rafmacalaba/datause-encoder-data huggingface dataset:
# [Table 2]

As already mentioned, economics and other disciplines have offered a wide range of indices of relative deprivation, few of which have been used in empirical applications and none of which has been tested to see whether effectively captures sentiments of deprivation. In Table 3, we test the Yitzhaki (1979) relative deprivation index, which is perhaps the most popular relative deprivation index used in economics and the one that originated most of the contributions on the subject. We also test the Silber-Verme (2010) index, which builds on predicted incomes as the generalized indices proposed in this paper.

The Yitzhaki (1979) index performs rather well and better than the population weighted indices of Table 1 but worse than the income weighted indices of Table 2 (Table 3). In three of the ten years considered for the UK, the individual deprivation score is nonsignificant. The Silber-Verme (2010) index of relative deprivation is instead always negative and significant for all years and countries considered and its performance is comparable to the income weighted index when alpha is equal to two. This index, together with the income weighted index of Table 2 is the best performing index in terms of correlation with self-reported income satisfaction. As for the indices presented before, the coefficients of the Yitzhaki and Silber-Verme indices seem to increase as we move from Western to Eastern European countries.

# [Table 3]

As a final test, we reduce the sample to balanced observations and use only the panel components of the surveys. This can be done for the UK, Germany (West and East) and 17
R<sup>2</sup> =0.97). Projecting this trend forward to 2015, the estimated headcount index for that year is 16.1% (standard error of 1.4%). Given that the 1990 poverty rate was 43.1%, this calculation implies that the developing world as a whole is on track to achieving the first Millennium Development Goal (MDG) of halving the 1990 poverty rate well before 2015.<sup>26</sup> The 1% point per year rate of decline in the poverty rate also holds if one focuses on the period since 1990 (not just because this is the base year for the MDG but also recalling that the data for the 1980s are weaker). The $1.25 poverty rate fell 9% points in the 10 years of the 1980s (from 52% to 43%), and a further 20 points in the 18 years from 1990 to 2008.

China’s success against absolute poverty has clearly played a major role in this overall progress. Tables 2 and 3 repeat the calculations excluding China. Strikingly, the number of people outside China living below $1.25 a day is no lower in 2008 than 1981, although it rose then fell, with a marked decline since 1999, from 1.3 to 1.1 billion.

Figure 3 plots the poverty rates over time with and without China. Excluding China, the $1.25 a day poverty rate falls from 40% to 25% over 1981-2008, with a rate of decline that is half the trend including China; the regression estimate of the trend falls to -0.53% per year (standard error of 0.05%; R<sup>2</sup> =0.94). Based on our new estimates, the projected value for 2015 is 23.5% (standard error=1.05%), which is well over half the 1990 value of 37% (Table 2). So past trends do not suggest that the developing world as a whole outside China is on track to reaching the MDG for poverty reduction.

Our new estimates suggest only slightly less progress in absolute terms for the $2 per day line than $1.25 (though less in proportionate terms). The poverty rate by this higher standard has fallen from 70% in 1981 to 43% in 2008 (Table 3). The trend is also about 1% per year (a regression coefficient on time of -0.97; standard error=0.09); excluding China, the trend is only 0.4% per year (a regression coefficient of -0.44; standard error=0.07%). Clearly, in proportionate terms, however, the rate of progress has been lower for the higher poverty line.

The number of people living below $2 per day has fallen over 1981-2008, but only because of the progress since 1999 (Table 3). The number of people living between $1.25 and $2 a day has almost doubled from 648 million to 1.18 billion. Most of the 649 million fewer poor by the $1.25 per day standard over 1981-2008 are still poor by the standards of middle-income

> 26 Our preliminary estimate for 2010 using survey data representing about 80% of the population of the developing world indicates that the first MDG was in fact achieved in that year.

14
are willing to pay differential premiums to live near others who share common socioeconomic or demographic characteristics. These characteristics include religion, class, language, educational attainment, and duration of stay and tenure in the city.

# **3. Data**

Our data come from Pune, a city located in the State of Maharashtra, approximately 200 kilometers east of Mumbai. Pune has a population of 2.8 million, of whom close to one million live in slum settlements distributed throughout the city (Bapat and Agarwal 2003). The empirical analysis draws on a household survey which was collected between August and October 2002, and designed to be representative of the Pune Municipal Corporation area. All households of the city are part of the sampling universe, with the exception of residents of military cantonments and institutional populations (e.g. prisons). The sample was chosen to be representative of each of the 48 wards (administrative units) in proportion to the preliminary estimates of the Census of March 2001. For our survey, 2850 households were randomly selected and asked questions regarding socioeconomic characteristics, quality and quantity of housing, tenure status, and access to infrastructure services. Non-response and other missing data problems reduce the size of the dataset for analysis to 2703 households.

The value of the dwelling unit is the household’s willingness to pay (WTP) for a set of dwelling unit and neighborhood characteristics. The survey asked each household the following questions: “What would be the estimated present market price for a similar unit in this neighborhood?” and “What would be the estimated monthly rental value for a similar unit in this neighborhood?” We use the latter measure in the empirical work 9
Musacchio (2018) to eliminate another 30 firms that have minority state ownership (any firm with more than 5 percent state ownership) in the roads, airports and airlines, power, and railway sectors.<sup>8</sup> Having some partially privatized firms in our set of private firms would be a problem only if we find that SOEs outperform private firms. In this case, the partially privatized firms could be reducing the mean performance of private firms, biasing our matching results to indicate better performance for SOEs. If, however, we find that SOEs underperform a sample of private firms and partially privatized SOEs, it is likely that our estimates represent lower bounds on the “treatment effect” of being state owned and that the effect would actually be larger if all partially privatized firms were eliminated from the comparison group.<sup>9</sup> As we explain below, our results show that SOEs perform worse, are larger, accumulate more liabilities, and have higher employment costs than private firms. We are therefore confident that our results present either a lower-bound estimate of the effect of state ownership or at best a good reflection of the average effect of being an SOE.

# **_5.2 Matching_**

In order to estimate the effect of state ownership on several indicators of performance, we first perform a matching exercise in which we treat our SOE sample as the treatment group and a dataset of private firms as the control group. We use matching techniques, because our database is observational rather than randomly selected; there can therefore be systematic differences in characteristics between the treated group (SOEs) and the untreated subjects (private firms) that can cause significant imbalances between these groups.

We use the coarsened exact matching (CEM) algorithm described in Iacus, King, and Porro (2012). This technique minimizes imbalances between groups and provides a larger comparison group for each of our SOEs than we would get with exact matching techniques, which tend to produce only a few matches. The underlying idea of the CEM algorithm is to yield coarsened groups, or strata, to match the treated and the control subjects, which bounds the imbalance between the two groups. The CEM has several benefits, as Blackwell et al. (2009) note. First, it meets the congruence principle, which states that the data space and analysis space should be the same, thereby reducing the possibility of obtaining strange or counterintuitive results. Second, the CEM bounds the matched data to areas of common empirical support, thereby reducing the possibility

> 8 There is still the slim possibility that we may have left firms in the sample that are owned by entities that are ultimately controlled by governments. A more specialized database that tracks ultimate ownership, such as Bureau van Dyk’s Orbis, can be used to resolve this issue in future work.

> 9 We run the same matching exercise but with partially privatized firms, using the sample of Lazzarini, and Musacchio (2018), and find no significant differences with fully owned SOEs.

15
We use the 2011-2015 GEPU uncertainty index to interact with the instrumented tradable job gains or losses in each county. We expect the job gain multiplier in the years with high level of economic policy uncertainty to be smaller. Table 6.4 reveals that the interactions between the exogenous job gains and GEPU uncertainty is negative and significant at 1% level. Specifically, a 1-point increase in the global uncertainty index (for example, increasing from 100 to 101) will reduce the job gain multiplier by about 0.04 in all the recovery periods (2011-2013; 2011-2014; 2011-2015).

In addition to uncertainty, we test for the presence of credit constraints during the job recovery period. Following Mian and Sufi (2014) we splits the counties into two groups, one primarily served by national banks before the Great Recession (National=1), and one primarily served by local banks (Local=1)<sup>9</sup> . Mian and Sufi (2014) define a national banking county as a county that has banks with a very low fraction of their deposits in that county. They therefore should not be as sensitive to local credit supply conditions. If credit were to play a key role in the transmission, the multiplier would be smaller in counties with more local banks, as local banks would be less likely to get deposits and funding beyond their respective counties.

Table 6.5 presents the employment multipliers by two types of counties: those that were dominated by national banks compared to those that were dominated by local banks before crisis. We find that the job gain multipliers for counties primarily served by national banks are consistently statistically significant in all recovery periods, whereas the job gain multipliers for counties primarily served by local banks are consistently statistically insignificant. This finding is consistent with the notion that credit constraints existed during the recovery – counties with national banks have larger job gain multipliers during the recovery

> 9 Data on national banks and local banks are from Mian and Sufi (2014)

28
- Feng, S., Gr ́epin, K. A., and Chunara, R. (2018). Tracking health seeking behavior during an ebola outbreak via mobile phones and sms. _NPJ digital medicine_ , 1(1):1–8.

- for Health Initiative: NCD Mobile Phone Survey, B. P. D. (2020a). Executive summary: Zambia ncd mobile phone survey 2017. Technical report, Bloomberg Philanthropies.

- for Health Initiative: NCD Mobile Phone Survey, B. P. D. (2020b). Ncd mobile phone survey in the philippines. Technical report, Bloomberg Philanthropies.

- Friedman, J., Beegle, K., De Weerdt, J., and Gibson, J. (2017). Decomposing response error in food consumption measurement: Implications for survey design from a randomized survey experiment in tanzania. _Food Policy_ , 72:94–111.

- Garlick, R., Orkin, K., and Quinn, S. (2020). Call me maybe: Experimental evidence on frequency and medium effects in microenterprise surveys. _The World Bank Economic Review_ , 34(2):418–443.

- Gibson, D. G., Wosu, A. C., Pariyo, G. W., Ahmed, S., Ali, J., Labrique, A. B., Khan, I. A., Rutebemberwa, E., Flora, M. S., and Hyder, A. A. (2019). Effect of airtime incentives on response and cooperation rates in noncommunicable disease interactive voice response surveys: randomised controlled trials in bangladesh and uganda. _BMJ global health_ , 4(5):e001604.

- Gibson, J., Beegle, K., De Weerdt, J., and Friedman, J. (2015). What does variation in survey design reveal about the nature of measurement errors in household consumption? _Oxford Bulletin of Economics and Statistics_ , 77(3):466–474.

- Gigliotti, L. and Dietsch, A. (2014). Does age matter? the influence of age on response rates in a mixed-mode survey. _Human Dimensions of Wildlife_ , 19(3):280–287.

- Glazerman, S., Rosenbaum, M., Sandino, R., and Shaughnessy, L. (2020). Remote surveying in a pandemic: Handbook. _Innovations for Poverty Action_ .

- Gourlay, S., Kilic, T., Martuscelli, A., Wollburg, P., and Zezza, A. (2021). High-frequency phone surveys on covid-19: Good practices, open questions. _Food Policy_ , 105:102153.

25
- **Durnev, A., R. Morck, and B. Yeung** , “Value enhancing capital budgeting and firm-specific stock return valuation,” _Journal of Finance_ , 2004, _59._

- **Edmans, A., Itay Goldstein, and W. Jiang** , “Feedback Effects and the Limits to Arbitrage,” _Working Paper_ , 2011.

- **Fama, Eugene and Merton Miller** , “The Theory of Finance,” _New York: Holt, Rinehart & Winston_ , 1972.

- **Foucault, Thierry and Laurent Fresard** , “Learning from Peers’ Stock Prices and Corporate Investment,” _Forthcoming Journal of Financial Economics_ , 2013.

- **Gilchrist, Simon, Charles P. Himmelberg, and Gur Huberman** , “Do stock price bubbles influence corporate investment?,” _Journal of Monetary Economics_ , 2005, _52._

- **Goldstein, I., E. Ozdenoren, and K. Yuan** , “Trading Frenzies and Their Impact on Real Investment,” _Journal of Financial Economics, Forthcoming_ , 2012.

- **Goldstein, Itay and Alexander Guembel** , “Manipulation and the Allocational Role of Prices,” _Review of Economic Studies_ , 2008, _75._

- **Grinblatt, M.S. and S.A. Ross** , “Market Power in a Securities Market with Endogenous Information,” _The Quarterly Journal of Economics_ , 1985, _100_ (4), 1143–1167.

- **Guntay, Levent and Dirk Hackbarth** , “Corporate bond credit spreads and forecast dispersion,” _Journal of Banking and Finance_ , 2010, _34._

- **Hayek, Friedrich** , “The Use of Knowledge in Society,” _American Economic Review_ , 1945, _35_ (4), 519– 530.

- **Kyle, A.S.** , “Continuous Acutions and Insider Trading,” _Econometrica_ , 1985, _53._

- **Lambros, Louis and Victor Zarnowitz** , “Consensus and uncertainty in economic prediction,” _Journal of Political Economy_ , 1987, _95._

- **Leland, H.E.** , “Insider Trading: Should it be Prohibited,” _The Journal of Political Economy_ , 1992, _100_ (4), 859–887.

24
Indicators, which provides information on 203 countries in each of our six aggregate indicators. Primarily for reasons of cost, household and firm surveys have much smaller country coverage. Our largest surveys, the Global Competitiveness Report survey and the Gallup World Poll each cover around 120 countries in 2006, and several regional surveys cover necessarily smaller sets of countries. Some of the expert assessments provided by NGOs and public sector organizations have quite substantial country coverage, but others, particularly regionally-focused ones again have much smaller country coverage. Table 2 summarizes the distribution of country-level data points for each of the six indicators in 2006. The 2006 WGI are based on a total of 10,985 country level data points (after averaging multiple questions from individual sources), of which slightly less than half (46 percent) come from commercial business information providers. The remaining data points are fairly evenly distributed between the remaining three types of data providers.

This year, we continue the practice we started last year of reporting the underlying data from virtually all of the individual data sources that go into our aggregate indicators. The sources we have made available on our website are noted in Table 1. A number of our data sources, such as Freedom House and Reporters Without Borders have always been publicly available through the publications and/or websites of their respective organizations. Several of our other sources provided by commercial risk rating agencies and commercial survey organizations have only been available for a fee. In the interests of greater transparency, these organizations have kindly agreed to allow us to report their proprietary data in the form in which it enters our governance indicators. As mentioned above and as documented in detail in Appendix A and B, we in some cases use a simple average of multiple questions from the same source as an ingredient in our governance indicators. We do this when we find more than one question from a single data source that is relevant to one of the dimensions of governance that we measure. On our website we report either the individual question, or the average of individual questions, from each source that enters into our governance indicators. All the individual variables have been rescaled to run from zero to one, with higher values indicating better outcomes.

These individual indicators can be used to make comparisons of countries over time, as all of our underlying sources use reasonably comparable methodologies from 6
**Figure 4. Nonparametric decomposition of changes in poverty headcount ratio**

<!-- Start of picture text --> 4<br>2<br>0<br>‐2<br>‐4<br>‐6<br>‐8 All Rural Urban<br>‐10<br>Household Salaried/Wage work Self‐employed work Other sources of income<br>composition<br>Change in poverty (%)<br>Residual Share of adults Remittances (per capita) Benefits (per capita) Other income (per adult)<br>worker)<br>agr. worker)<br>percentage of workers) percentage of workers)<br>Share of workers (as percentage of adults) Share of wage/salaried agr. workers (as Share of wage/salaried non‐agr. workers (as percentage of workers) Wage/salary agr. Income (per wage/salaried Wage/salary non‐agr. Income (per wage/salaried non‐agr. worker) Share of self‐employed agr. workers (as Share of self‐employed non‐agr. workers (as percentage of workers) Self‐employed agr. income (per farm worker) Self‐employed non‐agr. income (per business<br><!-- End of picture text --> Source: IHDS, 2005 and 2012 Aggregating the contributions of components by sector, changes in the non-agricultural sector are found to account for the largest share of both urban and rural poverty reduction, followed by other income sources, household composition, and then the agricultural sector (Figure 5, panel a). The nonagricultural sector contributes as much as 46 percent of national poverty reduction, which includes 42 and 57 percent of rural and urban poverty reduction, respectively. Agriculture has no contribution to urban poverty decline, while the contributions of other income sources and household composition to poverty decline are similar for urban and rural areas.

Aggregating the contributions of components by type of work, changes in wage/salaried work and self-employment have together contributed 54 percent of the national poverty decline, which includes almost similar contributions to reductions in rural and urban poverty (Figure 5, panel b). Thus rising labor income, rather than changes in household composition and other income sources, has been the main driver of poverty reduction (see Figure A1, Appendix), consistent with what has been reported by and large for other countries as well. Most of the contribution of labor income comes from changes in wage/salaried work, which accounts for 46 percent of national poverty reduction compared to only 8 percent of the decline being attributable to changes in self-employed work.

19
Policy Research Working Paper 5695

# **Abstract**

This paper analyzes previously unreleased firm-level customs transaction data from the Lao PDR in order to assess the determinants of cohort survival among exporters. The authors find that export flows in value terms are dominated by the intensive margin, with large firms continuing to supply the same products to the same markets. On the extensive margin, new export spells for firms, products and firm-product-destination units are very small and short-lived, suggesting that although there is significant experimentation and discovery by firms, there is only limited capacity to stay in markets once an entry is made. Regression analyses of the factors that influence survival past the first year reveal that this is positively correlated with the initial dollar value (starting big makes a difference) and is helped by the firm’s experience with the product and the destination, but hindered by a lack of focus. Agglomeration of exporters in the same destination with the same product is beneficial, an effect analogous to external economies of scale. The authors conclude by recommending that the focus of export promotion activity should be on helping existing exporters find and stay in new markets.

This paper is a product of the Poverty Reduction and Economic Management Sector Department, East Asia and Pacific Region. 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 author may be contacted at rrecord@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
TABLX 1.3t Nuw are of Teachers in Secondary Schools, 1979-1989

|Year|3T<br>PIndexw||LlIntrainad|l**_ndercralIfi**<br>**_d+_**|
|---|---|---|---|---|
|1979|35.1)|100|n.a|6|
|i980|3736|106|3|7|
|1981|6112|173|5|7|
|1982|8349|236|12|12|
|1983|1119;|317|22|46|
|1984|14718|416|41|18|
|1985|17315|489|45|19|
|1986|19487|551|47|17|
|1987|21981|622|49|16|
|1988|23899|676|n.a.|n.a.|
|1989|24856|703|50|n.a.|

n.a. not available.

_*_ The teacher index baseline is the 1979 figure.

+ Underqualifisd is defined as Standard 6 plus 2 year. teachor training, Junior Certificate (J.C.) plus 2 or 3 years teacher training, and Cambridge School Certificate (C.S.C.) plus 2 or 3 or 4 yeare primary teacher training.

Source: See Table 1.1 The rapid increase in student enrolments was matched by a rapid growt<sup>t</sup> :n the number of teachers. As can be seen from Table 1.3, the teaching force which stood at 3,534 in 1979 rose to 24,856 in 1989. However, the steady increase in numbers of teachers had another side to it, namely the increase in the number of untrained and underqualified teachers employed in secondary schools. Whilst only 3% of secondary school teachers were untrained in 1979 the percentage rose to 50% in 1989. The percentage of underqualified teachers has fluctuated between 7 - 46% in the period 1980 - 87. Government efforts to train more secondary school teachero have been significant. While there were only two secondary teacher training colleges in 1979, the number had increased to four by 1985 and a fifth college is due to opr. shortly in Chinhoyi. The data in Tables 1.2 and 1.3 clearly demonstrate that the Zimbabwean government 4