rafmacalaba/datause-encoder-data huggingface dataset:
becomes larger, while fast population growth affects the dependency ratio and thus also poverty. Regarding the former, as underscored by Henderson (2003) and Behrens and Bala (2013), political interference and rent seeking by the ruling elite often favors urban primacy over more dispersed urbanization patterns. Favoritism, as in Indonesia (Henderson and Kuncoro, 1996), could for example involve restrictions in capital markets and export/import licensing, all favoring firms that locate in the capital, and helping central bureaucrats and politicians to extract rents when allocating loans and licenses, without competition from lower ranked bureaucrats in other locations. Similar considerations could lead national governments to underinvest in interregional transport and telecommunications, favoring producers and investors in the capital over those in the hinterlands. Yet, rent seeking may also be related with other interventions that affect changes in poverty. It may for example decrease the likelihood of redistributive programs such as Bolsa Familia in Brazil, which proved to have important poverty reducing effects (Ferreira, Leite, and Ravallion, 2010). Henderson (2003) finds democratic regimes to display less concentrated urbanization patterns.

Second, as exemplified in the Kagera case study, both differential unemployment rates (labeled here the Harris-Todaro effect) as well as differential opportunities and ability to move to proximate versus more distant sources of growth (the size effect) may explain potential differences in the poverty reducing effect of the transition to non-farm activities and secondary towns versus the poverty reducing effect of migration to metropoles. One way to explore this is to control for the relative size of the middle and the metropoles: ~ ~ and test whether β _U_ = β _N_ . If the latter is still bigger than the former, as hypothesized before, then substantial Harris-Todaro effects are still possible. If they are no longer different, the size effect is likely dominant. Any advantage from one sector over the other would then, for example, come from the fact that the poor are better able to move to the middle instead of to 15
The probability of attending a general school does not change and the probability of attending a technical school decreases. Program impacts do not differ by gender, with the exception of the school fixed effects specification where the interaction between female and being a beneficiary reduces the probability of attending a general secondary school.

With respect to enrollment in high school (Tables 12a through 12c), the results are similar as was the case for the entire population in poor and very poor areas; that is, the program increases the probability of attending a general high school and reduces the probability of attending a technical school. No differences in the size of the effects by gender are observed.

Table 12a: Impacts of PROSPERA on Type of High School 6<sup>th</sup> Graders Enrolled in Indigenous Primary Schools in 2008 State Fixed Effects

||General<br>HS|Technical|Tec Prof|Private|
|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|
|PROSPERA beneficiary<br>2008|0.083***|-0.082***|-0.001|0|
||(0.014)|(0.015)|(0.003)|(0.003)|
|Female beneficiary|-0.026|0.025|0.003|-0.002|
||(0.02)|(0.02)|(0.004)|(0.004)|
|Female|0.008|-0.008|-0.002|0.002|
||(0.018)|(0.018)|(0.004)|(0.003)|
|Constant|0.362***|0.616***|0.005|0.017|
||(0.078)|(0.08)|(0.017)|(0.015)|
|Observations|9,253|9,253|9,253|9,253|
|R-squared|0.101|0.104|0.029|0.007|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

31
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

|**Study**|**Hypothesis**|**Dependent**<br>**Variable(s)**|**Independent**<br>**Variable(s)**|**Control Variables**|**Notes: # of countries, times**<br>**period, data source**|
|---|---|---|---|---|---|
||||court justices’ in the index;<br>tenure of justices as separate<br>variable.<br>-alternative political variables:<br>-sum of ‘law & order’,<br>‘bureaucracy’ and ‘corruption’<br>variables plus ‘index of<br>likelihood of contract<br>modification w. foreign business’<br>and ‘index of likelihood of<br>confiscation nationalisation’,<br>-index of inst. constraints on<br>chief executive decision by<br>accountability groups,<br>-index of institutionalised<br>democracy,<br>-gastil index of political and civil<br>liberties.<br>_Result: significant_|||
|**Holborn**<br>**(2001)**|_H0: The probability that an_<br>_individual IPP will enter a_<br>_country with monopsony_<br>_market is increasing in the_<br>_amount of the firm’s prior_<br>_monopsony market_<br>_experience._<br>_H1: The probability that an_<br>_individual IPP will enter a_<br>_country with a competitive_<br>_generation market is_<br>_increasing in the amount of_<br>_the firm’s competitive_<br>_market experience._<br>_H2: The probability that an_<br>_IPP will enter a country is_|-the firm’s decision to enter<br>a country in a given year –<br>takes the value of 1 if<br>expected profits net of cost<br>of capital are non-negative;<br>otherwise takes the value of<br>0<br>(PI)|**-country-level** **variables**: GDP<br>growth (CL)<br>_Result: significant at the 0.1%_<br>_level_<br>GDP (US$) (CL)<br>_Result: significant at the 0.1%_<br>_level_<br>a dummy variable to indicate if a<br>country has implemented a<br>competitive or a monopsony<br>market (RM)<br>_Results vary according to model_<br>a measure of political risk<br>(higher for stronger political<br>constraints) (CL)<br>_Result: significant (either at 5 or_<br>_at 0.1%, depending on the_<br>_model)_|-first year firms were able<br>to enter a country (where<br>hard to observe when<br>liberalisation started,<br>assumes this year to be the<br>year of the first IPP entry)<br>(RM)||

54
# **1 Introduction**

A growing literature documents the importance of management quality to productivity growth and other measures of firm progress.<sup>1</sup> Bloom and Van Reenen (2007) have given the field a quantum push by undertaking globally comparable surveys of management practices. For the US, Germany, the UK and Sweden, they find a correlation of their management scores with firm level productivity, growth and survival. They document large within-country heterogeneity<sup>2</sup> and identify the degree of product market competition and ownership structure as critical determinants of the average level of management quality, particularly working through the extended lower tails of the distribution.<sup>3</sup> More recently, exploring a broader range of middle-income and developing countries, Bloom and Van Reenen (2010) show average management scores to be highly correlated with aggregate labor productivity (see Figure 1). Improving managerial quality thus appears to be an important and heretofore understudied ingredient in fomenting economic development.

These findings raise the question of the process through which countries achieve such improvements. This paper uses recent advances in unconditional quantile decompositions (Machado and Mata, 2005; Firpo et al., 2009) and the Bloom and Van Reenen international firm level data base to analyze the changes in the within-country distribution of management quality associated with convergence to the managerial frontier.<sup>4</sup> That is, we would like to know if convergence arises from progressively trimming the left tails, a more general rightward shift of the distribution due to, perhaps, the general accumulation of human capital, or perhaps the emergence of superstars in the right tails?<sup>5</sup> Our empirical approach allows us to move beyond the central tendency and more fully characterize the entire country distribution relative to the frontier and generate estimates of the drivers of distributional

> 1See Syverson (2011) for a review and Bertrand and Schoar (2003); Kaplan et al. (2012); Malmendier and Tate (2009); Ichinowski et al. (1997); Lazear (2000); Bloom et al. (2013).

> 2Their findings are perhaps expectedly analogous to those found for firm productivity more generally. See Syverson (2011) for a full survey of this literature; Syverson (2004); Eslava et al. (2004); Foster et al. (2008).

> 3They conclude that “Low competition and primogeniture in family firms account for about half of the tail of poorly performing firms.”

> 4For other work using this data see, for example, Lemos and Scur (2013) who focus on the role of firm ownership structure.

> 5For instance Malmendier and Tate (2009) show that compensation, status, and press coverage of managers in the United States follow a highly skewed distribution with a very small number of “superstars.”

1
effect is present when privatization is accompanied by hard budget constraints and indepth institutional reforms. Bennett, Estrin and Giovanni Urga (2007) use a panel data model and GMM estimation methods for almost all the transition economies (26 countries), controlling for country or time specific factors with fixed effects. They do not identify a significant relationship between private sector share and growth; hence their results do not indicate a direct relationship between privatization and growth. However, they do have results concerning methods of privatization in that they find countries which used mass privatization enjoyed significantly higher growth post-privatization relative to pre-privatization, compared with countries that used other privatization methods. Their study suggests that, the advantage of speed in privatization brought about by mass privatization may have yielded long-term benefits in terms of economic growth.<sup>12</sup> Using similar data, Fabian Gouret (2007) provides complementary evidence about the impact of privatization methods on growth. He also finds a positive effect from mass privatization but it is smaller than from the more gradual methods of privatization. The difference in the results of the two studies stems from differences in specification, not completely overlapping data sets and the use of different estimation methods.

The macro studies hence suggest that privatization, especially when accompanied by complementary reforms, may have a positive effect on the level of aggregate output or economic growth, but the effect of speed, and the accompanying dispersed versus more concentrated ownership, on aggregate output and growth is unclear.

# **_4. The Effects of Privatization on the Performance of Firms_**

Earlier surveys of firm-level studies examining the effects of privatization on firm performance range from ones that find a large variation of outcomes but no systematically significant effect of privatization on performance (Bevan, Estrin and Schaffer, 1999) to those cautiously concluding that privatization improves firm performance (Megginson and Netter, 2001), to ones that are fairly confident that privatization tends to improve performance (Mary Shirley and Patrick Walsh, 2001; and Djankov and Murrell, 2002).

> 12 They argue that their result is due to an increase in ownership concentration following mass privatization that had strengthened control over firms.

15
ZAIRE: The IMR is a Bank projection from the 1984 census. Life expectancy at birth are from _World Population Pro:pects 1990._ The U.N. estimate is based on extrapolations from a 1955-57 survey.

**ZIMBABWE:** Both IMR and life expectancy estimates are Bank projections from the 1988 DHS.

# North Africa

ALGERIA: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ The U.N. based its estimates on an official life table for 1985 and projected them to 1985-89 with the U.N. medium mortality decline scenario. EGYPT: The IMR is estimated by linear interpolation between the 1980 World Fertility Survey and the 1988 Demographic and Health Survey. Life expectancy at birth estimates is from _World Population Prospects 1990,_ which based its estimates on adjusted registered deaths for 1975-77 and projected them to the 1985-89.

LIBYA: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ The U.N. based its estimates on the 1973 and 1984 censuses. Current estimates are obtained by projecting these estimates with the U.N. medium decline scenario.

**MOROCCO:** The IMR is from the 1987 Demographic and Health Survey. Life expectancy at birth estimates are from _World Population Prospects 1990,_ and are based on Coale-Demeny "South" model life tables selected on the basis of IMRs from the 1979-80 World Fertility Survey, the 1983-84 Contraceptive Prevalence Survey and the 1987 Demographic and Health Survey.

TUNISIA: The IMR is from the 1988 Demographic and Health Survey obtained through linear extrapolation. Life expectancy at birth estimates are from _World Population Prospects 1990,_ which are based on adjusted registered deaths through 1988.

# Latin America and the Caribbean

**ANTIGUA AND BARBUDA:** The IMR is projected by linear extrapolation from official estimates for 1980 and 1985, as published in _PVSR._ Life expectancy at birth is obtained from Coale-Demeny model life tables on the basis of the official CDR and population distribution by age and sex.

ARGENTINA: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ The U.N./CELADE based their estimates on the adjusted 1980 census and on vital registration, and projected these estimates to 1985-89.

BAHAMAS, THE: The IMR is the average of official vital registration estimates for 1985 to 1989 as published in _PVSR._ Life expectancy at birth is from Coale-Demeny model fife tables, selected on the basis of the official CDR and age-sex distribution.

BARBADOS: The IMR is the average of official vital registration estimates for 1985 to 1989 as published in _PVSR._ Life expectancy at birth is obtained from Coale-Demeny model life tables, based or. the official CDR and age-sex distribution.

13
15 females from wealthier households seem slightly more likely to opt out of the labor market than their counterparts from poorer families. The same trend is observed for youth: while 67.3 percent of all individuals aged 15 to 24 are out of the labor force, the proportion increases to 71.4 percent for those from non poor families. Finally, 38.4 percent of the poor aged 55 to 64 do not participate in the labor market compared to 48.2 percent of the same age group among the non poor.

**Table 6. Non-Participation Rate by Poverty Status, Gender, Living Area, and Age Group (in %)**

||_All_|_Non Poor_|_Poor_|_Extremely Poor_|
|---|---|---|---|---|
|_All_|50.7|51.4|49.9|49|
|_By gender:_|||||
|_Male_|26.6|26.5|26.7|26.9|
|_Female_|72|73.5|70.1|68.5|
|_By living area:_|||||
|_Rural_|50.2|52.3|48.5|48.3|
|_Urban_|51.4|50.5|53.4|51.4|
|_By age group:_|||||
|_15-24_|67.3|71.4|62.6|61.8|
|_25-54_|41.2|40.2|42.5|41.7|
|_55-64_|44|48.2|38.4|34.6|

Source: EPCV 2004. Figures are displayed in percentage.

_While women’s labor market participation seems to be affected by their poverty status, wealth does not appear to influence men’s decision to participate in the labor market._ As shown by Table 8, male non-participation rate remains relatively constant across all wealth groups amounting to 26.7 percent for the poor and 26.5 percent for the non poor.

_Although rural non-participation is slightly lower than the urban one for the overall population, when it comes to the non poor, the opposite holds_ (Table 6) _:_ a higher proportion of the rural non poor is out of the labor force than of the urban non poor (respectively 52.3 percent and 50.5 percent). Moreover, in rural areas, the poor appear more likely to participate in the labor market than the non poor. The converse is observed in urban centers. This might suggest that low returns on income-generating activities contribute to poverty in rural areas whereas exclusion from the labor market is more of an issue for the poor in urban centers. Further analysis would however be required to confirm this point.

# <u>3.1.2 Unemployment</u>

_The poor display substantially higher unemployment rates than the non poor._ As shown by Table 7, 17.2 percent of the poor and 19.8 percent of the extremely poor are unemployed compared to 12.6 percent for the non poor. This is mirrored by differences within gender groups. Hence, the
# **Table 2a: Trends in Real Unit Labor Costs in Local Currency and US Dollars: 2003 = 100, High Remittance Recipient Transition Economies**

||2000-04|2005-08|2009-11|2012-14|2015-17|
|---|---|---|---|---|---|
|AlbaniaLC|98|98|105|104|119|
|USD|91|131|136|124|121|
|ArmeniaLC|100|112|134|157|159|
|USD|103|185|244|278|244|
|Bosnia andherzegovinaLC|96|91|106|102|93|
|USD|88|114|143|128|99|
|GeorgiaLC|97|140|168|165|166|
|USD|100|220|296|313|249|
|Kyrgyz RepublicLC|93|135|160|165|161|
|USD|88|190|269|318|256|
|MoldovaLC|94|114|101|85|88|
|USD|92|173|215|205|170|
|MontenegroLC|101|120|148|153|145|
|USD|108|170|238|230|193|
|SerbiaLC|89|104|94|94|98|
|USD|73|130|121|113|98|
|Tajikistan LC|98|152|194|245|277|
|USD|94|232|367|519|410|
|UkraineLC|92|109|117|122|118|
|USD|89|195|222|253|162|
|Uzbekistan LC|98|na|228|239|279|
|USD|107|na|543|607|612|
|Mean LC|96|118|141|148|155|
|Median LC|97|113|134|153|145|
|MeanUSD|94|174|254|281|238|
|MedianUSD|92|179|238|253|193|

Sources: UNECE, ILO and SEE jobs Gateway for wages, WDI for real GDP, persons employed, GDP deflators 11
# <u>Table A1: Regional dimension</u>

||<br>**Region name (code in parenthesis)**|
|---|---|
|1|Cambodia(KHM)|
|2|China(CHN)|
|3|Indonesia(IDN)|
|4|Lao PDR(LAO)|
|5|Malaysia(MYS)|
|6|Philippines(PHL)|
|7|Thailand(THA)|
|8|Vietnam(VNM)|
|9|Rest of East Asia(XEA)|
|10|Bangladesh(BGD)|
|11|India(IND)|
|12|Nepal(NPL)|
|13|Pakistan(PAK)|
|14|Sri Lanka(LKA)|
|15|Rest of South Asia(XSA)|
|16|EU ECA(ECU)|
|17|Russian Federation(RUS)|
|18|Poland(POL)|
|19|Rest of Eastern Europe(EEU)|
|20|Kazakhstan(KAZ)|
|21|Kyrgyz Republic(KGZ)|
|22|Rest of Central Asia(XCS)|
|23|Turkey (TUR)|
|24|Egypt,Arab Rep.(EGY)|
|25|Iran, Islamic Rep.(IRN)|
|26|Rest of MENA(XMN)|
|27|Ethiopia(ETH)|
|28|Kenya(KEN)|
|29|Tanzania(TZA)|
|30|Rest of Sub-Saharan Africa(XSS)|
|31|Latin America and Caribbean(LAC)|
|32|United States(USA)|
|33|Rest of High-income(XHY)|
|34|Rest of Western Europe(E_U)|

- 62 -
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**Table 3.3 Allocation of teachers to schools (national)**

||**2017**|**2018**|**2019**|
|---|---|---|---|
|**A. All schools**||||
|% schools allocated new teachers|38%|43%|33%|
|Number of teachers allocated – mean*|2.2|2.4|1.9|
|**B. All schools that needed 0 new teacher**||||
|% schools allocated new teachers|25%|32%|24%|
|Number of teachers allocated – mean*|1.7|1.9|1.6|
|**C. All schools that needed at least one new teacher(s)**||||
|% schools allocated new teachers|59%|61%|45%|
|Number of teachers allocated – mean*|2.6|3.0|2.2|

*Denotes number of teachers allocated to schools which were allocated a teacher.

**Table 3.4** (in Section 8, Tables) shows the performance of individual districts in allocating teachers to the correct schools, as well as the national total performance. At national level, the share of deployed teachers who were allocated correctly fell from 56 percent in 2017 to 49 percent in 2018, reflecting the fact that the extra-large allocation was not well targeted as noted above. The share correctly allocated fell further to 43 percent in 2019, showing a further deterioration in adherence to the new rules following their introduction.

This aggregate picture, however, masks a high degree of variation between districts. On average across the three years, the share of teachers received which were allocated correctly varies from as low as 22-23 percent (Rumphi and Mzimba North districts) to 75 percent (Dowa district). Strikingly, each district varies considerably year-on-year in its adherence to the guidance, with only four districts (Mwanza, Kasungu, Mangochi and Machinga) allocating more than half their teachers correctly in all three years.

To provide a deeper exploration of the dynamics of district allocations over time, we focus on 13 districts which received at least 50 teachers in all three of the years 2017-19. **Figure 3.2** shows the performance of these districts in allocating teachers to schools across the three years. In a number of cases, the quality of allocations fell in 2018 in response to the larger-than-normal allocation before improving in 2019. However, in other cases the quality of allocations increased in 2019 before falling, or declined consistently. None of the 15 districts achieved a consistent improvement in the share of teachers which were allocated according to the modified rules.

12
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capital expenditure Ooperations and maintenance