rafmacalaba/datause-encoder-data huggingface dataset:
is then a function of both health information and the worker’s actual health status, as well as other dimensions of human capital.

In this randomized control trial described in more detail below, we exogenously vary access to a malaria testing and treatment intervention by randomly assigning workers to a day of testing. For malaria positive workers, the effect of the intervention provides an estimate of the impact of changes in the workers’ health status and information about their malaria status. For malaria negative workers, who do not receive treatment, the sole effect of the intervention is that of health information. Becker’s model motivates the experimental design, as effort alone is not the only determinant of productivity, and our discussion of worker fixed effects, particularly β and unobservable components of h, map effort into worker productivity.

Despite the theoretical attention to effort in earnings functions, the measurement of physical activity has typically generated most interest in the fields of medicine, public health, and nutrition sciences. In medical and public health studies, whether assessing the health risks associated with certain (e.g. sedentary) lifestyles, or estimating gains from health promotion activities, measurement efforts aim to capture the duration, frequency, intensity, or setting of physical activity (Bauman et al., 2006). Nutritionists tend to be interested in physical activity as a major component of individual energy needs. Calorie (food) deficits or surpluses are generally evaluated comparing estimated energy expenditure against a benchmark energy requirement. Physical activity is the most variable component of human daily energy expenditure and the second most important after the Basal Metabolic Rate (BMR) (FAO and WHO, 2001).

A number of tools are used for the measurement of physical activity that can be ranked according to their degree of practicality. Nutritionists and physiologists consider the doubly labeled water method (DLW) as the gold standard to measure energy expenditure in free-living conditions (Speakman, 1997; FAO and WHO, 2001). As an alternative to this gold standard, subjects are sometimes asked to keep diaries or logs of physical activity. While widely used in medical and nutrition studies, these methods are cumbersome and costly to implement with large samples. More common are individual self-reports of physical activity. Instruments that have 6
Whether we estimate the CPI change on the subsidized product or on all products, the general equilibrium case implies deflating household expenditure and government revenues by the inflation rate generated by the change in the price of the subsidized good. With inflation adjustments, the final government revenues are simply described as Note that .

## **Estimating elasticities**

We have seen that to estimate changes in total revenues we need to know the demand elasticity of the subsidized good to changes in prices. This elasticity may be known for the particular good treated. For example, country and sector specialists may already know rather well demand elasticities for specific subsidized goods and, in this case, all we need to do is to use these known elasticities. However, in many cases, demand elasticity is not known in advance and will have to be estimated. The economic framework is simply the classic demand schedule where the slope of the demand and the elasticity is given by - _d_ q/ _d_ p or. With only a cross-section survey, we may have variation in price (say across regions or districts). In such case it is possible to estimate the demand elasticity with a simple equation as proposed by Deaton (1997): where is the expenditure (income) elasticity and is the own-price elasticity, which is our parameter of interest. This equation is a very general equation that can rarely be applied as such. Depending on the type of good considered, we may want to control for various factors such as substitutes goods or use fixed effects for spatial indicators such as urban or rural areas or municipalities. Moreover, with subsidized goods, it is rare to find variation in prices across regions or districts in cross-section surveys. Indeed, this would imply different subsidies across areas. Therefore, the equation above can only be considered as a broad baseline equation that need to be adjusted and tailored to the particular good and country treated.

The equation above also assumes that demand elasticity is equal for all consumers while we may want to consider different elasticities for different types of consumers such as the poor and the non-poor or people living in urban or rural areas. This can be easily done by estimating different equations that may also be adjusted for the specific conditions of the different groups by adding specific control variables. However, the important point to retain here is that there is no universal model to estimate demand elasticities and researchers will have to find the most appropriate model case by case.

# **Multiple pricing**

For some goods, pricing is not homogeneous across quantities consumed, a case often referred to as nonlinear pricing. The generic term _nonlinear pricing_ refers to any case in which the tariff is not strictly 23
_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_ Nonetheless, manufacturing firms in Africa have been able to attract a well educated labor force, allowing upgrade in skill levels since the 1990s. Table 7 shows that on average, workers in our sample had 11 years of education; 12 years in firms with over 100 employees. This compares favorably with China, where workers in manufacturing have on average 9.8 years of education, and Morocco, where only 27 percent have more than 12 years of education.<sup>20</sup> Even unskilled workers have on average over 8 years of education, which roughly corresponds to having completed a junior secondary education.<sup>21</sup> Similarly, average education (years) for professional workers corresponds nearly to a university degree, and there is relatively little variation across countries within this category. Firms in both country groups have older workers with slightly less education. Larger firms and exporters tend to have slightly more educated workers.

# INSERT TABLE 7

We tried to compare the levels of education from the previous wave of enterprise surveys but the sample of countries differs, and there were also changes in the sampling methodology. For the countries where we do have data in both waves, the numbers are shown in Table 8. In Ghana and Kenya the share of workers with post-secondary education seems to have increased significantly.<sup>22</sup> Likewise, the average number of years of education increased in both countries.

# INSERT TABLE 8

A word of caution is in order here: although the worker sampling has been made as randomly as possible, it is still likely that some worker categories are being overrepresented in this sample, particularly high-skilled workers, because of the stratification technique. While there is no precise way to establish this (nor to construct appropriate weights), we can still roughly compare our sample averages with a few responses from the firm’s survey, which give us an indication of the “true” averages at the firm level. We can do this in particular for the distribution of education (albeit only for those firms interviewed before 2006) and for the distribution of occupations (only for the categories of skilled and unskilled production workers).

INSERT TABLES 9 AND 10

> 20 The source of education in China is the National Bureau of Statistics, One Percent Population Sample (2005). We’re grateful to John Giles for providing us with this number.

> 21 Readers should exercise caution in interpreting cross country comparisons of education years, because they do not necessarily reflect similar levels of skills due to the variation of education quality within and across countries. In this paper we do not correct for education quality, although in our regional regressions we argue that one potential reason for differences in returns to education is quality.

> 22 However, numbers for Kenya must be interpreted with caution as differences might be largely due to sample composition (oversampling of large establishments).

10
Figure 7 Portfolio Allocation of PFR-VEB (2004-2009) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40% cash, bank deposit<br>government securities<br>30%<br>20%<br>10%<br>0%<br>2004 2005 2006 2007 2008 2009<br><!-- End of picture text -->

# Portfolio Allocation of PFR-AM

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2004 2005 2006 2007 2008 2009<br>government securities regional state securities municipal bonds<br>commercial bonds equity cash, bank deposit<br><!-- End of picture text -->

# Portfolio Allocation of PFR-NPFs

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2005 2006 2007<br>government securities regional state securities commercial and municipal bonds<br>equity cash, bank deposit other<br><!-- End of picture text -->

## Source FFMS

27
0.1 to 0. 15 percent of the initial loan balance for each prompt repayment. This incentive system will be extended to all loans in new provinces.

_(Group Formalization atnd Up)grading._ In the past. the program attempted to link groups into broader associations with the idea that these "super" groups could become financial intermediaries, as occurs in PHBK's credit group model (Model I). However, generally only the more affluent groups and group members could take advantalge of these more sophisticated structures and, in any case. the concept was generally not very successful. The program will not focus efforts on this area in the future.

_Program Supervision._ As indicated above, the program is split between group development which is controlled by the Ministry of Agriculture. and lending. which is controlled by BRI. Program managers receive data from BRI but appear to have virtually no influence over BRI's P4K policies and procedures. Within BRI, the program is overseen by the Small Business, Food, and Cooperative Division. Program managers exert somewhat greater influence over the Ministry of Agriculture's involvement with the program.

# **Loan Products**

_First_ **_fotur_** _Loans._ Loan terms are usually from 12 to 18 months. Repayment frequency can be monthly, quarterly, semi-annually or annually, depending on the nature of the use of the loan.

Groups must save at least Rp. 50,000 (US$2 1) to obtain their first loan. Group savings for the second loan must be at least 10 percent of the loan capital required, and savings for the third and fourth loan must be at least equal to 20 percent of the loan required. After the fourth loan, the groups' forced savings requirement is 25 percent of the loan amount. Savings earn interest at the same rate as savings placed in BRI's voluntary savings account program - SIMPEDES. This rate is about 9 percent for small balances. Groups can withdraw their savings at any time after they have repaid their loan.

For the first four loans, the interest rate charged on loans is fixed at I percent per month on the initial loan amount. Assuming monthly installments, and taking into account the forced savings requirement, this is equivalent to a monthly interest rate of about 1.8 to 2.4 percent on a declining balance basis or 24 to 33 percent annually. The effective interest rate varies depending on the amount of forced savings required. A small incentive in the form of an interest rebate is provided for on-time payments. This rebate reduces the effective annual interest rate yield to about 22 to 31 percent. A stamp duty of Rp. 1,000 (about US$0.43) is charged on all credit agreements.

For a first loan, the maximum loan size per group member is Rp. 100,000 (US$43). This figure increases to Rp. 300,000 (US$128) for the fourth loan. The initial maximum loan is equal to about 5 percent of GDP per capita, and the final maximum, about 15 percent.

66
<!-- Start of picture text --> 2S<br>=<br>Ss<br>2xs<br>3<br>82<br>”<br>Ss<br><!-- End of picture text --> <!-- Start of picture text --> BS<br>o<br>2006 2007 2008 2009 2010 2011 2012 2013 2014<br>EEE Financial reforms MQ Trade reforms<br>HEE Fiscal-related reforms MEE Real sector reforms<br><!-- End of picture text -->
of the different lockdowns that were enacted to contain the COVID-19 pandemic, to help assess them.

# **3. Data**

# _3.1 Electricity Consumption_

Monthly electricity consumption in Bangladesh is available from the Bangladesh Bureau of Statistics (BBS) from 1993 until 2013, when the data series was discontinued. Daily electricity consumption data can be scraped from the Bangladesh Power Development Board and is available for all eight divisions (Dhaka, Chattogram, Mymensingh, Sylhet, Khulna, Rajshahi, Barisal, and Rangpur) from 2010 until now, with only a one-day lag. For this study, daily data from February 2010 to October 2021 has been compiled.<sup>1</sup> For 2.5 percent of all the days during this period, data are missing and were hence linearly interpolated using the day before and day after the missing one.

# _3.2 Economic Activity_

For the analysis of the relationship with economic activity, we use annual data on GDP and industrial production (2000–21) from the National Accounts statistics published in the BBS Bluebook and the monthly Quantum Index of Industrial Production data (July 2012 to April 2021) from the BBS. Monthly export data (2016–20) were taken from the Export Promotion Bureau.

# _3.3 Other Data_

We analyze the correlations between electricity consumption per capita and subnational poverty levels, the share of households with access to electricity (from the Bangladesh Household Income and Expenditure Survey), and firm density (from the Economic Census 2013). To assess the efficacy of using electricity consumption to analyze the impacts of disasters, we extract disaster data (2016–20) from the Center for Excellence in Disaster Management & Humanitarian Assistance. For estimation of the electricity consumption model, we use temperature data (2010–21) from the Bangladesh Meteorological Department and the dates of holidays and Ramadan from timeanddate.<sup>2</sup> To measure the stringency of the COVID19 containment measures Bangladesh adopted and their impact on mobility, we rely on the Oxford COVID-19 Government Response Tracker (OxCGRT)<sup>3</sup> Stringency Index (2020–21) and Google mobility data from the Google Community Mobility Reports. Finally, data on daily COVID-19 cases and division-level data on the number of cases and deaths were retrieved from

> 1 Using the Tabulizer tool pack from the R statistical software, the data were scraped from PDF documents published on the Bangladesh Power Development Board website.

> 2 https://www.timeanddate.com/.

> 3 OxCGRT collects publicly available information on 20 indicators (Containment and Closure Policies, Economic Policies and Health System Policies, and so forth) of government responses.

4
African economy did not face the macroeconomic instability, external sovereign debt problems, and forced adjustments that provided the initial impetus for privatization in the developing countries.

However, even within South Africa’s moderately well-run electricity system several problems have emerged which, if not addressed adequately, could pose a serious threat to the system’s future viability and sustainability.

# **...Yet, Continuing Inefficiencies in the Distribution Segment Can Seriously Undermine System Security and Reliability**

During the past decade, numerous studies [e.g. the analysis of boundaries, ownership, asset valuation, regulation, and human resources of the distribution industry undertaken by a consortium led by Price WaterhouseCoopers (PwC)] and consultations [e.g. the, and the Electricity Distribution Industry Restructuring Committee (EDRIC)] led to a growing consensus that the problems in the electricity distribution industry needed urgent attention. High levels of nonpayment for services, insufficient asset management strategies and levels of recapitalization, and a lack of appropriate technical resources at the municipal level have contributed to an alarming deterioration in the condition of distribution assets. A number of surveys conducted by the NER revealed a growing dissatisfaction of customers with their electricity service mainly due to a worsening reliability and availability of supply. There have also been growing complaints about the lack proper customer attention on the part of municipal distributors.

One of the key structural deficiencies of the distribution industry has been excessive fragmentation—there were too many small, non-viable, poorly-run municipal distributors. After several years of studies and consultations, the Cabinet finally approved in June 1999 a plan to merge the large number of municipal distributors and Eskom’s distribution businesses into six Regional Distribution Companies (REDs). **The continuing uncertainty** However, implementation has been painfully **regarding the status of the** slow and convoluted. The first RED was **distribution industry is likely to** established only in 2005 and only on paper.<sup>106</sup> **exacerbate the problems of** Moreover, recent draft Cabinet memos and **inadequate investment in and** interventions by National Treasury officials **deterioration of system reliability.** have created some uncertainty and confusion about the consolidation model that will ultimately be implemented.

The original consolidation plan, if successfully implemented, will correct the past failure of small municipal distributors to capture economies of scale, skill and specialization; and reduce the substantial differences in the financial status of municipal distributors and the wide disparity in the prices paid by geographically segmented customer groups. However, unfavorable microeconomic conditions were not the only 106 Eberhard, A. 2005. “From State to Market and Back Again: South Africa’s Power Sector Reforms”. _Economic and Political Weekly_ : 5309-17.

77
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- K. E. Semrau, K. A. Miller, S. Lipsitz, J. Fisher-Bowman, A. Karlage, B. A. Neville, M. Krasne, J. Gass, A. Jurczak, V. Pratap Singh, S. Singh, M. Marx Delaney, L. R. Hirschhorn, B. Kodkany, V. Kumar, and A. A. Gawande. Does adherence to evidence-based practices during childbirth prevent perinatal mortality? a post-hoc analysis of 3,274 births in uttar pradesh, india. _BMJ Global Health_ , 5(9), 2020.

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37
# **Table 17: Upgrading Opportunities for Penang, 1995**

|sitc4|short description|haveRCA|exporting|invdensity|Lall Class|
|---|---|---|---|---|---|
|8811|Photographic cameras, flashlight apparatus, parts, accessories, nes|0|1|2.837|HT2|
|7762|Other electronic valves and tubes|0|1|2.912|HT1|
|9510|Armoured fighting vehicles, war firearms, ammunition, parts, nes|0|1|2.969|MT3|
|452|Oats, unmilled|0|1|3.026|PP|
|344|Fish fillets, frozen|0|1|3.039|PP|
|7528|Off-line data processing equipment, nes|0|1|3.054|HT1|
|7648|Telecommunications equipment, nes|0|1|3.058|HT1|
|6841|Aluminium and aluminium alloys, unwrought|0|1|3.061|PP|
|5982|Anti-knock preparation, anti-corrosive; viscosity improvers; etc|0|1|3.085|MT2|
|7415|Air conditioning machines and parts thereof, nes|0|1|3.090|MT3|
|6413|Kraft paper and paperboard, in rolls or sheets|0|1|3.097|RB1|
|6671|Pearls, not mounted, set or strung|0|1|3.109|RB2|
|8852|Clocks, clock movements and parts|0|1|3.127|MT3|
|2331|Synthetic rubber, latex; factice derived from oils|0|1|3.155|RB1|
|7523|Complete digital central processing units; digital processors|0|1|3.161|HT1|
|2881|Ash and residues, nes|0|1|3.199|RB2|
|5849|Other chemical derivatives of cellulose; vulcanized fibre|0|1|3.209|MT2|
|5161|Ethers, epoxides, acetals|0|1|3.213|RB2|
|7149|Parts, nes of the engines and motors of group 714 and item 71888|0|1|3.221|MT3|
|113|Pigmeat fresh,chilled or frozen|0|1|3.235|PP|

_Note:_ PP: Primary Products, RB1: Agro-Based, RB2: Other Resource-Based, LT1: Textile, garment & footwear, LT2: Other, Low-Technology, MT1: Automotive, MT2: Process, MT3: Engineering, HT1: Electronic & Electrical, and HT2: Other, High-Technology. Source: Authors’ calculation 40
Figure 4: Event Study Analysis: Manufacturing and Others <!-- Start of picture text --> 2013 2014 2015 2016 2017 2018 2019 2013 2014 2015 2016 2017 2018 2019<br>MNCs Non MNCs MNCs Non MNCs<br>(a) Manufacturing (b) Non-Manufacturing<br>.08 .08<br>.06 .06<br>.04 .04<br>.02 .02<br>0 0<br>Log Employment -.02 Log Employment -.02<br>-.04 -.04<br>-.06 -.06<br>-.08 -.08<br><!-- End of picture text --> _Notes:_ The figure plots event study analysis results from equation (1) where the dependent variable is the log of employment. In panel (a), the results are illustrated for the manufacturing sector; in panel (b), the results are illustrated for the non-manufacturing sector. Coefficients along with 95% confidence intervals are illustrated. Standard errors are clustered at the industry level.

# **4.2 Employment Adjustments in Manufacturing**

The event study estimates for the manufacturing sector may be biased if time-varying industry-specific, region-specific, and/or industry-region-specific shocks were compounded and thus affected the MNC and non-MNC groups differently. We address such concerns by employing a difference-in-differences estimator with industry-region-year fixed effects.

## **4.2.1 Difference-in-Differences**

We estimate the following difference-in-differences specification: where _i_ is a firm, _j_ is an industry, _r_ is a region, and _t_ is time (i.e., year). The dependent variable is the log of employment for firm _i_ in year _t_ . MNC _i_ is an indicator variable that equals 1 if a firm _i_ has foreign affiliates in the years 2013~2016 and 0 otherwise. Post _t_ is an indicator variable that equals 1 if the time _t_ is after 2016 (i.e., 2017, 2018, and 2019) and 0 otherwise. _γi_ and _ψjrt_ capture firm fixed effects and industry-region-year fixed effects, respectively. Standard errors are clustered at the industry level. The sample is restricted to the manufacturing sector.

16