rafmacalaba/datause-encoder-data huggingface dataset:
strengthening is a major theme in the Paris Declaration agenda for reforming aid practices. The Paris Declaration (PD) created a set of numerical indicators on improved “alignment” of aid activities with country systems and inter-donor coordination, to be monitored through surveys of donors and recipient countries, with defined targets for the year 2010.

Although the PD principles and associated indicators reflect a broad consensus within the donor community (most notably in the OECD Development Assistance Committee), the empirical basis for this new aid effectiveness agenda is thin (Easterly, 2007). Advocacy for reform of donor practices is based on theory, intuition and scattered anecdotal evidence.<sup>1</sup> This paper does not attempt to add to the thin evidence base underpinning the Paris Declaration principles and indicators. Rather, it is premised on the fact that they have been endorsed by the DAC bilateral donors and multilateral agencies providing the bulk of ODA, and by most recipient countries. Donor behavior is assessed relative to donors‟ own assertions about what constitutes more effective aid practices.<sup>2</sup> Knack and Eubank (2009) provide a theoretical framework for understanding the incentive problems among donors that can produce suboptimal levels of harmonization and alignment in their aid activities. This framework and supporting empirical evidence are relevant so long as most of the donor community believes that use of country systems is currently below the optimal level. The caveat is that we cannot offer confident conclusions about which donors

> 1 See World Bank (2003: ch. 11) for representative anecdotes.  Azam et al. (1999) construct a formal model with learning-by-doing externalities in tax administration, in which aid dependence with low institutional capacity can be an equilibrium outcome.  What little systematic empirical evidence exists in support of the Paris Declaration agenda bears mostly on its harmonization provisions, not the alignment provisions that cover (inter alia) use of country systems.  Anderson (2011), Djankov et al. (2009), Knack and Rahman, 2007) and Knack and Smets (2012) all provide empirical evidence on the adverse effects of fragmenting a country‟s aid among a larger number of donors. 2 Here we follow Easterly (2007) and Easterly and Pfutze (2008: 3-4): “The academic aid policy literature and the aid agencies themselves agree on many elements of „best practice‟ . . . By taking this consensus as our standard, we are asking in effect if aid agencies operate the way they themselves say they should operate.”

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# **5.1 A Back-of-the-Envelope Cost-Benefit Analysis**

Our results suggest both EFS and LTFF have had a positive impact on boosting exports, but at what cost? To assess the costs and benefits of the schemes, we carry out a backof-the-envelope cost-benefit analysis of the two export finance support schemes, similar to the one conducted by Cadot et al. (2015) to evaluate the impact of an export promotion program in Tunisia. We consider two scenarios. In the first we assume that the State Bank of Pakistan (SBP) aims at improving the consolidated fiscal position of the government. In this sense, it is important to compare the financial opportunity cost that SBP incurs by providing subsidized credit to exporters (instead of, for example, lending to the central government) against the estimated tax revenues that additional exports bring into government coffers. In the second scenario we assume instead that the SBP’s objective is to secure additional foreign exchange, so we contrast each extra dollar of additional exports with the financial opportunity cost of the schemes to the SBP.

Before presenting our results, we first discuss the parameters we use in our evaluation. We use the US 6-month Treasury bill rate, which is 13 percent per annum during our period of analysis, to pin down SBP’s opportunity cost of raising external funds to finance the schemes because this is the key reference interest rate that the bank uses to determine both the refinance and end-user interest rates to firms that participate in EFS and LTFF. We assume a 20 percent profit margin for exporters; this figure is very close to the markup estimates for exporters found by Loecker and Warzynski (2012). The statutory corporate income tax rate in Pakistan is 30 percent. Based on the estimates presented in the previous section, and assuming that the exports of each participating firm increase by the estimated average treatment effect, we assume that EFS and LTFF generate US$500 and US$270 million worth of additional exports. We assume the refinancing rates offered by SBP are 2 and 4 percent per annum for EFS and LTFF respectively. It is important to remark that we impose strong assumptions such as constant profit and tax rates across exporters and we are not considering any indirect tax effects that could be brought about by the additional 24
# _(c) Heterogeneity dimensions_

We perform heterogeneity analysis along the dimensions of gender, social capital, family socioeconomic background and skill level at baseline to understand whether program impacts vary between subgroups of beneficiaries. Overall, we find little robust evidence of heterogeneity. There are no dimensions of heterogeneity that consistently or strongly show a specific sub-group benefitting more in the short-term and in the medium-term. A handful of coefficients suggest significant effects for some sub-groups in the medium-term, but they do not consistently highlight significant heterogeneity.

One of the most robust heterogeneity results based on the short-term 2011 survey indicates that students with a stronger set of skills at baseline (proxied by higher grade in the previous year at university) were more likely to succeed in becoming self-employed and in setting up active projects at midline (Table 9, Panel A). However, by the endline, these differences in program impacts are not significant anymore between the two groups, indicating that academically stronger students at baseline were not more likely than their less skilled peers to be selfemployed and have active projects by endline.

On the other hand, there is some indication of a positive medium-term impacts on project ownership for students from families of better socio-economic background (Table 9, Panel B). This effect only comes through in the medium-term, with no significant heterogeneity by family wealth in the short-term. One explanation could be that graduates from wealthier backgrounds have more resources allowing them to stay without a job and taking the time to prepare a more sustainable project. Still, these patterns of heterogeneity remain weak so that they should not be overinterpreted.

We do not find robust heterogeneity by gender and results on gender are not entirely conclusive. In the short-term, impacts on entry into self-employment (independent or seasonal activity during the last 7 days) are mostly observed among men, but with no significant heterogeneity between men and women (Table 9, panel C). On the other hand, positive effects are found for women for self-employment in the 12 months prior to the 2011 survey, but again with no 20
changes in the labor market outcomes by quarter for rural and urban areas at the province level.

The master sample is based on the latest three population censuses for 2006, 2011 and 2016.

# **3. Country background and COVID-19 3.1. Iranian context**

As in other MENA countries, the employment and labor force participation rate of Iranian women is low, about one-fourth that of men. This occurs despite high education and below replacement fertility, a combination that Assaad _et al._ (2020) call the MENA paradox. The sharp contrast between male and female LFP rates for prime age men and women (25-54) since 2005 is shown in the left panel in Figure 1. This figure draws on data from all rounds of LFS since 2005. During the period 2005-2022, participation rates for women fluctuated between 15% to 25%, while men’s participation rates stayed above 85%.

The impacts of COVID-19 on participation of both men and women are noticeable in this graph after the winter quarter of 2020 (2020_q1). The right panel of the figure brings the divergent effects on men and women into sharper focus and shows a more dramatic decrease in women’s participation. In this graph, participation is measured as change relative to winter 2019 (2019_q1). A few months after the first COVID-19 cases were reported in the Islamic Republic of Iran, women’s participation rate fell by about 15% in spring 2020 and by more than 20% in 2021. In contrast, men’s participation rates fell by 3% only.<sup>3</sup> Personal characteristics like age and education that affect the participation of women in the labor market could also have influenced the severity of COVID-19’s impacts on labor market outcomes. In MENA, marital status is perhaps the most important determinant of employment.

> 3 Plotting the changes in employment produces a figure that mirrors those in labor force participation (not shown). Women’s employment rates are lower than their participation rates, by about 5 percentage points, but follow the same path as participation.

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Bird et al. (2006) measure tax effort as tax revenue as a share of gross domestic product (GDP). Data for both measures of the dependent variable came from the World Development Indicators (WDI). The model was estimated using cross-section data with mean values for the years 1990 to 1999 for 110 transitional and developing countries.<sup>4</sup> _Table A2_ in the Appendix presents a summary of the results. It can be seen that institutions and governance play a significant role in the determination of the level of tax effort in transition and developing countries using the Kaufmann et al. (2004) data set and the International Country Risk Guide database. The ICRG provides an alternative set of data to the Quality of Governance Index, with special emphasis on aspects affecting private foreign investment decisions. The data contain annual values for indicators of the quality of governance, constructed by Stephen Knack and the IRIS Center, University of Maryland, and provided by The PRS Group. The results indicate the institutional factors are always statistically significant at the 1% level with substantial quantitative effects. Using panel data Torgler et al. (2010) extend the previous studies covering a time period of 16 years using the ICRG risk guide data (1990-2005). They also implemented the Kaufmann et al. (2006) data set working with the years 1996, 1998, 2000, 2002, and 2004. Such an analysis goes beyond the previous studies that have explored this framework, mainly in a cross-sectional environment (e.g., Bird et al. 2006, 2008). Their results also indicate that governance and institutional conditions have a strong impact on tax performance.

Looking at further factors and focusing more on tax administration using US and data from Turkey, Torgler, Demir, Macintyre and Schaffner (2008) report that positive attitudes towards the tax authority (e.g., how taxpayers rated tax administrations‘ job, their honesty and fairness, and their helping and information behavior) and tax system significantly increase tax morale. As Smith (1992) argues, ―cycles of antagonism between the tax administration and the taxpayer might begin to break with a positive concession by the administrator‖ (p. 226). He points out that a respectful and fair treatment of taxpayers induces respect for the tax system and thus leads to co-operation. On the other hand, a higher perceived tax burden crowds out the intrinsic motivation to pay taxes. Interestingly, complexity does not have a significant effect on tax morale,

> 4 The use of average values over a period allows maximizing the number of observations.

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concentration. We find little evidence for the hypothesis that these firms are restricting investment.

# **1 Identifying Star Firms**

We use data from Compustat that provides detailed financial information on publicly traded firms in the US over an extended period of time. We drop cross listed ADRs and restrict the sample to firms incorporated in the US. We also drop firms in Utilities (SIC 49), Finance, Insurance and Real estate (SIC 60-69) and Public Administration (SIC 90-99), observations with missing SIC codes, negative values for employees, sales, total assets, current assets and current liabilities, fixed assets, cash, and goodwill and missing total assets or sales.

The advantage of using Compustat is that we have detailed balance sheet information that allows us to compute intangible capital. The caveat however, is that there are firm selection issues. First, it may be that listed firms, as a class, might not consistently represent star firms. Doidge, Kahle, Karolyi, and Stulz [2018] and Kahle and Stulz [2017] show that there are fewer US listed corporations today than 40 years ago. However, Grullon et al. [2017] argue that the void left by listed firms has not been filled by an increase in the number of private unlisted businesses. Using US Census data that includes both private and public firms, they show that even though more private firms have entered the economy, their marginal contribution to the aggregate product market activity has been relatively small. Public firms also account for one third of total US employment (Davis, Haltiwanger, Jarmin, Miranda, Foote, and Nagypal [2006]) and about 41% sales (Asker, Farre-Mensa, and Ljungqvist [2014]). Also using U.S. Census data, Maksimovic, Phillips, and Yang [2017] show that high initial firm quality at birth predicts subsequent listing decision. These findings suggest that while our sample will not be picking up small and young potential star firms in their private stages, we are targeting the sample of firms among which economically significant stars are highly likely to arise.

The second potentially more important issue, as pointed out by Doidge et al. [2018], is that small, young, high-technology firms may benefit from private status where specific financial institutions, such as venture capital partnerships and private equity firms better meet their financing needs than public capital markets. Thus, such firms may be underrepresented in our sample of star firms. To 6
and 27 _μg/m_<sup>3</sup> , respectively. Smoke plumes during the year 2009 did not contain density information and were removed from analysis. Since crop type data starts in 2008 (Section 4.1.2), our final smoke plume dataset spans 2008–2020, excluding 2009, and includes 332,547 plumes total. Of these, 250,406 are low density, 58,847 medium density, and 23,294 high density. Due to the small number of high-density smoke plumes, we grouped medium- and high-density smoke plumes together and refer to this group as high-density smoke in this paper.

From these raw plumes, we created daily smoke exposure polygons over the growing season. We first took the union of all low-density smoke plumes at daily resolution from May 1 to September 30. We did the same for all high-density smoke plumes. We then subtracted the footprint of high-density smoke from low-density smoke daily, because low-density smoke plumes are often drawn in ways that include high-density smoke (i.e. they may represent _at least_ low density smoke). Since smoke plumes were aggregated to daily temporal frequency, if a given area is under a low-density smoke plume early in the day and then under a high-density smoke plume later, we classified it as being exposed to a high-density, and not a low-density, plume for that day. These steps were performed in `Python` using the `geopandas` package.

Lastly, we rasterized the daily plume polygons into images at 30m resolution, using the same projection as our crop type maps (Section 4.1.2). Every day has two rasters, one for low smoke and one for high smoke. Each pixel of a raster has value 1 if a smoke plume covered that pixel that day, and value 0 otherwise. We summed the daily images to obtain one final raster for low and high smoke, where each pixel represents the total number of days during the growing season that the pixel was exposed to a low- or high-density plume (Figure 5 panel **B** ).

# **4.1.2 Corn and soybean exposure to smoke**

We use Google Earth Engine<sup>31</sup> to access the USDA’s Cropland Data Layer (CDL),<sup>32</sup> which maps crop types across the coterminous US at 30m spatial resolution (Figure 5 panel **C** ). CDL is available from 2008 to 2020 and allows us to precisely map corn and soybean exposure to smoke plumes. We overlaid CDL with our growing season low- and high-density smoke rasters (Figure 5 panel **D** ), and, for each county and year between 2008 and 2020 (excluding 2009), we computed the average number of days that crops in a given county were under low- and high-density smoke for corn and soybeans from May 1 to September 30. These values are the treatment variables on the right-hand side of Equation (1).

# **4.1.3 Meteorological variables**

To measure temperature and precipitation in each county-year, we collected an updated version of the modified PRISM dataset used in ref.<sup>33</sup> We modified this data to pixel-weight it by crop 16
# **6. Urban poverty and income inequality in the informal sector**

Poverty and income inequality are particularly significant in the informal sector. This section examines these two issues among the three distinct groups of informal firms.

Table 7 shows the distribution of quintile of income by informal firms. The top performers are better off than the other categories of informal firms. While more than 17 percent of survivalists are in the bottom quintile of revenue only respectively 13 percent of constrained gazelles and 9 percent of top performers are found in this quintile. Moreover, the logit estimates of the probability of being poor confirm that survivalists and constrained gazelles are more likely to be poor than top performers (table 8).<sup>7</sup>

**Table 7. Quintiles of Per Adult Equivalent Consumption, Informal Firms, %**

|Quintiles of consumption|Top performers|Constraine<br>Gazelles|d<br>Survivalists|
|---|---|---|---|
|1|8.94|12.85|17.55|
|2|18.43|21.17|26.23|
|3|21.37|17.17|21.94|
|4|23.04|21.47|19.66|
|5|28.23|27.35|14.63|
|Total<br>_Source:_Calculations using 1-2-3<br>Notes: Percentages are weighted|100<br>Survey data<br>using sampling weigh|100<br>ts.|100|

**Table 8. Poverty between Informal Firms, Logit Estimates**

||(1)<br>Coefficients<br>Poor=1|(2)<br>Marginal effects|(3)<br>Coefficients<br>Poor=1|(4)<br>Marginal effects|
|---|---|---|---|---|
|Top-performers|Ref.||Ref.||
|Constrained gazelles|0.320**|0.0681**|0.409***|0.0715***|
||(0.146)|(0.0309)|(0.154)|(0.0270)|
|Survivalists|0.736***|0.157***|0.582***|0.102***|
||(0.132)|(0.0277)|(0.154)|(0.0269)|
|Constant|0.276**<br>(0.124)||1.339***<br>(0.193)||
|Sector fixed effects|||yes||
|Region fixed effects|||yes||
|Pseudo-R2|0.0111||0.1517||
|Observations|3,398|3,398|3,398|3,398|
|Standard errors in parent|heses *** p<0.01,|** p<0.05, *p<0.1|||

A Lorenz curve illustrates inequality across the three distinct groups of informal firms (figure 3). It shows that the profit – which constitutes entrepreneurs’ revenue – is more equally distributed across top performers than across the other two groups of informal firms. For instance the poorest 25 percent of top performers share about 8 percent of the profits, whereas the poorest 25 percent of the constrained gazelles possess only 1.5 percent of the profits. The Gini coefficient is about 0.40 among top performers whereas this index is higher among constrained gazelles (0.57) 7These results are robust to two other classification methods (using the mean in capital and in value added per hour worked) for the three groups of informal firms.

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Figure 2.2 <!-- Start of picture text --> GDP Breakdown by Reglonal Group -19S9<br>a,<br>70<br>a,~ ~ ~ -<' -<br>10<br>20<br>ASEAN ASEAN 3 EMU<br>Brunei Darussalam and Myanmar not Included.<br>Source: The World Bank, World Development Indicators 2001<br><!-- End of picture text --> East Asian economies have a very high savings rate. [See Figure 2.3 showing Gross Domestic Savings (GDS) and Gross Capital Formation (GCF) 43 as a percentage of GDP for East Asian countries. Data for 1990 and 1999 can be found in Table _S.5_ in the Statistical Appendix.] For 8 out of the 12 countries included in Figure 2.3, the savings rate is above the average (in most cases well above) for economies of their respective income level.'<sup>4 4</sup> Also revealed in this figure is that most of these countries have a surplus of savings over investment. With few exceptions, the ratio of GDS to GCF is over 100 percent, which is near the average for high-income countries.'<sup>45</sup> Although the overall savings rate for the region is very high, rates for individual countries vary considerably; e.g., Cambodia's savings rate (GDS/GDP) is only 5 percent and Singapore's is over 50 percent, a difference of 45 percentage points.

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6 willing to pay more for reliable power supply. Finally, although most households have economically meaningful willingness to pay for a reliable power supply, our VoLL figures appear to be below the marginal cost of avoided load shedding (i.e., utilizing high-cost operating reserves or importing electricity at times of high demand). These findings suggest that if the government's goal is to improve the quality of residential electricity consumption, it must find less expensive sources than the ones available at the time of the study.<sup>4</sup>

# **2. Background**

# _A. Nepal's load shedding crisis, 2008-2016._

Nepal, like many other developing countries, is striving to provide reliable electricity to the population. Although the country has made significant progress in improving electricity access,<sup>5</sup> reliability of power supply has until very recently remained a major issue. Most of the electricity supplied by the Nepal Electricity Authority (NEA) is hydropower and is subject to seasonal fluctuations. Historically, inefficiencies and poor management issues have plagued the national grid, which in practice resulted in power supply lower than demand. As a result, households and other entities were subject to severe load shedding schedules.<sup>6</sup> The outage schedule was rotated across different service territories.

In October 2016, the NEA was able to resolve some of these issues, and the load shedding schedule was suspended (World Bank, 2019). Households may, however, still experience unscheduled outages.

> 4 In 2018 the Nepali authorities have effectively eliminated all remaining scheduled outages after completing addition of low-cost generation capacity, while peak power demand remained flat.

> 5 The World Bank Sustainable Energy for All (SE4ALL) database estimates that 93 percent of residential households had access to electricity in 2018.

> 6 Shrestha (2014) reports that there were at most 2 hours daily of load shedding in 2005, and 3 in 2006. Starting in 2007 the situation appears to have worsened considerably, with 8 hours/day in 2007, 16 in 2008, 18 in 2009, 14 in 2010, 12 in 2011, 10 in 2012, and 12 and 8, respectively, in January and July 2014.