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

After the Thatcher government popularized large-scale privatizations four decades ago, the policy has been widely used throughout the world, especially by developing and transition countries with large shares of state-owned enterprises (SOEs). The literature surrounding the effects of privatization is huge, as shown in several influential surveys across different generations of research (Megginson and Netter 2001; Djankov and Murrell 2002; Estrin et al. 2009; Megginson 2017). These studies tend to find modest gains in firm performance, while acknowledging the selectivity of privatizations. In this paper, we revisit this literature by addressing three questions: What determines a politician’s decision to privatize an SOE? How large are the causal effects of privatization in China? How do the effects of privatization depend on local government activism (i.e., its direct involvement in local firms)?

To make sense of the large literature on privatization effects, we need to consider several facts. First, privatizations tend to be selective based on past firm performance and characteristics. The public or private ownership of firms is a key dichotomy in shaping the basic structure of an economy, and has occupied the attention of generations of economists who have debated the merits and flaws of state ownership (Bardhan and Roemer 1992, 1993; Shleifer and Vishny 1994; Stiglitz 1994). Privatizations of SOEs, thus, not surprisingly, reflect deliberate considerations of many involved parties. When SOEs are profitable, the politicians in charge benefit from controlling access to these firms’ cash flows, and through arranging jobs in these lucrative firms. When SOEs are unprofitable, the government shoulders the burden of keeping them afloat. Thus, privatization decisions are not made in pure pursuit of efficiency. Instead, they are made to benefit politicians and advance their careers. The types of firms that can be privatized also depends on investor demand. Highly non-profitable firms do not attract buyers and are difficult to privatize. Most existing studies on the effects of privatization rely on the firm fixed effect model, and do not otherwise deal with selectivity of privatization. Second, the causal effects of privatizations should differ across countries and periods. A convincing conclusion from the survey of privatization literature is that the effects of privatization differ by the institutional context of privatizations (Djankov and Murrell 2002; Estrin et al. 2009): more positive effects or associations, for instance, have been consistently ``` 2 ```
evaluated to make sure that the reported innovation activities of establishments indeed reflect significant product and process innovations.<sup>7</sup> Moreover, we use information on firms’ revenues per worker (in logs). Capital is only observed for manufacturing firms for which we derive revenue-based total factor productivity (TFPR) as the log-difference of firms’ sales, capital, intermediate inputs, and labor, weighted by the different input shares relative to the corresponding sector averages. We also use two different measures of firms’ capital intensity: (i) the (log) capital-labor ratio for manufacturing firms and (ii) a dummy which is equal to 1 if the firm uses a generator which is available for firms in all sectors.

# **Policy privileges:**

The 2016 Egypt establishment survey contains representative information on several country-specific variables measuring the governments direct support for specific firms in the form of subsidies, state contracts, and access to the more favorable official exchange rate in a period when Egypt had a (de facto) multiple exchange rate regime. Especially, we use the following establishment level information:

- _Over the last three years has this establishment received access to subsidized input or energy prices?_

- _Over the last year, has this establishment secured or attempted to secure a government contract?_

- _It is sometimes said that establishments access foreign currencies at different exchange rates than the official one. Would you please estimate approximately how much more expensive as a percentage it would be to access foreign currency at an unofficial exchange rate?_

Moreover, we use information on the share of investments financed by banks to measure as establishments’ access to bank financing and a dummy which is equal to 1 if an establishments main market is international and 0 if domestic which is available for manufacturing firms.

# **Other control variables:**

> 7 The survey also asks for firms R&D activity but the activities reported by establishments revealed substantial misreporting so that this variable has not been used.

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# BOX 113:Eductionspenine and enrollMent at tiMe tlands: **Costa Rica and** Ghana

Do public financing of education and enrollment rates follow similar time trends? Data from Costa Rica and Ghana tell different stories. Education's share of the public budget and GDP in Costa Rica declined from 1981 to 1983, increased slowly after 1983, but in 1987 remained below the 1980 level (real expenditures also declined). Public education spending as a share of GDP is also below the 1983 level. Enrollment rates declined between 1980 and 1985, apparently in conjunction with declining public financing.

||1980|1981|1983|1985|1987|1988|
|---|---|---|---|---|---|---|
|% of public budget<br>|14.9|15.1|12.1|12.7|13.7|12.9|
|%of GDP<br>|6.7<br>|5.8<br>|_5.0_|_5.0_|_5.0_|4.8|
|Gross primary enrollment rate<br>|106%<br>|||101%|||
|Gross secondaty enrollment rate|47%|||41%|||

# Source: World Bank (1990d).

In Ghana, trends in financing levels and enrollment rates do not coincide. Ghana suffered a severe recession in the early 1980s, when central government spending on education declined markedly (both real expenditures and central government education spending as a proportion of the public budget and GDP). Enrollment rates at both the primary and secondary levels suffered during the 1980s despite rising central government spending after 1981. The increase in expenditures in 1986 resulted from a wage increase for teachers, which was not intended to expand coverage. The enrollment rate declined because population growth exceeded the rate of expansion of the school system.

||1975 1980|1981 1982|1984 1985|1987|1988|
|---|---|---|---|---|---|
|% of public budget<br>|20.6 22.0<br>|17.1 18.7<br>|20.2 18.0<br> <br>|23.9<br>|25.7|
|% of GDP<br>|4.5 2.4<br>|1.8 2.1<br>|2.0<br>2.4<br>|3.3<br>|3.5<br>|
|Real expenditures<br>(index)<br>|174.5 100.0|73.6 78.0|78.3 98.8|147.7|167.0|
|Enrollment rates:<br>Gross primary<br>|71% 80%<br>|77%<br>|76% 72%<br>|71%<br>||
|Gross secondary|37% 41%|38%|41% 39%|40%||
|S=cg:World Bank<br>....<br>**.**<br>.... .|(1990c).|||||

3.10 The coexistence of adjustment programs and declining enrollment rates does not imply causality, nor are the means through which adjustment policies translate into changes 26
Policy Research Working Paper 10446

# **Abstract**

This paper studies the effect of a local sectoral productivity shock on subnational structural transformation. The analysis is based on regional input-output tables constructed for 2004 and 2013 and available censuses of firms in 2003 and 2013 for Ghana. Based on the data, the analysis confirms the occurrence of a mining productivity shock. Between 2004 and 2013, mining grew dramatically as a share of gross domestic product. The mining shock occurred primarily in the south of Ghana with much larger increases in mining’s share in regional output, the number of mining firms, and mining employment than in the north of the country. The findings show that the mining productivity shock led to growing regional (north-south) differences in intersectoral linkages, with greater intermediate use of mining output and a larger sectoral total factor productivity ratio between mining and manufacturing in the south than in the north. Informed by international evidence of strong intersectoral linkages between mining and heavy manufacturing industries, the paper examines the performance of heavy manufacturing in response to the mining productivity shock. The elasticity of heavy manufacturing to mining employment growth is 50 percent larger in the south than in the north, generated by an increase in both average firm employment and the entry of new firms. These north-south differences are interpreted as possibly due to weak interregional production linkages.

This paper is a product of the Social Protection and Jobs Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at at saumik.paul@newcastle.ac.uk and draju2@worldbank.org.

_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
Kerala - the focus of this paper - is regarded as a model of social development in the developing world. When last recorded in 2014, life expectancy was 74.9 years, the highest among states in India. The literacy rate was 94 percent in 2011, compared with 73 percent for India as a whole.<sup>9</sup> Yet despite these laudable achievements in health and education, Kerala has long fared poorly in terms of job creation, suffering from double-digit unemployment rates for decades.

Since the 1960s, these poor prospects at home have compelled Keralites to pursue jobs both in other states within India and abroad. In the 1970s in particular, rising oil prices in the Arabian Gulf countries and the resulting construction boom fueled a significant demand for temporary labor, mostly in low-skilled manual work. Keralites responded to this demand in sizeable numbers (Zachariah et al., 2003). By 1998, 7.3 percent of Keralite households had at least one member in a foreign labor force. In 2003 (the end of the five-year period examined in this study), that figure had risen to 8.4 percent. More recent data from the 2014 wave of the KMS survey show that international migration continues to trend upward, with 18 percent of households having at least one member in a foreign labor force that year.

The share of Keralite households with one or more members participating in the labor force of other Indian states has been considerably lower over time. Between 1998 and 2003, it increased marginally from 3.2 percent to 3.4 percent. By 2014, 4.9 percent of Keralite households had members working in another Indian state (KMS, 2014).

Domestic (or interstate) and international migration from Kerala appear to be competing options for households, with one almost always being chosen over the other. Only infrequently does a single household send family members both overseas and to another Indian state. In 1998, approximately 6.5 percent of migrant households had both domestic and overseas migrant members. This share had fallen in 2014, with less than 3.5 percent of migrant households having members both working abroad and within India.

> 9 Planning Commission, Government of India, data tables as of August 12, 2014.

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||_Urban_|-|50|96|-|
|---|---|---|---|---|---|
||_National_|20|13|61|3.5|
|Togo|_Rural_|-|3|40|-|
||_Urban_|-|26|89|-|
|Sub-Saharan|_National_|32.4|30.7|61.1|1.4|
|African|_Rural_|-|23.4|48.6|-|
|countries|_Urban_|-|42.4|82.7|-|

<u>Source: World Development Indicators</u> The statistics in Table 1 show that Burkina Faso and Togo are deprived in basic utilities compared with Sub-Saharan Africa as a whole. Electricity access rates are respectively about 15% and 20% for these countries against 32% for the region. Concerning access to a telephone line, Burkina Faso has the lowest access rate with 1%, while Togo displays a relatively high rate of 3.5% against 1.4% for the whole region. Access to improved sanitation seems to be a great challenge since the rates remain low (17% and 13% respectively for Burkina Faso and Togo) against 31% for the region. Finally, regarding access to improved water, the situation seems better in Burkina Faso than it is in the SubSaharan region whose access rate is the same as that for Togo (61%). As expected, the situation is always worst in rural areas than in urban ones.

# **_2.3 Assets_**

The asset dimension considered in this study refers only to physical assets such as durable goods. Although assets are not targeted by the MDGs, they can be seen as one of the major concerns of the first MDG, which is to eradicate extreme poverty and hunger. Regarding the gender equality perspectives addressed by the third MDG, OECD (2010) suggests considering asset ownership. In fact, the ownership of physical assets can decrease the probability of being monetary poor (Sackey, 2005a). Given that income is most often volatile, assets are helpful for smoothing consumption (Brandolini et al., 2010) and thus they are likely to capture more closely the permanent part of consumption for households or individuals (Stifel and Christiaensen, 2007; McKay, 2009). Therefore, according to McKay (2009), a lack of assets could be considered to be a good proxy for chronic poverty.

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inflow period and the final four years, 1993-96. Bank borrowing played a relatively minor role during 1988-92, accounting for only 10 percent of total flows, but increased sharply to 60 percent during 1993-96. This occurred as a result of the establishment of the BIBF and was due mainly to two reasons: first, as outlined above, BIBF institutions were granted considerable tax advantages; and second, many Thai firms who could not directly access overseas capital markets were able to borrow from BIBF Thai banks. As a result, foreign bank loans through the Bangkok International Banking Facility soared from US$ 8 billion in 1993, its first year in operation, to US$ 50 billion in 1996, US$ 30 billion of out-in transactions and- 20 billion of out-out transactions.<sup>3 4</sup> Figure **3. Composition of Capital Flows** <!-- Start of picture text --> 100% _<br>80%<br>60%<br>40% 1<br>20%<br>o% _<br>1988-96 1988-92 1993-96<br>*iTotal nonbank Total banks<br><!-- End of picture text --> _Source: Bank_ of Thailand

**_Nonbank Capital_** _Inflows._ There are three salient facts regarding the composition and trends of nonbank net capital inflows:

- The composition of nonbank capital flows to Thailand over the full period 1988-96 has been relatively balanced. Net foreign direct investment (22 percent), portfolio flows (25 percent), nonresident Baht deposits (29 percent) and other loans (20 percent) all account for similar amounts.

- Again, however, the averages hide significant changes over time. Most importantly, other borrowing accounted for some 41 percent of nonbank inflows over the initial period 1988-92, but during 1993-94, there was a net _outflow_ of capital under this category. The data suggest that Thai firms used bank lending to refinance their direct borrowings from foreign financial institutions, especially since the establishmnent of the BIBF (Figure 4). In 1995-96, however, as explained below, the authorities implemented several measures to reduce the magnitude of bank, in particular shortterm, inflows and net direct extemal borrowing by firms became again positive.

34 Kawai (1997).
**Figure 4. Distributional Effects of Indirect Taxes in Mali and Niger, 2014**

<!-- Start of picture text --> 4.a Indirect taxes as a share (%) of market income per decile<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Mali Niger<br>4.b Share (%) of indirect taxes paid by market income deciles<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Mali Niger<br>Source: Authors<br><!-- End of picture text -->

# _Direct Transfers_

Direct transfers include a cash transfer program in Mali and scholarships and school meal programs in Niger. The first is the _Jigisemejiri_ cash transfer program, of which the World Bank is the main funder. The aim of this program is to cover 75 percent of households below the poverty line by the end of the project, which targets poor and food-insecure households. In 2014, the program covered Bamako and Sikasso. The second is support of grant to university students meeting the criteria of the _Direction des Bourses et des Aides Financières_ (e.g., citizenship, submitting an application). The third is a meal program that covers primary and secondary students 8
facilitating women business leaders’ relative advantage. We thus expect that availability of internet would reduce women business leaders’ productivity performance disadvantage.

_The personal network hypothesis_ : The effects of female corporate leadership on productivity improvement would be especially negative where access to personal network is important, that is, where trade credit relationship is important, where access to electricity and to bank credit is scarce and important, but less negative and pronounced where the rule of law is stronger, and where the availability of internet is better _._

# **3. Data and Empirical Results**

The key data source is the World Bank’s Enterprise Surveys (WBES) in 579 cities of 103 countries. The WBES data are collected by the World Bank to benchmark the business climate in developing countries across the world and to understand the determinants of firm performance. In each country the survey is based on the universe of eligible firms obtained from the country’s statistical office with stratified random sampling with replacement, and the result is a representative sample of the non-agricultural private economy in the country.<sup>11</sup> Stratification is based on two criteria: the sector of activity and firm size. Typically, the stratified sampling yields between 100 – 1,000 firms per country, with 108 firms for the median city. Industries range from manufacturing and construction to services and retail and wholesale trade.<sup>12</sup> We include data collected after 2006, although some WBES were conducted earlier. Prior to that year, there was considerable heterogeneity across countries in terms of the questionnaire format, sectors covered, and sampling methodology. Moreover, the samples for surveys conducted before 2006 were not generally representative. A complete list of variables and data sources is shown in Table 1; the summary statistics for our key variables are in Table 2. Our final sample consists of up to approximately 65,000 firms covering 579 cities in 103 countries.<sup>13</sup>

> 11 Thus, wholly state-owned firms are not in the sample.

> 12 See http:\\www.enterprisesurveys.org for a more detailed description of the WBES. 13 For each dependent variable, the number of observations differs, and the figure of 65,000 is for the dummy variable of female head which has fewer missing observations. The 579 cities in 103 countries refers to the sample where the missing data issue does not arise.

12
# **Table 9: Results table for Kyrgyz Republic, by occupation**

||**All**<br>**Occupations**|**Store Clerk**|**Farm**<br>**Worker**|**Receptionist**|**Software**<br>**Engineer**|**Manager**|**Medical**<br>**Doctor**|**Taxi Driver**|**Schoolteacher**|
|---|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
|**_Random assignment_**|**_sex_**|||||||||
|Male|0.055***|0.021|0.076**|0.091***|0.048|0.066*|0.112***|0.033|-0.000|
||(0.012)|(0.034)|(0.035)|(0.034)|(0.034)|(0.036)|(0.034)|(0.034)|(0.034)|
|**_Random assignment_**|**_age_**|||||||||
|Age 35|0.036**|-0.003|-0.094*|0.038|0.055|0.101**|0.056|0.054|0.110**|
||(0.017)|(0.051)|(0.049)|(0.047)|(0.049)|(0.049)|(0.051)|(0.048)|(0.046)|
|Age 45|0.049***|-0.004|-0.009|0.026|0.110**|0.096**|0.054|0.009|0.120***|
||(0.018)|(0.050)|(0.048)|(0.049)|(0.050)|(0.047)|(0.049)|(0.049)|(0.046)|
|Age 55|0.033*|-0.081|-0.041|-0.029|0.040|0.112**|0.143***|0.028|0.096**|
||(0.018)|(0.053)|(0.049)|(0.050)|(0.050)|(0.049)|(0.048)|(0.046)|(0.047)|
|**_Random assignment_**|**_pay_**|||||||||
|Pay 20000 KGS|-0.685***|-0.676***|-0.730***|-0.751***|-0.586***|-0.645***|-0.631***|-0.614***|-0.828***|
||(0.022)|(0.057)|(0.055)|(0.053)|(0.056)|(0.053)|(0.053)|(0.051)|(0.052)|
|Pay 30000 KGS|-1.204***|-1.119***|-1.196***|-1.255***|-1.112***|-1.155***|-1.195***|-1.173***|-1.436***|
||(0.022)|(0.054)|(0.054)|(0.052)|(0.053)|(0.050)|(0.051)|(0.051)|(0.046)|
|Pay 40000 KGS|-1.545***|-1.290***|-1.522***|-1.617***|-1.564***|-1.544***|-1.553***|-1.560***|-1.699***|
||(0.023)|(0.056)|(0.053)|(0.053)|(0.052)|(0.053)|(0.052)|(0.051)|(0.048)|
|Pay 50000 KGS|-1.855***|-1.607***|-1.807***|-1.900***|-1.811***|-1.922***|-1.929***|-1.857***|-1.997***|
||(0.026)|(0.060)|(0.057)|(0.058)|(0.057)|(0.057)|(0.055)|(0.052)|(0.053)|
|Constant|4.407***|4.268***|4.428***|4.362***|4.409***|4.377***|4.389***|4.489***|4.511***|
||(0.022)|(0.058)|(0.054)|(0.053)|(0.053)|(0.052)|(0.053)|(0.050)|(0.049)|
|Sample|All|All|All|All|All|All|All|All|All|
|Observations|23,928|2,991|2,991|2,991|2,991|2,991|2,991|2,991|2,991|
|R2|0.357|0.273|0.344|0.363|0.350|0.381|0.385|0.389|0.408|
|Adjusted R2|0.357|0.271|0.342|0.361|0.348|0.379|0.383|0.387|0.406|

notes: Standard error in parenthesis; .01 - ***; .05 - **; .1 - *; Ordinary least squares; Dependent variable: 1 = Severely overpaid, 2 = Somewhat overpaid, 3 = Fair, 4 = Somewhat underpaid, 5 = Severely underpaid; Controls for survey round not shown 16