rafmacalaba/datause-encoder-data huggingface dataset:
- East Asia and Pacific: Indonesia, Solomon Islands

- Europe and Central Asia: Albania, Bulgaria, Croatia, Georgia, Kosovo, Romania, Ukraine

- Latin America and Caribbean: Argentina, Brazil, Peru, Uruguay

- South Asia: Bhutan

- Sub-Saharan Africa: Burundi, Ethiopia, Ghana, Kenya, South Africa.

The database classifies an enterprise as an SOE if the state directly or indirectly owns more than 50 percent of its shares or the state is the ultimate controlling entity, through majority ownership of common stock or any other mechanisms of control. This definition is in line with the European Union’s definition of public undertakings in Commission Directive 2006/111/EC.

The database provides panel data on financials of SOEs in the power and transport sectors at the SOE/year level that are consistent over time and comparable across SOEs no matter where they operate. To ensure consistency and reliability, researchers collected the data using a standardized accounting data template that was populated using the information on financial statements. To ensure that quantities like <mark>earnings before interest, taxes, depreciation, and amortization</mark> (EBITDA) or operational subsidies in the database are comparable across SOEs and years, they identified each item as defined by the template using the notes to the financial statements rather than relying on the way such items are presented in the SOEs’ main financial tables. Data reliability was further ensured through quality assurance checks by alternate analysts and accounting experts.

The database provides a standardized representation of the income statement, balance sheet, and cash flow statement of each SOE. It also contains supplementary items, including currency risk, debt/loan analysis, maturity profiles of assets and liabilities, and SOE ownership structure.

We used financial data on 135 SOEs in 19 countries for the 2009–18 period. Some of the regressions are based on fewer SOEs (92–118), because of data availability issues regarding subsidies, retained earnings, and other financial information. Table 2 presents the country and sectoral distribution of the SOEs in the sample.

**Table 2 SOEs in the sample by country and sector**

|_Country_|_Power_|_Airlines and airports_|_Roads_|_Railways _|_Total_|
|---|---|---|---|---|---|
|Albania|3|0|0|1|4|
|Argentina|2|2|0|3|7|
|Bhutan|2|1|0|0|3|
|Brazil|8|1|0|0|9|
|Bulgaria|4|3|0|2|9|
|Burundi|1|0|0|0|1|

9
the customs duties collection (which is primarily of interest to customs) but indirectly in the whole port clearance processes.

In this section we intend to make use of explanatory statistics to analyze shipment level data collected through ASYCUDA by customs administration and test the assumptions and findings presented in previous sections and recalled in table 12 below. Different models are tested and areas for further research are identified.

|**Factor**|**Type **|**Impact**|
|---|---|---|
|**Fiscal regime**|Shipment specific|High fiscal pressure leads to high dwell time|
|**Bulking of containers**|Shipment specific|LCL containers stay longer in the port|
|**Density of value**|Shipment specific|Higher value leads to higher dwell time|
|**Commodity type**|Shipment specific|Commodity category is a crucial determinant|
|**Concentration of C&F market**|External Factor|Dominant C&F players have a low<br>performance|
|**Low volume per operation**|External Factor|Lack of regularity leads to poor performance|
|**Concentration of shipping flows**|External Factor|Disruption in ship arrivals leads to discrete<br>behaviors|

**Table 12 – Early assumptions about determinants of long dwell times**

# **Parametric fit using continuous distributions**

We first attempt to fit the distribution of container dwell times using parametric asymmetric distributions of continuous data with positive values. The analysis of cargo dwell time qualifies as survival analysis since the research output is the expected time at which cargo will exit the port (continuous positive values with right-censoring patterns<sup>30</sup> ). Such methods have not been successful however at this stage.

Univariate analysis shows for example that standard parametric distributions (Gamma, Lognormal, and Weibull) are not fitting the dwell time data well. Data is processed to try and attempt to reduce some discrete patterns but neither seasonality nor simple stratification improves distribution fit. We also fit a Cox Proportional Hazard (PH) model (refer to Hosmer, Lemeshow 1999 for details) - a semi-parametric model – to Cargo Dwell Time (CDT) with help of covariates identified in earlier sections (e.g. fiscal regime, full-container-load, density of value, cargo type, C&F agents

> 30 Survival techniques enable the consistent analysis of expected time before occurrence of a discrete event such as illness, death or in our case, exit from port terminal. They have not been use yet to our knowledge for the analysis of cargo dwell time in ports and further research would be very useful to make use of the powerful scope of analysis. In particular, survival theory copes with biases attached to right or left censoring. In the case of cargo dwell time right censoring is needed since many containers in the sample are not cleared before cut-off date but have to be included in the modeling structure.

24
POLICY RESEARCH WORKING PAPER 2745

# Summary findlings

Using panel data from Peru, Ilahi investigates the determinants of the allocation of boys' and girls' time to schooling, housework, and income-generating activities. Specifically, she explores whether sickness, female headship, access to infrastructure, and employment of women in the household have different impacts on the time use of boys and girls.

Girls mostly engage in housework, and boys mostly work outside the home. As a work activity, housework responds to economic incentives and constraints.

The author's econometric findings suggest that changes in household welfare affect girls' work and schooling more than boys'. Even though boys' and girls' educational attainment rates are the same, girls' education responds more to changes in household welfare than does boys'. Similarly, girls are more likely than boys to adjust their home time in response to changes in adult female employment and to sickness of household members. Lack of access to energy infrastructure lowers the educational attainment of both boys and girls but has little affect on their labor.

The traditional approach to the determinants of child labor and education excludes housework and may understate children's time use, particularly that of girls. It may therefore also overlook an important gender dimension of education policy. Safety nets that protect household incomes from employment shocks and sickness, and childcare programs that allow women to work, would reduce the likelihood of girls being pulled out of school.

This paper-a product of the Gender Sector Unit, Latin America and the Caribbean Region-was originally written for the recentWorld Bank publication _EngenderingDevelopment._ Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Selpha Nyairo, room 18- 11 0, telephone 202-473-4635, fax 202-5220054, email address snyairo@worldbank.org. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. December 2001. (37 pages) _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 view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
**-** **<u>Table 6: Predicting growth acceleration and deceleration panel data</u>**

||<br>**Dep. variable: d**<br>**accele**|**ummy of growth**<br>**ration**|<br>**Dep. variable: d**<br>**decele**|**ummy of growth**<br>**ration**|
|---|---|---|---|---|
|**Variable**|**Odds ratio**|**p-value**|**Odds ratio**|**p-value**|
|Savings|1.152|.000|.929|.000|
|Investment in fixed capital|||.956|.062|
|Foreign direct investments net flow|1.146|.000|.811|.000|
|GDP deflator|||1.010|.016|
|Consumption|1.051|.004|||
|Government consumption|.904|.000|||
|Trade|||.980|.008|
|Minor conflict|||1.744|.045|
|Major conflict|.435|.064|||
|LR (chi2)|127.6|.000|97.4|.000|
|N|825||647||

Note: fixed effect logit regression.

Taken together our results suggest that growth decelerations are more predictable than growth accelerations. In the African context it may be easier to understand what policy makers should avoid to prevent growth collapse rather than what they should do to achieve sustained growth.<sup>13</sup>

# **6.  Conclusions**

This paper investigated growth acceleration and deceleration in Africa, seeking to uncover stylized facts and identify growth challenges not captured by standard long term growth analyses. Contrary to the common wisdom, African countries have experienced numerous growth acceleration episodes in the last 30 years, but have also gone through a comparable number of growth collapses that cancelled out most benefits of growth. We find that during growth accelerations the representative African country managed to grow by 3.6%, while during decelerations it grew by -2.7%. Ceteris paribus, had Africa avoided its growth decelerations, it would have grown in per capita terms at 1.7% a year instead of 0.7%, and GDP per capita would have been more than 30% higher in 2005.

We find that it is easier to predict a growth deceleration than an acceleration. One explanation is that there may be few commonalities among success stories. Opportunities for growth vary with sound policies and good governance, but also with timing, initial conditions, inherited institutions, geography, availability of natural resources. Leadership, and other less observable factors may also have a role in achieving

> 13 HPR using a much larger set of countries also find that growth accelerations tend to be highly unpredictable, even after controlling for the standard determinants of growth, including political changes and economic reforms.

21
Policy Research Working Paper 9115

# **Abstract**

Using multiple sources of national- and firm-level data around the world, this paper investigates how the effects of the business environment depend on whether the measure is de jure or de facto, and how business environment effects differ by ownership and size. The paper compares estimates of business environment effects from three data sources, and modifies the priors on the relative importance of various elements of the business environment. Among four aspects of the business environment, at least two sets are robust and a third set does not contradict the other two: access to finance, electricity, internet, and human capital. The effects of de jure business environment indicators on firm performances depend on measures of contract enforcement. Foreign-owned firms benefit more from the maintenance of physical safety and ease in obtaining construction permits, and gain competitive advantage in productivity when domestic infrastructure or access to finance is worse. Relatively small firms benefit more from corruption control, basic and modern infrastructure, human capital, and land access. Relatively large firms benefit more from physical safety. Access to finance is important for the expansion of smaller firms and the productivity of large firms.

This paper is a product of the Development Research 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 lxu1@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
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DESIGN FOR WOMEN5 DEVELOPMENT<sup>CENTRRE5CALa</sup> 6
**Table 8: Rice Prices, Child Labor, and the Presence of Younger Siblings** _limited to children with siblings in the household_

||Girls<br>Participation|Hou|rs Worked|(OLS)|Boys<br>Participation|Ho|urs Worked|(OLS)|
|---|---|---|---|---|---|---|---|---|
||-1|-2|-3|-4|-5|-6|-7|-8|
|Dependent Variable:|Active|Total|Market|Household|Active|Total|Market|Household|
|Rice Price|-0.251|-7.227|-3.888|-3.59|-0.215|-2.829|-1.45|-1.276|
||[0.105]**|[3.091]**|[2.019]*|[2.078]*|[0.098]**|[2.633]|[2.077]|[1.526]|
|RP*# Siblings|-0.007|-0.219|-0.351|0.148|-0.001|-0.423|-0.089|-0.376|
||[0.022]|[0.680]|[0.467]|[0.479]|[0.022]|[0.640]|[0.536]|[0.324]|
|# Siblings|-0.005|-0.097|0.378|-0.489|-0.002|0.323|0.189|0.166|
||[0.023]|[0.755]|[0.496]|[0.542]|[0.025]|[0.722]|[0.589]|[0.370]|
|Observations|4496|4492|4510|4494|4730|4723|4749|4726|
|R-squared|0.41|0.46|0.39|0.23|0.37|0.41|0.34|0.21|
|Rice Price|-0.254|-6.565|-3.647|-3.188|-0.223|-2.491|-1.144|-1.248|
||[0.104]**|[3.104]**|[2.027]*|[2.112]|[0.099]**|[2.674]|[2.131]|[1.535]|
|RP*# Siblings|0.003|0.626|0.061|0.572|0.016|0.462|0.453|-0.024|
||[0.025]|[0.722]|[0.524]|[0.466]|[0.026]|[0.687]|[0.558]|[0.370]|
|RP* # younger sibs|-0.083|-2.853|-1.803|-1.045|-0.065|-4.178|-3.021|-1.224|
||[0.045]*|[1.528]*|[1.276]|[0.961]|[0.042]|[1.137]**|[0.991]**|[0.596]**|
|RP*# young male sibs|0.133|1.206|1.41|-0.142|0.051|3.396|2.856|0.631|
||[0.057]**|[1.815]|[1.663]|[1.370]|[0.054]|[1.462]**|[1.264]**|[0.783]|
|# siblings|-0.036|-1.454|-0.198|-1.272|-0.038|-1.011|-0.547|-0.441|
||[0.027]|[0.799]*|[0.562]|[0.529]**|[0.029]|[0.767]|[0.614]|[0.416]|
|# younger sibs|0.157<br>[0.047]**|4.385<br>[1.610]**|2.332<br> [1.356]*|2.102<br>[1.050]**|0.121<br>[0.045]**|5.446<br>[1.242]**|3.457<br> [1.089]**|2.063<br> [0.663]**|
|# younger male sibs|-0.143|-0.905|-1.407|0.401|-0.036|-2.925|-2.412|-0.6|
||[0.060]**|[1.923]|[1.778]|[1.487]|[0.059]|[1.586]*|[1.390]*|[0.883]|
|Observations|4496|4492|4510|4494|4730|4723|4749|4726|
|R-squared|0.43|0.48|0.39|0.26|0.38|0.42|0.35|0.22|

Top panel and bottom panel are separate regressions. See notes to table 5 for additional explanation of regressions. Robust standard errors in brackets are corrected for psu-time clustering. * significant at 10%. ** Significant at 5%.
89 in Bangladesh. It had lent to some 413,000 "members" and 84 percent of these were womon, and they received 70 percent of the loans. Groups of members borrow with no collateral and complete their repayment within one year or lose the group's rights to borrow in the future. Membership **is** restricted to only persons with less than .5 acres of cultivatable land or assets not exceeding the valu, of one acre of medium quality land. **Repayment** rates **have** been favorable compared to other commercial banks, but the cost for loan placement and monitoring of assets is not easily derived from reports. Evaluation studies of the Bank are favorable but not sufficiently analytical. They do suggest, however, that here is an institutional innovation that mitigates the disadvantages of poor womon in expanding their means of production. Comparisons of the consequences of the Bank being in **a** village on average productivity and earnings of village women and men would strongthen future evaluation studies. Is economic progress more rapid in "bank villages" than control villages without access to Grameen Bank resources? (Hossain and Afsar, 1988; Hossain, 1987).

To promote the more efficient allocation of talent across the entire economy, women should be helped to compete in male-dominated professions and industries as men should be encouraged to enter female dominated segments of the economy. Women may work in declining sectors, where modern emerging technologies are likely to depress their incomes by the generation of new man-made substitutes. These wonen need opportunities to enter more favorably situated sectors. If they do not have as much education as men, women will need more specific training and assistance to redeploy their skills. Nonetheless, general education remains the most reliable means to improve the mobility of women in the long run. Women have fared poorly relative to men in
A second implication of the model is that the effects of the cap on electricity prices can be significant, if fuel prices increase together with carbon permit prices as is likely. In this case, our estimates suggest that for every 10 percent rise in carbon and fuel prices, the marginal cost of electric power generation increases by 8 percent in the short-run. Consequently, if EUA allocations are fixed and fuel prices exogenous, the degree to which the costs of a carbon cap are passed on to consumers in the short run will be determined by how open the system is to alternative carbon offset, such as CERs, and the relative price of those allowances.

The European experience points to the importance of starting early down a low-carbon path. Because fixed investments in power generation are long-lived and irreversible, inflexibilities resulting from past investments will be long-lived as well. Consequently, it is important for countries that do not currently cap greenhouse gas emissions but hope to promote growth that is less carbon intensive to find alternative polices that consider the costs of future adjustments.

# **References**

Blackorby, Charles and R.Robert Russell. 1989. Will the real elasticity of substitution please stand up? American Economic Review 79(4), 882-888.

Caves, Douglas W. and Laurits R. Christensen. 1980. Global properties of flexible functional forms. American Economic Review 70(3), 422–432.

Considine, Timothy J. and Donald F. Larson. 2006. The environment as a factor of production. Journal of Environmental Economics and Management 52(3), 645-62.

Convery, Frank J. and Luke Redmond. 2007. Market and price developments in the European Union Emissions Trading Scheme. Review of Environmental Economics and Policy 1(1), 88111.

Ellerman, A. Denny. 2008. The EU Emission Trading Scheme: prototype of a global system? The Harvard Project on International Climate Agreements, Harvard University, Kennedy School of Government, August, Discussion Paper 08-02.

19
showing that after controlling for differences in endowments (education, housing characteristics, and ownership of consumer durables), differences in per capita expenditure between ethnic minority households and others remain, but are much smaller.

# **3.2 Education**

In both urban and rural areas, the level of schooling of the head of household is a good predictor of a household's per capita expenditure.<sup>7</sup> The five dummy variables that represent the education of the head are jointly significant at the 1 percent level in both rural and urban areas (see Table 3). In rural areas, heads of household who complete primary school earn 6 percent more than those not completing primary school. In urban areas, households whose head has completed primary or lower secondary school do not seem to be better off than those whose head has not completed primary school, but higher levels of education are associated with significantly higher earnings (see Table 2).

In general, the educational level of the spouse is less significant than that of the household head as a predictor of per capita expenditure.<sup>8</sup> In the rural areas, only the highest two levels of education of the spouse (advanced technical training and post-secondary education) show any significant effect relative to the base level (not completing primary school). The education of the spouse is a better predictor in urban areas than in rural areas (see Table 2).

# 3.3 **Occupation**

The occupation of the head of household is a statistically significant predictor of per capita expenditure in rural and urban areas.<sup>9</sup> In rural areas, the first three 7 We also experimented with using the number of years of the education for the household and spouse as explanatory variables, but found that the level of education completed gave better results.

8 Education of the spouse may have other benefits, such as improved health or nutrition, that are not captured by the measure of welfare used in this analysis, per capita expenditure. Note that 11.4 per cent of spouses in the VLSS are male.

**9** Although information on the employer of households heads is available in both the Census and the VLSS, the categories they use to describe different categories of employers differ substantially and cannot be reconciled.

15
Table 5: Robustness I, Average Effect of the PIP on Assisted Firms.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Dif<br>in|Dif<br>in|Dif<br>in|Dif<br>~~i~~n<br>~~D~~if<br>with|Two stage<br>Heckman|BDM<br>Correction|
||Dif|Dif|Dif|Matching|||
|treatment_ikt_|0.100|-0.023|-0.115|-0.114|-0.669|-0.410|
||(0.215)|(0.264)|(0.305)|(0.327)|(0.813)|(0.564)|
|ln<br>~~s~~ales_it_||0.909***|0.923***|0.948***|0.890***||
|||(0.217)|(0.287)|(0.293)|(0.290)||
|ln<br>~~N~~employee_it_|||0.063|0.074|0.078||
||||(0.105)|(0.107)|(0.103)||
|experience_ikjt_|0.299|-0.252|-0.228|-0.253|-0.213||
||(0.248)|(0.373)|(0.473)|(0.504)|(0.485)||
|_λ_|||||0.333||
||||||(0.357)||
|constant|8.95***|-9.36|-10.01|-18.98***|-8.54|0.17|
||(1.008)|(5.834)|(5.975)|(5.128)|(6.054)|(0.311)|
|Observations|681|368|288|281|286|93|
|R-squared|0.153|0.211|0.227|0.218|0.230|0.036|
|Number of id|373|189|142|139|140|78|

Robust standard errors clustered at firm level are in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

21