rafmacalaba/datause-encoder-data huggingface dataset:
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Table 3: VARMA(AICc) results at PM2 _._ 5 _>_ 10, _R_<sup>ˆ2</sup> = 0 _._ 722, 37 estimated parameters on ( _N −_ max( _p, q_ ) _× T_ ) _×_ 3 = 2160 data points with 213 fixed demeaning components. _AICc_ = _−_ 3876 _._ 735.

||Pollution|Bottom Expenditures|Expenditures|
|---|---|---|---|
||(pol)|(bot)|(exp)|
|_φ polt−_1|-0.578***|-0.542***|-0.413***|
||(-17.043)|(-4.875)|(-2.811)|
|_φ polt−_2|-0.234***|-0.320***|-0.204**|
||(-4.764)|(-4.866)|(-2.459)|
|_φ polt−_3|0.064*|||
||(1.883)|||
|_φ bott−_1|0.138*|-0.440***|-0.248**|
||(2.327)|(-11.281)|(-2.558)|
|_φ bott−_2|0.083**<br>|||
||(2.374)|||
|_φ bott−_3||-0.061*<br>(-1.85)||
|_φ expt−_1||0.151***|-0.172**|
|||(3.049)|(-2.654)|
|_φ expt−_2||0.056*|-0.219***|
|||(1.837)|(-5.579)|
|_φ expt−_3||||
|_Mpolt−_1||0.569***|0.342**|
|||(4.903)|(2.244)|
|_Mpolt−_2|-0.184***<br>(-3.11)|||
|_Mpolt−_3|-0.134***|-0.294***|-0.208***|
||(-2.594)|(-4.529)|(-2.751)|
|_Mbott−_1|-0.188***||0.348***|
||(-2.644)||(3.247)|
|_Mbott−_2||-0.344***|-0.154**|
|||(-6.453)|(-2.138)|
|_Mbott−_3||||
|_Mexpt−_1||-0.106**|-0.303***|
|||(-2.021)|(-4.569)|
|_Mexpt−_2|-0.079***|||
||(-2.579)|||
|_Mexpt−_3|0.054*||-0.286***|
||(1.789)||(-6.83)|
|_ρ_<br>_b_||||
|_σ_|0.094|0.079|0.101|
|_ν_|4.107|9.474|4.573|
|p white-noise|1.000|0.311|0.498|

_Note: ∗_ p _<_ 0.1; _∗∗_ p _<_ 0.05; _∗∗∗_ p _<_ 0.01 _Constant omitted, t-statistics in parenthesis for the ARMA components._ xvii
25

# **Sources and Selected Bibliography**

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- Alba, Pedro, Leonardo Hernandez and Daniela<sup>Klingebiel.</sup> "Financial Liberalization and the Capital Account: Thailand, 1988-97." World<sup>Bank Policy ResearchWorkingPaper, No.</sup> 2188. Washington, D.C.

- Arellano, Jose Pablo, and Manuel Marfan.<sup>1986."Ahorro-Inversiony Relaciones Financieras</sup> en la Actual Crisis Economica Chilena." _Coleccion_<sup>_Estudios Cieplan_20, 61-93.</sup>

- Arnason, Birgir, Ousmane Dore, and<sup>LuisCatao.</sup> 1995. "Norway, Recent Economic Developments." International Monetary Fund Staff Countries Report,<sup>No.95/26.</sup> Washington, D.C.

- Balino, Tomas J. T. 1991. "The Argentine Banking Crisis of 1980." In Sundararajan, Vasudevan and Balino, Tomas J.T., eds. _Banking_<sup>_Crises: Cases and Issues._International</sup> Monetary Fund, Washington, D.C.

- Barandiaran, Edgardo and Leonardo Hernandez.<sup>1999."DealingwithBankingCrisis:Chile</sup> 1982-86." Mimeo.

and Rolf Luders. 2000. E-mail to the author. April 8.

Barbone, Luca. 2000. Interview by author. April<sup>12.</sup>

- Belka, Marek. 1994. "Financial Restructuring<sup>of Banks andEnterprisesin Poland."</sup><sup>_Economic_</sup> _Policy in Transitional Economies,_ 4; No. 3:71-84. Netherlands.

- Berg, Jesper. 1995. "The Nordic Banking Crisis-Lessons<sup>to be Learned."Mimeo.International</sup> Monetary Fund Research Group. July. Washington, DC.

- Berg, Sigbjorn A. 1997. "Bank Failures<sup>inFourScandinavianCountries."Mimeo. Norges</sup> Bank. Stockholrn.

- _____, Barbro Hexeberg. 1994. "Early Warning Indicators<sup>forNorwegian Banks:ALogit</sup> Analysis of the Experiences from the Banking<sup>Crisis."</sup><sup>_Norges BankArbeidsnotat._No.</sup> 1994/1:1-24. January. Norway.

- Bonin, John P. 1993. "On the Way to Privatizing<sup>CommercialBanks:Poland and Hungary take</sup> Different Roads." _Comparative Economics Studies,_ 35:103-19.

1997. "Bank Privatization in Poland: The Case of<sup>BankSlaski."</sup> _Journal of_ _Comparative Economics,_ 25:31-61.
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_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_ 21. ILO, 2004. Yearbook of Labour Statistics.

22. International Comparison Program, 2008. 2005 International Comparison Program. Tables of final results, the World Bank, Washington DC.

23. Krueger, A. and L. Summers, 1988. “Efficiency Wages and the Inter-industry Wage Structure,” _Econometrica_ , Vol. 56, pp. 259-293.

24. La Porta, R., López-de-Silanes, F., Shleifer A. and Vishny, R., 1998. “Law and Finance,” _Journal of Political Economy_ , Vol. 106(6), pp. 1113-1155.

25. Loayza, N., Oviedo, A.M. and Servén, L., 2005. “The Impact of Regulation on Growth and Informality Cross-Country Evidence,” World Bank Policy Research Working Paper No. 3623.

26. Manda, D., Bigsten, A., and Mwabu, G., 2001. “Trade Union Membership and Earnings in Kenyan Manufacturing Firms,” Working Paper No. 50, Department of Economics, Göteborg University.

27. Mazumdar, D. and Mazaheri, A., 2002. _Wages and Employment in Africa,_ Aldershot, Ashgate.

28. Murphy, K., and F. Welch, 1990. “Empirical Age-Earnings Profiles,” _Journal of Labor Economics_ , Vol. 8(2), pp. 202-229.

29. Ndulu, B., L. Chakraborti, L. Lijane, V. Ramachandran and J. Wolgin, 2007. “Challenges of African Growth: Opportunities, Constraints, and Strategic Directions,” Washington DC. World Bank.

30. Psacharopoulos, G. and Arriagada, A.M., 1992. “The Educational Composition of the Labor Force: An International Update,” PHREE Background Paper Series No. 92/49, The World Bank.

31. Söderbom, M. and Teal, F., 2001. “Firm Size and Human Capital as Determinants of Productivity and Earnings,” Working Papers Series 2001-9, Centre for the Study of African Economies, University of Oxford.

32. Söderbom Måns, Francis Teal and Anthony Wambugu, 2004. “Does firm size really affect earnings?” Development and Comp Systems 0409011, EconWPA.

33. Söderbom, M., Teal, F., Wambugu, A. and Kahyarara, G., 2006. “The Dynamics of Returns to Education in Kenyan and Tanzanian Manufacturing,” _Oxford Bulletin of Economics and Statistics_ , Vol. 68(3), pp. 261-288.

34. Stifel, D., Rakotomanana, F. and Celada, E., 2007. “Assessing Labor Market Conditions in Madagascar, 2001-2005,” Working paper, The World Bank.

35. Teal, F., 1996. “The Size and Sources of Economic Rents in a Developing Country Manufacturing Labor Market,” _Economic Journal_ , Vol. 106, pp. 963-976.

36. Topel, R., 1991. “Specific Capital, Mobility, and Wages: Wages Rise With Job Seniority,” _Journal of Political Economy_ , Vol. 99(1), pp. 145-76.

37. Van Biesebroeck, J., 2007. “Wage and Productivity Premiums in Sub-Saharan Africa,” WP 291, University of Toronto, Department of Economics.

38. Verner, Dorte, 1999. “Wage and Productivity Gaps: Evidence From Ghana,” World Bank Policy Research Working Paper No. 2168.

39. World Bank, 2007(a). World Development Indicators 2007, The World Bank, Washington DC.

40. World Bank, 2007(b). Africa Development Indicators 2007, The World Bank, Washington DC.

41. World Bank, 2008(a). Doing Business Report. <u>www.doingbusiness.org</u> 42. World Bank, 2008(b). Enterprise Surveys. www.enterprisesurveys.org 29
# 2 Baseline and End-Line Survey Outcomes

## Baseline Survey Outcomes

### **Baseline Characteristics**

This chapter goes over the baseline and end-line characteristics of applicants in the treatment and control groups. It serves at least two primary purposes. One is to establish the comparability of the treatment and control groups. If they are not significantly different from each other on observable characteristics, we can reasonably assert that these two groups are similar in both observable and unobservable characteristics. Second, we analyze the overall characteristics of our sample.

A descriptive analysis of baseline characteristics is shown in table 2.1. It is reassuring to see that the control and treatment groups are very similar in all the variables. None of the differences between treatment and control groups were statistically significant; hence the two groups are comparable across all the employment- and earnings-related characteristics and socioeconomic backgrounds. They are also not different in their attitude toward work and confidence in their capability and future career.<sup>5</sup> On average, the applicants appear confident that they have adequate technical and soft skills for their work and believe that they can achieve their career aspirations in the future.

**Table 2.1 Balance** **<u>of Baseline Characteristics in Treatment and Control Groups</u>**

|Variables|All<br>(n =<br>1000)<br>Mean|Treatment<br>(n=500)<br>Mean|Control<br>(n = 500)<br>Mean|Difference<br>(T -C)|
|---|---|---|---|---|
|_Employment and earnings status_|||||
|Employed|0.63|0.62|0.63|-0.01|
|Wage employed|0.48|0.47|0.49|-0.02|
|Self-employed|0.15|0.15|0.15|0.00|
|Unemployed|0.08|0.08|0.08|0.00|
|Studying or unavailable for work|0.30|0.31|0.29|0.02|
|<br>Current wage earning (monthly)|9,970|10,283|9,670|613|
|_Confidence and aspirations: opinion on a scale of 1–10_|||||
|Mastered the technical skills needed for my work|8.42|8.43|8.40|0.04|
|Have adequate soft skills|8.64|8.67|8.61|0.06|
|Can achieve career aspirations|9.20|9.17|9.22|-0.05|
|Confident I can work through challenges|9.08|9.05|9.11|-0.06|
|Willing to take risks for achieving better at work|9.12|9.09|9.14|-0.05|
|In the long run, hard work brings a better life|9.78|9.79|9.76|0.03|
|_Socioeconomic background_|||||
|<br>Female|0.33|0.33|0.32|0.01|
|Age (in years)|25.20|25.22|25.18|0.04|
|Education: primary education|0.28|0.28|0.28|0.00|
|<br>Secondary education|0.29|0.28|0.29|-0.01|
|<br>Higher secondary education|0.28|0.27|0.30|-0.03|
|<br>Degree or above|0.15|0.16|0.13|0.03|
|Ever attended any vocational training|0.39|0.40|0.38|0.02|
|<br>Have a prior certificate for the skill|0.23|0.22|0.23|-0.01|
|Ever worked before|0.83|0.82|0.84|-0.02|

5 The survey asked six questions related to motivation and aspirations on a scale of 1 to 10 (1 = fully disagree; 10 = fully agree). The questions are in table 2.1.

7
three-to-six-year-old schemes, but the chances declined to the 10% level for schemes older than eight years. Finally, for all water schemes, using low groundwater storage as the base level, having a high volume of groundwater storage reduced the likelihood of water scheme failure, and this was significant at the 5% level. The regressions on the other two hydrogeological variables, groundwater productivity and depth, were not statistically significant.

# (c) _Shapley Decomposition for Water Scheme Failure_

As discussed earlier, to understand the relative importance of the factors driving the functionality of water schemes, we use logit analysis to calculate the effect of a number of variables on water scheme failure (Table 3). To determine the reasons for failure in the short and medium to long term, we group together water schemes that are one year old, three to six years old, and over eight years old. In this section, we calculate the predicted impact of hydrogeology, technology, location, promoters, management, and maintenance and operation in explaining the likelihood of water scheme failure in each of these three age groups, and calculate the Shapley values (Table 3) for each component’s contribution to failure (Figure 10).

Further, we group the factors discussed above into three broader categories: (1) **factors that cannot be changed** , such as _political regions_ and _hydrogeology_ ; (2) **those that are influential at the design and implementation stages** (and so can be taken into account when developing new schemes)— namely _technology_ and _how promoters operate and implement interventions_ ; and (3) **factors that are influential at the operational stage** (and can be addressed for existing schemes), that is, _management_ and _maintenance_ (whether it is managed by WASHCOM and the different individuals/organizations in charge of carrying out the repairs). The Shapley values are presented in Table 3, columns 1, 3, and 5. The marginal contribution of most of these factors to the rate of failure has a general downward trend as schemes age, with the exception of the geographical zone and WASHCOM, whose contributions increase.<sup>11</sup>

> 11 The Shapley decomposition values presented here differ slightly from those presented in the _A Wake Up Call: Nigeria Water Supply, Sanitation, and Hygiene Poverty Diagnostic_ (World Bank, 2017). This is because, given the

16
In this subsection, we discuss the biases in the IGRC and IGC estimates for the intergenerational link between the father and sons, and the mother and daughters. While father-son intergenerational persistence in economic status has been the most widely researched topic both in developed and developing countries, it is probably equally (if not more) important from a policy perspective to understand the barriers faced by the girls in education. The results on father-son linkage are reported in the upper panel of Table 3, and the bottom panel contains the corresponding estimates for mother-daughter persistence in schooling. We report the estimates of bias, and test the null hypothesis of zero bias (i.e., that the estimates from the coresident and the full samples are equal). For the sake of brevity, we omit the underlying estimates of IGRC and IGC. The estimates for Bangladesh are in the first two columns, and the last two columns refer to the corresponding results for India.

# **Bangladesh**

The estimates of father-son intergenerational link in schooling for Bangladesh shows that the IGRC estimate in the coresident sample suffers from strong downward bias; the bias is 29 _._ 5 percent (row 1, column 1 in the top panel of Table 3). The bias in father-son IGRC estimate is thus similar to the average bias for the all children sample discussed above: 29 _._ 7 percent. The corresponding bias in the estimated IGC is much smaller: only 8 _._ 9 percent (row 2, column 1).

The results for mother-daughter in Bangladesh are reported in columns 1 and 2 of the lower panel of Table 3. The bias in the IGRC estimate from the coresident sample is much stronger at 45 _._ 6 percent, a very high magnitude indeed. This illustrates starkly that relying on the coresident sample can lead to a grossly misleading picture of intergenerational persistence between mother and daughter(s). This high bias reflects the fact that the degree of sample selection is very high in the daughters’ case; only 26 percent of the full sample satisfies the coresidency restriction in Bangladesh data (for sons it is 52 percent of the full sample). The bias in the IGC estimate from coresident sample is again much smaller in 16
CHAPTER I. INTRODUCrION: PURPOSE **AND** SCOPE OF **REVIEW**

1.1 The objectives of this Population, Health and Nutrition (PHN) Annual Sector Review (ASR) are (a) to provide an overvlLt of the FY91 work portfoIio, including ,tistics on the volume and mix of lending and sector work and a brief discussion of salient features and trends, and (b) to address in some depth the special topic of institutioial development for poverty alleviation.*

agreed with Operations colleagues that the issue would be dropped from this years sector review in favor of a more tncused exercise.

1.2 This report covers three components of the PHN work portfolio. First, the vortfolio of FY91approved _knding_ opelations considered in this review comprises the twenty-eight projects officially categorized as PHN sector, including a number of multisectoral operations that include relatively small PHN components. While the PHN secor lending work undertaken during FY91 actually encompasses a larger sampling of projects (i.e., those at the preparation, appraisal, and negotiation stages at the end of the fisal year), resources did not permit a rigorous review of all projects developed during the fiscal year. Second, the review provides statistics on _sector work,_ but no critical analysis; such a review is under consideration. Third, the coverage of the ninety-seven projects under _supervision_ is limited to some basic statistics on the size and composition of the supervision portfolio. The Seventeenth Annual Review of Implementation and Supervision (ARIS), currently underway, will provide a thorough review of supervision work This report did, however, take great care to incorporate into the lessons learned matrix the findings of the Sixteenth ARIS and of all seven Project Completion Reports (PCRs) produced during FY91.

1.3 r.. years Annual Sector Review guidelines, prepared oy the Office of the Vice-President for Sector Policy and Research (PRSVP), explicitly noted that reviews of _research and poly work_ were not to be included, as had been the case in the past. Such a review is being considered as a separate eercise and would, more appropriately, be undertaken by Operations. Also, while the _teholg topic_ proposed in the PRSVP guidelines was widely considered to be important, it was discussed and

* The review team (Denise Villancourt, Jane Nassim, and Stacy. Brown, PHRHN) consulted about forty operations staff on the process and substance of this exercls. These consultations heavily influenced the choice of the special theme as well as the overal approach to the review.
9 fuel use across income groups in rural China suggests that the income gradient for clean energy use in rural China is rather muted (Hao, 2005; Jiang and O’Neill 2004).

Furthermore, while there is a tendency for coal to play a relatively greater role in the fuel mix for better-off households, at 33 percent of the time of fuel use, fuelwood remains the most used fuel even in the richest quartile, despite the fact that it displays the largest income gradient (a 21 percentage point decline in going from the poorest to the richest quartile). These results confirm that income growth alone will not be sufficient to displace dirty solid fuels, closely mirroring findings in other studies of household energy switching in developing countries generally (Heltberg et al, 2000; Heltberg, 2004 and 2005; Cooke et al., 2008) and China in particular (Jiang and O'Neill, 2004). The dominance of solid fuels, even among households who should be able to afford alternatives, motivates our interest in energy technology interventions such as biogas that might help speed up the transition to cleaner and more sustainable energy use.

Interestingly, while the use of biogas is larger for the 2nd and 3<sup>rd</sup> quarter households, it is lowest in relative terms among the wealthiest. This is possibly linked with the move out of small animal husbandry as households get richer. It also positions smallholder biogas holding as a transitional rural fuel, especially in rapidly developing economies such as China.

Fuel use patterns also differ by location, both because of the temperature, as highlighted in the context of biogas, but also because of differences in the ease of buying coal and collecting fuelwood. In Hubei province, fuelwood and coal account for 52 and 32 percent of total energy use in winter, while in Hunan, they account for 35 and 42 percent respectively. In Anhui, Chongqing, and Guangxi, fuelwood and crop residues are the two most common fuels. Yet, biogas and other clean energy also accounted for a considerable share. In Guangxi and Hubei provinces, biogas comprises 23 and 13 percent of summer energy use, while electricity accounted for more than 10 percent in Anhui, Chongqing and Guangxi.
(AGE_DEP), defined as the ratio of the non-working (<=15 and >=65 years) to the working population (>15 and <65 years). To disentangle the effects of a relatively young vs. an old population, we also study the young (AGE) and old age dependency ratios, respectively.

Third, we examine the effects of _social and cultural variables_ . They include the number of schooling years (SCHOOLING) and an indicator variable that captures whether the country is predominantly Muslim (MUSLIM). These variables are probably more relevant for the life sector, as they capture primarily the motivations and incentives of individuals to look for insurance coverage. However, we tested the impact of MUSLIM in the non-life sector as well, as the cultural environment could also have an effect on the decision of businesses to look for insurance protection.

Finally, we investigate a set of _institutional and market structure variables_ . We include the mandatory contribution rate for social security as a proxy for the size of the social security system (CONTRIB_RATE). As mentioned in the previous section, a generous social security system could reduce the incentives and the need to buy retirement products from the life sector. We also include an indicator that measures the share of the private sector in total assets (PRIVATE) to test whether insurance flourishes when the private sector plays a dominant role in the industry. To assess the impact of market concentration (CONCENTR), we create a variable that assumes a value of 1 if the largest market player covers ≥ 50% of life-insurance industry assets and/or, the top 2 largest players cover ≥ 60% of life-insurance industry assets and/or, the top 3 cover ≥ 70% and/or, the top 5 cover ≥ 80%. The indicator is 0 otherwise. Finally, we study the impact of financial development, proxied both by the ratio of private credit to GDP (PC) and the ratio of private bonds to GDP (PR_BOND).

For our baseline results, we employ multivariate regression analysis on pooled countryyear data because of some correlation between the variables. We drop very small economies from the sample (countries with GDP less than 5 billion US constant 2000 dollars) because they tend to have idiosyncratic insurance industries due, for example, to their offshore nature. We adopt Ordinary Least Square (OLS) regressions since most of our explanatory variables are arguably exogenous. All OLS regressions are estimated with and year-fixed effects to address 11