated with high costs. Clearly under status quo where nothing is done about these problems, Africa will continue to experience a large amount of missed opportunities. It is hard to see how such a strategy can yield growth and create jobs on a scale necessary for significant poverty reduction.

The third broad policy option is to create incentives for African firms to strive to participate and be competitive in international markets. In our view, this is the most likely route to better economic performance. This view is directly supported by some of the findings based on the firm data. Harding, Söderbom and Teal (2004) find that the strongest explanatory factor of productivity growth over five years among firms in Ghana, Kenya and Tanzania, is the competitive pressure faced by the firms. Firms that face little competitive pressure, and have high profits as a result, record slower subsequent productivity growth than firms that face a lot of competitive pressure. Thus, opting to embrace, rather than to shy away from, increased competition is likely to improve the performance of manufacturing firms in Africa. Secondly, as discussed in Section 3.5, there is relatively strong evidence that participation in exports markets is a source of improved firm performance. We do not yet understand fully why this is so – it could be that international competition “disciplines” firms to get better organized and become more efficient, or that contacts with firms and customers in developed countries help to speed up the rate of technology transfer. Nevertheless, we know from the data that these positive effects exist. Apart from the productivity effect, international trade leads to concentration of investments in the more efficient sectors according to comparative advantages as well as the exploitation of scale advantages
) The third approach relies on homeowners’ subjective evaluations of the market rental values of their residences. This method has been widely used. For example, half the 21 economies in the Asia and Pacific region that participated in the International Comparison Program for the computation of 2011 purchasing power parities use self-assessed rental values to impute rents going to homeowners (ADB 2014). The African Development Bank also presents equivalent results for countries that include rents in national accounts: half the countries use self-assessment (AfDB 2013). The weights of the CPI in the United States are computed based on the values of dwellings found in the Consumer Expenditure Survey. The rental values of owner-occupied dwellings revealed in the survey depend on owner estimates (Lebow and Rudd 2003). According to a recent review of national methodologies used to produce official poverty estimates (World Bank 2020), poverty estimates in 16 percent of the countries surveyed include rental values that are self-assessed by homeowners.

Using homeowner assessments relies on the assumption that homeowners can give an accurate estimate of the rental market values of their dwellings, perhaps with the help of interviewers, as suggested by Garner and Kogan (2007). This information is frequently collected in household budget surveys, in which homeowners are often asked to estimate the rents they would pay to live in their current dwellings. However, owner-occupiers may not be able to provide accurate estimates of the rental prices for their dwellings, and the direction and magnitude of the inaccuracies become an empirical issue. In countries in which rental markets are thin because of high rates of homeownership, one may reasonably expect that homeowners might merely lack information about rental prices. In other circumstances, homeowners might overestimate the true rental values of their dwellings compared with rented dwellings having similar characteristics because of their attachment to specific features of their neighborhoods or of their residences, particularly if they designed or built the homes (Frick et al. 2010; Heston and Nakamura 2009). Extensive evidence on the United States suggests that self-assessed rental 2
subnational level, particularly in the developing world. I thus use NTL data to expand the sample of Salvadoran municipalities in my analysis.

NTL density data come from the Defense Meteorological Satellite Program (DMS) - Operational Linescan System (OLS). Every night from 8 PM to 9:30 PM the DMSP captures night light intensity from space. The high-frequency measure of NTL is a six bits digital number calculated for each location (i.e., pixel) of approximately 0.86 square kilometers in the Equator (its annual measure is an average across all nights for a given year). A digital number can go from 0 (a non-lit area) to a maximum of 63. Panel (a) in Figure 5 shows the distribution of digital numbers across pixels in El Salvador and its neighboring countries for the year 2004. The darker the pixel, the higher the density of NTL. As can be seen in the map, the intensity of NTL is higher in the most developed parts of El Salvador, such as San Salvador, Santa Ana, and San Miguel.

It is important to note that the satellites’ technologies changed in 2013, which makes the direct comparison between the 2000-2012 and 2013-2018 periods difficult. To overcome this problem Li et al. (2020) created a harmonized global nighttime light dataset for the entire period for which NTL data is available (i.e., 1992–2018). I exploit this granular data to compute density of NTL at the municipality level by averaging across all pixels within the boundaries of each Salvadoran municipality. Panel (b) in Figure 5 depicts the average NTL density by municipality in 2004. A closer look at the data reveals that even within regions of El Salvador substantial heterogeneity in development exists across municipalities.<sup>5</sup>

# **2.2.1 Validation of the Proxy for Income at the Municipality Level**

To validate the use of NTL data as a proxy for income in El Salvador I first show
<br>Japan<br>0 toy_export_kor<br>toy_export_sgp<br>Source: calculated based on COMTRADE data, SITC Rev 1, 3-4 digits, extracted from WITS .<br>1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2010 2010<br>1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010<br>1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010<br>1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010<br><!-- End of picture text --> 26