rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 6. Time Trends by Firm Size</u>**

|||1995|1996|1997|1998|1999|2000|t-test|
|---|---|---|---|---|---|---|---|---|
|Nominal Sales Growth|Small||0.776|-5.024|-4.250|-1.635|-10.529|(<sup>a</sup>)|
||Medium||5.371|9.043|3.606|-1.820|-1.050|(**)|
||Large||10.928|9.522|6.362|4.313|-0.587|(***)|
|Real Sales Growth|Small||-5.993|-9.201|-8.109|-4.500|-12.881|( )|
||Medium||-1.706|4.247|-0.570|-4.680|-3.651|( )|
||Large||3.477|4.705|2.075|1.275|-3.200|(***)|
|Retained Earnings|||||||||
||Small|0.041|0.047|0.036|0.024|0.028|0.018|(***)|
||Medium|0.048|0.049|0.049|0.038|0.033|0.041|( )|
||Large|0.032|0.022|0.025|0.033|0.025|0.038|(*)|
|Operating Income / Total Assets|||||||||
||Small|0.033|0.032|0.020|0.017|0.018|0.012|(***)|
||Medium|0.064|0.067|0.063|0.071|0.053|0.044|(**)|
||Large|0.086|0.077|0.071|0.092|0.091|0.080|( )|
|Total Liabilities / Total Assets|||||||||
||Small|0.470|0.427|0.430|0.357|0.376|0.323|(***)|
||Medium|0.606|0.592|0.542|0.538|0.485|0.489|(***)|
||Large|0.627|0.666|0.647|0.604|0.602|0.553|(***)|
|Interest Coverage|||||||||
||Small|1.978|2.721|2.993|1.764|2.625|1.359|(**)|
||Medium|2.214|2.404|2.934|2.097|2.057|1.733|( )|
||Large|4.009|3.117|2.562|4.569|3.768|2.923|( )|
|Paid-in-Capital|||||||||
||Small|0.368|0.365|0.367|0.442|0.443|0.532|(***)|
||Medium|0.185|0.193|0.248|0.271|0.250|0.291|(***)|
||Large|0.140|0.137|0.129|0.153|0.178|0.197|(***)|

Notes: Source CMA (own calculations). Firms are split in three groups based on their nominal sales with equal number of firms in each group. The t-test indicates significance for equality of the medians (or means) between years 1996 and 2000. ***,**,*, and<sup>a</sup> indicate significance at 1 percent, 5 percent, 10 percent, and 15 percent, respectively.
trends before the program started in 2005. We plot trends for the other two dependent variables of interest and the pre-program trends also look similar.

**Table 3 Summary Statistics by Sector**

||**Participa**|**nts**|**Non-particip**|**ants**|**Difference**|
|---|---|---|---|---|---|
||Mean|_N_|Mean|_N_|Mean|
|**POWER GENERATION**||||||
|Material cost|116399.5|1780|23962|2216|92178.7|
||(257650.6)||(44261.5)||(6194.2)|
|Unit material cost|0.59<br>(0.23)|1836|0.48<br>(0.29)|2189|0.11<br>(0.01)|
|Turnover|96664.6<br>(175802.3)|2447|41877.4<br>(66538.0)|2954|54098.4<br>(3772.2)|
|Number of employees|400<br>(739)|1615|105<br>(199)|1925|299<br>(19)|
|**CEMENT**||||||
|Material cost|86427.6<br>(155944.9)|694|29922<br>(33863.5)|466|55880.7<br>(6128.8)|
|Unit material cost|0.33|732|0.49|464|-0.17|
||(0.13)||(0.17)||(0.01)|
|Turnover|252651.7<br>(457188.5)|848|63294<br>(75279.7)|558|189595.1<br>(16313.3)|
|Number of employees|526<br>(1172)|823|145<br>(151)|519|390<br>(42)|
|**IRON AND STEEL**||||||
|Material cost|325580.0<br>(365430.2)|590|235068.3<br>(213408.6)|408|90797.0<br>(17841.2)|
|Unit material cost|0.62<br>(0.16)|643|0.68<br>(0.15)|406|0.06<br>(0.01)|
|Turnover|467611.6<br>(513468.6)|617|305319.5<br>(243698.7)|489|160209.6<br>(22978.4)|
|Number of employees|935|561|509|444|424|
||(1116)||(541)||(54)|

Note: For the first two columns the standard deviations are recorded in respective parentheses, while the standard errors are recorded in the last ‘difference’ column. The difference column also controls for year fixed effects. _N_ denotes number of observations.

10
It is noteworthy that in certain respects, these synthetic panel data may possess advantages over true panel data. When true panel data are affected by issues such as attrition bias or panelspecific measurement error, they may no longer be representative of the population and can provide biased estimates.<sup>3</sup> Cross sectional data may be less exposed to these issues since these data by design comprise fresh and representative samples of the population.<sup>4</sup> Since our synthetic panel data are constructed from cross sections, this advantage may extend also to our analysis. Thus beyond our contribution to the policy dialogue in Senegal, this paper represents an early application of the synthetic panel methodology to a country in Sub-Saharan Africa. And since the questions on poverty dynamics raised above may apply not just to Senegal but also to other Sub-Saharan African countries, this paper has potential relevance beyond Senegal, and can potentially motivate studies in other contexts where panel data are not readily available or of poor quality.<sup>5</sup> Our findings indicate that, beneath the relatively stable poverty rates estimated with cross sectional data, there has been much “churning” out of as well as into poverty in Senegal in the period between 2006 and 2011. Poor households were somewhat more likely to escape poverty (45 percent) than non-poor households were likely to fall back into poverty (40 percent), and were more likely to have stronger consumption growth as well. Overall, the population was rather mobile with more than half (53 percent) of the population experiencing either upward or

> 3 This situation is more common than one might think. For example, between 15 percent and 42 percent of measured mobility in Vietnam in the 1990s may be caused by upward bias due to measurement error (Glewwe, 2012). See also Gottschalk and Huynh (2010) and Lee et al. (2010) for related studies on the effects on measurement errors on earnings inequality and poverty mobility.

> 4 Strictly speaking, cross sectional data can be mis-measured just as with true panel data. However, while measurement errors can be correlated over time for the latter and thus can result in severe bias, these errors are uncorrelated across different rounds of cross sections since data on different households are collected in each round. 5 Synthetic panels constructed using the Dang et al. (2014) and Dang and Lanjouw (2013) methods have recently been applied to study poverty dynamics in various settings including multi-country analysis for Latin America (Ferreira et al., 2013; World Bank, forthcoming), South Asia (World Bank, 2014b), and Europe and Central Asia (World Bank, 2014e). Specific country case studies using synthetic panels investigate countries including the Kyrgyz Republic (Bierbaum and Gassmann, 2012) and Bhutan (World Bank, 2014c). We are also applying synthetic panel methods in our ongoing studies of other countries.

5
The first two selection equations can thus be correlated with each other besides each being individually correlated to the main productivity equation. This fully incorporates the correction for the product-only sample selection into the bivariate switching model. Of course, once a bivariate (rather than an univariate) selection is implemented, four instead of just two different regimes are identified, accounting for the potential specificities that characterize each possible combination of the two switching variables: (1, 1); (0, 1); (1, 0) and (0, 0).

From a computational point of view, four productivity equations should be estimated, each of them augmented by two additional terms (inverse Mills ratios) correcting for the double selection bias. Thus, for instance, focusing on the sub-sample identified by the combination (funding=1 & PDT_ONLY=1), the estimated performance equation will be: where: Where the lambda equations are obtained through probit estimations. The same procedure applies to the other three sub-samples. For our purposes, the relevant ATET will be: where, following the same procedure is adopted for the univariate endogenous switching model. The coefficients obtained on the sub-sample of non-supported product innovators will be applied to the supported ones in order to obtain an estimate of their potential productivity had they not received the subsidy (counterfactual).

# Meta-Analysis Effect Sizes

The quantitative relationships of interest may be univariate, bivariate, or multivariate. Nearly all meta-analysis is done on bivariate relationships, e.g., treatment-control differences on 40
Reinhart and Rogoff 2010) investigate the short-run effect of external debt on growth. In contrast, this analysis emphasizes the long-run relationship. Third, this analysis uses a different methodology to provide the core findings. In contrast to previous studies, which relied on spline functions (Cordella, Ricci, and Ruiz-Arranz 2010; Pattillo, Poirson, and Ricci 2002, 2004) or histograms (Reinhart and Rogoff 2010), this analysis relies on the threshold estimation techniques developed by Hansen (1996, 2000).<sup>1</sup> The chapter is organized as follows. The next section, section II, describes the data. The third section describes the methodology. The fourth section presents the results. The last section provides some concluding remarks.

# **_II. Data_**

The analysis is based on a data set of 101 countries (75 developing and 26 developed), consisting of annual observations for the period 1980–2008 (countries are listed in the annex). By including a large group of both developing and developed countries, this data set improves on previous data sets.<sup>2</sup> The main variables are gross public debt, GDP growth, and a set of control variables known to influence economic growth (table 1). Public debt is measured as the ratio of general government gross debt to GDP. When considering the debt-growth nexus, debt at all levels of government is relevant, because it influences the government’s ability to engage in growth-enhancing potentially countercyclical policies. The average debt to GDP ratio was 67.1 percent for the entire sample (59.9 percent for high-income countries). Average GDP growth was 3.8 percent for the entire sample (2.6 percent for high-income countries).

**Table 1:  Data Sources**

|_Variable_|_Time series_|_Data Source_|
|---|---|---|
|Real GDP growth|GDP (constant 2000 dollars)|World Development Indicators<br>(World Bank)|
|Public debt|General government, gross debt,<br>GDP(current dollars)|World Economic Outlook (IMF)|
|Openness|Imports of goods and services (current dollars)<br>Exports of goods and services (current dollars)<br>GDP(current dollars)|World Development Indicators<br>(World Bank)|
|Inflation|Consumer price index|World Economic Outlook (IMF)|
|Initial GDP|GDP per capita in 1970 (constant 2000 dollars)|World Development Indicators<br>(World Bank)|

_Source_ : Authors.

3
decrease and the Gini index at that national level also decreased albeit this decrease was extremely small.

**Figure 3: Growth incidence curve for Mali**

<!-- Start of picture text --> (a) Simulation 1c (b) Simulation 2b<br><!-- End of picture text --> <!-- Start of picture text --> Absolute propoor curves Absolute propoor curves<br>Mali - Simulation 1c Mali - Simulation 2b<br>.05 .23 .41 .59 .77 .95 .05 .23 .41 .59 .77 .95<br>Percentiles (p) Percentiles (p)<br>Difference Upper bound of 95% confidence interval Difference Upper bound of 95% confidence interval<br>Null horizontal line Null horizontal line<br>.015 .02<br>.01<br>.01<br>.005<br>0<br>0<br>-.01<br>-.005<br>-.01 -.02<br><!-- End of picture text --> Source: Computed by authors from ELIM 2006 with DASP package Figure 4 depicts the GIC for the rural households for simulation 1a (panel a) and simulation 2a (panel b). In panel a, we have a relatively horizontal curve with the exception of the two tails where the poorest are just below the 0 line and households between the 80<sup>th</sup> and 93<sup>rd</sup> percentile are the winners of this simulation. On the other hand, the simulation 2a consisting of taxing fossil fuels and subsidizing biofuel has a very slight positive slope. Interestingly, this simulations was the most efficient to reduce poverty in rural areas. The positive slope is coherent with the increase in inequality measured with the Gini index (+0.16).

**Figure 4: Growth incidence curve for Rural**

**(a) Simulation 1a                                                (b) Simulation 2a**

<!-- Start of picture text --> Absolute propoor curves Absolute propoor curves<br>RURAL- Simulation 1a RURAL- Simulation 2a<br>.05 .23 .41 .59 .77 .95 .05 .23 .41 .59 .77 .95<br>Percentiles (p) Percentiles (p)<br>Difference Upper bound of 95% confidence interval Difference Upper bound of 95% confidence interval<br>Null horizontal line Null horizontal line<br>.02 .02<br>.01 .01<br>0 0<br>-.01<br>-.01<br>-.02<br>-.02<br><!-- End of picture text --> Source: Computed by authors from ELIM 2006 with DASP package The other curves presented were computed for urban households for simulations 1c and 2b. These two curves are interesting insofar as the trend observed is not consistent through the entire distribution. For sim1c (panel a) we have a negative slope from the bottom of the distribution up to the 30<sup>th</sup> percentile and a flat curve to the top of the distribution. Hence, 24
10. As noted earlier, these estimates are national level figures and cannot be useful in predicting losses at the local level, where much bigger amounts can be experienced per capita. Nevertheless, they do provide some indication of magnitudes for different disasters and for different groups of countries. More work and more data are needed to get a dynamic profile for the losses of capital and income; the best we can say is that the time profile is typically between 2 and five years.

11. Broadly, quantitative analysis shows that the impacts of natural disasters on a nation’s income or output come through their indirect effects on capital, i.e., human and intangible capital and natural capital. This has some important policy implications, one of which is that, when considering which recovery measures to implement, attention needs to be paid to re-building these forms of capital.

# **References (including the Appendix)**

Bacolod, M. and P. Ranjan. 2008. “Why Children Work, Attend School, or Stay Idle: The Roles of Ability and Household Wealth.” Economic Development and Cultural Change, 56(4): 791-828.

- Barro, R.  1989. “A Cross-country Study of Growth, Saving and Government.” _NBER Working Paper No. 2855_ . National Bureau of Economic Research, Cambridge, Massachusetts, USA.

- Chang, S. 1997. “Regional Economic Structure and Capital Investment in the Built Physical Environment.” _Journal of Regional Science Association International_ , 76(4): 385-407.

- Dancer, D. and A. Rammohan. 2007.”Determinants of Schooling in Egypt: The Role of Gender and Rural/Urban Residence.” _Oxford Development Studies_ , 35(2): 171-195.

- Deolalikar, A. 1997. “The Determinants of Primary School Enrollment and Household Schooling Expenditures in Kenya: Do They Vary by Income?” _Working Paper_ . Department of Economics, University of Washington, Seattle, WA.

- Emergency Events Database (EM-DAT). 2008. _Data on Natural Disasters by type_ . Centre for Research on the Epidemiology of Disasters (CRED), School of Public Health of the Université Catholique de Louvain, Brussels, Belgium.

- Feinberg, R. and M. Meurs. 2008. “Market Reform and Infrastructure Development in Transition Economies.” _Review of Development Economics_ , 12(2): 237-247.

- Gourio, F. 2008. “Disasters and Recoveries.” _American Economic Review,_ 98(2): 68-73.

- Gaddis, E.B., B. Miles, S. Morse and D. Lewis. 2007. “Full-cost Accounting of Coastal Disasters in the United States: Implications for Planning and Preparedness.” _Ecological Economics,_ 63: 307-318.

- Holmes, J. 1999. “Measuring the Determinants of School Completion in Pakistan: Analysis of Censoring and Selection Bias.” _Center Discussion Paper No. 794_ . Economic Growth Center, Yale University.

- Irwin, E. 2006. “Determinants of Land-Cover and Land-Use Change at Different Spatial Scales.” Presentation prepared for the Urban Ecology Workshop, Duluth, Minnesota, USA (May, 2006).

- Kauffmann, R.K., A. Bradford, L.H. Belanger, J.P. Mclaughlin and Y. Miki. 2008.

“Determinants of OPEC Production: Implications for OPEC Behavior.” _Energy Economics,_ 30(2): 333-351.

22
exceptionally large listing (Celtel Kenya) accounted for nearly half of total corporate bonds outstanding on Kenya’s stock exchange. Excluding South Africa, telecoms accounted for 12 percent ($124.3 million) of the total value of bond issues outstanding in all sectors in the focus countries.

# **Table 5.5 Outstanding corporate bonds issued by infrastructure providers in the focus countries, excluding South Africa**

As of end-2006 except for issues traded on the BRVM (April 2007)

|Issuer of corporate bonds|Domicile of<br>issuer|Securities<br>exchange|Sector|Issue<br>date<br>(yr.)|Maturity<br>terms<br>(yrs.)|Outstanding<br>value of<br>issues (US$ millions)|% share<br>of all<br>corporate<br>bond<br>issues in<br>issuer’s<br>domicile|
|---|---|---|---|---|---|---|---|
|RFA10 Loan Stock 2010|Namibia|NSX|transport (roads) construction|2004|6|251|55|
|RFA16 Loan Stock 2016|Namibia|NSX|transport (roads) construction|2006|10|47|10|
|Port Autonome de Dakar|Senegal|BRVM|port management|2004|7|62|93|
|Total transport||||||361|34|
|Celtel Burkina Faso|Burkina Faso|BRVM|telecoms|2003|6|6|16|
|ONATEL|Burkina Faso|BRVM|telecoms|2005|6|33|84|
|Celtel Kenya|Kenya|Nairobi SE|telecoms|2005|4|65|51|
|Uganda Telecom|Uganda|USE|telecoms|2003|5|7|22|
|TDB|Mozambique|Maputo SE|telecoms|2004|6|3|13|
|MCEL|Mozambique|Maputo SE|telecoms|2005|5|10|42|
|Total telecoms||||||124|12|
|Comunaute Electrique de Benin|Benin|BRVM|electricity generation|2003|7|33|60|
|Comunaute Electrique de Benin|Benin|BRVM|electricity generation|2004|7|19|34|
|Lunsemfwa Hydro Power|Zambia|LuSE|electricity generation|2003|7|7|43|
|Total electricity generation/power||||||59|6|
|Shelter-Afrique (Senegal)|Senegal|BRVM|provides regional housing<br>finance & other development<br>assistance including for<br>infrastructure|2003|7|4|0.4|

Source: National securities exchanges.

Note: For individual issues, percent share of all listed issues for the country of domicile; for sector totals, given as a share of all listed corporate bond issues in the focus countries except South Africa. BRVM = Bourse régionale des valeurs mobiliéres; SE = stock exchange; LuSE = Lusaka Stock Exchange.

Transport infrastructure accounted for just over one-third ($360.6 million) of all corporate issues outstanding in these markets (excluding South Africa and Sudan) at end-2006. Two very large issues loom large in that total: one of $298.4 million for road construction by Namibia’s Road Fund Administration, and another of $62.2 million for transport infrastructure development by Senegal’s Port Autonome de Dakar. Bonds listed by corporations in other infrastructure sectors included power generation (Communauté Electrique de Benin and Zambia’s Lunsemfwa Hydro Power Company), which accounted for 6 percent of all total issues in the focus countries. Bank issues also dominated in several countries, constituting 100 percent and 96 percent of the total amount outstanding in the corporate bond markets in Ghana and Nigeria at end-2006. Corporate bonds issued by banks made up 35 percent of the total amount outstanding in Namibia.

50
**Table A 2: Hourly earnings by sector and type of employment - Thousand VND - 2009**

||Emp<br>|loyer<br>|Self-acco<br>|unt worker<br>|Wage<br>|worker<br>|
|---|---|---|---|---|---|---|
||Mean|Median|Mean|Median|Mean|Median|
|1. Agriculture, hunting, forestry and fishing|16.2|9.3|7.6|5.3|7.7|6.6|
|2. Mining and quarrying|29.0|25.7|7.1|5.9|14.1|12.4|
|3. Manufacturing|27.0|15.6|7.7|6.3|9.7|8.0|
|4. Electricity, gas|19.8|19.8|9.5|9.5|15.4|13.3|
|5. Water supply|38.2|41.2|7.8|6.1|13.3|11.0|
|6. Construction|19.3|14.7|11.4|9.3|10.0|8.7|
|7. Wholesale and retail trade, repair of motor vehicles|28.1|16.5|9.8|7.4|11.0|8.7|
|8. Transportation and storage|28.6|18.6|12.0|8.5|13.4|11.4|
|9. Accommodation and food service activities|18.5|14.1|9.8|7.4|8.4|6.4|
|10. Information and communication|27.7|13.0|12.5|10.3|18.9|16.5|
|11.Financial , banking and insurance activities|-|-|26.3|16.8|25.0|19.1|
|12. Real estate, renting and business activities|92.2|51.6|226.6|74.2|22.4|20.6|
|13. Professional, scientific and technical activities|38.0|23.6|13.6|10.8|17.8|15.5|
|14. Administrative and support service activities|30.5|26.5|56.7|9.9|13.0|9.5|
|15. Communist Party, socio-political organization|-|-|6.2|3.3|11.9|9.9|
|16. Education|52.7|27.2|18.4|14.1|16.4|14.8|
|17. Human health and social work activities|31.8|31.8|14.0|11.8|14.3|12.9|
|18. Arts, entertainment and recreation|22.2|17.7|8.0|6.2|11.3|9.7|
|19. Other services|14.5|11.9|9.3|7.1|8.8|7.4|
|20. Active households|-|-|11.8|5.9|6.4|4.9|
|21. Extraterritorial|-|-|-|-|24.5|16.1|
|Total|22.2|13.9|9.1|6.2|11.2|8.8|

_Source: LFS 2009._

**Table A 3: Proportion of low-wage earners by sector – Percent – 2008.**

||Mean|95% confi|dence interval|
|---|---|---|---|
|1. Agriculture, hunting, forestry and fishing|22|20|25|
|2. Mining and quarrying|5|0|10|
|3. Manufacturing|10|8|11|
|4. Electricity, gas|0|.|.|
|5. Water supply|5|-2|13|
|6. Construction|4|3|5|
|7. Wholesale and retail trade, repair of motor vehicles|7|5|10|
|8. Transportation and storage|2|1|4|
|9. Accommodation and food service activities|11|6|16|
|10. Information and communication|6|0|11|
|11.Financial , banking and insurance activities|3|-1|6|
|12. Real estate, renting and business activities|0|.|.|
|13. Professional, scientific and technical activities|1|-1|3|
|14. Administrative and support service activities|5|-1|12|
|15. Communist Party, socio-political organization|8|6|11|
|16. Education|3|2|4|
|17. Human health and social work activities|4|1|7|
|18. Arts, entertainment and recreation|5|0|10|
|19. Other services|8|3|12|
|20. Active households|18|9|27|

_Source: VHLSS 2008_ 46
Whalen, C., and F. Reichling. 2015. "The Fiscal Multiplier and Economic Policy Analysis in the United States." Working Paper 2015/02, Congressional Budget Office, Washington, DC. World Bank. 2015a. "Commodity Markets Outlook." July. World Bank: Washington, DC.

<u>.</u> 2015b. Having Fiscal Space and Using It: Global Economic Prospects January 2015. Washington, DC: World Bank.

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<u>.</u> 2017. Weak Investment in Uncertain Times: Global Economic Prospects January 2017. Washington, DC: World Bank.

Yifan, S., and T. Abeysinghe. 2016. International Transmission of Growth Shocks and the World Business Cycle. National University of Singapore, Department of Economics SCAPE Working Paper Series 2016/02.

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33
i

# **AN INTERNATIONAL STATISTICAL SURVEY OF GOVERNMENT EMPLOYMENT AND WAGES**

## _CONTENTS_

## Preface.

|**1. Scope of the survey and methodological problems**<br>**............................................**<br>**l**|
|---|
|2. The key findings on**goverrnent employment ......................................................**<br>**3**|
|**3.**The key findings on governm ent wages**......................................................**<br>**5**|
|4. Changes over the last decade**.....................................................**<br>**9**|
|**5. Determinants of government employment .....................................................**|
|**6. A concluding word ..................................................... .**<br>13|
|Statistical Appendix:|
|Government Employment and Wages, 1990's, by country and region ........................................<br>14|
|Text tables:|
|1. Government Employment,_early 1990's_.....................................................<br>5|
|2. Government Wages,_early 1990's ........................_<br>_;7_|
|3. Government Employment and Wages,_early 1980's and early 1990's .............................................._<br>9|
|Text Figures:|
|1. Defining Government Employment ...................................................<br>2|
|2. Countries Covered in the Survey ..................................................<br>2b|
|3. Government Employment, early 1990's ...................................................<br>5|
|4. Government Wages, early 1990's ...................................................<br>8|
|5. Government Employment and Wages,_early 1980's and early 1990's ._<br>_..........................................._<br>_10_|