rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 7: Return on equity for distributed solar: Sensitivity to input parameters.**

<!-- Start of picture text --> Investment cost -3.1 4.5<br>Capacity factor -3.7 3.6<br>Lending rate -0.9 0.9<br>Fixed O&M cost (%) -0.8 0.8<br>Loan tenor -0.4 0.3<br>Income tax rate -0.3 0.3<br>-20%<br>+20%<br>Share of equity (%) 0.2 -0.2<br>-4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5<br>Percentage points (100s of basis points)<br><!-- End of picture text --> The fourth finding is that off-grid distributed solar to replace diesel consumption is highly dependent on the local price of diesel. Figure 8 shows the ROEI for off-grid applications of distributed solar as a function of the investment cost, as these costs can differ significantly between off-grid configurations. When the solar system replaces diesel bought at the subsidized price (currently EGP 2.35 per liter), distributed solar has a positive return on equity for investment costs below around 1150 US$ per kW, but generally is not a very attractive investment even at very low investment costs. If the solar system replaces unsubsidized diesel (currently estimated at US$ 450 per ton, or about EGP 6.75 per liter), the ROEI increases to between 8.0 and 43.1 percent, depending on the investment cost. RCREEE found that in practice, when there are no diesel shortages, most consumers pay an average price somewhere between the subsidized and unsubsidized prices, as diesel is sold by third parties at prices above the government rate. Thus, the investment case for off-grid distributed solar is currently fragmented, as it is still unclear exactly how local prices of diesel vary across the country.

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# 3   Fieldwork—Establishing a quantitative baseline

## Target institutions and documents for collection

This chapter identifies and defines documents that are to be collected in support of setting the baseline. Spending data are among the most complex infrastructure data to be collected. The standardized collection of spending data requires the potentially elaborate recoding of national budgets, which are country specific and vary in complexity. It also demands the careful reclassification of financial information of nonfinancial public institutions‘ as to guarantee the comparability and consistency of spending categories across operators, sectors, and countries.

Norms and levels of transparency vary by country. In some countries making budget documents and financial accounts available to the general public is the exception rather than the norm. This is why the first step (and challenge) in setting the fiscal baseline is to have access and collect budget and financial account documents. To decide which institutions are the best sources of such documents, see the updated institutional map presented in template 1.

Fiscal data documents are generally collected from central ministries (finance and economic planning) as well as from key line infrastructure ministries, autonomous agencies, parastatals, and NGOs. The involvement of key official and technical personnel is therefore critical to the document collection process.

There are essentially five types of documents to collect:

- _Approved budgets_ . These are final budget books that have been authorized by parliament in the form of budget laws. They often consist of both original estimates and supplementary budgets.

- _Actual budgets_ . These are reports of funds that have actually been spent. When budget books are available, the actual execution of budget estimates for the previous year is reported in the same documents.

- _Annual reports/financial accounts_ . These are documents published or made available internally by SOEs or special funds as they report their annual performance at the end of each fiscal year.

- _Audited budgets and annual reports_ . These are the final actual budgets and annual reports, prepared after auditing. They may not be made available across all countries or to all entities within a country. A document is considered audited when it has been revised and approved in accord with a comprehensive set of audit policies and standards. Such standards should follow best international practice, such as the auditing standards published by the International Organization of Supreme Audit Institutions (INTOSAI) and the International Standards on Auditing prepared by the International Auditing Practices Committee of the International Federation of Accountants (IFAC).

33
2018, Clemens, Lewis and Postel, 2018, Mobarak, Sharif and Shrestha, 2023). Information on country or region-specific bilateral or multilateral agreements is often more readily available whereas aggregate global analysis on the impact of labor agreements has been limited due to the lack of global datasets documenting these bilateral and multilateral treaties. This has changed recently with the emergence of large datasets on BLAs (Chilton and Woda, 2022, Chilton and Posner, 2018, Peters, 2019 _b_ ). Using these datasets, new and emerging research has examined the relationship between BLAs and trade flows between countries (Maximova and Paraschiv, 2022), the relationship between cross-border property rights and innovation (Bian, Meier and Xu, 2023) and emigration and democratization (Peters and Miller, 2022). However, to our knowledge, no paper has so far documented the impact of BLAs on migration flows between countries. Both Peters (2019 _b_ ) and Chilton and Woda (2022) find positive association but do not document the impact rigorously.

This paper relates to literature on how explicit policies related to migration affect migration. A few papers examine the role of policies such as border walls, minimum wage, passport and other legal hurdles in facilitating or hindering migration (Allen, de Castro Dobbin and Morten, 2018, Feigenberg, 2020, McKenzie, 2007, McKenzie, Theoharides and Yang, 2014). Yet, another set of papers study the impact of specific migration restriction programs (Clemens, Lewis and Postel, 2018, Di Iasio and Wahba, 2023, Mayda et al., 2018). In a similar vein, Ortega and Peri, 2013 examine the relative effects of income and a broader set of immigration policies on immigration to the OECD countries and find that restrictive policies can rapidly reduce migration. While the literature has largely examined the role of restrictive immigration policies, this paper examines the impact of a migration facilitation policy.

The paper also adds to the large literature on relieving constraints to migration more broadly. Studies have reconciled the high returns to migration with the low observed rates of migration as stemming from high (utility) cost of migration (Bryan and Morten, 2019, Imbert and Papp, 2020). Other studies have examined the role of reducing various dimensions of costs, such as informational frictions, insurance failure, social networks, risk aversion, difficulty to learn about risks, liquidity, and financial costs of migration (see Angelucci, 2015, Bah et al., 2023, Baseler, 2023, Bazzi, 2017, Bazzi et al., 2021, Beam, McKenzie and Yang, 2016, Bryan, Chowdhury and Mobarak, 2014, Cai, 2020, Dao et al., 2018, Gazeaud, Mvukiyehe and Sterck, 2023, Munshi, 2020, Munshi and Rosenzweig, 2016, Shrestha, 2019, 2020, for key studies on these). This 6
with accommodation as a pillar of the ALSM's survival. This accommodation was of two sorts, one between ALSM members, including the _ex officio_ director who had appointed the members, and one with Ministry officials. Accommodation came at the expense of bold development and strategic moves, and it did not allow the ALSM to develop and exercise a full governance role.

# **5.2 Human resource management**

By the late 1990s, apathy was prevalent among employees in the Lebanese public sector. During the war, people had difficulty getting to work, public sector wages were eroded by inflation and compressed, public sector arrears in wage disbursement were common. When salaries were disbursed, they sometimes went to dead people because personnel rosters were not updated periodically. These factors encouraged moonlighting, absenteeism, and/or the establishment of private businesses alongside public sector jobs.

To motivate its staff HDB created decision rights in the area of human resource management that allowed the hospital to emulate the private sector. Some of the decision rights HDB created granted bonuses to MOH hospital staff, and others allowed the hospital to hire its own (non-Civil Service Board/MOH) staff, compensate them according to market rates, then discipline and fire them for inadequate performance. Table 5.2 summarizes HDB's bundle of decision rights in the area of human resource management. Between 1991 and 1997, an average of 66% of the ALSM's contributions to HDB went toward HDB's wage bill, in the form of salaries and bonuses to non-civil service (private sector) employees and income supplements to civil service employees. The remaining 33% went toward various capital and operating expenditures (ALSM financial statements).

|**Table 5.2. HIDB's De**|**cision Rights Equilibrium in Human Resource Management**|
|---|---|
|<br>Rights Created|<br>Hiring (of non-civil service staff, including compensation)<br>Firing (of non-civil service staff)<br>Internal organizational decisions (conumittee formiation, etc.)|
|Rights Appropriated|<br>Promotion (bonuses to civil service employees, and pay increases<br>for private sector employees)<br>Discipline (mostly through financial incentives, this was an under-<br>exercised function of the central administration and Civil Service<br>Board)|

Source: Author's construction based on results from the Decision Rights Analysis Framework (Appendix A).

Using the decision rights created above, HDB designed an incentive program that improved the range and quality of care at HDB. Given that public sector staff often earned their pay without coming to work, HDB staff, including doctors who were civil service employees, received an income supplement if they came to work and fulfilled the service equivalent of the pay they were already receiving from the MOH. For physicians, this service equivalent was calculated 25
16 Moldova, and Vietnam.<sup>22</sup> Estimated returns at the sample mean for the four countries lie in the range of returns to scale estimated in developed countries (see Mizutani and Urakami, 2001, for a review). For the case of Brazil, our results in terms of returns to scale differ from the ones derived by Seroa da Motta and Moreira (2006) using DEA techniques. Using data on 107 operators (both regional and local) over the 1998-2002 period, they find returns to scale for the regional companies of about 2.5. The discrepancy between the two sets of results may be explained by the different sample sizes (our sample contains 26 regional operators and cover the period between 1996 and 2004) but also by the use of different techniques (regression analysis versus DEA). Cubbin and Tzanidakis (1998) compare regression and Data Envelopment Analysis techniques, and note that these techniques do not always end up with similar results especially when small sample sizes are used.

Because estimated returns at the sample mean (i.e., for the utilities’ services with average characteristics) may be misleading, we report estimated returns for different groups of utilities. We report estimated returns to production density, estimated returns to customer density, and estimated returns to scale, for utilities classified in either small, medium, or large sized utilities, with respect to: (a) total volume of water produced by the utility, (b) number of water connections served by the utility, (c) volume of water produced per residential customer, and (d) number of customers per kilometre of water supply network.<sup>23</sup> Estimated returns for Brazil, Colombia, Moldova, and Vietnam are shown in tables 6, 7, 8, and 9 respectively.

We find similar trends in the four countries. In particular, we observe that estimated returns to scale decrease with utility size, as measured by total volume of water produced or number of connections served. Also, in most cases, the larger the volume of water or wastewater produced per residential customer, the lower the returns to scale. In Brazil, we cannot reject the null of constant returns to scale in all cases, with estimated returns lower than 1 in most cases (even if not statistically different from 1). In Colombia, we find significant economies of scale for all groups of WSS utilities. In Moldova, we find evidence of economies of scale

> 22 The result that the WSS industry in Brazil exhibit constant returns to scale does not rule out the possibility that this industry has natural monopoly characteristics. Indeed, economies of scale are a sufficient but not a necessary condition for a natural monopoly (Joskow, 2005). 23 Water utilities serve also industrial and/or institutional customers so the volume produced per residential customer may vary significantly from one water utility to the other. We consider the ratio of total volume produced per residential customer instead of total volume of water sold to residential users per customer because the latter was not available in the four countries.
<!-- Start of picture text --> G6 India:Spousal education difference<br>sa<br>ss34<br>3t,,<br>62<br>co<br>Si<br>Be°<br>oo<br>Bg<br>sot<br>oo<br>co<br>£8<br>55%<br>of |¢-—@—@—44+ _+ + + + + #5<br>= °<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>-—— Quintile2 +——— Quintile3<br>+—— Quintile4 +——— Quintile5<br>Generated by regressing education difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->

<!-- Start of picture text --> c Bangladesh:Spousal education difference<br>go<br>ss[s}<br>SL<br>2g<br>54<br>58<br>5 eo<br>35<br>20<br>o2<br>G5<br>oy<br>=o<br>5 oN<br>a2<br>£Ww O°<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>— Quintile2 +——— Quintile3<br>+—— Quintile4 '——— Quintile5<br>Generated by regressing education difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> c Pakistan:Spousal education difference<br>g 2<br>ie<br>ol<br>Loe<br>of<br>mae)<br>Qo<br>=eoo<br>O68<br>ee<br>gf<br>£8<br>cn68<br>ge<br>wo<br>Ww<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>-—— Quintile2 +——— Quintile 3<br>-—— Quintile4 +——— Quintile 5<br>Generated by regressing education difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> c Nepal:Spousal education difference<br>gc<br>is<br>- x<br>boo<br>62<br>ame)<br>go<br>=coo<br>8<br>oe<br>54<br>£8<br>c5Non<br>ae<br>g Po<br>Ww<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>-—— Quintile2 +—— Quintile 3<br>-— Quinte4 '——— Quintile 5<br>Generated by regressing education difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
(Vol. w19725). Retrieved from http://www.nber.org/papers/w19725

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- Randell, H., & Gray, C. (2016). Climate variability and educational attainment: Evidence from rural ethiopia. _Global Environmental Change_ , _41_ , 111-123. Retrieved from https://www.sciencedirect.com/science/article/pii/ S0959378016302643 doi: https://doi.org/10.1016/j.gloenvcha.2016.09.006

- Randell, H., & Gray, C. (2019). Climate change and educational attainment in the global tropics. _Proceedings of the National Academy of Sciences_ , _116_ (18), 8840-8845. Retrieved from https://www.pnas.org/doi/abs/10.1073/pnas.1817480116 doi: 10 .1073/pnas.1817480116

- Seid, Y. (2016). Does learning in mother tongue matter? evidence from a natural experiment in ethiopia. _Economics of Education Review_ , _55_ , 21-38. Retrieved from https://www.sciencedirect.com/science/article/pii/ S027277571530282X doi: https://doi.org/10.1016/j.econedurev.2016.08.006

- Sexton, S., Wang, Z., & Mullins, J. T. (2022). Heat adaptation and human performance in a warming climate. _Journal of the Association of Environmental and Resource Economists_ , _9_ (1), 141 - 163. Retrieved from https://EconPapers.repec.org/RePEc:ucp: jaerec:doi:10.1086/715509

- Trisos, C., Adelekan, I., Totin, E., Ayanlade, A., Efitre, J., Gemeda, A., ... Zakieldeen, S. (2022). Africa [Book Section]. In H. O. P ̈ortner et al. (Eds.), _Climate change 2022: Impacts, adaptation and vulnerability. contribution of working group ii to the sixth assessment report of the intergovernmental panel on climate change_ (p. 12851455). Cambridge, UK and New York, USA: Cambridge University Press. doi: 10.1017/9781009325844.011.1286

- Wagner, P., Schober, B., & Spiel, C. (2008). Time students spend working at home for school. _Learning and Instruction_ , _18_ (4), 309-320. Retrieved from https://www .sciencedirect.com/science/article/pii/S0959475207000485 doi: https://doi.org/10.1016/j.learninstruc.2007.03.002

- World Bank Group. (2018). _Ethiopia - multi-tier framework (mtf) survey_ (Tech. Rep.). Retrieved from https://datacatalog.worldbank.org/search/dataset/ 0041725/Ethiopia---Multi-Tier-Framework--MTF--Survey

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**Figure 2: East Africa Deal Volume by Country (2013-2015)**

<!-- Start of picture text --> 2013 2014 2015<br>Rwanda Rwanda Uganda Rwanda<br>Uganda 6% Uganda 8% 7% 2%<br>6% 8%<br>Tanzania Tanzania<br>27% 30%<br>Tanzania Kenya<br>Kenya 27% 57% Kenya<br>61% 61%<br><!-- End of picture text --> _Source: KPMG/EAVCA_ As Kenya has taken a more prominent role in the African private equity landscape, the size of transactions has grown. Prior to 2007, there were no reported private equity deals with a ticket above US$5 million; from 2010 to 2014, however, there were 23 deals (29 percent) in which the investment amount totaled between US$5 million and US$10 million, while seven deals exceeded US$10 million. This increase in transaction size reflects a maturation of the market, the arrival of pan-regional players such as ECP and Helios that invest in much larger transactions, and investor confidence that they can find and fund regional champions that are based in Kenya.

The entrance of well-known regional or global players such as Emerging Capital Partners (ECP) and Helios is part of the ongoing growth in the number of private equity firms operating in Nairobi in the last five years. When the EAVCA was established in 2013, the group comprised just seven founding member firms. Today, it boasts 60 members (including service providers), of which approximately two-thirds have operations in Kenya.<sup>3</sup>

# **B. Industry Overview: Private Equity, Venture Capital, and Impact Investment**

The funds that constitute Kenya’s VC/PE industry can be divided into distinct groups. First, there are private equity funds. PE funds operating in Kenya may be classified based on their geographic targets: global (e.g. The Carlyle Group, Abraaj,<sup>4</sup> Actis), pan-African (e.g. Helios, AfricInvest, and ECP), and East African (e.g. Phatisa, Fanisi, Kibo). Second, there are venture and seed capital funds, such as Novastar and Safaricom Spark. Finally, there are impact investment funds, such as Grassroots Business Funds and Acumen Fund. The market for each of these investment types - private equity, venture capital, and impact investing - is profiled below.

# **_Private Equity Funds_**

Private equity funds invest in businesses ranging from SMEs and family-owned businesses through to large

> 3 This figure includes services providers such as lawyers and accountants.

> 4 Following completion of this report, the Abraaj Group and its fund management business entered a courtsupervised liquidation process in the Cayman Islands in June 2018.  The appointed liquidators and investors are in discussions about the future of the Abraaj funds, including the potential entry of replacement fund managers.

5
a measure of household permanent income in the parental generation, and we estimate returns to education equation (1) above using this measure of permanent income. For returns to education at the household level in children’s generation, we use the household expenditure data from the last two waves (2007 and 2014).

An important data issue for estimating intergenerational mobility is sample selection bias that arises from the fact that many of the existing household surveys such as LSMS and DHS include information on only those household members coresident at the time of the survey. The criteria used for household membership may vary, but when nonresident parents or children are not included in a survey, it can cause substantial biases in the estimates of intergenerational persistence in education. We utilize the household roster, nonresident parents module, and mothers’ marriage module to gather the education information on fathers and children. For details, please see the online appendix on data.

We use the Raven test scores and two memory tests to construct an index of cognitive ability. An advantage of these measures is that they do not require any knowledge of numeracy or literacy to do well in the tests. For a detailed discussion on the construction of the cognitive ability index, please see pp. 18-19 in section (5.1) below. Table 1 provides the summary statistics for our various estimation samples, separately for rural and urban areas. In our “mobility sample”(18-40 years old children in 2014 wave), the average education of rural fathers is 5.66 years, and 7.54 years for urban fathers. The rural children attain 9.05 years of schooling, while the urban children acquire more than a year more schooling at 10.18 years on average. The household income and education expenditure are expressed in the units of 10,000 rupiahs and are deflated by rural and urban average rice prices in each wave of the survey. In the education expenses sample (pooled 1993, 1997, 2000, 2007), the average educational expenditure by rural households is 0.64, and it is more than twice as high in the urban households at 1.51. The household average expenditures in the parental generation (using 1993, 1997, and 2000 waves) are 199.32 for rural areas, and 334.79 in the urban areas. The summary statistics thus provide a vivid picture of the rural-urban divide in Indonesia.

16