rafmacalaba/datause-encoder-data huggingface dataset:
Yet, there is no evidence that longer-term impacts of the program identified in this paper are driven by differences in pre-program trends. Two main checks were conducted to directly investigate this. The first uses pre-baseline and baseline data from PIHS (2001) and MICS (2003) to run double-difference models on schooling, labor market, marriage, and fertility outcomes for the same cohorts of girls.<sup>36</sup> In principle, one should not expect to see impacts of the FSSP since the program began after the period covered by this _placebo test_ . In fact, results of this analysis show that all outcomes examined in the paper were progressing in similar trends for stipend and non-stipend districts at the preprogram time. The second check consists of doubledifference models using data from MICS (2007-08) to examine the trends of school attainment for two slightly older cohorts of girls (ages 20-22 and 23-25) that, by definition, finished school before the program started and, thus, did not benefit directly from it. Again, results of these exercises do not reveal any differential pre-program trends that may contaminate the main findings of the paper (Table 12).

Another important concern stems from the fact that the empirical analysis is based on a proxy measure of the level of exposure of girls to the program. Consequently, program duration for the synthetic cohorts used in the analysis is likely measured with some error. This measurement error in the indicator of treatment could produce imprecise or biased estimates of program impacts. If this is the case, the parameters that measure the effects of the FSSP on the longer-term outcomes would be biased toward zero (also known as ―attenuation bias‖). Although there is no sensible way to correct for this type of bias in the context of this evaluation, the results of the analysis are still very informative as they can be interpreted as lower bounds of the true impacts.

There are various problematic compositional changes in the sample that could take place. An example is that the FSSP likely induces the self-selection of lower–ability female students into middle schools. The marginal girl who is most likely to be brought into school by the

> 36 A limitation of this analysis is that PIHS 2001, the household survey used for the prebaseline period, is representative only at the province level. The survey was structured around three main domains: rural, major cities, and other urban areas. The first two domains (rural and major cities) were stratified by district and, thus, can be used to construct the treatment status of girls. The ―other urban‖ domain was stratified by division, a higher administrative level that may include both stipend and non-stipend districts. Therefore, the DD analysis is restricted to the ―rural‖ and ―major cities‖ domains and assumes that the sample statistics based on PIHS 2001 are representative for the two groups of analysis as a whole (that is, stipend and non-stipend districts).

26
<!-- Start of picture text --> Polsr Paning, and lRcshm<br>Fln ndal Podby and Sytems<br><!-- End of picture text --> Fmancial liberalization was carried out in a period when the nonfimancial corporate sector was in financial distress due to reduced profitability. The consequent emergence of substantial nonperfonming **lo-is** in the banking sector, especially among smaller banks, created fierce competition for financial resources. The result was a rapid expansion of deposits and high real interest rates. Instead of forcing insolvent borrowers into bankniptcy, banks refinanced nonperforming loans as a way to prolong their own survival, and real credit to the private sector increased dramatically. Furthermore, the market mechanism turned out to be il-equipped to induce the exit of insolvent banks and thereby increase the efiTciency of allocating loanable funds.

An analysis of finn-level data reveals that nonfinancial corpor ions were subject to both an earnings shock (increases in costs relative to sales income) and an interest rate shock. Although the debt-to-asset ratios cf profitable firms did not change, those of firms under distress actually increased, despite higher costs of borrowing.

The Turkish experience suggests that financial liberalization may not produce desired results when it occurs in a period of major macroeconomic realignments that adversely affect the profitability of the corporate sector, especially when it is implemented without an adequate regulatory framework.

This paper is a product of the Fmancial Policy and Systems Division, Country Economics DeparEment. Copies are available free from the **World** Bank, 1818 H Street NW, Washington DC 20433. Please contact Wilai Pitayatonakam, room N9-005, extension 60353.

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planming, **and** Research Complex. An objective of the series is to **get** these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, **and** conclusions in these papers **do** not necessarily represent official policy of the BankL

**Produced** at the PPR Dissemination Center
# **Appendix B: Documentation of the Calculation of Ownership Shares for Kenya**

# **I. Telecommunications Shares in Kenya**

The primary source of data was various publications of Paul Buddle Communications, including ―Kenya—Telecoms Market Statistics and Forecasts,‖ March 20, 2008. Table 10 contains mobile phone subscription statistics by company and Table 2 lists the number of fixed-line phone subscribers. We defined market share as the share of total subscribers, summing fixed-line and mobile subscribers.

The telecommunications companies are: Telkom Kenya, Safaricom and Celtel. Ownership shares are as follows. France Telecom purchased 51% of Telkom Kenya in 2007 with the Government of Tanzania holding the remaining 49 percent.<sup>32</sup> . Vodafone held 35% of Safaricom network, with the remainder held by Telkom Kenya (60%) and a local company Mobitelea (5%).‖<sup>33</sup> . ―Celtel was acquired by MTC of Kuwait for US$3.4 billion in March 2005‖. MTC was later renamed ―Zain Group‖.<sup>34</sup> The results for market share by country (in percent) are as follows: Kenya, 26; EU, 49; EAC, 0; COMESA, 0; Rest of World, 25.

# **II. Bank Shares in Kenya.**

# **Bank Market Shares**

The data source for bank market shares was Bankscope, an on-line data source for about 29,000 banks world-wide.<sup>35</sup> Through Bankscope, we obtained data on total assets by bank in Kenya, owners -shareholders of the bank and the percent of the bank owned by each owner-shareholder. Market share of each bank was defined based on the bank’s assets as a share of total bank assets in the country. We divided the regions into the European Union, East African Customs Union, COMESA and Rest of the World.<sup>36</sup>

# **Ownership Shares of Banks**

Each bank’s market share was then allocated among geographic regions according to the shares of ownership of the bank. We then summed across the banks to obtain total market shares by region. In many cases, however, the Bankscope data were inadequate to allocate ownership shares by region. In these cases, we investigated bank websites, to obtain the required ownership information. The results of our supplementary inquiries are listed below.

The results we get are that owners of the banking sector of Kenya are as follows, in percent: Kenya, 61.8.; EU, 28.7; EAC, 0; COMESA, 0.2; ROW, 9.3. Detailed results on the ownership of the banks are in the tables below.

> 32 <u>http://www.orange.com/en_EN/press/press_releases/cp080917uk.html Accessed 17 April 2009</u>

33 See Paul Buddle Communications, ―The Kenya Regulatory and Fixed-Line Telecoms Overview,‖ March 20, 2008.

34 See Paul Buddle Communications, ―The Kenya Mobile Market Overview,‖ March 20, 2008.

> 35 It combines data from the main information provider, Fitch Ratings, and nine other sources, with software for searching and analysis. Each bank report contains balance sheet and income statements with up to 200 data items. 36 Although we calculated data for the U.S. and the U.K. separately, these were aggregated into the Rest of the World and the European Union, respectively.

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That phenomenon of the impact of new suppliers appears even more strongly if we compare developed-country export price indexes for 1991 relative to 1981 based on developed-country weights with developed-country price indexes based on the weights of Hong Kong, Korea, Singapore, and Taiwan (China). .. Table 5 Effect on Price.Indexes for Developed-Country Exports of Manufactured Goods of Weighting by Export Patterns of Taiwan (China), Hong Kong, Korea, and Singapore Average Annual Rate of Growth (Per Cent) of Price Index with 1986 Weights, 1981-1991

|Developed Countries|4.42|
|---|---|
|Hong Kong|3.68|
|Korea-|3.54|
|Taiwan (China)|3.97|
|Singapore|3.37|

Source: Appendix Table 9 Even over this short period, developed-country export price indexes based on the weights of the fast-growing Asian exporters declined substantially relative to an index based on the- developed countries' own export weights. The differences ranged from a half per cent per year to more than one per cent.

# Ouality Change

Our attempt to adjust manufactured goods price indexes for changes in quality is based on the idea that neither unit value indexes nor price indexes based on specification pricing have captured the effects of changes in the quality of manufactured goods, particularly those that are complex or one-of-akind. Many such products are simply omitted from most price indexes and, when they are included, the prices tend to be list, rather than transactions prices,
# **Box 3**

**Locke and Latham (1984) Goal-Setting Questionnaire**

1. I understand exactly what I am supposed to do on my job.

2. I have specific, clear goals to aim for in my job.

6. If I have more than one goal to accomplish, I know which ones are most important and which ones are least important.

21. Usually feel that I have a suitable or effective action plan or plans for reaching my goals.

37. I find working toward my goals to be very stressful.

41. I have too many goals on this job (I am overloaded) Response Options: Five-point Likert scale ranging from (1) “strongly disagree” to (5) “strongly agree”.

Although these types of questionnaires go beyond just measuring the ability for setting well-defined goals, scales from adaptations of goal-setting questionnaires, especially streamlined versions developed within education and even sports psychology, could be useful for this purpose. For example, Stout (1999) validates a scale that includes questions such as “How often have you set goals for what you want to accomplish?” and “How often have you developed specific plans to help you achieve your goals?” (Ranked 1-9, from “Not Often at All” to “Very Often”), while Earley et al.’s (1987) goal-setting scale asks questions such as “How would you characterize your own objectives for performance?" (Ranked from 1 = my goals are general (e.g., "do my best") to 5 = my goals are specific (e.g., make 20 sales calls)) and “How often do you set specific goals for [X]?” (Ranked from 1 = not at all often to 5 = extremely often). To our knowledge, these standardized goal-setting questionnaires and scales have not been used in research in Sub-Saharan Africa.

Nevertheless, attention to the importance of this aspect of agency within development settings has recently increased due to research on its role in poverty perpetuation (Mullainathan and Shafir 2014). Specifically, an individual’s capacity to set goals – and thus achieve outcomes – is limited by the scarcity of attention and mental resources, which is itself exacerbated by poverty. For example, the World Development Report on _Mind, Society and Behavior_ describes how financial concerns and associated stress resulting from a lack of financial resources may deplete cognitive bandwidth, and hinder the capacity of an individual to develop well-defined goals and improve their financial situation (World Bank 2015). While the link between scarcity and goal-setting capacity highlights the importance of measuring this dimension of agency, it also underscores the need to assess the stability of goal-setting over time, since measures of this construct might be particularly susceptible to economic shocks or seasonal patterns.

Within studies conducted in Sub-Saharan Africa, goal-setting capacity has most commonly been measured by simple questions on what the respondent’s goals are in a certain domain, which are then assessed on their specificity and level of detail. Johnson (2015) uses this strategy in Kenya through in-depth 13
evidence that in the STE sample employment response in _T −_ good (manufacturing) sector is strong rejects the assumption of a tradable _T −_ good.

For services employment in large firms, the estimated coefficient is positive and statistically significant in both the rural and STE samples, but the magnitude of the effect in the STE sample is almost twice that in the rural sample. Thus the effects of agricultural productivity growth seem to be especially potent in those upazilas where the small towns are located. In the rural sample, the estimate for large-scale (formal) services is in sharp contrast to that for large-scale manufacturing, when a comparison is made with the corresponding estimates for all firms in columns (1) and (2) using population census data. The estimate for employment in large-scale services firms in the rural sample is much larger in magnitude (1.50) compared to the estimate for all firms in the services sector in column (2), while the relative magnitudes are reversed in the case of manufacturing. The evidence shows that the growth in large-scale firms in the services sector is favorably affected by higher agricultural productivity irrespective of whether there is a small town in the upazila. This probably reflects the fact that formal health and education services are effectively nontradables and also enjoy more income elastic demand. We explore the effects on large-scale health and education services in greater depth below.

Our results are partly consistent with findings from Foster and Rosenzweig (2004) for India. Using village level data, Foster and Rosenzweig (2004) find that agricultural productivity growth has a negative effect on the number of factory workers and a positive effect on the employment in services. Our evidence that there is no significant effect of agricultural productivity growth on large manufacturing employment in the rural sample, but the employment in large-scale services increases substantially is consistent with their findings. However, using the STE sample, we find that agricultural productivity growth can have a strong positive effect on the large-scale manufacturing when the villages are located in an upazila with a small town. Thus the conclusions depend critically on the sample used. In a related contribution, using district and state level data from India, Adhvaryu, Chari and Sharma (2013) on the other hand find factory employment to be affected negatively by a negative productivity (rainfall) shock which is 26
We employ different elasticity measures to investigate the substitutability/complementarity relationship between fuels. The Allen-Uzawa elasicitity of substitution between fuels  and , <sup>,canbecalculatedas</sup> where  is the cost function in (4) and  and  are the first and second partial derivatives of the cost function with respect to input prices,  =  and  = <sup>2</sup> . See Uzawa (1964) and Diewert (1974) for more details. If <sup>0(thatis,ifincreasingtheth</sup> price increases the optimal quantity of fuel ), we say that fuels  and  are Allen-Uzawa (net) substitutes. If <sup>0,theyareAllen-Uzawa(net)complements.</sup> The own-price elasticities can be calculated as where  is the cost share of fuel . Finally, Blackorby and Russell (1989) show that the Morishima elasticity of substitution between fuels  and , <sup>,canbeexpressedas</sup> Notice that the Morishima elasticity looks at the impact on the ratio of two inputs, . If <sup>(thatis,ifincreasingthepriceincreasestheoptimalquantity offuel relative</sup> <sup>0</sup> to the optimal quantity of fuel ), we say that fuel  is a Morishima (net) substitute for fuel . If <sup>fuelisaMorishimanetcomplementtofuel.</sup> <sup>0,</sup> In this paper, the estimation is performed in TSP/GiveWin (version 4.5), using the FIML procedure, and the regularity conditions are checked as in Feng and Serletis (2008). That is, positivity is checked by checking if the estimated cost is positive, monotonicity is checked by direct computation of the values of the first gradient vector of the estimated cost function with respect to p, and curvature is checked by examining the eigenvalues of the Hessian matrix provided that the monotonicity condition holds; curvature requires that these eigenvalues be negative or zero. See Feng and Serletis (2008) for more details.

# 4 Data

Energy consumption data are readily available for almost all countries, but fuel price data are available for only a small number of countries. Because of this data availability problem, we estimate models for those countries for which fuel quantity and price data are available for at least 15 years.

For the purpose of providing an international comparison, we use data for three groups of countries, in accordance with the most recent World Bank’s country classification by income.

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# Notes

*The authors are grateful to Dipa Nag-Chowdhury and Jane He for valuable computer assistance, and to Marjory-Anne Bromhead, Aly Ercelawn and Gary Fields for insightful comments.

1. On Pakistan, see Kruijk (1987), Mohammad and Badar (1985) and Ercelawn (1984); on other developing countries, see Glewwe (1986), Nugent and Walther (1982) and Pyatt, Chen and Fei (1980).

2. Among the decomposition studies cited in note (1), only Nugent and Walther (1982) use panel data in ungrouped (disaggregated) form to examine short-run fluctuations in the sources of rural income inequality.

3. For an overview of these four inequality measures, see Anand (1983: 89-91).

4. This study w!as undertaken by the International Food Policy Research Institute (IFPRI) working in collaboration with Pakistani research institutes -- Applied Economic Research Centre (University of Karachi), Punjab Economic Research Institute (Lahore), the University of Baluchistan (Quetta) and the Center for Applied Economic Studies (University of Peshawar).

5. The sample was randomly drawn with all rural residents in the selected districts having an equal probability of being included. Landowners who reside in urban areas, therefore, are not included in the sample. Since unweighted samples generally tend to miss the apex of a distribution, the fact that there are, for example, far fewer households owning 3,000 acres of land than there are households owing 3 acres may lead to a slight underrepresentation of the skew of landholding in any moderately sized sample.

6. The 727 households were distributed among the districts as follows: 148 from Attock District (Punjab), 239 from Badin District (Sind), 193 from Dir District (Northwest Frontier) and 147 from Faisalabad District (Punjab).

7. The Gini coefficients of household income recorded for ten Asian countries in Lecaillon et. al (1984: Table 3) range from a low of 0.351 (Korea) to a high of 0.561 (Iran). It should, however, be noted
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Aguilar-Gomez et al. (2022) find that abnormally high temperatures lead to higher rates of delinquency among private firms in Mexico. While effects are driven by firms in the agricultural sector, they also document higher rates of delinquency among non-agricultural firms who rely on local demand. However, they cannot address to what extent lenders have accurately priced in such climate-driven delinquency risks, or whether lenders offer more favorable terms to firms less exposed to climate risk. Miguel, Pedraza, and Ruiz-Ortega (2022) find that a policy in Brazil that explicitly required banks to include environmental risks in capital assessments had no impact on overall lending because firms with higher environmental risks simply started borrowing from smaller lenders. They do document that the policy change caused some smaller firms to close, as such firms faced higher costs from switching lenders. Collier and Babich (2019) study the decisions of community lenders in LMICs and conclude that such lenders reduce credit supply following natural disasters due to capital constraints rather than lower expected returns on loans, suggesting that lenders may have already priced in climate risk. Collier (2020) go a step further and model local lending markets where lenders can access index-based insurance. They conclude that lender-level insurance has the potential to prevent the types of capital constraints documented in Collier and Babich (2019) following climate shocks. The scarcity of evidence from LMICs makes it difficult to understand how firm-level climate risk affects firm financing options and, by extension, firm incentives to invest in adaptation in ways that lenders can verify.

Although the role of climate risk in financial markets in LMICs is unclear, firms can also adapt to climate change by altering their financial decisions. Huang, Kerstein, and Chong Wang (2018) show that globally, firms facing higher climate risk hold more cash, less shortterm debt, and more long-term debt than those facing low climate risk (Dessaint and Matray (2017) find similar results in the U.S.). Elliott et al. (2019) show that Chinese firms attempt to buffer climate impacts by increasing debt and reducing liquidity. This evidence illustrates the range of tools for adaptation that firms may employ to mitigate direct risks to capital and indirect risks related to financing capital.

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