rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Early Life Exposure to Coffee Mills and Test Scores: Effects by Gender

|||All Subjects|||STEM|||Non STEM||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||||||Male|||||
|CatchmentArea X Post|0.0951***|0.1022***|0.0932***|0.0784***|0.0739***|0.0645**|0.0998***|0.1144***|0.1059***|
||(0.0317)|(0.0280)|(0.0272)|(0.0303)|(0.0271)|(0.0267)|(0.0317)|(0.0282)|(0.0274)|
||[ 0.004 ]|[ 0.001 ]|[ 0.001 ]|[ 0.012 ]|[ 0.009 ]|[ 0.009 ]|[ 0.003 ]|[ 0.001 ]|[ 0.001 ]|
|R-squared|0.3793|0.4222|0.4222|0.3699|0.4099|0.4100|0.3574|0.4026|0.4027|
|Observations|668639|668285|668285|668639|668285|668285|668638|668284|668284|
||||||Female|||||
|CatchmentArea X Post|0.0837***|0.0973***|0.0801***|0.0604***|0.0662***|0.0513***|0.0929***|0.1108***|0.0932***|
||(0.0257)|(0.0209)|(0.0202)|(0.0222)|(0.0196)|(0.0193)|(0.0273)|(0.0219)|(0.0210)|
||[ 0.002 ]|[ 0.001 ]|[ 0.001 ]|[ 0.009 ]|[ 0.002 ]|[ 0.002 ]|[ 0.002 ]|[ 0.001 ]|[ 0.001 ]|
|R-squared|0.3984|0.4432|0.4433|0.3889|0.4300|0.4300|0.3713|0.4188|0.4189|
|Observations|818714|818396|818396|818717|818399|818399|818714|818396|818396|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exam Year FE|Yes|No|No|Yes|No|No|Yes|No|No|
|YOB FE|Yes|No|No|Yes|No|No|Yes|No|No|
|Municipality X Exam Year FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Municipality X YOB FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|CatchmentArea X Exam Year FE|No|No|Yes|No|No|Yes|No|No|Yes|

Notes: Dependent variable is the standardized aggregate test scores in the respective subject groups in the Rwanda Primary School National exam. STEM subjects include Math and Science while Non STEM courses include English, Social Studies, and Kinyarwanda. CatchmentArea X Post is an indicator variable equal to 1 if a student was born within the vicinity ( _≤_ 2.5km) of an operational coffee mill and 0 if otherwise. Controls include gender of the student, average annual temperature, and precipitation in the exam year, as well as baseline controls such as coffee suitability and road density interacted with time trend. Robust standard errors clustered at school level in parenthesis. False discovery rate (FDR) adjusted p-values for CatchmentArea X Post are reported in square brackets (Anderson 2008). The outcome variables accounted for in the p-values adjustments include the standardized test scores of: All subjects (aggregate), STEM, and Non-STEM subjects, as well as the subject-specific test scores i.e., Maths, Science, Social, English, and Kinyarwanda.

> _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level

34
17 described above in that we assume small open price taking behavior on both imports and exports.

Counterfactual policy analyses can be performed with the model for changes in a variety of social policies, including income and value-added tax changes, targeting programs, and changes in government expenditures (on education, for instance), domestic price controls, export policies, and others. When used in counterfactual mode, the model is solved using the MPS-GE software package developed by Rutherford (1988). This software package explicitly incorporates the optimizing behavior of consumers and firms, together with the activities of central and provincial government agencies. The numerical specification of behavioral relationships and policy parameters in the model are generated using the calibration techniques set out in Mansur and Whalley (1984) using the 1986 SAM referred to above.

The MPS-GE software used captures four sets of economic constraints (for commodities, consumers, producers and auxiliary constraints), and two classes of functions (utility and production functions). Agents are assumed to behave competitively; i.e., consumers allocate their incomes to maximize utility from consumption, producers adjust their production plans to maximize profit, and both groups take market prices as given. The number and types of agents and commodities, together with the characteristics of production and utility functions, constitutes the input formulation of the model for the purposes of MPSGE. Primal and dual constraints require that supply satisfies demand and that no sector earns excess profit; MPS-GE uses these conditions to compute equilibrium prices and quantities.
Figure 1: Distribution of coefficients when dropping one country at a time <!-- Start of picture text --> (a) Education SP aid (b) Education TC<br>.8 1 1.2 1.4 1.6 −.04 −.02 0 .02 .04<br>Education SP aid Education TC<br>(c) Health SP aid (d) Health TC<br>.6 .7 .8 .9 1 1.1 −.3 −.2 −.1 0 .1<br>Health SP aid Health TC<br>80<br>80<br>60<br>60<br>40<br>Frequency 40 Frequency<br>20 20<br>0 0<br>80 80<br>60 60<br>40 40<br>Frequency Frequency<br>20 20<br>0 0<br><!-- End of picture text --> Source: Author’s analysis based on data described in the text.

This assumption would be violated if, for instance, the allocation of education (health) SP aid and TC were partially determined on the basis of past or current values of public education (health) expenditure. In fact, Table 6 contains some evidence indicating that strict exogeneity for SP aid is unlikely to hold. If a firstdifferenced version of equation (11) is estimated with OLS (columns 2 and 4, labeled FD), then the effect of SP aid differs markedly from its FE estimate and even becomes negative. This stark difference between the FE and FD estimates of the SP aid coefficients suggests a violation of the strict exogeneity assumption because such a violation causes both FE and FD to be inconsistent and to have different probability limits (Wooldridge, 2002, pp. 284-285). However, the effect of TC is similar in the first-differenced model. There is some evidence of a negative effect of TC, especially in the education sector, in which the hypothesis of no fungibility can be rejected at a 10% significance level, but any displacement of sectoral public expenditure is minimal. Hence, the conclusion that the fungibility of TC is limited is confirmed in the FD model. A second indication that the FE model may be misspecified emerges from a serial correlation test of the idiosyncratic errors.<sup>30</sup> For both sectors, I reject the null hypothesis of no serial correlation at a significance level of less than 1%. Although clustering standard errors on the recipient country should ensure that inferences are valid, the presence of a serial correlation in _εit_ may indicate that the model is dynamically misspecified, which would again render the 21
# Financial Viability of Electricity Sectors in Sub-Saharan Africa

Quasi-Fiscal Deficits and Hidden Costs Chris Trimble, Masami Kojima, Ines Perez Arroyo, and Farah Mohammadzadeh Key words: cost of electricity service, electricity utilities, electricity tariffs, quasi-fiscal deficit, subsidies, reform, Sub-Saharan Africa JEL codes: H20, Q48, L94 Sector Board: Energy and Mining
- Ito, Kasuhiko, and G. D. Thurston. (1996). Daily PMlo/mortality associations: An investigation of atrisk subpopulations. _Journal of Exposure Analysis and Environmental Epidemiology_ 6(1):79-95.

- Katsouyanni, K. (1995). Health effects of air pollution in southem Europe: Are there interacting factors? _Environ Health Perspect_ 103(Suppl 2):23-27.

- Kelsall, J. E., J. M. Samet, S. L. Zeger, and J. Xu. (1997). Air Pollution and Mortality in Philadelphia, 1974-1988. _Am JEpidemiol. 146(9):750-62._

- Kinney, P., and H. Ozkaynak. (1991). Associations of Daily Mortality and Air Pollution in Los Angeles County. _Environmental Research 54:99-120._

- Kinney, P. L., Kasuhiko Ito, and G. D. Thurston. (1995). A sensitivity analysis of mortality/PMo0 associations in Los Angeles. _Inhal. Toxicol._ 7:59-69.

- Krupnick, A. (2000), Personal communication, Washington, D.C., Oct 17 2000.

- Lee, J. T., and J. Schwartz. (1999). Reanalysis of the effects of Air Pollution on Daily Mortality in Seoul, Korea: A Case-Crossover Design. _Environ Health Perspect._ 107(8, August):633-36.

- Mazumdar, S., and N. Sussman. (1983). Relationships of Air Pollution to Health: Results from the Pittsburgh Study. _Arch Environ Health_ 38(1, January/February): 17-24.

- Mitchell, R. C., and R. T. Carson. (1989). _Using Surveys to Value Public Goods: The Contingent Valuation Method,_ Resources for the Future, Washington, D.C.

- Moller, L., D. Schuetzle, and H. Autrup. (1994). Future research needs associated with the assessment of potential human health risk from exposure to toxic ambient air pollutants. _Environ. Health Perspect._ 102 (Suppl 4):193-210.

- Moolgavkar, S. H., and E. G. Luebeck. (1996). A Critical Review of the Evidence on Particulate Air Pollution and Mortality. _Epidemiology_ 7(4, July):420 -28.

- Moolgavkar, S. H., E. G. Luebeck, T. A. Hall, and E. L. Anderson. (1996). Air pollution and daily mortality in Philadelphia. _Epidemiology_ 6(5, September):476-84.

- Navrud, S. (1999). _Linking weak and strong sustainability indicators: Critical Loads and Economic Values._ John Wiley & Sons Ltd.

- NCP (1998). _La situacion demografica de Mexico,_ National Population Council (Consejo Nacional de Poblacion), Mexico DF, p. 9-18.

- Neas, L. M., J. Schwartz, and D. Dockery. (1999). A Case-Crossover Analysis of Air Pollution and Mortality in Philadelphia. _Environ Health Perspect_ 107:629-631.

- Ostro, B.D. (1987), Air Pollution and Morbidity Revised: A Specification Test. _Journal of Environmental Economics and Management_ 14:87-98.

- Ostro, B.D. (1989). Air Pollution and Acute Respiratory Morbidity-An Observational Study of Multiple Pollutants. _Environ Res._ 50(2):238-47.

- Ostro, B. (1995b) Fine Particulate Air Pollution and Mortality in Two Southern California Counties. _Environmental Research_ 70:98-104.

- Ostro, B., M. Lipsett, and J. Mann (1995'). Air pollution and asthma exacerbations among AfricanAmerican children in Los Angeles. _Inh Tox._ 7:711-722.

_48_
a simple OLS method while the backward elimination starts with all the candidate variables and then deletes each variable that falls below a p-value threshold. We use the backward elimination process while setting the p-value threshold to 0.05. This is chosen over the forward elimination approach because forward elimination depends on the order in which variables are chosen.<sup>11</sup> T Having chosen the set of candidate explanatory variables xch, equation 1 is originally estimated using ordinary least squares. The regressions are weighted using the sampling weights within the surveys. To allow for the possibility of intra-cluster correlations of household expenditures, the random disturbance term is defined as follows: T where η and ε are assumed independent, uncorrelated with xch and as having different data generating processes. These two components of the error term are assumed to have mean zero and 2 2 variances ση and σε,c, which indicates that the latter is permitted to be heteroskedastic and vary across households in a given cluster, while the former is assumed to be a constant. Clusters are defined as districts, the lowest level of spatial disaggregation that can be matched between CES 2011/12 and SCS Health 2017/18.<sup>12</sup> Our approach allows for the possibility of normal or nonnormal heteroskedastic error terms. The variance-covariance matrix of the error term is computed using the methods described in Nguyen et al. (2018).

Given the structure of the errors in equation 2, an OLS estimation of model 1 would underestimate uncertainty. Therefore, in the second step, the model is re-estimated using Generalized Least Squares (GLS) to control for the heterogeneity in the cluster specific errors, so: Using a Monte Carlo approach, 100 samples from the training data are drawn to obtain 100 values of the coefficients β̂GLS and of the error components η̂C and ε̂ch (the latter based on assumptions about their distribution and estimates of their variances σ̂η2 and σ̂ε,c2 from previous stages).13 Using R these estimates and explanatory variables from the target survey, xch, we obtain 100 imputed values of per capita household consumption for household h in cluster c:

> 11 For an introduction to variable selection and regularization methods in general, and of the LASSO and Stepwise selection methods in particular, see chapter 6 of James et al. (2013).

> 12 Having a smaller number of clusters reduces the likelihood of heteroskedasticity in the cluster component of μch. 13 See Nguyen et al. (2018) and Newhouse and Vyas (2019) for more details on the distributional assumptions.

8
**Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (+30% across the board)**

||Coeff.|Std. Error|Sig.|
|---|---|---|---|
|(Constant)|6.718|0.378|0.0%|
|LN full-time employees|0.171|0.039|0.0%|
|LN of q55 after imputed values were introduced (imputed "no|0.142|0.030|0.0%|
|answer")||||
|dummy for full in-house (0) and full outsourcing|-0.436|0.106|0.0%|
|Guateng provincial dummy|0.363|0.087|0.0%|
|Manufacturing|-0.239|0.119|4.5%|
|PAYE|0.542|0.140|0.0%|
|Adj. R2|0.302|||
|Mean of the dependent variable|9.633|||
|**Dependent Variable: Sensitivity analysis: LN Total cost of tax**<br>**board)**|**compliance**|**(+20% acros**|**s the**|
||Coeff.|Std. Error|Sig.|
|(Constant)|6.638|0.378|0.0%|
|LN full-time employees|0.171|0.039|0.0%|
|LN of q55 after imputed values were introduced (imputed "no|0.142|0.030|0.0%|
|answer")||||
|dummy for full in-house (0) and full outsourcing|-0.360|0.106|0.1%|
|Guateng provincial dummy|0.364|0.087|0.0%|
|Manufacturing|-0.240|0.119|4.5%|
|PAYE|0.540|0.140|0.0%|
|Adj. R2|0.293|||
|Mean of the dependent variable|9.568|||

|**Dependent Variable: Sensitivity analysis: LN Total cost of tax**<br>**board)**|**compliance**|**(+10% acros**|**s the**|
|---|---|---|---|
||Coeff.|Std. Error|Sig.|
|(Constant)|6.550|0.378|0.0%|
|LN full-time employees|0.171|0.039|0.0%|
|LN of q55 after imputed values were introduced (imputed "no|0.142|0.030|0.0%|
|answer")||||
|dummy for full in-house (0) and full outsourcing|-0.277|0.106|0.9%|
|Guateng provincial dummy|0.365|0.086|0.0%|
|Manufacturing|-0.240|0.119|4.4%|
|PAYE|0.539|0.140|0.0%|
|Adj. R2|0.285|||
|Mean of the dependent variable|9.498|||
|**Dependent Variable: LN of total cost of tax compliance**||||
||Coeff.|Std. Error|Sig.|
|(Constant)|6.455|0.377|0.0%|
|LN full-time employees|0.171|0.039|0.0%|
|LN of q55 after imputed values were introduced (imputed "no<br>answer")|0.142|0.030|0.0%|

46
# **I. Introduction**

In the 1960s, Africa’s economic growth was similar to South Asia (Collier and Gunning 1999a). However, between 1970 and 2000, the average GDP per capita growth rate was only 0.5 percent per annum, and sub-Saharan Africa is now the poorest continent.<sup>2</sup> In the decade before the recent financial crisis, however, the continent experienced a resurgence in growth. Growth in GDP for the continent averaged 5.9 percent annually (World Economic Forum 2009). Is this trend sustainable? How can Africa keep growing? What are the key policies that facilitate Africa’s economic performance?

This paper sheds light on these questions using micro data. We use recent surveys of the World Bank’s Enterprise Survey for more than 80 countries. We combine these surveys with other cross-country datasets on politics, macro policies, geography and the business environment, to study the determinants of performance of formal manufacturing firms. Our goal is to explore the key factors behind Africa’s disadvantage, if any, relative to other regions.<sup>3</sup> We focus in particular on African firm performance relative to firms in countries from other continents with GDP per capita below 3000 U.S. dollars. We look at a comprehensive set of firm performance outcomes, including static efficiency, dynamic efficiency, export shares, and investment rates.

Our paper adds to the literature explaining Africa’s economic performance (see Collier and Gunning 1999a, 1999b; Bigsten and Soderbom 2006). There are many previous studies examining one aspect of firm performance (such as investment rates, sales growth, exports, or productivity), often using one or several African countries’ firm-level data. However, there is no

> 2 The numbers are based on World Development Indicators of the World Bank.

> 3 In the rest of this paper, we use SSA and Africa interchangeably.  Africa does not include the North Africa region in this paper. It is important to keep in mind that all firms in the World Bank Enterprise Survey that we use consist of formal firms. In a few African countries, informal firms were also surveyed, but the number of countries is too small to merit a full scale cross-country firm-level study.

2
- The offer of government guarantees to cover a specified lifetime minimum real rate of return for accumulated balances at the time of retirement.

- The close monitoring of the marketing and pricing policies of annuity providers to ensure effective protection of pensioners by deterring deceptive practices, preventing excessive fees, promoting sound asset valuations and ensuring a fair distribution of profits.

- The development of efficient electronic quotation systems to lower search costs and minimize the influence of brokers and the creation of central registers with comparative data on the performance and bonus policies of different providers.

- The use of a centralized agency for variable annuities and a decentralized competitive market for fixed real and nominal annuities.

- The specification of a default option, covering both the product and provider, for retiring workers who are unable or unwilling to make a timely decision.

- The further study of the complex issues that arise from granting annuitants the right to switch providers.

10
In the section below, we match these national, regional and sectoral mandated prices with plant-level information to construct plant-specific energy prices for Mexico.

# **3 Data**

Both Indonesia and Mexico maintain some of the most detailed manufacturing plant-level datasets available in developing countries. These include information on expenditure of energy consumed by energy source, investment by type of asset and other key information on production that can be used to estimate productivity. The plants are tracked over time with very high response rates, which makes the panel representative in every year. That is unusual especially for developing economies, for which the scarcity of granular data has constrained the evidence on the impact of environmental policies on firms’ performance.

## **3.1 Indonesian Data**

Plant level data are taken from the Indonesian survey of manufacturing plants with at least 20 employees (Statistik Industri) administered by the Indonesian statistical office (BPS). The coverage of the survey is extensive; in fact it becomes an actual census in 1996 and 2006 and it is very close to a census in the remaining years, hence ensuring high representativeness even at the provincial level. Plants are grouped into 5 digits sectors following the definition Klasifikasi Baku Lapangan Usaha Indonesia (KBLI), a classification mostly compatible with ISIC Rev.3. The KBLI classification has been adjusted to be consistent over the whole sample, ranging from 1998 to 2015. The data also include information on products defined at a more refined level (9-digit Klasifikasi Komoditi Indonesia), which we use in the computation of product level TFPQ as explained below. The plant level data provide information on several variables such as output, capital stock, employment, materials and energy usage (price and quantities) by type of energy.<sup>20</sup>

> 20To maximize the reliability of the data, we dropped an observation from the raw sample when at least one of the variables used in the analysis had an implausible value. For instance, we dropped an observation when a plant reported expenditure on fuel and electricity larger than its revenue.

15
**Table 6A: Relative Mobility and Absolute Mobility Across Urban vs. Rural Areas**

|||**CHINA**|||**INDIA**||**IN**|**DONESI**|**A**<br>|
|---|---|---|---|---|---|---|---|---|---|
||RIGRC|RES|Linear<br>ES|RIGRC|RES|Linear<br>ES|RIGRC|RES|Linear<br>ES|
||||||Urban|||||
|No Sch|0.446|6.411|7.564|0.833|3.545|5.498|0.593|6.053|7.225|
||(0.015)|(0.174)|(0.147)|(0.012)|(0.102)|(0.074)|(0.020)|(0.203)|(0.171)|
|Primary|0.411|8.970|9.700|0.681|7.255|8.314|0.533|9.400|10.033|
||(0.014)|(0.114)|(0.104)|(0.008)|(0.060)|(0.052)|(0.017)|(0.104)|(0.096)|
|Junior High|0.401|10.187|10.768|0.629|10.512|11.130|0.518|10.974|11.436|
||(0.013)|(0.105)|(0.099)|(0.007)|(0.052)|(0.048)|(0.017)|(0.082)|(0.078)|
|Senior High|0.393|11.377|11.836|0.618|11.758|12.256|0.507|12.511|12.840|
||(0.013)|(0.112)|(0.107)|(0.007)|(0.056)|(0.052)|(0.016)|(0.089)|(0.086)|
|College|0.385|12.933|13.260|0.605|13.591|13.946|0.498|14.521|14.712|
||(0.013)|(0.139)|(0.134)|(0.007)|(0.067)|(0.064)|(0.016)|(0.130)|(0.127)|
|**IGRC**|0.356|||0.563|||0.468|||
||(0.013)|||(0.007)|||(0.016)|||
||||||Rural|||||
|No Sch|0.473<br>(0.021)|3.583<br>(0.220)|5.271<br>(0.165)|0.973<br>(0.016)|1.336<br>(0.082)|3.798<br>(0.048)|0.583<br>(0.016)|5.071<br>(0.144)|6.331<br>(0.115)|
|Primary|0.402|6.177|7.195|0.711|5.362|6.741|0.523|8.357|9.134|
||(0.017)|(0.121)|(0.103)|(0.009)|(0.057)|(0.046)|(0.014)|(0.090)|(0.081)|
|Junior High|0.384<br>(0.016)|7.355<br>(0.102)|8.157<br>(0.088)|0.648<br>(0.008)|8.730<br>(0.073)|9.685<br>(0.065)|0.509<br>(0.013)|9.903<br>(0.092)|10.536<br>(0.085)|
|Senior High|0.372|8.488|9.119|0.636|10.013|10.862|0.500|11.416|11.937|
||(0.016)|(0.106)|(0.094)|(0.008)|(0.084)|(0.076)|(0.013)|(0.110)|(0.103)|
|College|0.362|9.588|10.081|0.623|11.901|12.628|0.492|13.398|13.806|
||(0.015)|(0.128)|(0.117)|(0.007)|(0.101)|(0.093)|(0.013)|(0.146)|(0.139)|
|**IGRC**|0.321|||0.589|||0.467|||
||(0.014)|||(0.007)|||(0.012)|||

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia. (2) The RIGRC refers to the Risk Adjusted Intergenerational Regression Coefficient. The RES refers to the Risk Adjusted Expected Schooling. The Linear ES refers to the Expected Schooling from a linear CEF mobility model. And the IGRC is the Intergenerational Regression Coefficient from a linear CEF mobility model.

30