rafmacalaba/datause-encoder-data huggingface dataset:
# **4.   Seasonality in financial intermediation of MFIs: Descriptive evidence**

Household borrowing and its seasonal pattern are very likely a reflection of both demand for and supply of loans. If household borrowing is lowest during _Monga_ season, for example, even though the demand for loans for smoothing income and consumption is high, this is perhaps due in part to the supply of loans being lowest during _Monga_ season, compared to other seasons. So we ought to verify whether there is a seasonality of rural financial intermediation. We use the data on MFIs supported by a microfinance wholesale agency to verify this as they are readily available on a monthly basis than that of formal financial institutions. This also makes sense as MFIs are largely engaged in rural financial intermediation in Bangladesh. The question is, do MFIs suffer from the seasonality of agriculture?

Palli Karma-Sahayak Foundation (PKSF), an apex organization for Bangladeshi microfinance institutions (MFIs), finances various programs of 262 MFIs—called Partner Organizations (POs) of PKSF (PKSF 2011). These POs are scattered all over the country and are part of the 750 registered MFIs in Bangladesh.<sup>7</sup> The analysis of the 262 PKSF POs captures well the microfinance sector of Bangladesh. PKSF lends to POs at a subsidized rate of interest, while POs lend to household borrowers at market rates and repay PKSF from revenues generated.<sup>8</sup> We use monthly data from all five major projects of the 262 POs from FY2004-05 to FY201011 (see the discussion of program level data in Appendix A). The monthly data over the seven years help establish a seasonal trend of MFI portfolio. If there are any distinct seasonal patterns (persistent upward or downward movements) in indicators in most years, we can say that seasonality is at play for financial intermediation. We present the monthly distribution of loan disbursement, loans recovered, and other indicators of program performance.

> 7 In 2010, Bangladesh Microfinance Statistics reported around 750 MFIs. Instead of PKSF data on its sectoral level portfolio, it would have been better if PO level data were available for analysis. Unfortunately, PO level data are not available.

> 8 Subsidizing funds for on-lending by POs by PKSF along with the grants for institutional development of MFIs has helped promote the viability of microfinance institutions in Bangladesh (see Khandker, Khalily and Samad 2016).

9
The wage differentials tend to be higher in rural as compared to urban areas, and are higher among women than among men. The wage differential also tends to be higher for low-skilled workers. Moreover, there is considerable evidence of an increase in the wage differential between 1993-94 and 1999-00. The public sector wage advantage has increased for rural male and female workers according to all three sets of estimates. However, in urban areas the trend is more ambiguous. For male workers, two sets of estimates show an increased differential, while one method shows a slight decline in the differential. For female workers, the wage advantage of public sector has declined slightly according to two sets of estimates, and increased according to one.

Our review of wage differentials (estimated using similar methodologies) across the world shows that India has one of the largest differentials between wages of public workers and workers in the formal private sector.<sup>15</sup> Differentials as high as in India were found in only two African countries (Ghana and Côte d’Ivoire) and in regions of Brazil.

There are several qualifications and possible caveats to our results. First, it is quite possible (even likely) that our sample missed the most skilled workers in India’s labor market – managers, lawyers, and accountants – those who tend to work in the internationally competitive private sector. In particular, the 75<sup>th</sup> (90<sup>th</sup> ) percentile of the distribution of private-formal wages in the 1999-00 NSS sample is Rs 800 (Rs 1,500). The several highest weekly wage points are in the range of Rs 17,000 - 23,500. (The highest observed wage in public sector is Rs 21,500). At the same time, the median weekly wage of a “fully qualified professional” employed in multinational corporation operating in the financial or industrial sectors in India or working for a “non for profit” international organizations is Rs 16,550 (annually Rs 0.86 million). Clearly, only few workers from this internationally competitive segment of India’s labor market are covered in the NSS (although this might be a true representation of the size of this segment in the total Indian labor force). However, if these are the people the public sector is trying to attract, the results of this study are not directly applicable for setting their wages.

In this paper, we may have overestimated the size of the public–private wage differential for this small group, because we did not have a representative sample of

> 15 The review included North America, Africa, OECD countries, Eastern Europe, Turkey, Brazil, Vietnam, and Indonesia. A description of the differentials in these countries is not presented here, but is available in the longer version of the paper and from the authors upon request.

25
replacement sample was selected. A total of 105 such replacements were made resulting in 3,619 completed interviews from 3,724 attempted (97.2% response rate).

In addition, two of the ten sampled households were selected randomly for structured observations which took place on the second day (resulting in a sub-sample of 724 households). Structured observations consisted of a three hour visit to the household in the early morning (determined by the time the primary caregiver would wake up) where the enumerator would observe caregiver handwashing behavior. This was conducted before any household interviews were held in the village, with the intention of reducing any potential Hawthorne effects that could result from respondents being aware that the intention of the visit was to record handwashing behavior. Enumerators would record the various handwashing “critical junctures” experienced by the caregiver and target child, which include (i) before preparing, serving or eating food and (ii) post-toileting. They would then record whether critical junctures were accompanied by handwashing with soap and water.

Following the structured observations, household surveys were conducted on the second and third days of field work. The questionnaire<sup>29</sup> included modules on demographics, productive activities and assets, water and sanitation status (observed and self-reported), education, hygiene practices and knowledge, social capital, self-reported exposure to the program interventions and child health. Health results were collected for children between the ages of 6 months and 5 years. A description of the priority outcomes is as follows:

**Access to an improved latrine** : The variable construction follows the JMP improved sanitation definition based on observed latrine type and private ownership. Based on a specific interest of the Tanzanian Government, we also consider how access to an improved latrine varies when we include shared facilities, or expand the definition of sanitation quality as being fulfilled by only one squat hole with a slab that does not expose contents.<sup>30</sup> For households with latrines, we also ask when the latrine was constructed to determine whether this was done during the intervention period. While a small number of households may have flush/pour facilities, including septic tanks, we refer to “latrine” construction since 97.5% of households with a toilet facility are referring to some form of pit latrine.

**Open defecation** : This is a measure of households that report using “no facilities” as the usual defecation practice (as per JMP guidelines.) We present a second definition that asks households directly whether they practice open defecation always, sometimes, rarely or never. We then present these results as a measure of intensity.

> 29 Based on the global WSP survey instrument applied for HHWS impact evaluations in Senegal, Peru and Vietnam and TSSSM evaluations in India and Indonesia.

> 30 see http://www.wssinfo.org/definitions-methods/watsan-categories for the formal JMP definition

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# **The Impact of Electricity Shortages on Firm Productivity: Evidence from Pakistan**<sup>**1**</sup>

Corbett A Grainger Fan Zhang University of Wisconsin-Madison World Bank

**Keywords:** Manufacturing productivity, electricity shortages, Pakistan

**JEL:** D04, D24, L11, L94, O12, O13, Q48

> 1 This paper is part of a broader analytical effort to assess the cost of power sector distortions in South Asia being conducted by the Office of the Chief Economist of the World Bank South Asia Region. Comments from Anjum Ahmad, Martin Rama, Amena Raja, Mohammad Saqib, Shahzad Sharjeel, and Richard Spencer on earlier drafts are greatly appreciated. Umul Awan, Fatima Najeeb and Weijia Yao provided excellent research assistance. Financial support from Partnership for South Asia Trust Fund is greatly appreciated.
Freetown, Sierra Leone.” Summary Brief 19, International Centre for Tax and Development. Brighton, United Kingdom.

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33
classroom management tools, the curriculum, and the life skills of instructions and being considerate. These are generally more readily observable and less subjective items, but also ones where different questions or scales could lead to different measurements.

The moderate strength agreement items (0.4 to <0.6) were outdoor space, toilets, soap, a KG degree, continuous professional development, positive teaching, and all the multi-disciplinary items. The multi-disciplinary items have some potential for test-retest variation in children’s responses (but the better-performing foundational skills did as well), whereas the other items may be more readily measurable.

In terms of the class level and school level strength of agreement, interestingly, although categories and overall were weak, classroom level measures performed slightly better than school level averages in terms of kappa. Overall consistency was slight (kappa=0.174 on the class and 0.013 on the school level). Because most schools were at the minimum level of overall quality, exact agreement was higher (82.2% on the class level and 86.7% on the school level).

**Table 1. Proportions from QAS tool, diagnostic research tool, exact agreement, and kappa**

||**Mean**<br>**QAS tool**|**Mean**<br>**diagnostic**<br>**research**<br>**tool**|**Exact**<br>**agreement**|**Kappa**|**N (Obs.)**|
|---|---|---|---|---|---|
|**Infrastructure**||||||
|Desk|0.948|0.919|0.902|0.211|332|
|Seat|0.975|0.919|0.925|0.260|332|
|Indoor space|0.708|0.588|0.517|-0.043|332|
|Outdoor space|0.667|0.801|0.761|0.402|332|
|<br>Toilets|0.667|0.525|0.766|0.524|332|
|Soap|0.493|0.664|0.758|0.518|332|
|<br>Washing|0.300|0.121|0.729|0.221|332|
|Inadequate light|0.109|0.109|0.873|0.347|332|
|Inadequate ventilation|0.094|0.147|0.855|0.320|332|
|<br>Other hazards|0.588|0.646|0.816|0.613|332|
|Class size|0.575|0.567|0.980|0.958|333|
|Student-teacher ratio|0.203|0.215|0.982|0.947|333|
|**Materials**||||||
|Writing utensils|0.639|0.846|0.664|0.169|332|
|Manipulatives|0.250|0.253|0.656|0.086|332|
|Classroom management|0.416|0.771|0.564|0.200|332|
|**Teacher's supports**||||||
|KG degree|0.748|0.628|0.760|0.451|434|
|<br>Trained on ed. 2.0|0.257|0.279|0.879|0.692|434|
|Cont. Prof. dev.|0.132|0.108|0.884|0.453|434|
|**Pedagogy 2.0**||||||
|Curriculum|0.719|0.906|0.748|0.217|332|
|Individualized|0.863|0.894|0.786|0.001|332|

15
Fi ure 2b: Correlation in female activit for services b state <u>g y y</u> <!-- Start of picture text --> A. Female-owned firms vs. female employees D. Female employee share vs. segmentation in female-<br>45% owned firms<br>90%<br>40%<br>85%<br>35%<br>80%<br>30%<br>75%<br>25% 70%<br>20% 65%<br>60%<br>15%<br>55%<br>10%<br>50%<br>5%<br>45%<br>0% 40%<br>0% 5% 10% 15% 20% 25% 30% 35% 40% 0% 5% 10% 15% 20% 25% 30% 35% 40%<br>Share of establishments in state that are female-owned Share of employees in state that are female<br>B. Female-owned firms vs. segmentation in female-<br>E. Female employee share vs. segmentation in male-<br>owned firms<br>owned firms<br>90%<br>100%<br>85%<br>80% 95%<br>75%<br>90%<br>70%<br>65%<br>85%<br>60%<br>55% 80%<br>50%<br>75%<br>45%<br>40%<br>70%<br>0% 5% 10% 15% 20% 25% 30% 35% 40%<br>0% 5% 10% 15% 20% 25% 30% 35% 40%<br>Share of establishments in state that are female-owned<br>Share of employees in state that are female<br>C. Female-owned firms vs. segmentation in male-<br>F. Segmentation in female-owned vs. male-owned<br>owned firms<br>firms<br>100%<br>100%<br>95%<br>95%<br>90%<br>90%<br>85%<br>85%<br>80%<br>80%<br>75%<br>75%<br>70%<br>0% 5% 10% 15% 20% 25% 30% 35% 40% 70%<br>40% 50% 60% 70% 80% 90%<br>Share of establishments in state that are female-owned<br>Share of female employees in female-owned establishments<br>female<br>establishments<br>Share of employees in state that are<br>Share of female employees in female-owned<br>establishments<br>establishments<br>Share of female employees in female-owned Share of male employees in male-owned<br>establishments<br>establishments<br>Share of male employees in male-owned<br>Share of male employees in male-owned<br><!-- End of picture text -->
7 A surprising finding is that in urban schools improvement in the learning achievement of students appears lower for the experimental vis-à-vis control group. It is difficult to explain this result. It is possible that the selection of the control group was not properly done. Having said this, we note that the PARE program was not well implemented among the urban schools. Delivery of planned interventions were either not delivered or delayed. A further investigation of this result is needed. Lessons could perhaps be learned in understanding how a well designed project could end up hurting children’s education, if it is poorly implemented. It might be that delays and non-delivery of planned inputs created confusion, de-motivated schools and generated changes in schools that became harmful without timely delivery of complementary assistance and resources.

In a recent education sector study<sup>2,</sup> a two-stage least squares regression model, in which learning achievement and the monetary value of PARE assistance to schools were treated as endogenous, was estimated on the combined indigenous, rural and urban samples<sup>3.</sup> The model treats the amount of assistance received by schools to be dependent on the school’s learning achievement score, on the assumption that the program has systematically given more assistance to more disadvantaged schools. The analysis reveals that holding other things constant, there is a significant positive correlation between learning achievement and the monetary value of PARE assistance (Table 2).

> 2 World Bank, Mexico: Advancing Educational Equity and Productivity in the Context of Decentralization), November 1998 (Processed)

> 3 The dummy variable representing whether or not a student is in the experimental schools was used to identify the learning achievement equation.
# Figure 10: LAC: Investment growth

# A. Investment growth

B. Countries with investment growth below its long-term average <!-- Start of picture text --> Percent<br>2000-21 average<br>18<br>12<br>6<br>0<br>-6<br>LAC EMDEs<br>2010 2012 2014 2016 2018 2020 2022 2010 2012 2014 2016 2018 2020 2022<br><!-- End of picture text -->

# C. Five-year-ahead investment growth forecasts

<!-- Start of picture text --> Percent<br>Argentina Brazil<br>12<br>Chile Colombia<br>10 Mexico Peru<br>8<br>6<br>4<br>2<br>0<br>E. Investment growth by sector<br>Percent per annum 2004-08 2011-15<br>24<br>2016-21<br>18<br>12<br>6<br>0<br>-6<br>Argentina Brazil Mexico<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Construction Machinery Overall Construction Machinery Overall Construction Machinery Overall<br><!-- End of picture text --> <!-- Start of picture text --> Percent of countries<br>100 Below long-term average<br>75<br>50<br>25<br>0<br>2016 2017 2018 2019 2020 2021 2022<br>D. Public and private capital stocks per<br>head<br><!-- End of picture text --> <!-- Start of picture text --> US$, thousands LAC public stock<br>per capita (2011 LAC private stock<br>EMDE public stock<br>30 EMDE private stock<br>20<br>10<br>0<br>1960 1970 1980 1990 2000 2010<br>F. Public and private investment growth<br>Percent<br>Public Private<br>8<br>4<br>0<br>-4<br>-8<br>2000-09 2010-15 2016-19<br><!-- End of picture text --> Sources: Consensus Economics, Haver Analytics; International Monetary Fund; national sources; World Bank. Note: EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean. A. Average growth rates weighted by investment levels. Includes 98 EMDEs, of which 20 are in LAC. B. Economy coverage is the same as for panel A. Share of countries in LAC region with investment growth below the long-term (2000-21) average.

C. Five-year ahead consensus forecasts for investment growth.

E. For Argentina, 2004 is excluded. For Brazil, construction and machinery investment are derived using gross fixed capital formation indicators from the Instituto de Pesquisa Econômica Aplicada as proxies.
# 4.1 Production and the Labor Market

The basic distinction on the production side is that between rural and urban sectors. In the rural economy firms produce one good, which is sold domestically or exported. The urban economy consists of both formal and informal components. The informal economy produces nontraded services. The formal urban economy is separated between production of a private good, and a nontraded public good.

For all activities the production technology is represented by value added functions and fixed (Leontief) intermediate input coefficients. With exception of the public sector, the value added functions are represented by sets of nested constant elasticity of substitution (CES) functions and Cobb-Douglas (CD) functions. The rural sector uses land (assumed to be in fixed supply), unskilled labor, and the economy-wide stock of public physical capital. Production exhibits decreasing returns to scale with respect to the two latter inputs. Value added in the informal sector requires only unskilled labor and is subject to decreasing returns to scale. Urban private formal production uses as inputs both skilled and unskilled labor, as well as private and public physical capital. Skilled labor and private physical capital are assumed to have a higher degree of complementarity than private physical capital and unskilled labor. Furthermore, it is assumed that public capital is subject to congestion effects, that is, the positive externality associated with public capital decreases as its usage by the urban population increases. Value added in the public sector is measured from the government wage bill. Employment levels of skilled and unskilled workers in that sector are treated as predetermined variables. Firms in the rural and the private formal sector allocate their output to exports or the domestic market according to a production possibility frontier, defined by a constant elasticity of transformation (CET) function. Assuming imperfect substitutability, the ratio between exports and domestic sales depends on the relative prices of exported and domestic goods and the elasticity of substitution between these goods.

The demand for labor in the rural sector is derived from first-order conditions for profit maximization. Labor demand is related positively to the level of net output and negatively to the product wage (the nominal wage deflated by the net output price). The nominal wage in the rural sector adjusts to clear the labor market. The supply of labor in the rural sector is predetermined at any given point in time, but grows over time at the exogenous population growth rate net of worker migration to urban areas. Following 16