rafmacalaba/datause-encoder-data huggingface dataset:
28

|**Table**|**4. Impacts of speci**<br>Financial sector<br>f|**fic reforms on produ**<br>Fiscal sector<br>f|**ctivity – Conditional factors**<br>Real sector reforms<br>Trade sector<br>f|
|---|---|---|---|
|**Conditional factor: Exp**|reorms<br>**orting firm dumm**|reorms<br>**y**|reorms|
|Reforms|0.376***<br>(0.0223)|0.342***<br>(0.022)|9.670***<br>(0.543)<br>0.266***<br>(0.017)|
|Reforms* Exporting<br>firm dummy|-0.010<br>(0.013)|0.004<br>(0.018)|-0.003<br>(0.019)<br>0.001<br>(0.0192)|
|Exporting firm dummy|0.04367***<br>(0.0157)|0.0484***<br>(0.0180)|0.0493***<br>(0.0160)<br>0.0473***<br>(0.0163)|
|R-squared|<br>0.2361|<br>0.2361|<br> <br>0.2361<br>0.2361|
|**Conditional factor: Fin**|**ancial access**|||
|Reforms|0.505***<br>(0.040)|0.261***<br>(0.0401)|9.678***<br>(0.566)<br>0.220***<br>(0.0342)|
|Reforms* Financial<br>access|**-0.135*****<br>**(0.0414)**|**0.0891*****<br>**(0.0323)**|-0.007<br>(0.04593)<br>0.0514<br>(0.0376)|
|Financial access|0.0492***<br>(0.0163)|0.0484***<br>(0.0162)|0.0491***<br>(0.0164)<br>0.0491***<br>(0.0164)|
|R-squared|0.2368|0.2370|0.2360<br>0.2361|
|**Conditional factor: Dis**|**tortions**|||
|Reforms|0.358***<br>(0.0268)|0.374***<br>(0.0224)|9.635***<br>(0.579)<br>0.271***<br>(0.0193)|
|Reforms* Distortions|0.0133<br>(0.0111)|**-0.0424****<br>**(0.0170)**|-0.0150<br>(0.0219)<br>-0.0101<br>(0.0138)|
|Distortions|0.0377|0.0573|0.0055<br>0.0213|
||(0.0359)|(0.0573)|(0.0301)<br>(0.0529)|
|R-squared|0.2362|0.2372|0.2362<br>0.2361|
|**Conditional factor: Sm**|**all firms**|||
|Reforms|0.341***<br>(0.0334)|0.353***<br>(0.0484)|9.634***<br>(0.529)<br>0.282***<br>(0.055)|
|Reforms* Small firms|**0.0375***<br>**(0.0225)**|-0.0282<br>(0.0417)|0.0267<br>(0.0409)<br>-0.0180<br>(0.0393)|
|Small firms|-0.290***<br>(0.0382)|-0.294***<br>(0.0382)|-0.285***<br>(0.0448)<br>-0.295***<br>(0.0386)|
|R-squared|0.2364|0.2370|0.2361<br>0.2361|
|Observations|10,822|10,822|10,822<br>10,822|
|Control variables|YES|YES|YES<br>YES|
|CountryFE|YES|YES|YES<br>YES|
|Sector FE|YES|YES|YES<br>YES|
|Year FE|YES|YES|YES<br>YES|

Notes: The table presents standardized coefficients of the effects of reforms on firm-level labor productivity growth using a multilevel mixed effects model. All estimates use the weights and are standardized so that can be compared across structural reforms. Robust standard errors clustered at the country level are in parentheses. ***, ** * <u>, denote significance at the 1, 5, and 10 percent level.</u>
Figures 2 and 3 provide some _pnImafacie_ evidence that the distribution of FONCODES expenditures between 1991 and 1995 may have been responsive to electoral influences, something which has been argued elsewhere (Graham and Kane, Kay, Roberts and Arce). However, much of this previous work on the political use of discretionary social expenditures in Peru has some important limitations. First, it frequently does not use a multivariate regression framework to separate political from other effects. To see why this is likely to seriously bias the results, consider FONCODES expenditure patterns after 1993. FONCODES spent more on poor provinces before and after the 1993 referendum, and these poor provinces largely voted against the government in 1993. Were expenditures made by FONCODES after October 1993, then, motivated by electoral considerations, redistributive considerations, or both? Multivariate regression analysis allows for a parsing out of the "political" and "technical" influences on the distribution of FONCODES expenditures. Second, previous studies generally test for a linear effect of elections on the distribution of expenditures (Graham and Kane). This approach does not allow for non-linearities which are at the heart of the marginal voter models, or the dynamic changes in the buy6 back model. 1 Third, earlier work uses department-level (rather than province- or district-level) information on the distribution of FONCODES expenditures and the vote in various elections. This seriously diminishes the sample size and makes it difficult to run meaningful regressions. Even more important, however, in Peru there are often very large differences in the vote _within_ departments. In the department of Amazonas, for example, only 23.2% of the population of the province of Condorcanqui, but 60.0% of the population of neighboring Bongara voted for the government in 1993. More formally, the total variance in the distribution of the pro-government vote can be decomposed into inter-department and intra-department components (for example, Cowell 1995). Such a decomposition shows that less than half of the total variation in the vote in the 1990 election and the 1993 referendum (43.0% and 46.8%, respectively) is accounted for by differences across departments. Since all of this information was published, it is hard to believe that the government would deliberately have chosen not to make use of it. Finally, the results presented by Graham and Kane and others do not acknowledge that estimates of the influence of elections on FONCODES expenditures after 1993 may be contaminated by simultaneity bias. I now turn to this question.

> **16** This is a shortcoming of much of the empirica **l** iterature on political influences on expenditure patterns (i.e. Gershberg 1994). Wright's analysis of geographic patterns in the distribution of discretionary funds during the New Deal is one early and important exception. More recently, Case finds evidence that expenditure patterns in Albania conform to the predictions made in the Lindbeck-Weibull model.

16
Table A.4. Sampled villages according to village and strata rules and actual sample (50 units).

||According to<br>village/strata rule|Sample|
|---|---|---|
|Villages (total)|295|293|
|Villages, average in CA|6|6|
|Villages in strata 1, total|64|70|
|Villages in strata 1, average in CA|1|2|
|Villages in strata 3, total|117|121|
|Villages in strata 3, average in CA|2|3|
|Villages in strata 5, total|114|102|
|Villages in strata 5, average in CA|2|2|

Source: UBOS maps and census data.

Table A.5. Total number of households, villages and enumeration areas in actual sample

||Total|Within 1 km<br>radius|Within 3 km radius<br>excl. those within<br>the 1 km radius|Within 5 km radius<br>excl. those within<br>the 3 km radius|
|---|---|---|---|---|
|Households|29,405|7,696|11,653|10,056|
|Villages|293|70|121|102|
|Enumeration areas|242||||

Table A.6. Village characteristics of sample.

||Mean|Median|Min|Max|
|---|---|---|---|---|
|Number of households in village|102|92|22|232|
|Distance to facility|3.2|3|1|5|

35
# **Table 4. Main commodity flows in the South Caucasus, 1999 (Contd.)**

|_(The cov_<br>_Exporting_<br>_countries_|_erage of the re_<br>|_levant trade flow,_<br>_Imp_<br>|_in percent, i_<br>_orting count_<br>|_s presen_<br>_ries_|_ted in pare_<br>|_nthesis)_<br>|
|---|---|---|---|---|---|---|
||Armenia|Azerbaijan|Georgia|Iran|Russia|Turkey|
||Cigarettes,<br>oil pro-<br>ducts,<br>processed<br>food,<br>wheat,<br>chemicals<br>(30 %)|Machinery and<br>mechanisms,<br>processed food<br>(53 %)|Processed<br>food,<br>chemicals,<br>apparel<br>(49 %)|N/A|N/A|N/A|
|Turkey|I|I_I||I|<br>|I|

_Sources: National Statistical Agencies of Armenia, Azerbaian, Georgia, and Russia. N/A: Not applicable for the purposes of this report._ The table shows that the South Caucasus countries export primarily energy, mineral resources, and semi-finished products. Armenia exports copper and molybdenum concentrate. Azerbaijan exports oil and gas. The scrap of ferrous and non-ferrous metals, released in the process of the physical dismantling of the Soviet industrial infrastructure, is among the most important exports. Some ex-Soviet industries continue to operate in the region, including Armenia's chemical industry, Georgia's metallurgy, and Azerbaijan's oil refinery; the outputs of these industries enter regional trade. Agricultural and forest products (such as animal skins in Armenia, wood and sunflower seed in Georgia) are also traded. In the last decade, the exports of agricultural products have decreased, and the exports of industrial products (especially, light industry and machinery) have collapsed, along with the collapse of regional industry. Despite transport disadvantages, products with low value-to-weight ratio continue to dominate the exports of the South Caucasus countries.

The South Caucasus countries import energy, cereals (a lot of which are offered as humanitarian aid), processed food, consumer goods, and machinery.

# **2.4. Geography of Trade and Transport Costs**

This section describes the prevailing trade routes between the three South Caucasus countries and their main trading partners. It also looks at associated transport and handling costs. Energy trade is relegated to the dedicated chapter. Table 5 presents various transport costs within and outside the region.

13
between the two periods. While for Costa Rica and El Salvador the additional emissions seem to come mainly from dirty industries, both type of industries played a role in the expansion of emissions for Nicaragua and Honduras. A more detailed analysis is necessary to assess the extent to which the variation in pollution relates to changes in composition and to control for underlying trends in emissions.

**Figure 1. Average pollution per year, before and after DR-CAFTA negotiation (pounds)**

<!-- Start of picture text --> CRI SVL<br>40000000 80000000<br>35000000 70000000<br>30000000 60000000<br>25000000 50000000<br>20000000 40000000<br>15000000 30000000<br>10000000 20000000<br>5000000 10000000<br>0 0<br>before after before after before after before after before after before after<br>air water total air water total<br>clean dirty clean dirty<br>NIC HND<br>35000000 18000000<br>16000000<br>30000000<br>14000000<br>25000000<br>12000000<br>20000000 10000000<br>15000000 8000000<br>6000000<br>10000000<br>4000000<br>5000000<br>2000000<br>0 0<br>before after before after before after before after before after before after<br>air water total air water total<br>clean dirty clean dirty<br><!-- End of picture text --> _Source_ : IPPS and authors calculations Figure 2 presents the results of the decomposition analysis under the baseline scenario for total emissions<sup>11</sup> (results for air, water and metal pollution can be found in ~~the appendixA~~ <u>nnex 2). The first</u> striking fact of the analysis is the importance of scale effects. More than 90 percent of pollution variation results from a scaling up of production. This is consistent across all countries in the sample. Composition effects not only are smaller, but vary significantly across countries. For Costa Rica and El Salvador, composition effects further expand pollution, while for the remaining countries (including the regional

> 11 The results for air, water, and metal pollution are available on request from the authors.

15
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Ministry of Communication and Works, Public Works Department, 2015. Consultancy Services for A Feasibility Study for a Tramway System in Nicosia - Final Feasibility Study Report. Nicosia, Cyprus.

Neofytou, H., Nikas, A., Doukas, H., 2020. Sustainable energy transition readiness: A multicriteria assessment index. _Renewable and Sustainable Energy Reviews_ 131, 109988. <u>https://doi.org/10.1016/j.rser.2020.109988</u> Papathanasiou, J., Ploskas, N., 2018. _Multiple Criteria Decision Aid: Methods, Examples and Python Implementations_ . Springer International Publishing, Cham. <u>https://doi.org/10.1007/978-3-31991648-4</u> Pollitt, H., 2020. <u>Assessment of Green Recovery Plans After COVID-19. Cambridge Econometrics,</u> Cambridge, October.

Popp, D., Vona, F., Marin, G., Chen, Z., 2020. The Employment Impact of Green Fiscal Push: Evidence <u>from the American Recovery Act. Working Paper 27321, National Bureau of Economic Research,</u> Cambridge, MA, June.

Republic of Cyprus, 2020. Cyprus’ Integrated National Energy and Climate Plan, Nicosia, January.

Saaty, T.L., Sodenkamp, M., 2010. The Analytic Hierarchy and Analytic Network Measurement Processes: The Measurement of Intangibles, in: Zopounidis, C., Pardalos, P.M. (Eds.), _Handbook of Multicriteria Analysis_ , Applied Optimization. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 91– 166. https://doi.org/10.1007/978-3-540-92828-7_4 Seddiki, M. and Bennadji, A. (2019) ‘Multi-criteria evaluation of renewable energy alternatives for electricity generation in a residential building’, _Renewable and Sustainable Energy Reviews_ , 110, pp. 101–117. https://doi.org/10.1016/j.rser.2019.04.046.

Simoes, S. G. _et al._ (2019) ‘InSmart – A methodology for combining modelling with stakeholder input towards EU cities decarbonisation’, _Journal of Cleaner Production_ , 231, pp. 428–445. <u>https://doi.org/10.1016/j.jclepro.2019.05.143.</u> Sotiriou C. and Zachariadis T., Optimal Timing of Greenhouse Gas Emissions Abatement in Europe. _Energies_ 2019, _12_ (10), 1872.

Stern N., Bhattacharya A. and Rydge J. (2020) <u>Better Recovery, Better World: Resetting climate action in the aftermath of the COVID-19 pandemic. The Coalition of Finance Ministers for Climate Action, July</u> 2020.

Strand J. and Toman M. (2010), <u>Green stimulus, economic recovery, and long-term sustainable development. Policy Research Working Paper No. 5163. World Bank, Washington, DC</u>

- Taliotis C., Giannakis E., Karmellos M., Fylaktos N. and Zachariadis T. (2020a), <u>Estimating the economy wide impacts of energy policies in Cyprus.</u> _Energy Strategy Reviews_ 29, 100495.

Taliotis, C., Fylaktos, N., Partasides, G., Gardumi, F., Sridharan, V., Karmellos, M., Papanicolas, C.N., (2020b), The Effect of Electric Vehicle Deployment on Renewable Electricity Generation in an Isolated Grid System: The Case Study of Cyprus. _Front. Energy Res._ 8. <u>https://doi.org/10.3389/fenrg.2020.00205</u> UN (United Nations), ‘Shared Responsibility, Global Solidarity: Responding to the Socio-economic <u>Impacts of COVID-19’, March 2020.</u> 29
in the probability of staying minus the probability of leaving. To characterize this, we do not need the flow data between non-conflict locations or inflows to conflict locations, which may not be captured in the absence of a humanitarian situation. However the number of internally displaced people, i.e. flows out of conflict regions, is often well documented by the international organizations to program assistance.

Note that the calculated utility change in Proposition 1 is an upper bound rather than a precise point estimate because it is missing the term<sup>�</sup> _l∈_ Φ<sup>_φl_</sup> �∆ _Ut_<sup>_l−_∆</sup><sup>_C_</sup> _t_<sup>_kl_</sup> � from equation (3), which accounts for the average utility loss in all regions weighted by _φl_ , net of the changes in moving costs. As long as this term is non-positive, Proposition 1 will hold.

The next proposition extends this result to changes in welfare.

**Proposition 2** _The upper bound of change in welfare in conflict location k (i.e., the lower bound of the welfare impact) can be expressed as_

_if the condition_<sup>�</sup> _l∈_ Φ<sup>_φl_∆</sup> � _Ut_<sup>_l−C_</sup> _t_<sup>_kl_</sup> � _≤_ 0 _holds, where φl ≥_ 0 _are weights for destinations such that_<sup>�</sup> _l∈_ Φ<sup>_φl_= 1</sup><sup>_._</sup> Similar to the previous proposition, Proposition 2 only needs the flow of agents out of conflict locations and the migration elasticity parameter to calculate the upper bound of welfare changes. The expression in Proposition 2 calculates the upper bounds of welfare change (lower bound of the welfare impact of the conflict) rather than the location-specific utility change, which takes the possibility of moving to other locations into account. If agents are not likely to stay in the conflict region after the conflict, then the impact on their expected welfare will be smaller. Therefore, the change in expected welfare is a better measure of the impact of conflict on people compared to the location-specific fixed utility.

# **3 Case Study: The Conflict in Eastern Ukraine**

In this section, we take our model to data using the conflict in Eastern Ukraine between 2014 and 2019. A few characteristics of this conflict make such an application feasible. First, interregional migration patterns were documented in detail before the conflict. Second, the conflict was regionally contained for the period covered in this analysis and the war in 2022 (3 years after the last data in our analysis) was not foreseen. Third, with a relatively low-intensity conflict, demographic mobility remained feasible throughout the period of 8
26

- digging is equal to the difference in wage rates, f,-fl_, (l-r)W, and it is readily verified that the surplus maximizing value of s is a strictly increasing function of W.

7. This is also observed between sub-perioos I and 2, though this may partly reflect seasonality; we shall model seasonality explicitly in the next section.

8. From April 1987 to February 1990 the two variables are very highly correlated (r-.996 in levels, .989 in logs).

9. Data on labor attendance on EGS and the EGS time wage were obtained from the Planning Department, Government of Maharashtra.

10. Foodgrain output data disaggregated by kharif and rabi seasons are available only up to 1987-88. Since we are particularly interested in seeing how the model predicts labor attendances after mid-1988, we are unable to use the seasonally disaggregated data on foodgrain output.

11. Ideally, we would also like to include variables for the non-foodgrain output. We are however constrained by the non-availability of data for the last two tears of our sample period. Foodgrains nevertheless still account for about 70 per cent of the gross cropped area in Maharashtra (GOM 1987, 1989).

12. The fact that we do not have a monthly series on the EGS wage rate means that there is measurement error in this variable, which will bias OLS estimates. We also estimated a generalized instrumental variables estimator, in which the EGS wage was not used as its own instrument. The instrumental variables included the average EGS wage rate of the previous year and the rank of the current year's EGS wage, as well as all other RHS variables. The IV estimator gave very similar results to Table 3, though with a slightly lower wage elasticity (0.15 rather than 0.20). In view of this, and the fact that we gain 11 degrees of freedom using the OLS estimator, we decided to stay with the latter.

13. The model parameters also contain a dummy variable for December 1977. We believe there is an error in the data for this month. Excluding that variable had little effect on other parameters, though it did yield significant nonnormality of residuals, as indicated by the LM test.

14. As evident in Figure 1, the actual increase in the wage rate was staggered over June to September 1988, so starting the post-sample forecasting in July (rather than June) is reasonable. This also coincides with the beginning of the new crop-year.

15. 1988 was not, of course, the first instance when piece rates on EGS increased, though it was the largest increase. However, in the earlier cases we do not find any evidence of structural instability, as indicated by the evolution of recursive residuals since December 1978. Both the CUMSUM and CUMSUMSQ statistics (Brown et al., 1975) are well within the 95% confidence intervals.
40 Finally, job search assistance and employment services currently favored by many African governments can indeed help match jobs and job seekers. They are usually inexpensive and often help reduce the length of unemployment. But they typically benefit only a small number of active job seekers. Promoting access to the information and communications technologies (ICT) sector could help remove supply and demand-side constraints in the labor market. Recent sociopolitical developments across the Arab world have demonstrated that the youth are the biggest consumers of technology, which they use to enhance their skills, seize opportunities, and connect to a global audience. Well-targeted policy measures to provide disadvantaged groups with access to ICT (IT alphabetization, investments to create ICT communal areas for rural youth and women, etc.) could foster the emergence of new economic activities and generate employment.

# **4. Conclusion**

The multidimensional benefits of employment for all human societies have always been well understood by researchers and policy makers and are now empirically documented (World Bank 2012). Economists have long attempted to design theoretical and policy frameworks aimed at optimal labor market conditions. But too often these intellectual ventures have focused on unemployment—a poor predictor of growth and economic performance—and have been tailored to suit the structure of advanced economies. While there has been a rich and vibrant economic literature on unemployment in developing countries, especially since the 1970s and 1980s, the models derived from it have not resulted in actionable policy recommendations that yield satisfactory results. Often designed for high-income countries where the labor market is relatively homogeneous, they have tended to focus on generic business environment issues and labor market institutions while strategies and policies to raise the demand for workers in competitive industries were neglected on the rationale that they would imply activist and inefficient industrial policies. Yet almost all economies that have succeeded in moving from low to high-income countries—especially the East Asian countries—have also proactively addressed the issue of employment creation by competitive private firms.
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