rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 8. firm performance: Evolution and determinants**

**Panel A. Evolution of firm performance**

|Year|ROE|Investment/profit|Export/sales|
|---|---|---|---|
|1995|0.221|0.417|.|
|1997|0.335|0.587|.|
|2000|0.245|0.743|.|
|2002|0.215|0.308|.|
|2004|0.266|0.404|.|
|2006|0.305|0.465|0.053|
|2008|0.276|0.469|0.045|
|2010|0.338|0.218|0.032|
|2012|0.345|0.098|0.034|

**Panel B. Determinants of firm profitability**

|VARIABLES|(1)<br>ROE|(2)<br>ROE|
|---|---|---|
|Share of state economy|-0.029|-0.067|
||(0.123)|(0.125)|
|Marketization index|0.006|0.012|
||(0.020)|(0.018)|
|Ln GDP percapita|0.027|0.051|
||(0.132)|(0.129)|
|FDI/Total investment|0.000|-0.000|
||(0.000)|(0.000)|
|Ln asset|-0.076***|-0.103***|
||(0.007)|(0.008)|
|Firm age|0.012***|0.009***|
||(0.002)|(0.002)|
|Female||-0.020<br>(0.018)|
|Year of schooling||0.023***<br>(0.003)|
|Party member||0.032*<br>(0.017)|
|Membership in Congress or||0.160***|
|Consultative Conference||(0.021)|
|Prov, Sector, year|Yes|Yes|
|Observations|12,324|10,431|
|R-squared|0.063|0.090|

_Data sources: National Surveys of Privately Owned Enterprises in China_ _Data sources: National Surveys of Privately Owned Enterprises in China._ Notes: Standard errors are clustered at provincial level and reported in parentheses. Significance levels 0.1, 0.05 and 0.01 are noted by *, **, and ***, respectively.

44
schools in the Indian states of Rajasthan (in the West) and Orissa (in the East). The sample was designed by first selecting districts using population proportional to size (PPS) sampling, where, in the absence of data on school-by-school enrollment, the PPS methodology was applied to the population of schools across districts. Following the selection of districts, the total number of schools to be selected in each district was arrived at and schools were surveyed in both urban and rural areas, as well as across institutional affiliation; that is, government schools, private unaided and private aided schools were all included in the sample.

There are three important features of the data. First, only children enrolled in school were tested. According to the World Development Indicators, gross enrollment in India in lower secondary schools is only 53 percent, so this leaves out half the children of the relevant agegroup. Consequently, there are intractable issues of trying to generalize the comparison of tested children to _all_ children across countries. For instance, the gross enrollment in secondary schools in three other countries close to Orissa and Rajasthan in the world rankings varies dramatically, from 44 percent in Ghana to 75 percent in Botswana and 90 percent in South Africa. To the extent that marginal performers are less likely to be enrolled, comparisons of India with South Africa would therefore favor India; alternatively comparisons between India and Ghana favor the latter. Although problematic for the mean, the lack of information on non-enrolled children may not be as problematic for different percentiles—it may be plausible to assume, for instance, that the 50 percent of children not tested are likely to join the group that performs “poorly”, in a sense to be made precise below.

Second, although all attempts were made to ensure that no type of school or location was left out of the sampling procedure, it has been difficult to accurately weight the data given paucity of data on enrollments in private unaided and aided schools at the district level. This is a general problem that any testing exercise has to address and it calls for an urgent compilation of a universal dataset that can be used for sampling in the future.

Third, the data are from two states only, and therefore generalizations to all of India may be misleading—Rajasthan and Orissa are both poorer states with larger tribal populations. Learning outcomes though may be different from those suggested by income rankings. The results from a countrywide testing exercise in rural areas (Pratham 2006) gives us some sense of where these states lie in the Indian distribution. Among children tested in Grade 8 countrywide 12
Determinants of Absolute Rolling Correlations - Subsample Analysis (Cont’d) Panel D: Sample Period 2007M05-2013M05

|||Re|gressor: Sta|ndard Devi|ation of S&|P500 Retu|rns (_σSP_500_,_|_t_)|||
|---|---|---|---|---|---|---|---|---|---|---|
||Crude oil|Coal|Natural|Soybean|Barley|Maize|Sorghum|Rice|Wheat|Sugar|
||||gas|oil|||||||
|Coal|0.017||||||||||
|Natural gas|0.007|0.018***|||||||||
|Soybean oil|0.011|0.022*|0.003||||||||
|Barley|0.043***|0.034*|0.012**|0.033***|||||||
|Maize|0.038***|0.039***|0.009|0.026***|0.016***||||||
|Sorghum|0.024**|0.030***|0.009|0.029***|0.020***|0.004**|||||
|Rice|0.015**|0.004|0.016***|0.021***|0.020|0.017**|0.026***||||
|Wheat|0.022*|0.028**|0.003|0.021***|0.017**|0.014*|0.018***|-0.021***|||
|Sugar|0.021***|0.015**|-0.001|0.009|0.023|0.011**|-0.010|0.011|0.026***||
|Cofee|0.030**|0.031***|-0.008|0.024***|0.040***|0.019**|0.020**|-0.014**|0.021***|0.021**|
||||R|egressor: R|eal Interest|Rate (_RR_|_t_)||||
||Crude oil|Coal|Natural|Soybean|Barley|Maize|Sorghum|Rice|Wheat|Sugar|
||||gas|oil|||||||
|Coal|0.006||||||||||
|Natural gas|-0.001|0.001|||||||||
|Soybean oil|0.001|0.000|0.003||||||||
|Barley|-0.005|0.002|0.005|-0.007*|||||||
|Maize|0.002|0.002|0.001|-0.005|0.004||||||
|Sorghum|0.001|-0.004|0.000|-0.001|0.000|0.001|||||
|Rice|0.006*|0.019**|0.017***|-0.004|0.006|0.009*|0.000||||
|Wheat|0.003|0.002|0.000|0.000|0.004|-0.001|0.000|-0.009*|||
|Sugar|0.010***|0.001|-0.006|-0.005|0.004|-0.005|-0.004|0.003|0.005||
|Cofee|0.003|-0.002|-0.004|0.000|0.003|0.001|0.003|-0.008*|-0.004|0.008*|

Note. This table reports the OLS estimates of _δ_ and _γ_ in the equation The model parameters are estimated over the indicated subsamples.<sup>�</sup> _ρ_ � _xz,w,t_ is the estimated value of the rolling correlation between the nominal price returns of _x_ and _z_ at time _t_ , where _w_ is the length of the window used to compute the rolling correlations ( _w_ = 24 months, in this case). _L_ = 12 is the maximum lag allowed in the model; _L_ is determined using the Schwarz criterion. A Newey-West correction is used to produce reliable standard errors. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels.

33
– – report higher SWB by any of the four different measures discussed in the next section compared to their counterparts who have had no IBLI coverage in any of the survey rounds. Rows 5-9 show that IBLI purchase is strongly positively correlated with the discount coupon and information treatments. In each sales period, about 93 percent of IBLI contract holders had received discount coupons.<sup>14</sup> Similarly, households who received information treatments (comic book or audio tape) were more likely to buy IBLI. As expected, higher discount rates are strongly correlated with IBLI uptake. These simple descriptive statistics suggest that the random, exogenous assignment of discount coupons and information treatments are suitable predictors of IBLI adoption.

## Table 2 here

Insured and uninsured households are not distinguishable by observable characteristics, apart from number of TLU owned, which is weakly statistically significant. The value of nonlivestock assets, annual income, expected livestock loss, gender and age of household head, household size and composition, and membership in _iqub_ groups vary insignificantly between those that purchased insurance and those who did not. These findings on observable characteristics do not rule out potential differences based on unobservable characteristics. However, so long as such unobservable differences are time invariant, we can control for them using a fixed effects estimator. Concerns that time varying characteristics may determine IBLI adoption nonetheless remain. We exploit the random assignment of discount coupon and information treatments, each strongly correlated with IBLI uptake, to address these concerns.

# **5 Estimation strategy**

A key challenge in evaluating policy interventions where respondents can voluntarily “optin” is that selection into the program may not be random. Rather, participation could be systematically correlated with respondents observable and unobservable characteristics. Peoples’ SWB is likely correlated with their subjective assessment of risk, their planning horizons, and other unobserved factors that influence insurance uptake. The experimental

> 14Since survey rounds 2 and 3 were preceded by two sales periods each, a household who purchased IBLI in sales period 2 but had received discount coupon in sales period 1 is reported to have received discount coupon for the survey round, hence the slightly higher figures in Table 2.

11
Table 3: Difference Between Actual vs Predicted Employment Shares (based on Pareto distribution)

|||S|ize class|– Number|of worker|s|
|---|---|---|---|---|---|---|
||_κ_|1 – 4|5 – 9|10 – 19|20 – 99|100+|
|||Pane|l a - Nar|row size cl|asses||
|Burkina Faso (2015)|1.383|-0.013|0.150|-0.013|-0.066|0.014|
|Cameroon (2008)|1.149|0.021|0.012|0.015|-0.015|0.011|
|Ghana (2013)|1.336|0.007|0.098|-0.006|-0.053|0.023|
|Rwanda (2013)|1.299|0.047|0.012|-0.015|-0.017|0.039|
|||Pan|el b - Bro|ad size cla|sses||
|||1 –|9|10 –|49|50+|
|Burkina Faso (2015)|1.454|0.0|27|-0.0|52|0.044|
|Cameroon (2008)|1.143|0.0|15|-0.0|11|0.008|
|Ghana (2013)|1.366|0.0|28|-0.0|42|0.032|
|Rwanda (2013)|1.294|0.0|22|-0.0|24|0.019|

_Source:_ Establishment censuses obtained from the statistical agencies of the select countries; see section 3. _Note:_ The Pareto shape parameter _κ_ is estimated based on minimizing Euclidean distance between observed and theoretical shares.

16
Policy Research Working Paper 8225

# **Abstract**

Telephone surveys enable us to collect data in a cost-effective and timely manner, but may not be conducive for collecting detailed consumption or income data for measuring poverty due to the required length of the interview and complexity of the questions. Combining telephone surveys with a survey-to-survey imputation technique may be a solution, as this technique can produce reliable poverty estimates from only 10 to 20 simple questions. However, this approach may lead to biased results if the interview mode, that is, face-to-face versus telephone interviews, affects how households respond to questions. By conducting the first survey experiment to examine potential differences in poverty estimates between interview modes, this study finds that the reporting patterns changed very little between the two interview modes, and the bias in poverty estimates due to interview mode is statistically insignificant. These findings suggest that poverty monitoring via telephone surveys is promising, but additional experiments in other country contexts are encouraged.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at nyoshida@worldbank.org, stakamatsu@worldbank.org, and rkatayama@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
<!-- Start of picture text --> 50,000 300<br>gs<br>©40,000 ?<br>ak Fa5<br>83 3<br>B $30,000 3<br>3570000 B=<br>a5 £150<br><s< <g<br>£ 8 10,000 re s<br>aaa 100<br>0<br>1990 1995 2000 2005 2010 2015 2020 1990 1995 2000 2005 2010 2015 2020<br>——— Bottom Billion ——— Emerging Markets === OECD —_—— Bottom Billion ——— Emerging Markets === OECD<br><!-- End of picture text -->
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

# **APPENDIX C IMPLICIT PENSION DEBT: CROSS COUNTRY COMPARISON**<sup>**37**</sup>

The IPD represents a fiscal burden that cannot be ignored in low- and middle-income countries. Indeed, it is important to take into account unfunded public pension liabilities as part of an assessment of the overall fiscal situation, including the fiscal positions of pension schemes pre and post reforms.

However, it is important to highlight that this indicator has different methodologies and assumptions and there is no standard way of calculation, hence careful consideration should be given to cross-country comparisons. The estimates are quite sensitive to methodology and definitions.

The definition of the IPD used in this paper represents the present value of pensions to be paid in the future on the basis of accrued rights by the year for which IPD is calculated; neither future contributions, nor the accrual of new rights on the basis of these contributions are considered (the value is assumed under the assumption that the systems closes today ).

Table below presents the IPD estimates for 35 low and middle income countries under alternative discount rate assumptions and compares them with official public debt, expenditure levels, and existing financial reserves (all in percent of GDP). Several results stand out.

First, the estimated IPD is large relative to national incomes in our sample of countries but there is a huge range. Using the two percent discount rate, Brazil’s IPD is 500 percent of GDP, ten times more than Morocco. Even using the higher, five percent discount rate, 18 out of 35 countries had IPDs greater than national income.

Not surprisingly, the former socialist countries and especially Central Europe, have the highest IPD burdens measured this way. Of the 19 countries with IDPs of at least 200 percent of GDP, 12 of them are from this region (using a discount rate of 2 percent). This reflects the high labor force coverage that characterized countries in this region and relatively generous benefit levels, combined with an aging population. Uruguay is another country with a large state apparatus and high social expenditure and has the oldest demographic structure in Latin America. At the other end of the spectrum are younger countries like Senegal, Morocco and Uganda where coverage is much lower than what had been found in pre-transition Central Europe. Nevertheless, the levels of pension debt even in these countries is too significant to ignore.

> 37 Source: Holzmann, Palacios, Zviniene, 2004

- 93 -
**Figure 1:** Productivity Dispersal - by Location and Gender

<!-- Start of picture text --> (a) Nigeria - by Location (b) Uganda - by Location<br>Kernel density estimate Kernel density estimate<br>0 5 10 15 -5 0 5 10 15<br>log of enterprise productivity log of enterprise productivity<br>Rural Urban Rural Urban<br>kernel = epanechnikov, bandwidth = 0.2094 kernel = epanechnikov, bandwidth = 0.2725<br>(c) Nigeria - by Gender (d) Uganda - by Gender<br>Kernel density estimate Kernel density estimate<br>0 5 10 15 -5 0 5 10 15<br>log of enterprise productivity log of enterprise productivity<br>Female Male Female Male<br>kernel = epanechnikov, bandwidth = 0.1944 kernel = epanechnikov, bandwidth = 0.2865<br>.4 .25<br>.2<br>.3<br>.15<br>.2<br>Density Density<br>.1<br>.1<br>.05<br>0 0<br>.4 .25<br>.2<br>.3<br>.15<br>.2<br>Density Density .1<br>.1<br>.05<br>0 0<br><!-- End of picture text --> by migrating to a large city, but that one in two did so by migrating to a secondary town.

We are therefore interested in providing a finer analysis of how spatial location affects labor productivity. We use data from Uganda and analyze it with regard to the country’s division into four main regions and the capital city: Central, Eastern, Northern, Western, and the capital Kampala. These four regions and the capital city are distinct in terms of population density, business environment and history. Over the past ten years economic growth has been largely concentrated in the Central region that includes the capital city, while violent conflict affected the Northern region between 1987 and 2006 (Blattmann et al., 2014). The Western region also has not escaped occasional (ethnic) conflicts (Espeland, 2007). This allows us to obtain some indication of how the local business environment may affect labor productivity, as regions that experienced a conflict are more likely to show a poorer business environment, as conflict destroys demand, markets and infrastructure (Brueck et al., 2013). We also expect that labor productivity is higher in the capital city, and the more densely populated and more peaceful regions (Eastern and Central regions) compared to the Northern region. Figure 2a confirms our expectations. Kampala reports the highest labor productivity, with the Central region following in terms of productivity level. The Northern region, with the lowest population density and a history of conflict, is home to the enterprises with the lowest productivity 20
Policy ReseaRch WoRking PaPeR 4928

# **Abstract**

Deliberative decision-making processes are becoming increasingly important around the world to make important decisions about public and private goods allocation, but there is very little empirical evidence about how they actually work. In this paper the authors use data from India extracted from 131 transcripts of village meetings matched with data from household surveys conducted in the same villages prior to the meetings, to study whose preferences are reflected in the meetings. The meetings are constitutionally empowered to make decisions about public and private goods. The findings show that the more land a person owns, the higher the likelihood her preference is mentioned in the meeting, the longer the amount of time spent discussing this preference, and the higher the likelihood that a decision to provide or repair this public or private good is taken. At the same time, the voices of disadvantaged castes, while not dominating the meeting, are also heard. By contrast, the preferences of Muslims are given less time. High village literacy and the presence of higher level officials during village meetings mitigate the power of the landed, but political reservations for low castes for the post of village president increase the power of the landed.

This paper—a product of the Poverty Team, Development Research Group—is part of a larger effort in the department to understand local government and citizen-based engagement in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at vrao@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
benefits of technology and learning spillovers. Moreover, EAP economies could also be investing in sectors from which they import goods, with the intention to acquire and secure resources (as many have argued to be China’s strategy of investing in resource-rich African economies).<sup>20</sup> EAP economies might also be investing abroad to hedge and/or diversify the risk of their exports (hence, mitigating possible terms-of-trade shocks). Besides the sectoral composition of trade and finance, further analysis could also inform whether foreign investments reinforce or loosen the existing comparative advantage forces observed in trade flows.

Although not discussed in detail in this paper, another aspect that deserves more research is the fact that EAP has sent more investments than it has received from the rest of the world. This pattern has generated on average positive net outward investments (except for greenfield investments). It could be interpreted as the counterpart to the persistent current account surpluses run over the years by many economies in the region, which has allowed them to invest those surpluses abroad. The large savings rate of some EAP economies might be important for economies in the region to act as the average developed economy is expected to do in theoretical predictions, exporting capital to other regions (Lucas, 1990). Moreover, the large increase in the investments that EAP has in South economies could be a sign of EAP’s strategy for diversification of its large pool of international reserve assets away from developed economies. This diversification does not need to be generated from central governments. The economy as a whole might be more willing to hold other types of assets or to diversify risk by investing in economies in the South. For example, as mentioned above, evidence suggests that China (partly through state-owned enterprises) has been investing in other regions. These types of flows are indeed captured by our data.

> 20 See, for example, Tull (2006), World Bank (2007, 2009), Besada et al. (2008), Gu (2009), Kaplinsky and Morris (2009), Christensen (2010), Klaver and Trebilcock (2011), Kolstad and Wiig (2011), Cheung et al. (2012), Ayodele and Sotola (2014), Shen (2015), and Chen et al. (2016).

24