rafmacalaba/datause-encoder-data huggingface dataset:
# **Charting the Diffusion of Power Sector Reforms across the Developing World** **_Vivien Foster, Samantha Witte, Sudeshna Ghosh Banerjee, and Alejandro Moreno_**

# **Acknowledgements**

This paper is primarily based on data collected as part of the World Bank/ESMAP project “Regulatory Indicators for Sustainable Energy (RISE), 2017” co‐led by Sudeshna Ghosh Banerjee, Alejandro Moreno, and Tanya Primiani, and implemented by Mallory LeeWong, Joonkyung Seong, Taylor Sloane, and Samantha Witte. A full list of acknowledgements for this project can be found at http://rise.esmap.org. The study also draws on the World Bank’s Private Participation in Infrastructure database: <u>https://ppi.worldbank.org. These data were supplemented by targeted desk‐based research and</u> crowdsourcing of information from numerous colleagues in the World Bank’s Energy & Extractives Global Practice. Helpful guidance on the fragility index came from Monty Marshall (Center for Systemic Peace).

The authors are particularly grateful to the following colleagues for peer review throughout the development of the paper: Robert Bacon (Consultant); Anton Eberhard (University of Cape Town); Marianne Fay (Chief Economist, SDNVP); Catrina Godinho (University of Cape Town); Stephane Straub (University of Toulouse); and Jonathan Walters (Castalia). Financial support from Energy Sector Management Assistance Program (ESMAP) and the Public Private Infrastructure Facility (PPIAF) is gratefully acknowledged.

**Keywords:** Power Sector Reform, Power Sector Market Design and Regulation, Power Sector Restructuring, Power Sector Regulation, Power Utility Reform

**JEL Classification** : L94 Electric Utilities

1
**Table 9. Parameters of selected log-linear models. Youths from 18-35 years of age of ENCEL 2017.**

|(a)<br>Model 10.<br>diagonal|Corners (4p)|with diverse|(b)<br>Model 11. Cor<br>diagonal.|ners (5p)|with diverse|
|---|---|---|---|---|---|
|**Parameter**|OR|Value of P|**Parameter**|OR|Value of P|
|dp1<br>(NM inheritance)|2.61|<0.001|dp1<br>(NM inheritance)|2.27|0.003|
|dp23|||dp23|||
|(HM, LMM, LMS|<br>1.80|<0.001|(HM, LMM, LMS|1.80|<0.001|
|inheritance)|||inheritance)|||
|dp6|||dp6|||
|(Agriculture|2.74|<0.001|(Agriculture|2.92|<0.001|
|inheritance)|||inheritance)|||
|esq|1.21|0.019|esq1|0.68|0.174|
||||esq2|1.32|0.002|

Source: Own elaboration from ENCEL 2017 (n=3,084).

The results of the log-multiplicative version of the models 2, 4, 8 and 9, which present values of the φ coefficient different from 1 (between 0.74 and 0.79) for these theoretical models, provide additional elements to define whether or not there are differences between both sexes. This leads us to conclude that there is a difference in relative mobility between females and males, although it is not very strong. Females, in comparison with males, experience greater occupational fluidity or equality in occupational opportunities. The four mentioned models propose that relative mobility of youths is characterized by intergenerational inheritance, which has the same strength regardless of the stratum of origin and, furthermore, they propose either the existence of shortdistance mobility or barriers to long-distance mobility at both extremes of the stratification.

The interpretation of differences in relative mobility between both sexes is based on the analysis of the parameters of the four non-constant fluidity models. These models consistently show that occupational inheritance is stronger for males than for females. If we consider model 9, we observe that males are 2.45 times more likely to inherit their provider’s occupation than to have a different occupation ( _dp=2.45, p<0.001_ ), while this value is only 1.87 times for females ( _dp=1.87, p<0.001_ ). Furthermore, this model suggests that males with an origin in the agricultural stratum experience a barrier to long-distance upward mobility ( _esq=1.51, p=0.002_ ), and for females we found no evidence of this ( _esq=0.99, ls_ ).

36
**Table 2. Summary statistics of explanatory variables at loan and SHG levels**

|**Variable**|**Mean**|**Std. Dev.**|
|---|---|---|
|**Loan level**|||
|Loan size (1,000Rs)|15.8|24.1<br>|
|Source is bank|0.28|0.45|
|Source is internal lending|0.19|0.39|
|From CIF|0.44|0.50|
|Source is mix|0.10|0.30|
|Annual interest rate (%)|14|4|
|Length of loan (year)|0.90|0.43|
|Installment weekly or fortnightly|0.74|0.44|
|No. of observations (loans)||3,350|
|**SHG level**|||
|SHG size (number of members)|13.1|2.4|
|SHG age (years)|5.30|2.39|
|Share of very poor members (%)|0.34|0.32|
|Share of poor members (%)|0.39|0.28|
|Share of members belong to Scheduled|||
|Caste/Tribe (%)|0.33|0.38|
|Share of members in other backward castes (%)|0.54|0.44|
|Share of members from dominant caste (%)|0.91|0.16|
|No. ofobservations (groups)||2,147|

8
|Received remitances from abroad|0.16|0.37|2399|
|---|---|---|---|
|Has the ability to migrate (network, liquid assets, and passport)|0.10|0.30|2400|
|Experienced reduced wages or business|0.36|0.48|2400|
|Experienced any employment shock|0.53|0.50|2400|
|Believe political situation will improve economic situation (ordinal, 0-5)|4.42|0.90|2400|
|Believe economic situation will improve political situation (ordinal, 0-5)|3.17|1.55|2400|
|Overall risk appetite (ordinal, 1-10)|6.04|2.76|2400|
|No. of confict events in township since Feb'21 (ACLED)|164.40|202.57|2400|
|No. of confict events in townshipin thepreceding year(ACLED)|33.25|52.12|2400|

# 4. Identification

Following the experimental set-up described in section 3.2, we attempt to estimate the following equation: Where: Y _i_ is the migration intention outcome (as described in section 3.1) for individual respondent _i_ S is the set of randomly assigned scenarios under which the respondent chooses whether to migrate (a baseline scenario presenting no signal of any changes in the future, and two scenarios representing political and economic stabilization, respectively) X is the matrix of individual- and household-level controls. 1 which represents the conditional effect of alternate scenarios on people’s migration intentions, is the main coefficient of interest. ββ X is the matrix of individual- and household-level controls.

Given the randomized allocation of S to the respondents, 1 is treated as the estimate of the causal effect of the treatment arms on migration outcomes, with respect to the base category. Table 2 ββ below shows the results from an ANOVA used to perform a joint test of significance with the nullhypothesis that the means of key demographic and other characteristics of the respondents are not statistically distinct from each-other across treatment arms. We see that the treatment arm assigned is on balance, not correlated with individual characteristics.<sup>4</sup>

> 4 While a few variables show some statistically significant differences of a small magnitude, we later examine the robustness of our estimates to controlling for these variables.

12
lO-a Figure 4

# (LOG-) CREDIT AND HONEY IN REAL TERNS: CANEROON

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Policy Research Working Paper 6398

# **Abstract**

In this paper, the effect of proximity to multinational exporters on the creation of new export linkages (the extensive margin of trade) is debated. Using panel data from Chinese customs for 1997–2007, the capacity for Chinese domestic firms to begin exporting new varieties to new markets is shown to respond positively to the export activity of neighboring foreign firms. These spillovers are shown to be product and country specific. This conclusion is robust to fixed effects and instrumental variable specifications that control for both supply and demand shocks that could bias the estimations. The impact is sizable. The marginal impact of productcountry-specific foreign export spillovers is five times as large as the effect of a 10 percent increase in the demand for the product in the destination country. Foreign export spillovers are also shown to be primarily limited to ordinary trade activities. Overall, our findings suggest that even for a country with an important cost-advantage such as China, there is room for initiatives from policymakers that will diffuse best practices regarding export experience among exporters.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at florian.mayneris@uclouvain.be and sandraponcet@cepii.fr.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
was a poor agricultural year. Beyond 1987/8 and until 1990/0, real wages rose in all states. However, between 1999/0 and 2004, there was a significant deceleration in real wage growth, from 2.6 percent per year between 1993/4 and 1999/0 to 1.6 percent per year in the period that followed. In some states real wages actually decline. Only in Assam, Orissa and UP is there evidence of real wages rising more rapidly between 1999/0 and 2004/5 than during the preceding two survey rounds.

This deceleration in wage growth, juxtaposed against potentially more rapid decline in poverty during the 1999/0 to 2004/5 has led to renewed attention to the nonfarm sector as a source of poverty reduction. We turn to trends in non-farm employment in the next section.

# **IV.  Non-Farm Employment**

As described in Section I, the rural non-farm sector is widely looked to as a source of momentum for rural growth and poverty reduction. Employment patterns in the non-farm sector have been widely scrutinized for evidence of economic dynamism in rural areas. Visaria and Basant (1993) carefully examine National Sample Survey and Census data and document a clear increase in the share of non-agricultural employment in the rural workforce during the 1980s, with the trend more clearly evident among males than among female workers. In addition, the evidence appears to point to a more rapid expansion of tertiary sector employment rather than of secondary sector employment,<sup>4</sup> and that the bulk of employment growth is of a casual nature, rather than permanent. Surprisingly, this expansion appears to have slowed during recent years. Unni and Raveendran (2007) suggest that long-term employment growth in rural areas slowed down substantially between 1993/4-2004/5 relative to 1983-1993.

Fisher, Mahajan and Singha (1997) conclude that 18-25% of rural employment occurred in the non-farm sector at the beginning of the 1990s. An important observation made in this study was that approximately one-fifth of total rural non-farm employment

> 1987/8 estimates but acknowledge that they may be less reliable than estimates for other survey years. In the econometric analysis in Section V, we do not include the 1987/8 data due to these comparability problems.

> 4 Secondary sector refers to manufacturing, utilities, construction and mining.  The tertiary sector includes activities such as retail trade, hotels and restaurants, financial services, transport and communication, public administration, education, health, and community services.

7
**_HoaswFt fai Chi,e InduurJy_** _Groy? 19_

state sector according to the 15-branch classification in use before 1986. Although the differences between the two sets of calculations are not large (again, note that branches 13 and 14 should be merged), the latter figures are preferable. Here again we see the importance of machinery and chemicals, which are the only large branches reporting above-average growth of energy productivity.

Table 14 presents trial estimates of energy consumption and productivity change for 15 branches of collective industry during 1980/85. In Panel 1 of Table 14, branch clergy consumption and branch GVIO are derived as residuals from the national totals and the state-sector figures presented above. Comparison of GVIO figures derived in this manner with published data showing branch GVIO for collective-sector independent accounting units at and above the &ing level (recall that the basic energy consumption data for 1980/85 appear to exclude village-level industry), reveals massive inconsistency. We therefore focus our attention on Panel 2 of Table 14, in which branch energy consumption for collective industries is derived as a residual, and then combined with published data on collective sector branch output to obtain productivity figures.

# Scrutiny of these results yields the following observations:

1. There is a major inconsistency in data for the electric power industry, in which data for the state sector alone indicate much larger energy consumption in both 1980 and 1985 than for the entire power branch! As a result, the calculation reported in Table 14 indicates large negative energy consumption in the collective power industry for both years. Less worrisome discrepancies appear in branches 4 (in which the collective sector minute) and 14 (probably reflecting need to merge with branch 13).

2. Output value per unit of energy consumed (partial energy productivity) appears much higher in the collective than in the state sector.

3. If valid, the foregoing observation appears to be the result of differences in output structure rather than superior coUective productivity on a branch-by-branch basis. Energy consumption per unit of real output by collective producers is markedly higher in branches 1, 6 and 9 (metallurgy, machinery, food processing) than in the state sector. Partial energy productivity seems to favor collective firms in branches 7, 8, 10, and 12 (building materials, forestry, textiles, leather), of which only 7 and 10 are major branches. In 7, the quality of small-plant output is far inferior to the state-sector norm [World Bank 1985-A3, p. 17]. Partial energy productivity is similar for state and collective firms in branches 5 (chemicals - here the comparison is blurred by major differences in product mix) and 11; the comparison is obscured by data problems for the remaining sectors.

4. Cumulative gains in energy productivity for the collective sector, summarized in Table 15, are far larger than comparable gains for the state sector in every significant branch except building materials (ignore the confused and minor branches 13-15). In most cases, the margin of difference is extremely large.
section while substituting bureaucracy quality by _government effectiveness_ . The use of that other variable of institutional quality does not fundamentally change our findings. Specifically, we find that fiscal rules may help promote counter-cyclical fiscal policy but are more effective when implemented in an environment with good institutions. Fiscal rules, together with strong institutions form a potent combination which promotes counter-cyclical policy.

Moreover, we get the same results with the control variables. Inflation targeting and government polarization are associated with more fiscal discipline. Less developed countries tend to perform pro-cyclical policies; and in the hedge of elections, governments tend to perform budgetary discipline.

|Dependent vari|able: Cyclical|ity of fscal p|olicy<br>ˆ<br>_βit_||||||||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|10|
|FRD|-0.022 ***|-0.002***|-0.001***|0.174|0.186|0.188|0.196|0.186|0.730|0.131|
||(0.007)|(0.008)|(0.013)|(0.011)|(0.010)|(0.010)|(0.010)|(0.096)|(0.546)|(0.086)|
|GE|-0.039 ***|-0.013***|0.246***|-0.232**|-0.148***|-0.147**|-0.176***|-0.122 ***|-0.049 *|-0.135 ***|
||(0.028)|(0.029)|(0.070)|(0.095)|(0.094)|(0.097)|(0.099)|(0.132)|(0.200)|(0.132)|
|ERR|0.020**|0.022***|0.036*|0.022*|0.023*|0.023*|0.022*|0.017 ***|0.019 ***|0.014***|
||(0.004)|(0.004)|(0.008)|(0.013)|(0.013)|(0.013)|(0.013)|(0.011)|(0.012)|(0.011)|
|FRD X GE|||||||||-0.205*||
||||||||||(0.130)||
|FRD X ERR||||||||||0.010|
|||||||||||(0.013)|
|IT||-0.238**|-0.244*|-0.387**|-0.342***|-0.341***|-0.345***|-0.360***|-0.346***|-0.352***|
|||(0.042)|(0.068)|(0.042)|(0.041)|(0.041)|(0.042)|(0.060)|(0.062)|(0.061)|
|ELECTIONS|||0.011***|0.002***|0.003***|0.003***||0.003 ***|0.002 ***|0.003 ***|
||||(0.002)|(0.002)|(0.002)|(0.002)|0.002***|(0.001)|(0.001)|(0.001)|
|POLARIZ||||-0.019**|-0.029***|-0.031***|(0.002)|-0.012***|-0.009 ***|-0.010 ***|
|||||(0.017)|(0.016)|(0.016)||(0.017)|(0.014)|(0.017)|
|GDPPC|||||-0.303***|-0.308***|-0.306***|-0.268***|-0.273***|-0.267***|
||||||(0.051)|(0.054)|(0.061)|(0.090)|(0.106)|(0.088)|
|OV||||||-5.772*|-6.542*|-4.509|-6.162|-4.145|
|||||||(2.675)|(2.655)|(3.574)|(3.338)|(5.424)|
|DEBT|||||||-0.001***|-0.001***|-0.001|-0.001 ***|
||||||||(0.000)|(0.001)|(0.001)|(0.001)|
|RENT||||||||-0.020|-0.018**|-0.020***|
|||||||||(0.008)|(0.008)|(0.007)|
|Obs.|1,316|1,316|724|493|487|487|486|479|479|479|
|Countries|137|137|78|70|69|69|69|68|68|68|
|Hansen J|0.269|0.288|0.204|0.816|0.962|0.966|0.982|0.964|0.949|0.963|

Bootstrapped standard errors in parenthesis (100 replications) * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01 Table 4: Robustness check 21
they were more likely to have higher education than male PFPS workers (see Table 5-15), and as we will see below, workers with higher education lost jobs at a lower rate. Therefore, our model would predict women retaining more of their (typically non-production) jobs than men. Of course, if one controls for educational

|_Table 5-15 Jordan: Priv_|_ate Sector W_<br>PFPS<br>(Percenta<br>Employe|_age earners_<br>ges of all PFP<br>es)|_by Educati_<br>S<br>Formal n<br>wage ear<br>(Share o|_on and Gender p_<br>on-permanent<br>ners<br>f PFPS workers)|_re-COVID_<br>Informal w<br>(Share of i<br>works)|age earners<br>nformal wage|
|---|---|---|---|---|---|---|
||Male|Female|Male|Female|Male|Female|
|Less than Basic|15.6|1.8|7.1|0.4|42.9|1.0|
|Basic Education|21.7|1.5|22.9|2.9|25.3|1.9|
|Secondary Education|12.7|1.7|10.5|3.8|15.6|1.1|
|Higher Education|30.2|14.7|29.7|22.7|10.7|1.5|
|Total<br>Source: LMPS 2016.|80.2|19.8|70.2|29.8|94.5|5.5|

attainment this may not be the case. Moreover, the female advantage does not carry over to non-PFPS workers. The CMMHH data reveals largely the reverse pattern for them: a higher percentage of female irregular workers became unemployed or left the labor force during under COVID-19 conditions than male ones (See _Table 5-17 Jordan: Percent of Non-Permanent Private Sector Workers Employed Pre-COVID No Longer Employed as of February 2021, by Gender_

||Unemployed,|2021<br>Out of labor force,2021|
|---|---|---|
|By Gender|||
|Female|34.8|9.2|
|Male|15.3|2.5|

Source: CMMHH, 2021.

Table 5-16).<sup>49</sup> By the first quarter of 2021, 35 percent of them were unemployed relative to 15.3 percent of males; and over 9 percent had left the workforce, whereas only 2.5 percent of men did so.

**Age** : As shown in Table 5-14 above, our method generates little age-related dispersion in job loss rates and therefore does not do well in predicting the large difference in job losses for older versus prime-age PFPS workers as captured in the CMMHH (38 percent versus 20 percent). These effects are most likely due to age-conditioned employer dismissals or labor

_Table 5-16 Jordan: Percent of Non-Permanent Private Sector Workers Employed Pre-COVID No Longer Employed as of February 2021, by Age Group_

|Age Group|Unemployed,2021|Out of labor force,2021|
|---|---|---|
|below 25|25.3|5.1|
|25-45|15.4|2.5|
|above45|15.8|4.4|
|Source: CMMH|H, 2021||

supply decisions.<sup>50</sup> Youth working in non-PFPS private sector jobs were disproportionately impacted as well. According to the CMMHH, by the winter of 2020/21, 25 percent of temporary workers under the age of 25 49 Although males comprised 70 percent of temporary formal private sector workers and 95 percent of informal workers pre-COVID, so women were relatively under-represented in these forms of work.

50Hypotheses one could examine with the available data are whether younger workers returned to education or became inactive at a higher rate; whether older workers withdrew from the labor force at higher rates than prime age workers (to retire).

37
engage in quality upgrading and technology adoption when they access wider varieties of imported inputs.

The development economics literature that analyzes empirically the impact of better telecommunications tends to focus on the supply side, and especially how technology upgrades improve the efficiency of suppliers in their operations as better information emerges to improve sales (e.g. about weather, input market conditions, local market prices, labor market trends), as better credit options materialize through fin-tech (e.g. wider banking service access, easier credit ratings), and also as knowledge spillovers disseminate faster – within firms, industry-wide and along value chain through both backward and forward linkages.

Notably, Hjort and Poulsen (2019) find that the availability of fast internet connection through submarine cables in Africa has resulted in large positive employment effect regardless of the skill profile of workers, and the positive employment effect is attributed to higher rate of firm entry, productivity gains and exporting that have come with the arrival of fast internet connection. In addition, Dutz et al. (2012) study how establishments’ innovation activities affect their employment growth using enterprise-level data from a large sample of countries, including 11 SubSaharan African countries. Their results show that innovating establishments registered significantly faster employment growth compared with their non-innovating counterparts, and this relationship is more pronounced when establishments use internet; it is shown that internet usage is a positive correlation of more innovation and higher productivity. Using Brazil’s staggered internet rollout, Dutz et al. (2017) also show that in the manufacturing sector, internet access induces positive employment and wage effects in both medium- and high-skill occupations.

In addition to our focus on manufacturing, another distinctive feature of the paper is the exploration of the demand channel whereby internet affects the behavior of producers through providing broader access to price and product information to customers rather than through technological impact on the production function or access to lower-cost inputs.

We investigate the relation between improved access to price information through the transition from 2G to 3G network (measured at the woreda level as share of households accessing data through smartphones) and firm performance measures (e.g., TFP, labor productivity, markups, 5