rafmacalaba/datause-encoder-data huggingface dataset:
private firms outperformed SOEs at any given level of competition. A study of thousands of Indonesian manufacturing firms reached similar conclusions (Bartel and Harrison, 2005).

The biggest gains may come not from improving the performance of the incumbent SOE but from insuring that future market growth is met by more efficient private operators. In urban bus services, for example, the SOE seldom achieves the economies of the private operator, at least as long as the state is still willing to provide it with some level of subsidies. Preserving the SOE allows the government to avoid layoffs or other controversies associated with closure or privatization. But opening the market to private operators puts an effective cap on SOE subsidies especially if the private operators can recover their costs while charging the same fare as the public company. Meanwhile the private operators and their employees are motivated to oppose any substantial expansion of the SOE’s market share.

One potential complication is insuring that the playing field between the SOE and the private firms is relatively level so that the customers’ choice of firms improves the welfare of society as a whole as well as that of the customers. In the case of urban buses, for example, critics often complain that the private minibuses are driven recklessly and generate more traffic accidents and congestion per passenger than the full-size buses typically operated by the SOE. Similarly, with mobile telephones questions often ariise as to whether the charges for accessing a competitor’s lines to terminate a call unduly favor either the incumbent SOE or the challengers. In principle, such problems should be correctable.

A more important limitation is that direct competition is hard to apply to industries with characteristics of natural monopoly. Sometimes activities that are potentially competitive can be separated from those that are not, as in the separation of mobile and fixed-line services in telephony. But in industries where there are no potentially competitive activities or where separation poses practical problems, direct competition is not an option.

# **Mixed-Capital Enterprises**

37
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Policy Research Working Paper 10282

# **Abstract**

This paper documents the relationship between legal gender equality and the use of financial services, using individual-level data from 148 developed and developing economies. The analysis, which combines data from the Global Findex and _Women, Business and the Law_ databases, highlights the existence of a significant and positive correlation between gender equality in the law and women’s access to financial products. The results show that greater legal equality alleviates women’s involuntary financial exclusion. The findings also suggest that prevailing adverse social norms can nullify the beneficial effects of legal equality, and that better implementation of the law can facilitate a stronger relationship between legal frameworks and women’s financial inclusion.

This paper is a product of the Global Indicators Group, Development Economics. 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 cperrin@worldbank.org and mhyland@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
statistics. Section 4 describes the empirical strategy used. Section 5 presents the main results. Section 6 summarizes the paper’s main findings.

# **2. Literature Review**

<u>Nordhaus (1975), Lindbeck (1976), and Tufte (1978) were the first researchers to model</u> government investment behavior when governments are constrained by political circumstances. <u>Nordhaus (1975)</u> introduced the concept of a political business cycle, describing a pattern of public expenditures that starts with relative austerity in the early years after an election and ends with a binge just before an election when transfers are increased, and government expenditures are redirected toward high-visibility projects. The simple explanation of this phenomenon is that incumbent politicians want to convince voters that they care about their interests and are effective in running the government. Voters are assumed to be myopic or suffer “fiscal illusion” (Alesina, — <u>Roubini, and Cohen 1997)</u> valuing expansionary fiscal policies and underestimating their costs in terms of inflation and the future tax burden (Rogoff 1990).

Recent studies examine why rational, forward-looking voters are influenced by pre-election budget spending and do not instead punish politicians for fiscal manipulations. The model developed by <u>Drazen (2008)</u> assumes that incumbent politicians seek to both increase social welfare by providing public goods and get reelected. Voters are uncertain about the ability of incumbent politicians to deliver public goods with available resources and about politicians’ preferences among different voter groups. Competent politicians may be able to provide more public goods without significantly affecting future spending and taxes and signal their ability by undertaking more visible spending before elections. Incumbents may also express their political preferences by targeting spending toward particular groups of voters. Under these assumptions, the political budget cycle would exist even in the presence of rational and fiscally concerned voters.

Extensive empirical analysis has been conducted on the effect of political competition on social policies. Akhmedov and Zhuravskaya (2004) find that the size and composition of government expenditures change during the political cycle in the Russian Federation. Expenditures increased nine months before the election date and went up again one month before the elections. Spending on education, culture, and health grew considerably during the two months before the elections and decreased sharply after the elections. Drazen and Eslava (2010) find in Colombia systematic 4
17 in modem panel buildings which are usually located on the town outskirts is noticeably below the overall average.

In Gorodets, the multi-unit housing stock consists primarily of the dilapidated, old low-rise stock possessing no so-called modern conveniences (hot water and centralized gas), and the Khrushchev-era buildings which, despite their small size at least have the advantages of these modem conveniences, which are clearly highly valued (especially in comparison with the neighboring private housing stock, which has no conveniences at all"). This is why the tenants of the Khrushchev-era buildings hastened to privatize their dwellings (over 60%). It should also be noted that even in the apartment stock devoid of conveniences a fairly large share of apartments was privatized (36%). This may be connected with the establishment of the housing market, which we shall examine in detail in Section 2. On the whole, it can be concluded that the quality factor is a primary one in small towns.

Table 1.14 shows the impact of the demographic factor on privatization decisions. The provinces displayed the same trends as Moscow: pensioner households are the leaders, and families with children are the least active. In small towns practically all pensioner households became the owners of their housing in the course of privatization.

**Table 1.14.** Private ownership of housing by demographic categories in Vladimir and Gorodets, 1996 (% of owners)

|Demographic category|Vladimir|Gorodets|
|---|---|---|
|.-.Pe. n- r--:<br>.. ......<br>---- ......**.**.....|-..--.......**..**......|.................|
|single|77|85|
|husband and wife|71|93|
|Single working age people:|||
|childless|77|60|
|childless + parents|58|52|
|with children|28|50|
|with children + parents|28|44|
|M'i<br>w''''''i'g ''"age<br>**people:**|||
|childless|71|50|
|childless + parents|35|63|
|with children|31|52|
|with children + parents|54|_55_|
|Average '-.<br>---.<br>::|.:...-4>8|-<br>.._66|

Source: Vladimir and Gorodets Longitudinal Household Survey, December 1996.

Fig. 1.3 shows the structure of the households living in privatized (including privatized apartments and privately-owned houses) and non-privatized housing in Vladimir and Gorodets. The trends are obviously the same as in Moscow.

"This is probably typical of many small Russian towns, whose housing stock has a similar stiucture.
# **Tables**

## **Table 1: Distributional Summary Statistics for the Survey-Based Consumption and Income Variables Used in the Analysis**

|**_Country_**|**Year**|**Households**<sup>**a**</sup>||**Consump**|**tion**|||**Incom**|**e**||
|---|---|---|---|---|---|---|---|---|---|---|
||||_Mean_<sup>_b_</sup>|_Gini_|_MLD_|_Theil_|_Mean_<sup>_b_</sup>|_Gini_|_MLD_|_Theil_|
|_Burkina Faso_|2014|9,229|2.796|0.358|0.207|0.249|0.620|0.568|0.608|0.658|
|_Ethiopia_|2014|4,733|1.694|0.425|0.314|0.436|0.712|0.575|0.674|0.675|
|_Ethiopia_|2016|4,262|1.489|0.371|0.232|0.242|0.943|0.607|0.736|0.754|
|_Ghana_|2005|7,659|4.067|0.419|0.305|0.324|0.712|0.605|0.759|0.698|
|_Kenya_|2005|11,700|6.009|0.517|0.471|0.542|4.500|0.674|0.971|1.037|
|_Malawi_|2013|3,873|3.135|0.380|0.239|0.278|1.115|0.569|0.625|0.642|
|_Malawi_|2017|11,783|1.864|0.394|0.260|0.363|2.665|0.884|1.941|5.688|
|_Niger_|2011|3,538|2.679|0.299|0.144|0.161|0.680|0.601|0.789|0.692|
|_Niger_|2014|3,290|2.632|0.344|0.194|0.211|0.627|0.593|0.753|0.672|
|_Nigeria_|2004|16,922|2.419|0.399|0.275|0.283|3.593|0.648|0.932|1.113|
|_Rwanda_|2014|14,174|2.646|0.457|0.350|0.458|1.426|0.619|0.765|0.762|
|_Tanzania_|2009|3,037|2.529|0.387|0.247|0.278|0.969|0.620|0.808|0.745|
|_Tanzania_|2013|4,587|2.840|0.405|0.274|0.294|1.426|0.599|0.729|0.674|
|_Uganda_|2005|7,199|2.257|0.471|0.373|0.457|1.328|0.612|0.717|0.807|

_Source:_ World Bank, based on microdata from RIGA/RuLIS database. _Note:_ MLD = mean log deviation.

a. Effective number of observations after removal of missing and nonpositive values.

b. Dollar-a-day money amount in 2011 purchasing power parity.

33
# **Table 3: Association between Shocks and Consumption per Capita, without Transfers and Aid**

||% cha|nge in to<br>exclu|tal consu<br>ding aid|mption,|% change<br>consumpt<br>transfers|in food<br>ion, excluding|
|---|---|---|---|---|---|---|
||_IHS2_|_-4_|_IHS_|_3-4_|_IHS3-4_||
|**1. Weather shocks**<br>|||||||
|Drought in growing season|-3.0<sup>∗∗∗</sup>|(0.9)|-3.5<sup>∗∗∗</sup>|(1.2)|-4.3<sup>∗∗∗</sup>|(1.3)|
|Days with excess rainfall (Nov-Dec)|-1.0|(0.7)|-1.6<sup>∗∗</sup>|(0.7)|-1.8<sup>∗∗</sup>|(0.8)|
|Days with excess rainfall (Jan-Feb)|1.1<sup>∗∗</sup>|(0.5)|1.0<sup>∗</sup>|(0.5)|0.7|(0.5)|
|Days with excess rainfall (Mar-Apr)|1.3|(0.8)|0.8|(1.0)|1.3|(1.2)|
|TA’s median Palmer Index|-7.4<sup>∗</sup>|(3.7)|-4.3|(3.9)|-7.9<sup>∗</sup>|(4.3)|
|TA’s 98th percentile of rainfall|0.1|(0.1)|0.2<sup>∗</sup>|(0.1)|0.2|(0.2)|
|**2. Self-reported shocks**|||||||
|Job loss<br>|2.7|(1.7)|1.2|(3.4)|0.7|(3.6)|
|Food price shock|-1.9|(1.4)|-5.1<sup>∗∗∗</sup>|(1.6)|-6.1<sup>∗∗∗</sup>|(1.9)|
|Death|3.8<sup>∗∗</sup>|(1.5)|1.0|(2.1)|-0.2|(3.3)|
|**3. Household head**|||||||
|Age|0.6<sup>∗∗∗</sup>|(0.1)|0.8<sup>∗∗∗</sup>|(0.2)|1.5<sup>∗∗∗</sup>|(0.2)|
|Age, squared<br>|-0.0<sup>∗∗</sup>|(0.0)<br>|-0.0<sup>∗∗∗</sup>|(0.0)<br>|-0.0<sup>∗∗∗</sup>|(0.0)<br>|
|Female|-2.5<sup>∗</sup>|(1.3)|-1.3|(1.5)|-5.6<sup>∗∗∗</sup>|(1.7)|
|Years of education<br>**4. Household**|3.4<sup>∗∗∗</sup>|(0.2)|3.9<sup>∗∗∗</sup>|(0.2)|3.5<sup>∗∗∗</sup>|(0.2)|
|% its members in services|0.3<sup>∗∗∗</sup>|(0.0)|0.4<sup>∗∗∗</sup>|(0.0)|0.4<sup>∗∗∗</sup>|(0.0)|
|% its members in agriculture|-0.0<sup>∗</sup>|(0.0)|-0.0|(0.0)|0.0<sup>∗</sup>|(0.0)|
|% its members in industry|0.2<sup>∗∗∗</sup>|(0.0)|0.3<sup>∗∗∗</sup>|(0.0)|0.4<sup>∗∗∗</sup>|(0.0)|
|<br>No one employed|19.7<sup>∗∗∗</sup>|(1.9)|21.7<sup>∗∗∗</sup>|(2.5)|11.8<sup>∗∗∗</sup>|(4.0)|
|Household size|-11.6<sup>∗∗∗</sup>|(0.3)|-11.3<sup>∗∗∗</sup>|(0.4)|-9.6<sup>∗∗∗</sup>|(0.4)|
|Dependency ratio|-10.4<sup>∗∗∗</sup>|(0.5)|-10.4<sup>∗∗∗</sup>|(0.7)|-9.2<sup>∗∗∗</sup>|(0.7)|
|Has electricity<br>|55.1<sup>∗∗∗</sup>|(2.6)|51.9<sup>∗∗∗</sup>|(2.8)|41.6<sup>∗∗∗</sup>|(2.9)|
|Owns toilets|43.9<sup>∗∗∗</sup>|(5.7)|45.3<sup>∗∗∗</sup>|(5.4)|24.6<sup>∗∗∗</sup>|(2.9)|
|Own more than 2 plots|10.6<sup>∗∗∗</sup>|(1.7)|7.9<sup>∗∗∗</sup>|(1.8)|11.4<sup>∗∗∗</sup>|(1.8)|
|**5. Aid received in the last**|||||||
|**year**|||||||
|<br>MASAF or education aid<br>**6. Geography**|3.7<sup>∗∗</sup>|(1.7)|3.9<sup>∗∗</sup>|(1.7)|1.9|(2.0)|
|Rural Household|-9.8<sup>∗∗∗</sup>|(3.4)|-14.0<sup>∗∗∗</sup>|(3.0)|-14.4<sup>∗∗∗</sup>|(3.0)|
|Distance to town (km)|0.0|(0.0)|-0.0|(0.0)|0.0|(0.1)|
|District FE|Yes||Yes||Yes||
|IHS round FE and Season FE|Yes||Yes||Yes||
|Observations|35627||24643||24643||

Note: FE stands for fixed effects. The dependent variables are per capita consumption without aid (food and cash) in columns 1 and 2 and per capita food consumption (without transfers) in column 3. In Column 2, we restrict the sample to households included IHS 3 and 4 so that we could measure food consumption without transfers in a similar way. Due to lack of access to Stata dofile used to construct consumption aggregates in IHS2, we could not 19
**Table 3. Default options in selected collective pension schemes**

||**United Kingdom **|**New Zealand**|**Italy**|**USA**|**Canada/Quebec**|
|---|---|---|---|---|---|
|Default Option: Manager|NEST<sup>£</sup>|Selected by the<br>government<br>every 7 years<br>(multiple)|No|No|Yes,<br>but<br>still<br>needs<br>to<br>be<br>regulated|
|Default Option:Investments|Life Cycle|Conservative|Conservative|Life Cycle is a<br>safe<br>harbor<br>provision|Life Cycle|
|Employee option for opting<br>for an individual plan|No|**Yes**|No|No|No|
|Withdrawals|55+|65+|Retirement<br>age+|59 1/2|55+|
|Liquidity options|Disability<br>and<br>major<br>health<br>issues|Severe financial<br>hardship, major<br>health<br>issues,<br>and<br>purchase<br>of a first house|<br>Not Available|Loans<br>and<br>withdrawals in<br>exceptional<br>cases, including<br>finacial<br>hardship,<br>disability<br>and<br>termination<br>of<br>employment|Employer<br>contributions<br>can be<br>withrawn only<br>in cases of<br>financial<br>hardship and<br>disability. More<br>flexibilityis|
|Lump sums|Yes|Yes|NotAvailable|Yes|No|

£ NEST is required to accept all participants at a given fee. Source: national sources

# _Default pension fund manager_

Most of the systems in the sample have followed decentralized default options. In the case of the United Kingdom, the government created a quasi-government provider of pension plans (NEST), and it has to accept all companies at a standard fee. NEST manages a passive portfolio, mainly based on market indices. In New Zealand, the government selects default providers from a list of private providers that apply for that position every seven years. Criteria for selecting the default providers in New Zealand include investment capability, corporate strength, administrative capability, track record, stability, and capacity to offer financial education. The government selected nine default providers in July 2014. According to the New Zealand regulation, new default members are allocated evenly across the appointed default providers. Quebec province still needs to enact regulation about the default providers.

Since automatic enrollment in the case of the 401(k) plans in the United States is voluntary, there is no default provider. However, plan sponsors have the fiduciary responsibility of prudently selecting plan providers and closely monitoring the investments of the pension funds.

# _Default Investment Portfolio_

16
The evolution of Africa’s edible groundnut exports prices has not been uniform across major African exporters. As Figure 6 shows, despite a decline in recent years, prices for South African groundnuts have held up on average at a high level since the mid-1980’s. In contrast, prices sharply declined for Gambia and, to a lesser degree, Senegal. The discount on the latter two countries’ prices reflect their lower groundnuts quality and stricter EU quality and technical standards. The EU has indeed become more demanding, both from a public health perspective (permissible level of aflatoxin)<sup>6</sup> , and from a technical standpoint (size, uniformity).

**Figure 6. Unit value of raw edible groundnut of African origin (US$/ton)**

<!-- Start of picture text --> 1200<br> Gambia<br>1000 Senegal<br> South Africa<br>800<br>600<br>400<br>200<br>0<br><!-- End of picture text -->

# **Groundnuts International Prices**

International prices of edible groundnuts and groundnut oil in Rotterdam market (the reference for groundnut trade) exhibit two distinct patterns since 1970 (Figure 7). First, during 1970-81, both prices were non-stationary, drifting away, and following an increasing trend. Cointegration tests show no co-integration between edible groundnut and groundnut oil during this period (Annex Tables 1 to 3). Prices were high and the world market was dominated by the US and SSA which supplied respectively 45 and 18 percent of world exports. China exported no edible groundnut or groundnut oil.

Second, in sharp contrast with the 1970s, groundnut prices over the last 20 years have been stationary, constantly reverting to their mean values following shocks (Annex Tables 4&5). Price fluctuations were thus important but two sub-periods can be distinguished. Prior to 1994, prices of groundnuts displayed a higher level of volatility. The coefficient of variation of prices stood at 20 percent between 1980 and 1994 against only 7 percent between 1995 and 2001. This shift in world price variability warrants at least two questions. First, what are the main causes? Second, is the change in price variability permanent?

> 6 Aflatoxin is a cancer-causing chemical produced by species of aspergillus moulds that can contaminate groundnuts. The spores of these moulds, present anywhere in the air and the soil, require specific temperature, moisture and nutrient substrates to germinate. Aflatoxin contamination of groundnuts can occur during its cultivation in the field, as well as during harvesting, post-harvesting, storage and processing. While aflatoxin disappears with crushing, they tend to be present in groundnut meal and edible groundnuts.

13
visits into doctor‘s visits for acute and chronic illnesses, the majority of these declines are in doctor visits for acute illnesses, which also account for the bulk of visits. Individuals and households report a higher labor impacts due to sickness in the weekly recall surveys. Across the observational and experimental samples, such reports drop from .09 per person-month to .06 (observational) or from .10 to .05 (experimental). Finally, there are large reported declines in the use of self-medication, both measured as the frequency of any selfmedication and the total number of medications taken.

Table 2 also shows how recall effects alter our perception of average household morbidity and treatmentseeking patterns. In the weekly data, households (outcome variables are aggregated up to the household level by summing across individuals) report a total of 5 acute illnesses a month. On average, 2.4 individuals in every household report at least one acute illness in a month, and household members spend a total of between 23 and 26 days sick per month. There are between 2 (observational) and 3 (experimental) doctor visits every month, the bulk of which are accounted for by acute illnesses—a result very much in line with our own experiences during the initial field work. Monthly-recall data present a completely different picture. Reported acute illnesses drop and doctor visits are down to 1.28 (observational) to 1.4 (experimental) visits per household per month. With an average of 5.2 members per household, this is just above the average of 3 visits per individual per year reported in the US data (National Ambulatory Medical Survey 2000).

In Table 3, we account for time-invariant individual heterogeneity and seasonality in the observational and experimental data. Specifically, for the observational data, we estimate where yit is the outcome variable for individual or household i at time t. Tit is a dummy variable that takes the value 1 for individuals/households who were surveyed using a weekly recall format at time t; γj represent coefficients on 12 seasonal dummies, one for each month of the year, and μi represents individual/household effects that are time-invariant but may be correlated with the format of the survey. The estimator β captures the effect of the recall period in the observational data under certain assumptions on the temporal autocorrelation of the outcome measures and the linear separability of recall from seasonal effects. In the case of the experimental data, we estimate a standard difference estimator with the treatment effect estimated as the interaction between the treatment group and the 2<sup>nd</sup> month of the survey, when the randomization into weekly and monthly recall was implemented. In all specifications, the lagged outcome variable increases the efficiency of the recall estimate and subsumes the individual fixed-effect. Given the nature of the experiment, 10
POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA using this kernel as a comparator would show the effect of re-weighting in a kernel. The convergence speed for all poverty-weighted PPPs are presented in Figures 2 and 3.

<!-- Start of picture text --> 10.00000%<br>1 2 3 4 5 6 7 8<br>(number of iterations)<br>1.00000%<br>0.10000%<br>0.01000%<br>0.00100%<br>Below IPL<br>Uniform Kernel (bw 1.0)<br>0.00010% Uniform Kernel (bw 0.5)<br>Uniform Kernel (bw 0.25)<br>Bi-weight Kernel (bw 1.0)<br>Bi-weight Kernel (bw 0.5)<br>Bi-weight Kernel (bw 0.25)<br>0.00001%<br>SD of PPP Ratios<br><!-- End of picture text --> FIGURE 2. ITERATION CONVERGENCE OF POVERTY-WEIGHTED PPPS, 28 COUNTRIES In the case of 28 countries, the fastest convergence, as expected, is produced by the bi-weight kernel based PPPs with bandwidths of 1.0 and 0.5. The same kernel with a bandwidth of 0.25 converges significantly slower. Poverty-weighted PPPs based on plutocratic weights below the IPL (“below IPL PPPs”) converge relatively fast in the beginning but then start oscillating. All indexes based on uniform kernels exhibit oscillations as well. It could be noticed that the only country that oscillated with the below IPL PPPs was Guinea. Removal of the Guinea data from the data set produces somewhat more consistent results for all indexes, with the below IPL PPPs discontinuing the oscillations altogether.

<!-- Start of picture text --> 10.00000%<br>1 2 3 4 5 6 7 8<br>(number of iterations)<br>1.00000%<br>0.10000%<br>0.01000%<br>0.00100%<br>Below IPL<br>Uniform Kernel (bw 1.0)<br>Uniform Kernel (bw 0.5)<br>0.00010% Uniform Kernel (bw 0.25)<br>Bi-weight Kernel (bw 1.0)<br>Bi-weight Kernel (bw 0.5)<br>Bi-weight Kernel (bw 0.25)<br>0.00001%<br>SD of PPP Ratios<br><!-- End of picture text --> FIGURE 3. ITERATION CONVERGENCE OF POVERTY-WEIGHTED PPPS WITHOUT GUINEA, 27 COUNTRIES The results without Guinea price data are presented in Figure 2. In general, the convergence picture is quite similar to the 28 country case. As before, the fastest convergence is produced by the bi-weight 7