rafmacalaba/datause-encoder-data huggingface dataset:
3

# 2. **THE PANAMA CASE**

# **Background**

Since 1961, water and sewerage services in the urban areas of Panama have been the responsibility of an autonomous public enterprise known as the Instituto de Acueductos y Alcantarillados Nacionales (IDAAN), which operates under the aegis of the Ministry of Planning. In the rural areas (encompassing all conurbations with fewer than 500 inhabitants), the Ministry of Health plays a leading role in the design and construction of water and sewerage infrastructure.

By 1995, IDAAN was manifesting a wide range of problems common to public-sector water companies in Latin America. The company had effectively lost much of the administrative and technical autonomy which-by law-it was supposed to enjoy, being subject to direct political involvement in its day-to-day managerial decisions. As a result, its operations had become highly inefficient and its financial situation extremely precarious. Only a substantial transfer of funds from the public purse, amounting to US$20m in 1997, was enabling the company to stay afloat.

Prompted by a desire to overcome these problems, the government of Panama began to consider options for water sector reform, including the possibility of private-sector participation. These deliberations occurred against the background of reforms in other utilities leading to the creation of a new multi-sector regulatory agency in 1996, the Ente Regulador de Servicios Publicos (ERSP), with jurisdiction over the electricity, telecommunications and water sectors.

The first major step towards reform of the water sector was the passage of Law No. 01/97 at the beginning of 1997. The law introduced a new regulatory framework for the water and sewerage sector, and, in particular, opened up the possibility of private-sector participation in water and sewerage services under a range of alternative forms.

Later that same year, the government announced an international bidding process for a concession to supply water and sewerage services to the areas currently supplied by IDAAN. As of mid-1999 the privatization process has been stalled pending a decision by the newly elected government.

# **The Subsidy Study**

One of the provisions of the new sector law described above related to the subsidy regime for water and sewerage services. The law allowed the government to grant subsidies to individual households for the consumption of water and sewerage services (as it had already been doing), but only under certain carefully defined conditions (which were not met by existing subsidy payments).

As part of the sector reform process, the government planned to introduce a direct subsidy scheme along the lines laid down in the new legal framework. The subsidy policy forms an
<!-- Start of picture text --> FCS Poverty, 2010 FCS Poverty, 2030<br>Nepa|<br>8.2M<br>I Myanmar<br>yN Dem. Rep. of Congo < A<br>< 53.4M . A VY<br>Madagascar \ Burundi}| ' Y A AXNyy|<br>17.1M 7.5M >» > Y<br>vy Y y Yemen<br>Liberia Sudan<br>yd >| |y 7 V 5.6M<br>a aa<br>Malawi<br>| 10.6M Madagascar32.4M<br>Yemen<br>Malawi<br>18.1M<br>Size of each bubble represents poverty headcount (also givne inside most bubbles); color<br>scale represents the severity of poverty rate. 2010 and 2030 bubble sizes to scale.<br>Source: International Futures v7.04<br>Size of each bubble represents poverty headcount (also givne inside most bubbles); color<br>Poverty Rate scale represents the severity of poverty rate. 2010 and 2030 bubble sizes to scale.<br>kT Source: International Futures v7.04<br>0.00 75.00<br>Poverty Rate<br>0.00 75.00<br><!-- End of picture text -->
Table 2 shows the profitability of different types of institutions and borrowers in a different way. The bottom row of Table 2 shows that, of the 315 institutions with data on profits, 57 percent were profitable according to the adjusted MIX data. Moreover, since profitable institutions tend to serve more customers, 87 percent of all borrowers were served by profitmaking institutions. Given that our data set is a self-selected sample of leading institutions, we also look to evidence from the larger data set of Gonzalez and Rosenberg (2006). There, profitmaking institutions are again much larger than others. But they find that only 44 percent of borrowers from microfinance institutions are served by profit-making institutions (in their data, profits are self-reported, so this estimate is likely an upper bound). The average is dragged down by some large and very unprofitable government banks. When focusing on private institutions and nongovernmental institutions, about 60 percent of borrowers are served by (self-described) profitable institutions. Most borrowers from profit-making institutions are customers of nongovernmental organizations.

As Table 2 shows, not surprisingly, banks are more likely to be profitable than others (73 percent of institutions are profitable), and nongovernmental institutions less profitable (54 percent). But because nongovernmental institutions are numerous and some are very large, eight million of the customers in the sample served by profit-making institutions are served by nongovernmental organizations. Banks serve under four million customers in the sample. Not all NGOs aim for profitability, and some that are profitable prefer to keep non-profit status since it often reduces the weight of regulation and taxes. But we will show that when it comes to serving poorer households and women, profit-making NGOs look much more like subsidy-dependent NGOs than they look like commercial banks. The bottom line so far is that, among these leading institutions, nongovernmental organizations are far from peripheral: they serve more borrowers overall and more borrowers on a profit-making basis.

# _Are loans repaid?_

Much has been made of the fact that microcredit innovations allow lenders to get their money back, even in the absence of collateral. The second panel of Table 2 divides the sample by lending method. Individual lending refers to traditional lending relationships between the bank 13
PPP is from Han's study (1982) where Han calculated the ratio of the sum of China's imports and exports at domestic prices in yuan to the sum of the same at world prices in dollars. This parity seems to only reflec **t** he relative price level differentials intradables between China and world market, which should be smaller than that in non-tradables as many studies in this field have demonstrted. Wharton Econometric Forecasting Associates (WEFA) constructed retail price PPPs to estimate the dollar net material product of China for 1981 (WEFA, 1984), which is below the exchange rate estimate. 9. The ICP has been under way for over two decades. Benchmark surveys have been made in 90 countries at one time or another (Kravis, et al., 1975, 1978, 1982). The benchmark estimates were extrapolated to other countfies and years in five versions of Penn World Tables (Summers and Heston, 1984, 1988, 1991).

10. Kravis (1981) used the ICP approach to approximate China's real income for 1975. His estimate was a "reduced information" exercise and involved the highest levels of expertise available in the international comparison research.

11. The Penn World Table (Mark 3) published in 1984 includes China's per capita GDP in U.S. dollars from 1950 to 1980, which are extapolations of Kravis' esimate (Summers and Heston, 1984). The 1988 Penn World Table (Maik 4) provides a revision of the previous estimate (Summers and Heston, 1988). The latest Penn World Table (Mark 5) gives an estimate of China's per capita GDP of $2308 for 1988, which was also extrapolated fiom Kravis' estimate (Appendix B of Summers and Heston, 1991).

5
Independent Evaluation Group (IEG) in 2014 concluded that women and female heads of households invest in livestock and agricultural tools as much as or more than men. However, the types of livestock or agricultural investment by women and men often differ (see e.g. Wheeler et al. (2012) for Ethiopia and Devereux and Ulrichs (2015) for Rwanda). Also, and particularly in relation to the reduction of future poverty, both Wheeler et al. (2012) and Devereux and Ulrichs (2015) show that the well-being and living standards of men and women are not defined by the same factors. In the Ethiopian case, for example, women defined wellbeing as the ability to feed their children, whereas for men well-being is most commonly associated with the accumulation of assets (Wheeler et al., 2012). Along this line, men and women are also exposed to different risks and face different constraints. Taking the case of the PSNP in Ethiopia as an example again, men often identify low wages as program-specific constraints to change, whereas women frequently mention conflicting time constraints as obstacles to overcome (Wheeler et al. 2012). Differences in needs and experiences offer one explanation of why the effects of poverty-based income transfers may lead to different results for men and women.

With this paper, we aim to advance the debate on how household characteristics and endowments contextualize access to and benefits from social assistance programs. We do so based on experiences from the Vision 2020 Umurenge Programme (VUP) in Rwanda. We are contributing to the existing literature and policy discourse in two ways. First, we use quantitative evidence from a statistically representative sample of VUP beneficiaries and a constructed comparison group to provide evidence of the effects of large-scale social protection programs in Sub-Saharan Africa – in this case from Rwanda in particular. Second, this paper uses qualitative data and information to take an in-depth look at a set of household characteristics, exploring how these shape the access to and gains from the VUP. This is a key aspect for policy relevance, since the VUP is currently launching the second generation, where 4
cashiers in a grocery store with 60 employees. When collecting the five data points under the _Parenthood_ indicator, the research applies additional assumptions, such as that mothers and fathers have worked long enough to accrue maternity, paternity, and parental benefits.<sup>5</sup> In each data collection cycle, specific time frames are set, and only legislation entered into force in each time frame is measured. Only codified laws (including codified customary laws) are considered in data collection. WBL data were collected from primary legal sources and confirmed by local experts through the administration of standardized questionnaires. Historical data on laws and regulations before 2009 and going back to 1970 were collected through desk research of the primary legal sources and consulted with secondary sources where the primary ones could not be located.<sup>6</sup> We follow the WBL methodology in our analysis, and the total number of leave days available to a mother is calculated as the combination of maternity leave, parental leave days reserved for the mother and any additional days that are not specifically reserved for the use of either parent.<sup>7</sup> Fathers’ leave is the sum of paternity leave days plus any days of parental leave specifically allocated to the father. In our analysis, only paid leave is counted, and all leave days are calculated in calendar days. Continuing with the example of Germany, according to data for 2021, women in Germany are entitled to 98 days of maternity leave,<sup>8</sup> while there is no provision in place granting paternity leave specifically (fathers’ leave is only covered under parental leave policy). Thus, according to our method of counting leave days, total leave days for mothers is 458—of 98 maternity leave days, 60 parental leave days reserved for mothers, and 300 unallocated parental leave days. Total leave days for fathers is 60 days, comprised solely of parental leave days reserved for fathers.

> 5 The complete list of assumptions for the indicators can be accessed at https://wbl.worldbank.org/en/methodology.

> 6 The full dataset of legal bases can be accessed at

> https://wbl.worldbank.org/content/dam/sites/wbl/documents/2021/02/WBL1971-2022%20Dataset.xlsx.

> 7 This is based on the assumption that mothers are more likely than fathers to take the parental leave that is not assigned to either parent. In our analysis, we test the robustness of the results to this assumption.

> 8 Maternity Protections Act; sections 3 and 6.
financial penalty if they fail to generate a minimum return relative to a peer- and market-based benchmark. Hence, it is not surprising that the portfolios of these funds replicate to a large extent the market index. Figure 3 shows the monthly time series of the share of AUM by investor type. The figure displays the upward trend of passively managed funds by domestic institutions and the sharp rise of foreign passive funds after 2008.<sup>11</sup> Given the unique nature of our transaction database with investor ID, we document how passive and active investors trade during the day. Table 4 summarizes the intra-day profile of traded value when we divide the day into eight time intervals.<sup>12</sup> Panel A in the table reports the proportion of the traded value by each group relative to the total traded value in the interval. Excluding the closing batch, domestic institutions account for more than half of the value traded during each period, with foreign investors increasing their relative importance in the afternoons. In the closing batch, foreign investors represent 39.9 percent of the interval, with passive funds accounting for most of those trades (30.9 percent of the interval). Panel B in the table reports the proportion of traded value in each interval relative to the total daily value traded by investor group. Domestic institutions and individuals execute most of their trades in the early hours of the day. On the contrary, passive foreign funds trade on average 39.8 percent of their daily traded value in the closing batch. While this behavior is more pronounced for the latter years in the sample, passively-managed foreign funds were consistently very active in the last five minutes of the trading session since the closing batch auction was introduced in 2009 (Table 4-Panel C).

Trading at or near closing prices might be an efficient strategy for open-end funds. For example, mutual funds are “forwarded priced,” which means that while investors can place orders to buy or sell shares throughout the day, these orders will be executed at the same price, i.e. the end-of-day net asset value of the fund. A fund manager that trades at closing prices mitigates the timing risk due to non-simultaneous purchases or sales of the fund shares and the underlying portfolio. In this scenario, the price that new shareholders pay for shares that are created coincides with the value of the underlying securities of the fund. Conversely, the price that old shareholders receive for their

> 11According to Factset Institutional Equity Ownership Database, the percentage of assets managed by foreign owned self-declared index funds grew from 0.1 percent in the first quarter of 2006 percent to 33.6 percent in the last quarter of 2015. Our time series should capture “closet-indexers” as well as self-declared index funds.

> 12The sample includes the period between February 6, 2012, and January 29, 2016 when the CSE synchronized its operations with the NYSE. The table is quantitatively similar if we include the period from February 9, 2011, to February 6, 2012, when the market traded from 8:30 to 15:00 hours and if we match the trading session.

10
Figure A7: Rural vs Urban Informal Consumption

# (a) Rural: Budget Share (b) Urban: Budget Share

<!-- Start of picture text --> BI CD<br>NE MZ BFTD RWTZBJ CG BI CDNE BJ<br>ST KM CM BOPGMA TZ ST KM CG<br>BF<br>EC RSPE MZ TD RW CM<br>BO<br>TN MA<br>MX<br>SZ DOCOME BR PG EC RSPE<br>TN<br>CO<br>DO MX<br>CR SZ ME BR<br>ZA UY CR<br>UY<br>ZA<br>5 6 7 8 9 10 5 6 7 8 9 10<br>Log GDP per capita, Constant 2010 USD Log GDP per capita, Constant 2010 USD<br>(c) Rural: IEC slope (d) Urban: IEC slope<br>BI CD<br>BJ<br>NE BFTD ST CG BJ CG<br>MZ RWTZ CMBOPG TN CO CR BI CDNE TZ ST CM PG ZA CRBR<br>KM MA SZ DO PE ZA UYMXBR BFTD RW KM BO TN SZ CO UYMX<br>EC MZ<br>MA DO ME<br>PE<br>ME<br>EC<br>RS<br>RS<br>5 6 7 8 9 10 5 6 7 8 9 10<br>Log GDP per capita, Constant 2010 USD Log GDP per capita, Constant 2010 USD<br>100 100<br>80 80<br>60 60<br>40 40<br>Informal Budget Share Informal Budget Share<br>20 20<br>0 0<br>5 5<br>0 0<br>-5 -5<br>-10 -10<br>-15 -15<br>Informality Engel Curve Slope Informality Engel Curve Slope<br>-20 -20<br>-25 -25<br><!-- End of picture text --> This figure plots informality levels and the slopes of the informality Engel curves for households located in rural regions (graphs a and c) and urban regions (graphs b and d). It only contains 29 countries instead of 31, since the expenditure surveys in Chile and Senegal concern urban population only.

58
(1) presents business closure rates disaggregated by gender, where, on average, female-led businesses were 4 percentage points more likely to close their business during 2020 than maleled businesses. The wave-time fixed effects are not shown in Table 1b but the overall trend points to a reduction in business closure rates over time from wave 1 to wave 7: globally, 25% of male businesses were closed during the May 2020 survey wave, 18% in June, 15% in July, 14% in August, September and October 2020; and 12% in September 2021.<sup>31</sup> However, the gender gap in business closures persists across the survey waves even as the trend in overall closure rates improves over time.

McKenzie and Paffhausen (2019) find that firm death is, on average, higher for femaleled firms than male-led firms.<sup>32</sup> However, once they account for business sector, size and other owner characteristics they show that the higher death rate of firms owned by women is no longer statistically significant. In line with this analysis, in Table 1b panels B to C we sequentially add potential explanatory factors to the regression that may explain the gender gap in business closures and performance, including controls for: business sector (Panel B), business size, age and role in the business (Panel C). Women entrepreneurs may be more likely to be concentrated in consumer-facing sectors (services, hospitality, retail trade) where a demand shock is likely to hit hardest.<sup>33</sup> However, Table 1b Panel B column 1 suggests that while accounting for sectoral differences in the regression narrows the gender gap in business closures slightly to 3.7 percentage points it does not fully explain the difference. Similarly, in Panel C column 1 controlling for business size (dummy variables for self-employed, micro, small, medium and large firms), business age (dummy variable if business is less than 5years old), and role in the business (business owner, manager or both) explains some of the gender gap in closures but not all: controlling for business size, age and role narrows the gender gap to 2.9 percentage points.

> 31Wave 1 can be considered the peak of the lockdown in many countries when closure rates and the gap was highest.

> 32Research conducted on small firms across 12 developing countries (McKenzie and Paffhausen, 2019).

> 33Globally, the sectors with the most business closures were travel or tourism agencies, hospitality and event services, education and childcare services, performing arts and entertainment, and hotels, cafes, and restaurants. The biggest gender differences were in the type of services that men and women operate in.

20
_Figure 5 Stochastic Simulations for Net Public Debt to GDP ratio in percent._ <!-- Start of picture text --> Public sector debt, alpha=0<br>70<br>60<br>50 95-97.5<br>5-95<br>40<br>2.5-5<br>30<br>20 Baseline<br>10<br>0<br>2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Finally, this approach could also be used to show the probability distribution of the required deficit reduction – a measure that calculates the additional fiscal effort needed to stabilize public debt to GDP ratio in any given year throughout the projection period, as a result of random shocks to key debt determinants. As the results in Figure 6 indicate, if Turkey is to maintain its sizeable primary surplus of 6.5 percent of GNP, as in the base case, there is 97.5 percent probability that fiscal policy will be at least consistent with stable debt ratios and targeted inflation rate in 2006 and there is only 2.5 percent probability that additional fiscal adjustment maybe needed in 2001 to ensure consistency with inflation target in 2001 and to stabilize the debt to GNP ratio at its 2011 level. Now note again, that these results are once again underpinned by the aggressive fiscal adjustment strategy. However, vulnerabilities remain to the extent that the quality and durability of the fiscal adjustment may not be sustainable.

30
the estimated change in fires in the preferred specification.

## **6.2 Changes in crop choice induced by MNREGA’s role as insurance**

Table 5 provides no evidence that the overall production of non-fire associated crops, which includes higher value, higher variance crops, increases as measured either by area in production or total tons. Existing work (Gehrke, 2013; Raghunathan and Hari, 2014) has suggested that farmers do in fact shift into higher variance, higher value crops such as cotton after the implementation of MNREGA. I find no evidence of such a change but more importantly, the production process of the crops that farmers may have shifted into (Gehrke, 2013) does not typically include fire (Jain et al., 2014; Bhuvaneshwari et al., 2019). It is therefore difficult to explain the observed increase in fires as being driven by MNREGA’s implicit insurance provision leading to transitions in crop type. In Appendix 3.11, I show that the absence of an impact of MNREGA on crop production in the average persists when examining outcomes by quartiles of the mechanization index. That is, there is no differential impact of MNREGA on crop production in different quartiles of the mechanization index.

# **7 Implications for air pollution**

The primary concern regarding the use of fires to clear crop residue stems from concern that this practice may increase air pollution. Previous estimates of the contribution of crop burning to pollution in Delhi suggest that substantial amounts, from 17% to 60% of particulate emissions in Delhi in the winter months are the result of upwind crop burning (Liu et al., 2018; Bikkina et al., 2019). In a direct examination of the impact of upwind crop burning on infant mortality in India, Pullabhotla (2018) suggests that an increase of five upwind fires in a given year increases the infant mortality rate by approximately 10%.

The availability of satellite measures of emissions due specifically to biomass burning enables me to directly examine how the increase in agricultural fires caused by MNREGA translates to an increase in the emissions of three precursor pollutants of PM10 and PM2 _._ 5: black carbon, organic carbon, and sulfur dioxide (SO2). I use data from the MERRA-2 satellite platform that measures the monthly emissions rates from biomass burning of these three pollutants by district to determine the impact of MNREGA on emissions. To do so I replace agricultural fires ( _Fimy_ ) as the outcome of 21