rafmacalaba/datause-encoder-data huggingface dataset:
# 1. **Introduction**

Recent empirical work has indicated that privatized firms are often more efficient than comparable public enterprises (Lopez-de-Silanes, 1997; Mueller, 1989; Vining and Boardman, 1992). Similarly, many enterprises become more efficient following privatization (Galal et al., 1994; Kikeri, Nellis, and Shirley, 1992; La Porta and Lopez-de-Silanes, 1997; Megginson, Nash, and Van Randenborgh, 1994; World Bank, 1995).1 Although a shift from public to private ownership has typically improved firm level performance, that shift has often proven insufficient by itself. At different times and in different places, dispersed ownership, entrenched interests, weak regulation and supervision, a lack of competitive pressure, and soft budget constraints, or a subset of those factors, have provided sub-optimal incentives for private owners of firms. Among the transition countries of Central and Eastern Europe, for example, recent studies indicate that the persistence of extensive insider (worker or manager) ownership is associated with limited progress in enterprise restructuring and poor post-privatization performnance (Carlin and Landesman, 1997; Pohl et al., 1997; Frydman et al., 1999; Jones and Mygind, 1999). The variation in performance suggests that the question of _how_ to privatize is, in many cases, at least as important as _whether_ to privatize.

Moreover, the privatization literature has largely ignored the incentives of the politicians that craft privatization agreements. There are some exceptions. World Bank (1995) identifies a number of features of the policy environment that coincide with successful privatization, and Lopes-de-Silanes et al. (1997) and Clarke and Cull (2000a) identify political factors that make privatization more likely, but none of those papers formally analyze how political incentives affect the privatization agreements themselves. In certain transition countries, it appears that privatization to insiders has been relatively unsuccessful, but what were the possible alternatives? Was privatization to outside or foreign investors possible in all transition economies? Did time constraints militate against lengthy negotiation with such investors?

Laffont and Tirole (1991) note, however, that public ownership may be advantageous in some circumstances. For example, it might be easier for a government to pursue goals other than **profit** maximization through ownership rather than through regulation. Therefore, theory cannot resolve which form of ownership better promotes social 3
their high long term growth rates, ranking second through fourth out of the sample in terms of their overall rate of per capita income growth 1975-2005.<sup>8</sup>

|**Table 2: Decomp**|**osition of standa**|<br>**rd deviation of G**|<br>**DPper capita and growt **|<br>**h - 1975-2005**|
|---|---|---|---|---|
|**Variable**|**Mean**|**SD overall**|**SD between countries**|**SD within countries**|
|GDPper capita|2,306|2,633|2,490|809|
|GDPper capitagrowth|0.71|6.32|2.26|5.95|

Notes: Statistics calculated from panel data.

Kernel densities of the distribution of per capita GDP growth rates at ten year intervals are shown in figure 4. The growth acceleration of 1995-2005 is clearly visible in the rightward shift of the distribution.

**Figure 4: Density of GDP per capita growth across countries**

<!-- Start of picture text --> Figure 4a: 1976 vs. 2005 Figure 4b: 1976 vs. 1985<br>-10 0 10 20 -10 0 10 20<br>Growth Growth<br>kdensity growth_1976 kdensity growth_2005 kdensity growth_1976 kdensity growth_1985<br>Figure 4c: 1985 vs. 1995 Figure 4d: 1995 vs. 2005<br>-10 0 10 20 -10 0 10 20<br>Growth Growth<br>kdensity growth_1985 kdensity growth_1995 kdensity growth_1995 kdensity growth_2005<br>.2 .08<br>.15 .06<br>.1 .04<br>Kernel density Kernel density<br>.05 .02<br>0 0<br>.15 .2<br>.15<br>.1<br>.1<br>Kernel density Kernel density<br>.05<br>.05<br>0 0<br><!-- End of picture text --> The most striking change in the distribution over time, however, is the extent to which growth rates have converged (figure 4a). The 1976 distribution is remarkably flat. Since then, there have been increasingly more acute peaks around the mean (figures 4b - 4d). The SD of growth rates dropped from 8.2 percent in 1976 to 3.6 percent in 2005.

8 Equatorial Guinea, which ranks first, and Lesotho, which ranks fifth, have the same coefficient of variation of growth of about 1.7.

7
Table A.8: Robustness: alternative measures of Germany’s comparative advantage

|Dep. Variable:|log TFPR<br>(1)|log revenues<br>(2)|log employment<br>(3)|log exports<br>(4)|
|---|---|---|---|---|
|Post*Germany’s output share (log)|0.041**|0.091***|0.062**|0.605***|
||(0.018)|(0.041)|(0.026)|(0.211)|
|Post*Germany’s initial average wages (log)|0.053***|0.117***|0.064**|0.605***|
||(0.012)|(0.027)|(0.029)|(0.211)|
|Post*Germany’s initial capital per worker (log)|0.022|0.123|0.125**|0.605***|
||(0.056)|(0.104)|(0.054)|(0.211)|
|Post*Germany’s initial capital stock (log)|0.037**|0.094***|0.065***|0.605***|
||(0.014)|(0.028)|(0.017)|(0.211)|
|Post*RCA relative to Poland|0.089**|0.140**|0.033|0.605***|
||(0.036)|(0.057)|(0.043)|(0.211)|
|Post*RCA relative to world|0.124**<br>(0.045)|0.301***<br>(0.078)|0.129<br>(0.081)|0.605***<br>(0.211)|
|N (obs.)|134174|134174|134174|63992|
|N (frms)|18355|18355|18355|13245|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. Each cell is from a separate regression. All regressions include firm and year fixed effects. TFPR is estimated using the method in Levinsohn and Petrin (2003). RCA is a standard measure of comparative advantages of Germany versus Poland and the world. All measures of German influence refer to the period 1994-2004. Standard errors in parentheses clustered at 2-digit industry level. *10% level, **5% level, and ***1% level.

Table A.9: Placebo test: initial export share for Russia, Ukraine, Lithuania and Poland

||Po|land|R|ussia|Ukr|aine|Lith|uania|
|---|---|---|---|---|---|---|---|---|
|D. Dep. variable: TFPR (ACF)|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Post _×_ country’s export share (log)|-0.015|-0.058***|-0.010|-0.039***|0.014|-0.014|-0.033**|-0.046***|
||(0.025)|(0.020)|(0.014)|(0.013)|(0.026)|(0.026)|(0.015)|(0.013)|
|Post _×_ Germany’s export share (log)||0.061***||0.067***||0.049***||0.052***|
|||(0.020)||(0.019)||(0.017)||(0.014)|
|N (obs.)|134174|134174|134174|134174|134174|134174|134174|134174|
|N (frms)|18355|18355|18355|18355|18355|18355|18355|18355|
|R-squared|0.114|0.116|0.115|0.117|0.115|0.116|0.115|0.117|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. All regressions include firm and year fixed effects. Export shares are measured in the period 1994-2004. TFPR is estimated using the method in Levinsohn and Petrin (2003). Standard errors in parentheses clustered at 2-digit industry level. *10% level, **5% level, and ***1% level.

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small quantities, with the exception of a period of very low world prices during the mid 1980s when large quantities were imported (Fig 4 and Data S5 and S9). In 1985 there were imports of 1.7 million tons, equivalent to about 28 percent of Indian white sugar production, 17 percent of world trade in refined sugar in that year and to about 6 percent of the combined world trade in raw and refined sugar.

Figure 4: **Share of Indian imports of raw and refined sugar as a percent of world imports** <!-- Start of picture text --> ,,,,,~ ~ ~ ~~~~~~~6 W>As'.-w2 .<br>1-4~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~uv<br><!-- End of picture text -->

**Source: See Data S5 for details**

Data 58 gives details of the recorded trade in non centrifugal sugar, which is negligible alongside world production of over 12 million tons and the international trade in raw and refined sugar. As noted previously, in the past India was a major and at times (198 1-83) the only exporter of non centrifugal sugar, but this trade practically disappeared during the 1 980s . India accounted for _59%_ of total recorded world exports in 1965, 78% in 1975 and 98% in 1985. Indian exports ceased in 1987. Kenya, Uganda and Tanzania were the other three countriesg 111 h~~~~~~~1 exporting non centrifugal sugar in 1965. Uganda and Tanzania had dropped out by 1975 and Kenya has slowly decreased its exports until only 1 MT was exported in 1992, which was equivalent to the total officially recorded world trade in that year! Japan and Laos in Asia and Kenya, Tanzania, and Uganda were the only importers until they stopped importing after 1987. **During the 1 990s only the Cook Islands imported non centrifugal sugar, 22 MT on average per** annum! This was the only recorded trade left in non centrifugal sugar, but non-recorded border trade **between** countries such as India, Bangladesh, Pakistan and Nepal and also in Africa is probably considerable.
6 Figure 3: Linkages between the United States and EMDE regions <!-- Start of picture text --> (A) East Asia and Pacific (B) Europe and Central Asia<br><!-- End of picture text --> <!-- Start of picture text --> Percent of total Percent of total<br>United States China Germany Japan United States China Germany Japan<br>40<br>40<br>30 30<br>20 20<br>10 10<br>0 0<br>(C) Latin America and the Caribbean (D) Middle East and North Africa<br>Percent of total Percent of total<br>United States China Germany Japan United States China Germany Japan<br>80 30<br>60<br>20<br>40<br>10<br>20<br>0 0<br>(E) South Asia (F) Sub-Saharan Africa<br>Percent of total Percent of total<br>United States China Germany Japan United States China Germany Japan<br>40 50<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>0 0<br>Exports Inward FDI inflows liabilities Exports Inward FDI inflows liabilities<br>Exports liabilities Exports liabilities<br>inflows inflows<br>Inward FDI Inward FDI<br>Exports liabilities Exports liabilities<br>inflows inflows<br>Inward FDI Inward FDI<br>Remittances Portfolio Foreign claims Remittances Portfolio Foreign claims<br>Remittances Portfolio Foreign claims Remittances Portfolio Foreign claims<br>Remittances Portfolio Foreign claims Remittances Portfolio Foreign claims<br><!-- End of picture text --> Sources: World Integrated Trade Statistics, Bank for International Settlements, International Monetary Fund, World Bank.

Notes: Averages for 2010-15, except for FDI (2010-14 average). In percent of total exports of each EMDE region, total inward FDI stocks in each EMDE region, total portfolio liabilities (derived from creditor data) in each EMDE region, total foreign claims of BIS-reporting banks on each EMDE region, and total remittance flows to each region.
# **5.3.2 Migration**

Even if services and infrastructure across communities are distributed randomly, individuals may choose to migrate to communities that offer the preferred set of services. In particular, disadvantaged households with unhealthy children may decide to move to areas with better infrastructure and community services, a choice process likely to be reinforced by the fact that better employment opportunities are usually found in the same areas. In the absence of a valid set of instruments to explain migration decisions, we treat the households' place of residence as exogenous to child health. Moreover, since in rural Guatemala there is little migration of families with small children (96% of the children in the EGSF sample have spent all their life in the villages where they were born), community characteristics are likely to reflect the environment in which children grew up.

# **5.3.3 Clustering**

The multistage clustered sampling design adopted in the EGSF is typical of many demographic and health surveys conducted in developing countries, including the World Fertility Surveys and the Demographic and Health Surveys. In the first stage of sampling, 15 rural communities were randomly selected in each of the four chosen departments. The second stage involved random selection of about 100 households within each community." The data that we use for our empirical analysis are thus characterized by two levels of clustering: children are grouped within families and families are grouped within communities. The 2963 children for which we have complete information on all the variables that we use in regression analyses come from 1658 families distributed across 60 communities.<sup>12</sup> In our sample, the number of children per community ranges from 10 to 87 (mean = 49.4). The number of families per community ranges from 11 to 44 (mean 27.6) and the number of children per family ranges from I to 4 (mean = 1.8).

While the growth experience of children in different communities may be independent of one another, the anthropometric outcomes of children in the same community, especially if they belong to the same family, are likely to be similar since these children share many characteristics when growing up. Thus, a consequence of clustering is that observations in the same group may be more homogeneous than

- l The actual number of households selected in each community varied between 92 and 109.

> 12 The structure of the data is slightly more complex since more than one woman was interviewed in a few households. In such cases, children of different families (mothers) belong to the same household. The total number of households for the anthropometry sub-sample is 1636 (in contrast to 1658 families). For the sake of simplicity in modeling, in our statistical framework we treat children of different families (mothers) as if they belonged to different households. In the analysis that follows, family and household are used interchangeably.

10
30 lower income groups have only about 25% of their income in actual cash. (P. Gertler, personal communication) rune costs, travel expenses, and lost income are other costs which are often not taken into acunL Each visit for which a theoretical 0.5% of income would be charged, could easily represent a much higher percentage of income when travel costs, lss of a day's wages, and other factors are taken into accounL The statement is often made that 'people are willing to pay for drugs,' the point of reference being what people have paid in the past, and thereby demonstrated themselves willing' to pay. But people often make expenditures on drugs in what are perceived (rightly or wrongly) to be life-or-death situations. Since economic barriers to seeking care are greater for poor people, they may tend to delay seeking cae until the situation really i life.threatening. They may go into debt or seU important posa ions (cale, etc.) to be able to purchase drugs and medical care in an emergency situation. 'Willingness to pay' data, therefore, must be used areflly, with the understanding that the poorer the person, the more likely it is tQat the expenditure recorded reflects an extraordinary situation-and cannot be taken to represent an amount they would be willing (or able) to pay on a regular basis. Special caution must be exercised when using such data to predict revenues from or utilization of routine health services.

Finally, the workshop participants concluded that in view of the very low income levels in much of rural Afia, charging people for drugs would be more useful as a rationing mechanism than as a major source of revenue. It was important to be realistic about the potential of cost recovery, experiec with user fees has shown that in many low-income countries, such fees do not contribute greatly to revenue. Even full cost recovery for drugs has not been possible to achieve in many countries. (WHO, 1988d) The Bamako Initiative, which proposes to recover 2-3 times the base cost of drugs, could disqualify a considerable proportion of the poor inhabitants of rural areas. (Anonymous, 1988b)

# _Impact of the AIDS epidemic on drug supplies_

In countries with a high prevalence of HIV infection, the epidemic represents signfficant additional morbidity and mortality in a group of primarily young adults who were not previously ilL It will make demands onA all health resources including hospital beds, staff cae, and drugs, and will make it difficult to maintain present levels of care for other illnesses. There h already some indication that other non-HIV positive patients are being crowded out of the health care system in some countries. As HIV seropositivity often causes a longer hospital stay, the overall number of admissions may stagnate or decline as occupancy rates reach or surpass 100%. The intensity of care may also increase since only very sick people will be able to be admitted to hospital, and more care wi have to be provided on an outpatient basis; this in turn can be expected to result in an overall higher cost per admission and per bed/day.
9 efficiency, the electricity sector is the only one to enjoy a representative coverage thanks to a series of papers by Rossi (2001, 2002 and 2003). Rossi works with data on 39 firms covering a dozen countries between 1994 and 2000. He finds an annual average rate of productivity change around 1 percent for that period, mainly from the technical change component. Moreover, his evidence also suggests that private firms are the firms that are pushing the South American production frontier in the right direction. He finds no evidence of technical progress in public firms, whereas technical change in private firms is around 2 percent per year. Since the renegotiation rates are fairly low in this sector, it seems to suggest that both the regulators and the operators were satisfied with these gains.

There is unfortunately no comparable cross-country coverage for the other sectors; most of their evidence is for specific experiences. Accounting for this limitation, the various studies provide reasonable anecdotal evidence suggesting that reforms have on average improved efficiency quite significantly throughout the region. For railways, Estache et al. (2002a) estimated TFP with _Tomnqvist_ indexes for Argentina's passenger and freight railways companies and for Brazil's freight railways companies which both work under price caps. In Argentina, they find an average annual efficiency gain of 5.3% for freight and 9.8% for passenger concessions. In Brazil, the average TFP growth has been 8.4% in the first two years after the private operation of the sector started. Before the annual efficiency gains achieved during a gradual restructuring process started in 1985, the average improvement in TFP was 5.5%. An interesting difference between the two countries' experience, is that all Argentinean freight contracts ended up being renegotiated while all the Brazilian freight contracts worked out well and are on track for a scheduled tariff revision. Additional differences between the two countries include the fact that demand was grossly overestimated in Argentina in comparison to Brazil and that the resources allocated to the monitoring of the contracts were much larger in Brazil than in Argentina.

For telecoms, Benitez et al. (2002) relies on an engineering-economic model (also known as hybrid cost proxy models-HCPM) to estimate that in one of Argentina's provinces, the efficiency gains achieved between 1991 and 2000 were about 3.9% per year, including the benefits from the major technological changes the sector enjoyed. Some degree of renegotiation also took place in that sector to allow contract extensions. For Brazil, Resende and Facanha (2002) using a Data Envelope Analysis (DEA) do not find any statistically significant impact
competition between Pemex's transportation subsidiary and the local distribution company, which would assure competitive contracts for consumers inside local distribution zones.

Distribution zones are tendered through an open bidding process, and the winner is granted an exclusivity period. For each tender the CRE defines a distribution geographical zone and sets a minimum consumer-coverage target that the firm must reach by the end of the first five years. Participants present technical and financial proposals, including a market demand study. Evaluation is carried out in two stages. In the first stage the technical quality of the project is evaluated. Those that pass this test are evaluated according to the lowest quoted value of the average revenue for the first five years. Distributors that had a distribution concession prior to April 1995 are also incorporated into the permit regime.

# _Regulation of Distribution Tariffs_

Decisions on how to regulate distribution tariffs were also influenced by the greenfield nature of natural gas distribution in Mexico. The choice was between cost-of-service regulation and price caps to regulate price level, and between tariff basket and average revenue to regulate price structure.16 The main features of these types of regulation are shown below

> 16 Price-level regulation refers to the long-run distribution of rents and risks between consumers and the regulated firm. Price-structure regulation refers to short-run allocation of costs and benefits among the different types of consumers (Vogelsang 1999).

17
view of the fact that the calibration of _f_<sup>2</sup> ( _·_ ) is anchored to _e_<sup>2</sup> 100<sup>= 0</sup><sup>_._065, the differences</sup> in _qt_ associated with the difference between the two ‘epidemics’ _D_ = 1 and _D_<sup>_∗_</sup> = 1 will yield different values of _a_ 1 and _a_ 2.

# **5 The benchmark cases**

The evolution of the entire system is determined by the extended household’s decisions ( _ct, e_<sup>1</sup> _t_<sup>_, e_2</sup> _t_<sup>)ateachpointintime,whichthendetermine,inturn,theformationofnew</sup> human capital through the educational technology, as specified in (1) and (2). Put formally, given the current environment _Zt_ and future environment _Zt_<sup>_e_,asforecastby</sup> the household’s decision-makers, and given the assumption of stationary expectations concerning future investments in education, the household solves problem (17) for period _t_ . In doing so, it determines some of the elements of _Zt_ +10 and _Zt_<sup>_e_</sup> +10<sup>.The</sup> associated demographic elements are provided by the forecasts in Section 4. The values of _αt_ and _σt_<sup>_a_(</sup><sup>_a_=1</sup><sup>_,_2)areassumedtoremainconstantfrom2000and1990onwards,</sup> respectively. These complete the respective vectors, whereby the values _σ_ 90<sup>1= 185and</sup> _σ_ 90<sup>2=800aredrawnfromKenya(1996)andAppletonetal.(1999,Table6),after</sup> conversion into the units of _Yt_ . One period later, in _t_ + 10, the household solves its problem anew, and so forth. The results of this process for _D_ = 0 and _D_ = 1 are set out in Tables 6 and 7, starting in 1990.

A noteworthy feature of the counterfactual is that all children complete full primary schooling ( _e_<sup>1</sup> _t_<sup>=1)from1990onwards,aresultthatdoesnotsquarewiththeactual</sup> value of _e_<sup>1</sup> 90<sup>=0</sup><sup>_._76(seeTable1).Themainreasonforthisdiscrepancyisthatthe</sup> chosen value of _σ_ 90<sup>1relatestothemid1990s,atwhichtimeitslevelappearstohave</sup> been markedly lower than in the mid 1980s (Appleton et al., 1999, Table 6). Unable to find any estimates for the intervening years, we choose the later of the two, thereby erring on the optimistic side. With the promise of declining mortality and no further 32