rafmacalaba/datause-encoder-data huggingface dataset:
demand; (ii) modeling electricity production; (iii) modeling electricity imports and (iv) modeling capacity constraints. Electricity demand is the sum of final and intermediate demand. The previous section described the general sectoral setup, which allows us to also model each sector’s demand for electricity. Final consumption is split between electricity and other types of consumption using a CES nest – i.e., electricity demand for final consumption depends on the elasticity of substitution, while intermediate electricity demand depends on fixed shares derived from an IO table. Electricity production is the sum of electricity value-added and intermediate supply. Value-added should be derived from labor and capital, or OPEX and CAPEX. Unfortunately, capital and labor data for the electricity sector in most countries are not available (even if value-added is). The production of electricity thus takes the same form as the modeling of the other sectors as in the previous section. This is an obvious shortcoming since making sector specific investment choices are not that easy in this setup and keeping track of different capital vintages by electricity production unit is not feasible (i.e., modeling stranded assets is not straightforward). In the model, excess demand is imported up until the point capacity is met. I.e., capacity is a constraining factor and is assumed to be exogenous. These assumptions are relaxed in the soft-linking approach in the next section. The soft-linking approach tracks CAPEX and OPEX costs explicitly and hence also capital vintages.

Aggregate economic output is produced using capital ( _K_ ), labor ( _N_ ) and electricity ( _Q_<sup>�</sup><sup>_E_</sup> _t_<sup>).Theunitsforcapitalareinlocalcurrencyunits(LCU),foremploymentweusethe</sup> number of people employed and for electricity we use the amount of _kWh_ available for supply (installed capacity). In the modeling it is assumed that electricity generation and capital are complements. This means that disruptions in electricity supply have a direct and indirect effect on output. The aggregate production function (or potential throughput, not GDP because we are adding energy) is written as a CES function between capital and electricity and as a Cobb-Douglas technology for capital and energy with labor (see Allcott et al. (2016) and Fried and Lagakos (2023) for examples): Using the aggregate resource constraint and the first-order conditions, marginal costs are now an increasing function of wage costs ( _W_ ), rental costs ( _R_ ) and electricity costs ( _P_<sup>_E_</sup> ): 8
# 4. Results

## 4.1 Infrastructure availability

Infrastructure availability—defined as the availability of an improved water source, improved toilet, and electricity—varies from a country average of 77.2% in Kenya to 21.0% in Niger. Infrastructure availability is significantly higher in urban areas, driven partly by the higher rates of electricity availability. Infrastructure availability is also significantly higher at private facilities than at public facilities across all countries. While sample sizes for higher-level facilities such as hospitals are smaller than for lower-level, availability of basic infrastructure is generally highest at hospitals and lowest at health posts across countries. **Table 2** shows results for overall infrastructure availability. More detailed breakdowns of the components of infrastructure availability are presented in **Appendix Table A7** . Overall infrastructure availability does not appear to increase in lockstep with country GDP per capita. While three-quarters of overall variance (74.0%) is attributable to within-country differences, only one-quarter is attributable to between-country differences. Results of variance decompositions for all variables are reported in **Appendix Table A8** .

**Figure 1** shows infrastructure availability by country and the rural/urban differentials. In all countries, urban facilities have much greater infrastructure availability, but this gap varies across the sample. Niger has the greatest gap with 84.6% of urban facilities having basic infrastructure, compared to 17.6% of rural facilities. **Figure 2** highlights which components are lacking in facilities and when multiple components are missing. In Mozambique, 38.9% of facilities are missing a single component and 9.3% of facilities are missing multiple components, whereas in Niger, 24.8% of facilities are missing a single component and 57.6% of facilities are missing multiple components. Similarly, the SDI reveals that lack of access to electricity is a problem across all countries, whereas lack of improved toilet and improved water are more common in Nigeria and Tanzania, respectively.

_Figure 1: Availability of basic infrastructure (improved water, improved sanitation, electricity) in public facilities by urban/rural and country, ordered by increasing GDPpc_ _Figure 2: Breakdown of infrastructure availability (improved water source, improved sanitation, electricity) in public facilities by country, ordered by increasing GDPpc_ 8
- L ̈utkepohl, H. (1982). Non-causality due to omitted variables. _Journal of Econometrics_ , 19:267–378.

- Maddala, G. S. and Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. _Oxford Bulletin of Economics and Statistics_ , 61:631–652.

- Mark, N. C. and Sul, D. (2003). Cointegration vector estimation by panel dols and long-run money demand. _Oxford Bulletin of Economics and Statistics_ , 65:655–680.

- Ozturk, I. (2010). A literature survey on energy-growth nexus. _Energy Policy_ , 38:340–349.

- Pedroni, P. (1999). Critical values for cointegration tests in heterogenous panels with multiple regressors. _Oxford Bulletin of Economics and Statistics_ , 61:653–670.

- Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application hypothesis. _Econometric Theory_ , 20:597–625.

- Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross section dependence. _Journal of Applied Econometrics_ , 22:265–312.

- Saikkonen, P. (1991). Asymptotically efficient estimation of cointegrating regressions. _Econometric Theory_ , 7:1–21.

- Stock, J. H. and Watson, M. W. (1993). A simple estimator of cointegrating vectors in higher order integrated systems. _Econometrica_ , 61:783–820.

- Wagner, M. and Hlouskova, J. (2010). The performance of panel cointegration methods: Results from a large scale simulation study. _Econometric Reviews_ , 29(2):182–223.

- Worrell, E., Laitner, J., Ruth, M., and Finman, H. (2003). Productivity benefits of industrial energy efficiency measures. _Energy_ , 28:1081–1098.

- Zachariadis, T. (2007). Exploring the relationship between energy use and economic growth with bivariate models: New evidence from g-7 countries. _Energy Economics_ , 29(6):1233– 1253.

- Zhang, F. (2013). The energy transition of the transition economies: An empirical analysis. _Energy Economics_ , 40:679–686.

13
financed separately). As in Chile, workers have a choice<sup>amonginvestmentmanagers,</sup> known as AFORES in Mexico. Retirement income is<sup>fuirtheraugmentedby a5%</sup> contribution of each worker's wage to a housing<sup>fund,INFONAVIT.If the money in the</sup> account is not borrowed to finance the purchase<sup>ofahome,itbecomespartof the</sup> worker's retirement assets.<sup>6</sup> Upon retirement at age<sup>65for both genders,married workers</sup> have a choice between a gradual withdrawal spread<sup>overboth spouse'slifetimesand a</sup> joint annuity.

The state contributes toward the finances of this system<sup>in threeways: First,it</sup> pays a flat "social quota" (SQ) equal to 5.5% of one<sup>dailyminimumwage toeach</sup> account for each day of work. The SQ is price-indexed<sup>(asistheminimumwage)but</sup> initially it was 2.2% of the average wage, a percentage<sup>thatwilldeclineaswagesrise</sup> faster than prices over time. Adding the SQ to the<sup>worker's6.5%contributionbrings the</sup> total gross contribution of the average-wage worker to<sup>8.7%andthe netcontribution,</sup> after subtracting administrative expenses, to 6.8% (plus<sup>somepartof INFONAVIT).The</sup> SQ is designed to increase the accounts of low-income<sup>workersandtheincentivefor</sup> informal sector workers to join the system. It is<sup>financed out of generalrevenues.Second,</sup> workers are guaranteed a minimum pension, initially<sup>equalto the minimumwageor 40%</sup> of the average wage, indexed to inflation, providing<sup>they had25yearsof contributions.</sup> Third, although affiliation to the new system is mandatory<sup>inMexico,workersin the</sup> labor force at the date of the reform were guaranteed<sup>therighttoopt backintothe old</sup> system upon retirement. In this paper we focus<sup>on new workerswhoarenotentitledto</sup> this opt-back provision.

Table 1 presents a brief summary of the new and old<sup>systemsinthethree</sup> countries and Table 2 presents some key data on wages,<sup>employmentandretirement</sup> income.

# MehonodoROY

Analysis of how women fare relative to men in the new<sup>andoldsocialsecurity</sup> systems is made difficult by a number of factors.<sup>First,thenewsystemhas notbeenin</sup> effect long enough to be mature. That is, current<sup>retireesin Chile andArgentinaare</sup> subject to a mixture of old and new system benefits (the<sup>fonner in the formof recognition</sup> bonds and compensatory pensions) and we don't know<sup>for sure how someone willfarein</sup> 11
**27**

significant.<sup>26</sup> In this context it is especially worth highlighting how sectoral growth affects the poorest differently in the medium and low-income countries (as measured by changes in the SPG). While both agricultural and non-agricultural growth offer scope for extreme poverty reduction in the middle-income countries, it is only agricultural growth that appears to affect the poorest in the low-income countries. This may suggest that the poorest groups in the middle-income countries are more likely to rely also on non-agricultural activities—possibly because extreme poverty is associated with landlessness and concentrated in urban areas as in many Latin American countries which make up more than 40 percent of our middle-income group. This result also resonates somewhat with Bravo-Ortega and Lederman’s (2005) finding that growth in agricultural output per worker was slightly less effective as growth in non-agricultural output per worker in raising the incomes of the poorest quintile, at least where it concerns the middle-income countries.

Finally we consider the sectoral impacts in Sub-Saharan African (SSA) countries. Of the 111 observations in our low-income sample, 36 are from SSA. It is not clear, therefore, that the findings for the low-income group would necessarily apply to SSA.<sup>27</sup> It would not be appropriate to estimate the poverty regressions separately for SSA, given the small sample. Our approach is to apply an SSA interaction term to the right-hand-side variables in the low-income country data. The results are presented in panel E of Table 6. As in case of the low-income countries, we find that only agricultural growth affects poverty reduction, while the estimated coefficients on nonagricultural growth are not statistically different from zero, supporting the critical role of agricultural growth in poverty reduction in SSA. None of the SSA interactive terms is statistically significant, indicating that the relationship between sectoral growth and poverty in low-income countries also applies to the Sub-Saharan-Africa group of countries covered in our sample.

> 26   The non-significance of non-agricultural growth for poverty reduction in low-income countries (and the positive sign on some of the estimated coefficients) might be the result of counteracting effects of growth within nonagriculture.  Indeed, re-estimation of equation 6 using further disaggregated measures of non-agriculture into industrial and service growth, indicates that industrial growth is positively associated with poverty changes—it increases poverty, while service growth is negatively associated.  Yet, consistent with the results reported here, only the coefficient on agricultural growth is statistically significant and the coefficients on industrial and service growth are neither jointly nor individually statistically significant.  This holds across all poverty measures for the low-income countries.  Results are available upon request from the authors.

> 27  Two countries—China and India— between them contribute 30 observations to the low-income country sample.
- **Speckhard, Anne and Molly Ellenberg** , “ISIS in Their Own Words,” _Journal of Strategic Security_ , 2020, _13_ (1), 82–127.

- **UNEP-WCMC, UN Environment Programme World Conservation Monitoring Centre** , “Mountains and Treecover in Mountain Regions 2002,” `https://bit.ly/3FQmIUp` 2002. Accessed: 2021-04-28.

- **United States Senate** , “Senate Hearing. 114-438 - Jihad 2.0: Social Media in the Next Evolution of Terrorist Recruitment,” 2015.

- **Warren, Camber** , “Explosive Connections? Mass Media, Social Media, and the Geography of Collective Violence in African States,” _Journal of Peace Research_ , 2015, _52_ (3), 297–311.

- **Zelin, Aaron** , “Tunisian Foreign Fighters in Iraq and Syria,” _Policy Notes: The Washington Institute for Near East Policy_ , 2018, (55).

- , “Not Gonna Be Able To Do It: al-Qaeda in Tunisia’s Inability to Take Advantage of the Islamic State’s Setbacks.,” _Perspectives on Terrorism_ , 2019, _13_ (1).

- , _Your Sons are at Your Service: Tunisia’s Missionaries of Jihad_ , Columbia University Press, 2020.

- **Zhuravskaya, Ekaterina, Maria Petrova, and Ruben Enikolopov** , “Political Effects of the Internet and Social Media,” _Annual Review of Economics_ , 2020, _12_ (1), 415–438.

23
WPS 3253

# **FIRMS, JOBS AND EMPLOYMENT IN MOLDOVA**

# **Jan Rutkowski***

## World Bank Policy Research Working Paper 3253, March 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*  This paper was prepared for the Moldova Poverty Assessment study.  The author is grateful to Reema Nayar and Gordon Betcherman for useful comments and suggestions.
a new category that has not been emphasized in the literature, but recent papers find it important for explaining firm performance across the world (Clarke, Li and Xu 2017). It incorporates whether or not a firm is located in a large city, the extent to which a firm is surrounded by “capable firms”, measured as the share of firms whose number of employees exceeds 50 (capacity agglomeration), and whether the firm competes with firms in the informal sector (informal competition).

When looking at air pollution, we focus on measures of particulate matter. Even though many pollutants contribute to contaminate the air, particulate matter is the one that affects human health the most. In particular, small particles, those with a diameter of 2.5 microns or less, are the most dangerous because they can travel in the blood stream and sit in the lungs, producing long lasting health consequences.<sup>7</sup> We use remote sensing data (van Donkelaar, 2016) and assign pollution measures to each particular firm.

Finally, in order to measure firm performance, we use the same two variables as Reyes et al. (2017): employment growth and labor productivity growth. Employment growth corresponds to the one-year employment change, and labor productivity is measured as sales over the number of permanent employees.

Our findings can be summarized in two main points. Frist, among the business environment variables, capacity agglomeration is the only variable that shows a different effect for African firms. Capacity agglomeration (the share of firms with at least 50 employees) has a stronger positive effect in this region, increasing labor productivity growth. Second, concentration levels of PM 2.5 also have a distinct effect for African firms. The relationship between pollution and firm performance goes through three stages: negative, positive, and negative again (cubic relationship between PM 2.5 and labor productivity growth). Although, as expected, we find a range of pollution levels where the correlation with productivity is positive, it is interesting to see that pollution effects turn negative at considerably low levels of PM 2.5 (approximately 30 μ ). /mm<sup>3</sup>

> 7 Deaths attributable to ambient PM2.5 increased from 3.5 million in 1990 to 4.2 million in 2015. Ambient PM2.5 was the fifth-ranking mortality risk factor in 2015 (Cohen, et al. 2017).

5
# **Table 4**

**Composition of EU15 manufactures imports from LDCs, 2001**

||total EU15<br>imports<br>‘000US$ im|free<br>ports (no<br>duty<br>applied)<br>‘000US$|free<br>import<br>share of<br>category|share of<br>total<br>manu-<br>factures<br>imports<br>|EU MFN<br>tariffs,<br>weighted<br>by LDC<br>trade<br>M|aximum<br>tariff|
|---|---|---|---|---|---|---|
|Textiles|1,838,393|39,012|2.1|32.1|11.5|12.6|
|Clothing|1,977,100|164|0.0|34.6|12.2|12.6|
|Leather|325,254|38|0.0|5.7|7.4|17.0|
|Wood products|104,170|24,665|23.7|1.8|1.7|10.0|
|Paper products|6,926|2,709|39.1|0.1|1.7|9.8|
|Petroleum and coal products|27,544|0|0.0|0.5|3.9|4.7|
|Chemicals, rubber, plastics|84,751|29,399|34.7|1.5|3.7|12.8|
|Non-metalic minerals|21,430|176|0.8|0.4|9.7|12.0|
|Iron and steel|6,364|4,218|66.3|0.1|0.7|5.7|
|Non-ferrous metals|640,473|251,514|39.3|11.2|3.5|10.0|
|Fabricated metal products|7,671|2,166|28.2|0.1|2.3|8.5|
|Motor vehicles and parts|3,120|143|4.6|0.1|6.8|22.0|
|Other transport equipment|587,053|78|0.0|10.3|1.4|15.0|
|Electrical machinery|16,766|15,003|89.5|0.3|0.6|14.0|
|Other machinery|41,592|9,546|23.0|0.7|1.6|8.0|
|Other manufactures|32,025|15,258|47.6|0.6|1.4|9.0|
|TOTAL|5,720,632|394,087|6.9|100.0|9.0|22.0|

source: WTO integrated database 29
and Frank von Sonnenburg. 1999. "Reductions in risk behaviour provide the most consistent explanation for declining HIV-1 prevalence in Uganda." AIDS 13: 391—98.

Kinsman J., J. Nakiyingi, A. Kamali, L. Carpenter, M. Quigley, R. Pool, and J. Whitworth J. 2001. "Evaluation of a comprehensive school-based AIDS education programme in rural Masaka, Uganda." Health Education Research 16 (1): 85—100.

Kremer, Michael. 2001."Creating Markets for New Vaccines, Part I: Rationale and Part II: Design Issues." In Adam B. Jaffe, Josh Lerner, and Scott Stern, eds., Innovation Policy and the Economy. Cambridge. Mass.: MIT Press, Volume 1.

Lleras-Muney, Adriana. 2004. "The Relationship Between Education and Mortality in the United States." Forthcoming, Review of Economic Studies.

Lundberg, Mattias, and Mead Over. 2000. "Transfers and Household Welfare in Kagera." Development Research Group, World Bank, Washington, D.C. Processed.

Mbulaiteye, S. M., A. Ruberantwari, J. S.Nakiyingi, L. M. Carpenter, A. Kamali and J. A. G. Whitworth. 2000. "Alcohol and HIV: a study among sexually active adults in rural southwest Uganda." International Journal of Epidemiology 29: 911—15.

Mbulaiteye S.M., C. Mahe, J.A.G. Whitworth, A. Ruberantwari, J.S. Nakiyingi, A. Ojwiya, and A. Kamali. 2002a. "Declining HIV-1 incidence and associated prevalence over a 10-year period in a rural population cohort in south-west Uganda." The Lancet 360: 41—46.

Mbulaiteye S.M., C. Mahe, A. Ruberantwari, and J.A.G. Whitworth. 2002b. "Generalizability of population-based studies on AIDS: a comparison of newly and continuously surveyed villages in rural southwest Uganda." International Journal of Epidemiology 31: 961—67.

Meyer, Bruce D. 1990. "Unemployment Insurance and Unemployment Spells." Econometrica 58 (4): 757—82.

Morgan, Dilys, and Jimmy A. G. Whitworth. 2001. "The natural history of HIV-1 infection in Africa." Nature Medicine 7(2): 143—45.

36
# **III. Data and quality measures**

# **_i. Robot data_**

Data on the use of robots across countries and industries are provided by the International Federation of Robotics (IFR), the same data used by Graetz and Michaels (2018), Acemoglu and Restrepo (2020). IFR provides information on annual shipments (sales) and a measure of industrial robot stock across roughly 100 countries and manufacturing industries.<sup>9</sup> Their definition of industrial robots comes from the International Organization for Standardization (ISO) 8373:2012. From this definition, a robot refers to a machine that embodies the following characteristics: can be reprogrammed, is multipurpose in function, allows for physical alteration, and is mounted on three or more axes.

IFR constructs this data set by consolidating information on industrial robot sales from almost every industrial robot supplier in the world. From the data we know the number of robots that are being sold to manufacturing sectors in countries over time. In addition, the IFR data provides information on the robot types (by application such as handling, processing, welding, assembling and so on), which is only available at the country level. One limitation of the data is that robot sales and stock are denominated in units, and thus information on the value of the assets is not included. Following Graetz and Michaels (2018), we construct robot stock at the country-industry level by taking the initial stock starting value from the IFR, then adding to this robot sales from subsequent years and assume annual depreciation of 10%.

One of the limitations of our study is that our robotics data is not comparable between developed and developing countries before 2000, which constrains our sample period to 2000-2015 (IFR 2018). Furthermore, robots are measured at the industry-country-year level therefore we cannot assess differences in the use of robots across establishments within an industry. However, one key

> 9 Note our final sample of countries is 59, after dropping jurisdictions with missing robot information, those who are not represented in the trade data and those lacking data on industrial employment, value added and number of establishments over the sample period.  We also exclude Canada and Mexico as robots for these countries are jointly recorded with the United States for the earlier period.

11