rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4811

# **Abstract**

The paper evaluates the effects of privatization in the post-communist economies and China. In postcommunist economies privatization to foreign owners results in a rapid improvement in performance of firms, while performance effects of privatization to domestic owners are less impressive and vary across regions, coinciding with differences in policies and institutional development. In China relatively more estimates suggest that privatization to domestic owners improves the level of performance. Concentrated private ownership has a stronger positive effect on performance than dispersed ownership in the post-communist economies, but foreign joint ventures rather than wholly owned foreign firms have a positive effect in China. Worker or collective ownership does not have a negative effect. In the postcommunist economies new firms are equally or more efficient than firms privatized to domestic owners, and foreign start-ups are more efficient than domestic ones. Privatization is not associated with lower employment. When accompanied by complementary reforms, privatization has a positive effect on economic growth. Three factors appear to drive the more positive effect of privatization to foreign than domestic owners. Domestic managers have more limited skills and access to world markets, domestically privatized firms have been more subject to tunneling and in some countries new large shareholders artificially decreased performance. The important policy implication is that privatization per se does not guarantee improved performance, at least not in the short- to medium-run. Type of private ownership, corporate governance, access to know-how and markets, and the legal and institutional system matter for firm performance.

This paper—a product of the Development Economics Department, Research Support Unit (DECRS)—is part of a larger effort by the World Bank’s Research Committee, in consultation with Regions and Networks, to commission surveys of recent policy research and diagnostic analyses of the current state-of-the-art in priority areas for developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. For information, contact jdethier@ 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
# **Table A.5**

# **Average Village Characteristics in 1988 by Timing of ID Card Distribution**

|||Year ID|Cards Were I|ssued||
|---|---|---|---|---|---|
|||prior to 1988|in 1988|after 1988|p-value<sup>1</sup>|
|Share of Productive Assets Owned by the|mean|0.399|0.260|0.246|0230|
|Village Collective|std. dev|0.278|0.205|0.276|.|
|Mean Consumption Per Capita|mean|414.3|367.7|405.1|0566|
||std. dev|154.6|159.6|86.9|.|
|Mean Income Per Capita|mean|627.2|504.2|558.1|0278|
||std. dev|243.3|213.7|162.4|.|
|Cultivable Share of Total Land Area|mean|0.691|0.546|0.512|0203|
||std. dev|0.277|0.268|0.309|.|
|Share in Mountains|mean|0.14|0.23|0.31|5|
||std. dev|0.36|0.43|0.48|0.89|
|Share Near a City|mean|0.21|0.04|0.08||
||std. dev|0.43|0.20|0.28|0.341|
|Cropped Land Gini Ratio|mean<br>std. dev|0.21<br>0.07|0.15<br>0.06|0.17<br>0.05|0.024|
|Average Household Size|mean|4.40|4.70|4.68|11|
||std. dev|0.66|0.48|0.53|0.3|
|Total Village Land|mean|4508|4633|7676|0512|
||std. dev|4694|4676|9401|.|
|Male Share in Population|mean<br>std. dev|0.51<br>0.02|0.51<br>0.02|0.50<br>0.02|0.716|
|Share of Labor Force Earning Wage|mean|0.27|0.17|0.16|1|
|Locally|std. dev|0.21|0.19|0.22|0.37|
|Village Population|mean|1646|1288|1501|0429|
||std. dev|1089|537|925|.|
|Village Consumption Per Capita Gini|mean|0.18|0.16|0.16||
||std. dev|0.03|0.03|0.03|0.142|
|Village Income Per Capita Gini|mean|0.23|0.22|0.21|0855|
||std. dev|0.07|0.05|0.07|.|
|Average Years of Schooling, aged 18-22|mean|8.25|7.57|7.47||
||std. dev|1.73|1.33|1.21|0.186|
|Share of 15-18 Year Olds Enrolled in High|mean|0.35|0.33|0.32||
|<br>School|std. dev|0.47|0.47|0.47|0.861|
|Observations||14|25|13||

Notes:

1. p-values in column 4 test the hypothesis that the three means are equal.

2. Consumption and income per capita are reported in 1986 RMB Yuan.

3. Sources: RCRE Household and Village Surveys (1986 to 2003), and RCRE Supplemental Surveys (2004).

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These differences imply that the country-rankings of Egypt and Botswana differ considerably between _HS_ and _HHS_ . While Egypt is 11 places below Botswana by _HS_ , it is 18 places higher by _HHS_ . More generally, we could also estimate how many re-rankings might occur overall, on average, for all countries. When considering all countries and all years in the sample, we observe an average absolute difference in rankings between _HS_ and _HHS_ of 3.

**Table 2:** Egypt and Botswana: Poverty values and rankings in 2008

|||Values|||Rankings||_ω_(**y**)|Median inc.|
|---|---|---|---|---|---|---|---|---|
||_HS_|_HHS_|_HA_|_HS_|_HHS_|_HA_||(PPP$)|
|Botswana|29.7|22.9|15.8|89|104|112|0.51|117|
|Egypt|32.9|17.1|4.7|100|86|83|0.44|132|

Our next example illustrates how trends can diverge across the three poverty measures. Figure 6 displays the evolution of poverty in Samoa over 2002-2008, as captured by _HA_ , _HS_ , and _HHS_ . Samoa experienced unequal income growth over 2002-2008; that is, both the standards of living (measured by either mean or median income) and inequality (measured by the Gini index) increased. The greater inequality resulted in the fraction of individuals who were income poor, as measured by _HS_ , increasing by 7% over this period. However, the economic growth meant that over time, a considerably smaller fraction of those income poor remained absolutely income poor. In fact, the absolute headcount ratio decreased by 70% over this period. Thus, many absolutely income poor individuals became only-relatively income poor, an evolution not captured by _HS_ . _HHS_ , on the other hand, does capture the shift from absolute to only-relative income poverty, decreasing by 9% over 2002-2008. Moving beyond this illustrative example, we can estimate how often these opposite conclusions between _HS_ and _HHS_ occur in our sample. Considering only countries with _za < z_ <u>(</u> _<u>y</u>_ <u>), for which</u> _HS_ and _HHS_ do not coincide, we compare the evolution of _HS_ and _HHS_ over all _t/t −_ 4 periods for all countries in the sample.<sup>45</sup> The results show that the share of opposite trends between _HS_ and _HHS_ is 9.7%.

# **6.4 Global Poverty: Trends and Distribution**

We turn now to studying how global poverty varies between the absolute headcount ratio ( _HA_ ), the societal headcount ratio ( _HS_ ), and the hierarchical headcount ratio ( _HHS_ ). First, we analyze the trends in global poverty according to these three measures. Figure 7a shows that _HHS_ decreases by 48% between 1999 and 2015,

> 45We use the following reference years: 1999, 2003, 2007, 2011, and 2015.

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larger (Table 1). Second, we find a distribution of connected firms across detailed 4-digit sectors that is very similar to the distribution of connected firms in previous work on Egypt. We observe at least 1 connected firm in 42 percent of all included 4-digit sectors (51 out of 118) relative to 47 percent in Diwan et al. (2015).<sup>4</sup> Moreover, out of the 51 connected 4-digit sectors, 44 have also been identified as connected in Diwan et al. (2015) in 2011.<sup>5</sup> We further find that politically connected firms benefit from exclusive input subsidies, including energy subsidies, and have superior access to bank financing, both, concurring the findings for Egypt of Diwan et al. (2015). Third, the number of politically connected firms in our sample is comparable to the number identified in previous research.<sup>6</sup> Finally, it is important to note that false negatives, if existent, would create a bias against our finding that politically connected firms benefit from policy favors such as privileged access to input subsidies, state contracts, and favorable exchange rates. Our estimates on the impact of political connections should therefore be regarded as a lower bound.

# **Innovation and other firm performance measures:**

The survey for Egypt provides detailed information across different dimensions of establishments’ innovation activity which allow us to define the following five innovation variables which are equal to 1 if a firm introduced in the last 3 years (i) a new or significantly improved product, (ii) a new or significantly improved process such as a new production, logistics, delivery, or distribution method or supporting activities for these processes, (iii) either introduced a new product or a new process, (iv) a new or significantly improved product that is new to the establishments main market, and (v) invested in an internationally recognized quality certification.

The survey also provides information on each establishment’s specific new product they introduced as well as on the exact type of process innovation adopted. The information was

> 4 Diwan et al. (2015) define politically connected firms as firms that that have a manager, owner, or board director that has a high political post in the ruling National Democratic Party (NDP) or is a member of the cabinet of ministers of the central government.

> 5 Arguably, we should not expect major changes in the group of firms with political connections to high-level (former) government officials in this study relative to Diwan et al., 2015 (compare Section 3).

> 6 Fisman (2001) and Ryjkers et al. (2016) identify about the same number of politically connected firms with available financial data in Indonesia and Tunisia, respectively. Diwan et al. (2015) observe a somewhat larger number of connected firms in Mubarak’s Egypt in 2011, but their sample includes a significant number of connected firms observed in sectors not covered in our sample such as in mining and extractives and finance.

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18 As previously noted (Table 2), the alternative definitions of manufactures produce sizeable variations in the reported values of individual countries trade in these goods, but Table 4 shows that differences in country rankings and growth rates also result. Several countries like Brazil, India, Indonesia or Israel have their rank position changing by one to three places while a major change occurs for Saudi Arabia which jumps from 15th position under IBRD/UNCTADIGATT to 10th place when the UNIDO definition is used. Table 4 also shows that the alternative definitions can produce major differences in compound growth rates for manufactures exports (note the 1977-1987 statistics for Indonesia, Saudi Arabia, Philippines, etc.), a point that could lead to important discrepancies in country classifications of "fast growing" exporters of manufactures.

# V. Summary and Conclusions

While economists often stress the importance of increasing developing countries' exports of manufactures, there are major differences in how some national and international agencies define these goods. Using actual trade data for 72 developing countries this study showed that the alternative definitions lead to major discrepancies in the value, share and growth rates of manufactures exports. In addition, the various definitions produce important differences in country classifications such as "major" or "fast-growing" exporters of manufactures.

Sensitivity tests show that relatively few products are responsible for the major differences in results, with the key item being refined petroleum products (SITC 332). Since the product characteristics, value added, and capital intensity of refned petroleum is similar to petroleum based chemicals in SITC _5_ the rationale for excluding these items from manufactures is weak. Similarly, it is also difficult to see why some of the major pro(essed food products are excluded, given the high degree of processing and capital intensive production techniques employed for these items. As previously noted, excluding these items on the basis that they are consumables is inappropriate since similar products (falling in SITC _5)_ are included in the IBRD/UNCTAD/GAIT core definition of manufactures.
10 The MTF survey data has extensive information about households, dwellings and housing tenure, expenditures on various consumption and durable items, cooking equipment and habits, access to electricity, experience with electricity outages, equipment used for lighting, or to powering appliances during outages, and sources of energy use. The survey was conducted between July and November 2017, and resulted in 6000 completed questionnaires, with almost 90% of the interviews completed in August and September.

A total of 4047 out of 6000 households reported that they were connected to the NEA grid. Some 640 households produced the most recent electricity bill, which displays the exact consumption in kWh during the latest billing cycle. The remaining households reported their typical bill in Nepalese Rupees (NR), and we are able to compute their monthly consumption in kWh by matching the bill amount with the NEA tariffs.<sup>10</sup> Figure 2 shows two histograms of the distribution of monthly electricity consumption. Figure 2.a is based on the exact consumption in kWh from the bill. Figure 2.b is based on either exact consumption, when available, or calculated consumption if the respondent merely reported the bill in NR. The two distributions are very similar: The average consumption is 61 kWh, and the median is 34 kWh for the sample with exact consumption data, and 38 KWh for the blended exact and calculated consumption sample, indicating that Nepali households use about 1-2 kWh/day.

The questionnaire asks respondents to indicate whether there were some periods during the year that were worse than most in terms of unscheduled electricity outages. Based on a total of 1543 responses, people experience, on average, 76.8 outages (median 40.5) and 92.38 hours (median 63) per month during the worst months. In practice, this means that there are unscheduled outages every day, for a total of 2-3 hours a day with electricity gone without

> 10 At the time of the study the exchange rate of one United States dollar was 108 NR.
# **Figures**

**Figure 1: Logistics performance vs. trade in parts and components.**

<!-- Start of picture text --> 25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>1.5 2 2.5 3 3.5 4 4.5<br>Share of Parts & Components in Total Exports Fitted Values<br><!-- End of picture text -->

## _Source: Arvis et al. (2010)._

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Second, we find empirical evidence that is broadly consistent with the hypothesis that crypto-assets are regarded as a risk asset. Global financial conditions were extraordinarily loose during our sample period. Real U.S. 10-year Treasury yields turned negative driven by the pandemic’s impact and the policy response to support financial markets and stimulate the economy (e.g., quantitative easing). In this context, we find that countries’ crypto volumes increase when real U.S. 10-year Treasury yields fall as it also tends to loosen global financial conditions and increase risk appetite. We further find that crypto volumes fall when our proxies of global risk aversion rise (i.e., the VIX index and the U.S. BBB corporate bond spread). Related and consistent with earlier literature (e.g., Liu and Tsyvinski 2018), we document that volumes typically respond positively to crypto-assets price momentum, suggesting speculative motives are relevant as well. Some emerging financial industry analyses indicate that crypto-assets may be a useful addition to a balanced investment portfolio, also as correlations with traditional asset classes appear relatively low (at least prior to the COVID-19 pandemic), but their short and volatile history makes it too soon to draw conclusions (e.g., Goldman Sachs, 2021a and Citi, 2021). However, others have concluded that crypto-assets are not a viable investment for a diversified portfolio (e.g., Goldman Sachs, 2021b).

Cross-sectional OLS regressions on 2020 crypto volumes for all available countries show that financial development and economic freedoms do not robustly explain the cross-country variation in crypto-assets activity associated with both all transaction sizes and smaller transactions only. However, we find tentative support that crypto activity is higher in less economically developed countries and in countries with stronger ICT penetration, broadband subscriptions in particular. We also find that crypto activity is higher in countries where personal remittances play a more important role. This finding is consistent with Graf von Luckner et al (2021) who study “off chain” transactions on LocalBitcoins.com, a large peer-to-peer bitcoin exchange. They estimate that about 7 percent of bitcoin transactions are used to make payments, of which 20 percent represents transactions across borders. While more research is necessary, these results suggest that cryptoassets may be used as a response to overcome long-standing challenges in cross-border payments and remittances (including to evade capital controls). Nascent implementations of new promising technologies (e.g., the Lightning Network, see Poon and Dryja (2016)) that could address current transaction throughput challenges of crypto-assets may help accelerate adoption for (cross-border) payment transactions, but these technologies are largely untested at scale and may also pose new risks.

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(Table 1). The reverse is observed in 2016-17. The absence of a clear pattern could partly stem from the fact that data from national accounts are themselves a source of contention (see e.g. Subramanian, 2019 and Goyal and Kumar, 2020 for details).

In Figure 8, the variance of log consumption per capita in the CPHS is lower than the variance observed in the NSS-2011 (on average 0.267 in the CPHS compared to 0.368 in the NSS-2011). The gap in consumption inequality is larger in urban areas. The Gini coefficient of inequality obtained using reported CPHS weights would rank urban India at par with Sweden, the 25<sup>th</sup> most equitable country in the world. By comparison, the NSS-2011 would rank urban India around the 60th most unequal country in the world.

The third moment of the log consumption per capita distribution is also markedly lower in the CPHS when compared to the NSS-2011. Figure 9 compares the third moment between the two surveys for urban and rural separately<sup>21</sup> . The gap in the third moment is larger in urban India, and larger than the gaps observed for the second moment (Figure 8). The second and third moment of log per capita consumption in CPHS are on average about 27 and 70 percent lower than the respective moments from the NSS-2011.

||Mean per|Private fnal|||
|---|---|---|---|---|
||capita|consumption|||
||consumption|expenditure|Growth in|Growth in|
||expenditure|per capita|survey|nominal|
||(MPCE,|(PFCE,|nominal|PFCE per|
|Year|nominal)|nominal)|MPCE|capita|
|2015-16|2193|6334|||
|2016-17|2315|7026|5.6%|10.9%|
|2017-18|2558|7638|10.5%|8.7%|
|2018-19|2846|8457|11.3%|10.7%|
|2019-20|3143|9179|10.4%|8.5%|

Table 1: Comparison of levels and trends in nominal consumption per capita in CPHS and National Account Statistics (NAS). Notes: Per capita consumption estimates are in nominal terms. Private final consumption expenditure is based on Statement 1.12 of national accounts statistics (NAS). The population estimates are also from NAS. Consumption per capita in CPHS is approximately 32 to 34 percent of PFCE per capita from NAS across years.

Comparing expenditure and non-expenditure statistics derived from the CPHS to

> 21Defined as _E_ [( _x − E_ ( _x_ ))<sup>3</sup> ] where _x_ is the log consumption per capita

22
Hence natural benchmarks are the sort of price indexes that a statistics office would use if price data were available. Typically, this would be a fixed-weight index, like a Laspeyres, for temporal deflation. For spatial deflation a statistics office might use a variable-weight superlative index, like a Törnqvist, since substitution bias is likely a bigger concern over space than over time given that relative prices do not vary much over the short to medium term (Van Veelen and Van der Weide 2008). Our Weighted Country Product Dummy (WCPD) testing framework allows both fixedweight and variable-weight price indexes to be calculated (along with their standard errors), and we apply these dual benchmarks to evaluate the performance of the Engel curve method.

On top of the empirical results there are good reasons to doubt the Engel curve method. Anything varying over space and affecting food shares but omitted from Engel curve regressions gets attributed to price differences between areas. For example, calorie needs and food shares are high for hard working rural folk; equally poor but sedentary urbanites seem better off due to their lower food shares (Deaton and Dupriez 2011). Likewise, another Engel curve study for China found a much richer set of covariates than those of Almås and Johnsen—including temperature— were relevant to food shares and were correlated with spatial variables (Gong and Meng 2008). While these factors could be included in Engel curve regressions, they almost never are, yet they reflect long-standing spatial differences that likely vary much more than do short-term changes when food budget shares are compared over time. Thus the omitted variables bias problem is potentially much worse when the Engel curve method is used over space than over time.

Another problem for the Engel curve method is that food shares will vary with relative prices, but if there is no spatial price survey, then relative prices are unobserved. In temporal uses of the Engel curve method, the relative inflation rate for food versus nonfood is used, but this is no help for spatial comparisons. While unit values (surveyed expenditures divided by quantities) 6