rafmacalaba/datause-encoder-data huggingface dataset:
# **_VII.A. The estimated impact of management practices on profits and productivity_**

<u>Profits: Overall we estimate a total increase in profits of around $228,000, with our</u> calculations outlined in Table A5. We could not obtain accounting data on these firms’ profits and losses. Public accounts data are available only with a lag of 2-3 years at the firm level (rather than plant, which is what we would want), and in our interviews with firm owners they told us they under-report profits to avoid tax and also move profits to years when they want a loan (to have proof of income). When asked for their internal accounts the firms were evasive and would not provide them, beyond occasional comments that profits were in the range of $0.5m to $1m per year.<sup>25</sup> So we estimated the changes from the quality, inventory and efficiency improvements. Our methodology is simple: for example, if an improvement in practices is estimated to reduce inventory stock by X tons of yarn, we map this into profits using conservative estimates of the cost of carrying X tons of yarn. Or if it reduces the numbers of hours required to mend defects we estimated this reduction in hours on the firm’s total wage bill. These estimates are medium-run because, for example, it takes a few months for the firms to reduce their mending manpower.

These estimates for increases in profits are potentially biased. There is a downward bias because we take firms’ initial capital, labor and product range as given. But in the long run the firms can re-optimize, for example, with more machines per weaver if quality improves (as dealing with breakdowns is time consuming). Furthermore, many of the management practices are complementary, so they are much more effective when introduced jointly (e.g. Milgrom and Roberts, 1990). However, the intervention time-horizon was too short to change many of the complementary human-resource practices. The estimates are upward biased if the firms backslide on the management changes once the consultants leave.

To estimate the net increase in profit for these improvements in management practices we also need to calculate the costs of implementing these changes (ignoring for now any costs of consulting). These costs were small, averaging less than $3000 per firm.<sup>26</sup> So given the $250,000 this consulting would have cost these firms, this implies about a 90% one-year rate of return.

> 25 It is not even clear if firms actually keep correct records of their profits given the risk these could find their way to the tax authorities. For example, any employee that discovered these could use these to blackmail the firm. 26 About $35 of extra labor to help organize the stock rooms and factory floor, $200 on plastic display boards, $200 for extra yarn racking, $1000 on rewards, and $1000 for extra computer equipment (this is bought second hand).

24
effective in reducing the overall number of missing girls. Or are indirect measures that target the root cause of sex selection – son preference – more effective in improving girls’ survival prospects and life chances?

This paper seeks to answer these questions by reviewing governmental and non-governmental interventions that have been used to resolve the problem of missing girls. The purpose is to leverage cross-country experiences (especially from China, India, and Korea, which have been grappling with this problem for decades) to strengthen the evidence base of measures aimed at reducing sex selection and son preference, and identify which measures have worked/not worked and why. The stocktaking draws on experimental and quasi-experimental studies where available; in lieu of causal studies, it draws on qualitative and correlation-based studies.

Many of the countries that have substantial sex selection are starting to see reduced levels of it. However, they have not reached the normal SRB and child sex ratio yet. This review provides an overview of the measures that can be used to accelerate normalization of these ratios and improve parents’ investments in girls.

The structure of this paper is as follows: Section 2 looks at the underlying cause and mechanisms of sex selection, i.e. it outlines the demand-side and supply-side factors driving sex selection; Section 3 assesses the direct measures that have been taken to reduce sex selection – namely the ban on prenatal sex selection, and the conditional cash transfer programs to induce families to have girls, and invest in girls; Section 4 assesses the indirect measures (legislative reforms to dismantle patriarchal practices, and advocacy efforts) that can be used to tackle the underlying cause of sex selection: son preference; Section 5 is a case study of Korea’s success story; Section 6 concludes with a summary of the most promising approaches that can be used to reduce sex selection, and son preference more broadly.

# **2. Sex Selection: Causes and Mechanisms**

# **_2.1 Causes_**

The entrenched preference for sons over daughters is the root cause of sex selection. Countries that do not have strong son preference, do not practice sex discrimination, even if they have access to sex selection technology. Therefore, long-term success in normalizing SRB and child sex ratios is only possible when the cause(s) of son preference are understood and eradicated.

Son preference can arise because daughters provide lower economic returns than sons, making the relative cost of investing in girls higher, or it can arise due to cultural practices that raise the 5
Policy Research Working Paper 10649

# **Abstract**

This paper assesses the performance of Romanian stateowned enterprises with various degrees of ownership (minority owned with 10 to 24.9 percent stakes, minority owned with 25 to 49.9 percent stakes, and majority owned with at least 50 percent ownership stakes) and control levels (central versus local state-owned enterprises and directly versus indirectly owned state-owned enterprises) relative to privately owned enterprises. The paper uses the Romanian firm-level data from the Ministry of Finance covering enterprises of all sizes from 2011 to 2020, combined with the new World Bank Businesses of the State dataset, which tracks ownership of state business entities with at least 10 percent stake in Romania. The paper analyzes whether various degrees of state ownership and levels of control matter for state-owned enterprises’ performance.

The paper also assesses whether Romanian state-owned enterprises were able to act as stabilizers during the early period of the COVID-19 pandemic, and how the presence of state-owned enterprises in markets correlates with market outcomes. The findings show that relative to private firms, Romanian state-owned enterprises, particularly those that are majority owned, directly owned, and local ones, employ more people, pay higher wages, but are less productive. In addition, Romanian state-owned enterprises cushioned the job and wage losses associated with the COVID-19 pandemic better than private firms, especially in competitive sectors. Finally, there is evidence that the presence of state-owned enterprises may limit private firm entry and allocative efficiency.

This paper is a product of the Finance, Competitiveness and Innovation 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 sdauda@worldbank.org, gpop@worldbank.org, and miootty@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
available. Use of data on market capitalization and trading aim to capture the extent of investor confidence in market integrity, liquidity and efficiency.

Return variables are likely to have a large effect on the growth of different types of funds. Equity mutual funds and the demand for equity investments more generally are likely to be negatively affected by high real interest rates on bonds-and bank deposits. If investors can earn high real returns on less volatile instruments, they would be less likely to invest in equities and equity mutual funds. However, if real returns on equity funds are much higher than real interest rates and if the volatility of equity returns is not particularly high, then equity funds would benefit.

The demand for bond and money market mutual funds is likely to be affected by the rate differential between such funds and bank deposits. When banks are forced to widen their spreads because of large losses on their lending portfolios or because of the high operating costs of their large branch networks, mutual funds that are free from such burdens are able to offer attractive returns on deposit-like instruments. However, because of the lack of detailed and reliable data on interest rate differentials, we retain the level of real interest rates, real equity returns and the volatility of equity returns among the independent variables used in the empirical analysis.<sup>9</sup> Additional variables, which highlight the overall level of country development, have been used. These include an indicator of the development of the banking sector (given by the ratio of commercial bank assets to the combined total assets of commercial banks and the central bank), the openness to international trade and foreign investment<sup>10</sup> (given by the share of exports and imports in relation to national income), the importance of high-tech industries" (measured by the share of high-tech exports to total exports), and

> 9 Interest rates on bank deposits were used where available; otherwise rates on treasury bills were used. Real interest rates were constructed through a Taylor series approximation. Equity returns were based on the Morgan Stanley Composite Index (MSCI) for developing countries and the Morgan Stanley Global Index (MSGI) for developed countries. For countries not included in either index we used a composite stock market index of the main stock exchange.

10 Openness to international trade and foreign direct investment are often used as indicators of integration with foreign markets with a strong positive impact on economic growth (Dollar 1992, Levine and Renelt 1992, Vamvakidis 1998).

**"** During the 1 990s, the period covered by our sample, high-tech companies have been able to list on both local and global equity markets and have raised large amounts of capital, giving a boost to stock market development and publicizing the advantages of equity markets and equity mutual funds. We expect a

30
in the robustness checks we estimate an alternative specification in which well-being is a function of radicalization and find similar results.

Another serious challenge in identifying the parameters in (1) and (2) is unobservable heterogeneity. The observable covariates are unlikely to pick up all the latent heterogeneity in individuals’ subjective assessment of radicalization. For instance, individuals’ mental health may well influence both his income and his preference for attacks against civilians. If individuals with poor mental health, who are more likely to have lower income levels, tend to be more radicalized, the omission of the mental health status would bias downwards the coefficient on income from a univariate estimate of (2). We are unable to address the unobservable heterogeneity issue with the repeated cross-sectional data from the World Gallup Poll.<sup>15</sup> A non-random reporting bias may also be present. For example, if there is a systematic reporting error in the education level, the univariate estimate of the coefficient on education would be attenuated (over-estimated) if respondents tend to understate (overstate) education levels.

All of these concerns suggest that we should adopt a simple econometric strategy, relying on the UOP approach. In addition, we estimate the simultaneous system (1) and (2) (  0 , 0) using the BOP FIML estimator as this approach addresses some, but not all, simultaneity concerns.<sup>16</sup> We also perform a number of robustness checks with alternative specifications, including a fully observed recursive mixed-process model, and check the sensitivity of the baseline

> 15 If we had panel data we could have addressed unobserved heterogeneity using individual fixed effects.

> 16 The estimation procedure is described in Sajaia (2008).

18
-14-

mentioned above<sup>17</sup> . Two, do not allow for differences in preferences across households. Given these difficulties, a strong case can be made for<sup>measuring</sup> undernutrition using anthropometric indicators like height for age and weight for height. Unfortunately, the available evidence of undernutrition is not systematic, although there are signs that it does exist'<sup>8</sup> .

It is difficult to put together these conflicting pieces of evidence and to ascertain the significance of undernutrition in Mexico. But two<sup>points can</sup> be made: first, a sharper distinction between under and malnutrition<sup>is</sup> required (cf. Schiff and Valdes (1990)). Undernutrition reflects lack of resources to buy adequate amounts of food; malnutrition reflects improper choice of food given incomplete information on the part of the consumer and other problems of the environment. As section VI argues, the policy implications differ. Second, systematic data on nutritional status in Mexico that differentiates between under and malnutrition is needed. Until such evidence is gathered there is a risk that resources for poverty alleviation be targeted to the wrong population, or wrong policies applied to the right population. Pending such evidence, however, second-best methods to determine the poverty line for the extremely-poor and the moderately-poor have to be used. Before I discuss these methods, I turn to describe the construction of operational poverty indices.

7A related problem is generated by the presence of intra-household inequality (of which more in section VI). Even if sufficient food is available, it may be unequally distributed within the household. Sen<sup>puts the</sup> matter aptly: "The requirement-intake comparisons also suffer from the serious problem of getting accurate information on the food intake of each individual member of the family. Obviously, food-purchase data are not adequate<sup>for</sup> this. Family members need to be observed eating, and -more than that- the food partaken would have to be weighed in the process of its journey<sup>from the</sup> plate to the mouth... So the actual intake figures may well<sup>be no more</sup> reliable than the alleged 'requirement' figures, and the blind shall lead the blind. All of this is, in fact, quite the wrong way of going<sup>about the</sup> problem. If nutrition is what we are concerned with, then nutrition<sup>is what</sup> we must observe. We have to look not at food intakes, but at signs of undernourishment" (1984b, pp. 382-3).

18For example, a World Bank report based on 1988 data (World Bank, 1990a, p. 1, table 1) shows that 14X of all children show below normal weight<sup>for age</sup> (which can be thought of as a measure of cumulative nutrition), while 15X of all babies show low birth weight. In addition, one-third of<sup>women of</sup> reproductive age are underweight.
# **Table 6a: Predicted change in the probability of selecting each animal from uniform** **<u>climate scenarios</u>**

||**Beef cattle**|**Dairy cattle**|**Goats**|**Sheep**|**Chickens**|
|---|---|---|---|---|---|
|Baseline probability|11.8%|23.1%|23.4%|19.4%|22.3%|
|Increase temp 2.5°C|-1.7%|0.4%|0.8%|3.3%|-2.8%|
|Increase temp 5°C|-3.8%|2.1%|0.0%|8.7%|-7.0%|
|Decrease rain 15%|-0.3%|1.8%|-1.2%|1.1%|-1.4%|
|Increase rain 15%|-0.1%|-1.5%|1.5%|-1.1%|1.0%|

**Table 6b: Predicted change in net income per animal from uniform climate scenarios** **<u>(US$/animal)</u>**

||**Beef cattle**|**Dairy cattle**|**Goats**|**Sheep**|**Chickens**|
|---|---|---|---|---|---|
|<br>Baseline income|145.54|132.09|6.49|11.77|1.14|
|Increase temp 2.5°C|-27.80|-3.40|-0.81|-2.55|-0.34|
|Increase temp 5°C|-47.09|-21.36|-0.54|-3.49|-0.55|
|Decrease rain 15%|13.91|-0.42|-0.06|1.03|0.03|
|Increase rain 15%|-7.08|3.78|0.29|-0.58|-0.02|

**Table 6c: Predicted change in number of each animal per farm from uniform climate** **<u>scenarios (animals/household)</u>**

||**Beef cattle**|**Dairy cattle**|**Goats**|**Sheep**|**Chickens**|
|---|---|---|---|---|---|
|<br>Baseline number|63.47|23.84|15.36|34.05|790.09|
|Increase temp 2.5°C|-9.00|1.84|1.45|0.35|-112.61|
|Increase temp 5°C|-18.96|2.88|2.14|3.20|-183.84|
|Decrease rain 15%|7.52|0.13|-0.90|2.99|50.30|
|Increase rain 15%|-6.33|-0.11|0.99|-1.80|-45.20|

**Table** **<u>6d: Predicted change in expected income from uniform climate scenarios (US$)</u>**

||**Mean**<br>**(US$/farm)**|**Total**<br>**(billions US$)**|**% change**|**Bootstrap**<br>**lower 95 %**|**Bootstrap**<br>**upper 95%**|
|---|---|---|---|---|---|
|Expected income|3023|60||||
|Increase temp 2.5°C|-964|-19|-31.90%|-1077|-722|
|Increase temp 5°C|-2083|-41|-68.89%|-2452|-1631|
|Decrease rain 15%|184|2|2.49%|139|207|
|Increase rain 15%|-108|-2|-1.88%|-111|-60|

30
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Interestingly, in case the couple has two children, rather than one, the disincentive to work for the second earner is much less severe (Figure 9). Although the 500+ is the most generous benefit, it is withdrawn at the highest income level. The ‘poverty trap’ for this family type occurs when the combined gross income of both partners reaches AW levels. This means that the disincentive is shifted upwards considerably to about twice the MW. Therefore, while the family will be financially worse off because of the loss of the 500+ payments when both adults earn at MW, the disincentive does not affect families who are in the lowest two quintiles. Again, when accounting for the fact that if the previously inactive partner decides to enter the labor market, there will be a need for childcare, the incentive to enter further diminishes, and based on the assumption of childcare costs, total earnings from work would increase between 22 percent and 36 percent.<sup>17</sup>

**Figure 9: TBM for married couple with 2 earners (principal fixed approximately at MW) - 1 child**

<!-- Start of picture text --> 60000<br>50000<br>40000<br>30000<br>20000<br>10000<br>0<br>T_TAX_SS NETpl FBpl HBpl<br>SApl GROSSpl Net+500 500+<br>46.9 49 51.1 53.2 55.3 57.4 59.5 61.6 63.7 65.8 67.9 70 72.1 74.2 76.3 78.4 80.5 82.6 84.7 86.8 88.9 91 93.1 95.2 97.3 99.4 101.5 103.6 105.7 107.8 109.9 112 114.1 116.2<br><!-- End of picture text -->

# **V. Empirical Relevance of the** **_de jure_ Model**

There is, of course, a chance that the level of earnings that could make families susceptible to poverty traps is actually uncommon in reality. To verify this, we exploit the Polish HBS 2014, and map the frequency of families in and around those earning levels where working more does not pay off.

First, the family typologies that the model takes into account are frequent (about 4 million households): the largest are married couples with 1 child (2 million families), followed by married

> 17 Bar chart in the appendix only accounts for one child in need of nursery care, with the other one old enough to take advantage of a subsidized spot in care for 0–6-year-olds or in school. If we were to assume that (a) both children are in need of nursery care (0–3 years old) and/or (b) that only the regular, private spots are available for a much higher price, the percentage increase in MW for the second earner moving into work would decrease from 36 percent to 22 percent (for scenario a) or even lead to a loss of 4 percent of net income for the family (scenario b). In the case where both scenarios a and b were to occur at the same time, up to 59 percent of MW could be lost for the family.

16
**Table 1: Descriptive Statistics**

||Ave|rages and St|andard Devi|ations<br>Eti|
|---|---|---|---|---|
||Mexico|Bolivia|Peru|nerprse<br>Surveys|
|**Panel A: Firm characteristics**|||||
|Firm age|9.57|13.55|12.96|19.32|
||(9.15)|(10.67)|(10.02)|(15.58)|
|Employees|1.37|3.89|6.97|85.59|
||(0.90)|(5.43)|(6.21)|(239.75)|
|Property value (in thousands)|8.89|9.54||418.25|
||(16.98)|(52.45)||(2393.17)|
|Machinery value (in thousands)|3.12|4.97||456.34|
||(7.67)|(14.12)||(2590.59)|
|Sales (last month)|1.15|1.36|6.66|1944.25|
||(2.30)|(2.94)|(14.71)|(5047.84)|
|Operating costs (last month)||0.88|5.27|1533.88|
|||(2.33)|(10.91)|(4123.64)|
|Profits (last month, in thousands)|0.38|0.47|1.40|410.36|
||(0.41)|(0.97)|(6.64)|(1293.68)|
|Firm is registered with the authorities|0.31<br>(0.46)|0.55<br>(0.50)|0.65<br>(0.48)||
|Adopted new technology during past 3 years||||0.60<br>(0.49)|
|**Panel B: Owner characteristics**|||||
|Female|0.29|0.46|0.29|0.15|
||(0.45)|(0.50)|(0.45)|(0.35)|
|Hours worked|45.13|59.78|59.02||
||(23.11)|(29.65)|(23.51)||
|Owner's years of schooling|8.06<br>(4.93)|10.55<br>(4.54)|11.94<br>(3.51)||
|Owner participated in business training||0.25<br>(0.43)|||
|Owner's mother has no schooling||0.35<br>(0.48)|||
|Numberof firms|8,293|265|582|2,175|

_Note_ : Property and machinery values in the Mexican and Bolivian data are conditional on having property or machinery, respectively. All monetary values are in US Dollars. _Source_ : Encuesta Nacional de Micronegocios (ENAMIN), 2002, Bolivian World Bank Micro Enterprises Survey, 2007, Peruvian World Bank Micro Enterprises Survey, 2008, World Bank Enterprise Surveys for Brazil, Ecuador, El Salvador, Guatemala, Honduras, and Nicaragua, 2003.

8