rafmacalaba/datause-encoder-data huggingface dataset:
percentage point of GDP leads to a _net present value_ gain of 6 percentage points of per capita GDP for the first 25 years of implementation and 10.5 percentage points of per capita GDP for the first 50 years.<sup>31</sup> This is a sizable yet reasonable improvement, and it can be further increased if the program's financing and implementation are less burdensome to the economy. Finally, we must note that this improvement is only a fraction of the total effect of infrastructure on social welfare. A complete evaluation would also take into account the direct benefit of infrastructure on the health, comfort, and happiness of its recipients.

# **VI.  Summary and Conclusion**

In the last half century, Egypt has experienced remarkable progress in the provision of infrastructure, which is the result of decades of purposeful and costly investment. In the past 15 years, however, a worrisome trend has emerged: Infrastructure investment has suffered a substantial decline. To be sure, some of this decrease is to be expected as infrastructure projects mature and demand less costly outlays. Nevertheless, the investment decline should be of concern for two reasons. The first is that the rate of progress in the measures of infrastructure, particularly road networks and power generation, has slowed down. A developing country like Egypt cannot afford such stagnation. The second reason is that, although acceptable at Egypt’s current income level, infrastructure in the country has much room to improve to be consistent with its goals of future economic growth.

The study first reviews the current state of several infrastructure indicators in Egypt compared with the rest of the world. For this purpose, a dataset for 150 countries was collected containing different indicators of quantity and quality of services for four major sectors of infrastructure: transport, telecommunications, electricity, and water and sanitation. The overall results suggest that Egypt has attained a level of infrastructure

> 31 The first-order condition for consumer welfare maximization implies, _r = ρ + σ g,_ where _r_ is the real interest rate, _ρ_ is the subjective rate of time preference, _σ_ is the inverse of the coefficient of intertemporal substitution, and _g_ represents the per capita GDP growth rate.  It is customarily assumed that the coefficient of intertemporal substitution is equal to 1 and the subjective rate of time preference is 0.05.  This would entail a real interest rate equal to 0.05 over the growth rate, which is our working assumption here.  Thus, if the per capita GDP growth rate were around 5%, the real interest used as discount rate in the NPV calculations would be 10%.

48
WPS3988

# **Firm Productivity in Bangladesh Manufacturing Industries**

Ana M. Fernandes<sup>∗</sup> The World Bank

## **Abstract**

This paper studies the determinants of total factor productivity (TFP) for manufacturing firms in Bangladesh using data from a recent survey. TFP measures are obtained following Olley and Pakes (1996) and making use of firm-specific deflators for output and inputs. Controlling for industry, location, and year fixed effects, we find that: (i) firm size and TFP are negatively correlated; (ii) firm age and TFP exhibit an inverse-U shaped relationship; (iii) TFP improves with the quality of the firm’s human capital; (iv) global integration improves TFP; (v) firms with R&D activities and quality certifications have higher TFP, while more advanced technologies improve TFP only in the presence of significant absorptive capacity; (vi) power supply problems cost firms heavily in terms of TFP losses; and (vii) the presence of crime dampens TFP.

JEL Classification Numbers: D24, F23, L25 O33.

Keywords: Total Factor Productivity, Simultaneity and Production Functions, Skilled Labor, Openness, Business Environment, Bangladesh.

## World Bank Policy Research Working Paper 3988, August 2006

_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. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ∗ Development Research Group, World Bank, 1818 H Street, N.W., Washington, DC 20433, USA, email: <u>afernandes@worldbank.org. This paper draws on a background paper prepared for the World Bank 2006</u> report “Bangladesh: Strategy for Growth and Employment.” I thank Thorsten Beck, Sandeep Mahajan, Luis Serven, and seminar participants at the Dhaka workshop on “Bangladesh: Strategy for Growth and Employment” and at the South Asia Brown Bag Lunch for comments.

1
# REFERE3NCES

Adams, F.G. and J. Behrman, 1976. Econometric Models of World Agricultural Commodity Markets. Cambridge, MA: Ballinger Publishing Co.

Adams, F.G. and J. Behrman, 1978. Econometric Modeling of World Commodits Policy. Cambridge, MA: Ballinger Publishing Co.

- Akiyama, T and A. Bowers, 1984. Supply Response of Cocoa in Major Producing Countries. EPDCS Division Working Paper No. 1984-3. Washington, D.C.: World Bank.

- Akiyama, T. and R.C. Duncan, 1982a. Analysis of the World Cocoa Market. World Bank Staff Commodity Working Paper No. 8. Washington, D.C.

- Akiyama, T. and R.C. Duncan, 1982b. Analysis of the World Coffee Market. World Bank Staff Commodity Working Paper No. 7. Washington, D.C.

- Akiyama, T. and P.K. Trivedi, 1987. A New Global Tea Model: Specification, Estimation and Simulation. World Bank Staff Commodity Working Paper No. 17. Washington, D.C.

- Akiyama, T. and P.K. Trivedi, 1985. Specification of the Price Equation in a Prototype of a Model of a Perennial CroD. Internal Working Paper. Washington, D.C.

- Anderson, J., C. Blitzer, T. Cauchois and E.R. Grilli, 1981. "Dynamic Simulation Model of the World Jute Economy," in World Bank Commodity Models, Volume I, World Bank. Staff Commodity Working Paper No. 6. Washington, D.C.

- Askari, H. and J.T. Cumiings, 1976. Agricultural Supply Responses: A Survey of Econometric Evidence. New York: Praeger Publishers.

- Augusto, S. and P. Pollak, 1981. "Structure and Prospects of the World Fats and Oils Economy," in World Bank Commodity Models, Volume I, World Bank. Staff Commodity Working Paper No. 6. Washington, D.C.

- Bowers, A., 1985. Time Series Prediction of Commodity Prices. EPDCS Internal Working Paper. Washington, D.C.: World Bank.

- Brown, M., A. Dammert, A. Meerus, and A, Stoutjeskijk, 1983. Worldwide Investment Analysis: The Case of Aluminum. World Bank Staff Working Paper No. 603. Washington, D.C.

- Brook, E.M. and D. Nowicki, 1981. An Econometric Forecasting Model of the World Sugar Economy. World Bank Commodity Note No. 10 (Revised). Washington, D.C.

46
# Table 13: Brazil<sup>- Relative Differences on Earnings Capacity and Simulated Reductions in Income Gap</sup> by Household Type<sup>and Metropolitan Area, 1984</sup>

(percent)

|Area and<br>Household Type|Male<br>Earmings|Female<br>Earnings<br>|% Earnings<br>Female<br>|Head<br>Earnings|Other Adult<br>Earnings|% Area<br>Non-Head|
|---|---|---|---|---|---|---|
|||Rel|ativeDifference||||
|Recife<br>||97|2.44|.49|.94|0.94<br>|
|FHH<br>|.52<br>45|.<br>87|<br>2.38|_OA3_|0.80|0.89|
|FHH w/children|.|.|||||
|SicPaulo<br>||111|2.59|0.52|0.95|0.96<br>|
|FHH<br>|.48<br>|.<br>98|<br>259|0.46|0.73|0.96|
|FHHwlchildren|0.35|.|.||||
|Porto Alegre|045|117|2.44|0.61|0.93|1.09<br>|
|FHH<br>|.<br>|.<br>102|<br>2.39|0.52|0.79|1.04|
|FHHwJchildren|0.39|.|||||
|||Sinul|ated Income<sup>Gap</sup>|||Sc|
|Recife<br>|001|017|0.19|40.89|0.19|0.22<br>|
|FHH<br>|.<br>031|.<br>042|0.54|40.43|0.44|0.60|
|FHHw/children|.|.|||||
|SioPaulo<br>||023|0.03|-1.11|0.03|0.06<br>|
|FHH<br>|-0.19<br>024|.<br>056|<br>0.59|-.47|0.42|0.56|
|FHHwkchildren|.|.|||||
|Pott Alegre||040|0.01|-1.34|40.03|40.01<br>|
|FHH<br>|-0.23<br>|.<br>|<br>049|40.57|0.43|0.52|
|FHHwkchildren|0.21|0.55|.||||

Source: authorfs<sup>tabulations</sup> divided by the average<sup>for all households.</sup> Notes: Relative<sup>difference is the average value foragiven type of household</sup> Income A negative value gap is equal<sup>indicates the average income of household to the average income of the household</sup> type (YF) subtracted type is above that<sup>of other households.</sup> from the average<sup>income of all the other houses</sup> It is reported in<sup>minimum salaries.(Yc)divided by Yc or (Yc - Y, / YC).</sup> Simulated income Lay is the<sup>estimated</sup> average income of<sup>household</sup> type (YF) if this<sup>group had the mnean</sup> of household group (Yc) on dke<sup>simulation variable.</sup>
<!-- Start of picture text --> Chronic poverty<br>Peninsular Malaysia East Malaysia Urban Peninsular Rural East Malaysia<br>rS<br>2<br>“i<br>a<br>5<br>qt<br>2<br>an<br>=<br>a<br>a<br>PSamy SSSa SEre LPS| x STCn MKnr KSa x SKer KP| PSnh SRCr a OM ar8 a<br>Sharesin the firstare roundbasedofoneachsyntheticinterval.panel The estimatespoverty lineforused householdis RM 527.heads aged between25 and 60<br><!-- End of picture text -->
45. Radhakrishnan, S. (1923) Indian Philosophy. Volume 1. Reprinted in 1997 by Oxford University Press, USA.

46. Radhakrishnan, S. (1947) Religion and Society. London: George Allen and Unwin.

47. Rao V. (1993) ‘The Rising Price of Husbands: A Hedonic Analysis of Dowry Increases in Rural India’, Journal of Political Economy 101, pp. 666-677.

48. Rustein, S. (1999), _Wealth versus expenditure: Comparison between the DHS wealth index and household expenditures in four departments of Guatemala_ . Calverton, Maryland: ORC Macro.

49. Shamasastry, R (1951), Translation into English of Kautilya’s _Arthasastra_ . Mysore: Sri Raghuveer Printing Press.

50. Sen, B. (1998) ‘Why Does Dowry Still Persist in India? An Economic Analysis Using Human Capital’, in South Asians and the Dowry Problem, edited by Menski, W., Trentham Books; London: University of London, School of Oriental and African Studies, pp. 75-95.

51. Sen, A. K. (1992), ’Missing Women’, British Medical Journal, 304, March, pp. 586587.

52. Sen, A.K. (2003), ’Missing Women - Revisited’, British Medical Journal, 327, December, pp. 1297-1298.

53. Shariff, A. (1999), India Human Development Report, Oxford University Press: New Delhi.

54. Suryanarayana Murthy, C (2000), _Sri Lalita Sahasranama_ with Introduction and Commentary. Mumbai: Bharatiya Vidya Bhavan Publications.

55. United Nations. (1961) Mysore Population Study. Department of Social and Economic Affairs, New York.

56. Visaria, P. (1971) The Sex Ratio of the Population of India, Census of India, Monograph No. 10 (New Delhi: Manager of Publications).

57. Youssef, N. H. (1978) ‘The Status and Fertility Patterns of Muslim Women’ in Women in the Muslim World, edited by L. Beck and N. Keddie, Cambridge: Harvard University Press, pp. 69-99.

19
Caliendo, M., and S. Kopeinig. 2005. “Some Practical Guidance for the Implementation of Propensity Score Matching.” Institute for the Study of Labor, Discussion Paper N. 1588.

- Camacho, A., and D. Mejia. 2015. “The Health Consequences of Aerial Spraying of Illicit Crops: The Case of Colombia.” Center for Global Development WP 408, June.

- Cameron, S. 1988. “The Economics of Crime Deterrence: A Survey of Theory and Evidence.” _Kyklos_ , 41 (2): 301–23.

- Conley, T. G. 1999. “GMM Estimation with Cross Sectional Dependence.” _Journal of Econometrics_ 92 (1): 1–45.

- Corman, H., and N. H. Mocan. 2000. “A Time- Series Analysis of Crime, Deterrence, and Drug Abuse in New York City.” _American Economic Review_ 90 (3): 584–604.

- Davalos, L., A. Bejarano, M. Hall, L. Correa, A. Corthals, and O. Espejo. 2011. “Forests and Drugs: Coca-Driven Deforestation in Tropical Biodiversity Hotspots.” _Environmental Science and Technology_ , 45 (4):1219–27.

- Dehejia, R. 2004. “Program Evaluation as a Decision Problem.” _Journal of Econometrics_ 125: 141–73.

- Dehejia, R, and W. Sadek. 1999. “Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs.” _Journal of the American Statistical Association_ 94 (448): 1053–62.

- Di Tella, R., and E. Schargrodsky. 2004. “Do Police Reduce Crime? Estimates Using the Allocation of Police Forces After a Terrorist Attack.” _American Economic Review_ , 94 (1): 115–33.

- Dion, M. L., and K. Russler. 2008. “Eradication efforts, the State, Displacement and Poverty: Explaining Coca Cultivation in Colombia During Plan Colombia.” _Journal of Latin American Studies_ 40 (3): 399–421.

- Draca, M., S. Machin, and R. Witt. 2011. “Panic on the Streets of London: Police, Crime and the July 2005 Terror Attacks.” _American Economic Review_ 101 (5): 2157–81.

- Dubin, J., M. Graetz, and L. Wilde. 1987. “Are We a Nation of Tax Cheaters? New Econometric Evidence on Tax Compliance.” _American Economic Review_ 77: 240–45.

- Eck, J., and E. Maguire. 2000. “Have Changes in Policing Reduced Violent Crime? An Assessment of the Evidence.” In A. Blumstein and J. Wallman, eds., _The Crime Drop in America._ New York: Cambridge University Press, 207–65.

- Evans, W., and E. Owens. 2007. “COPS and Crime.” _Journal of Public Economics_ 91 (1–2): 181–201.

- Frey, B. 1997. _Not Just for the Money: An Economic Theory of Personal Motivation_ . <mark>Cheltenham, UK:</mark> Edward Elgar Publishing.

- Garcia, J. F., D. Mejia, and D. Ortega. 2013. “Police Reform, Training and Crime: Experimental evidence from Colombia’s Plan Cuadrantes.” Documento CEDE.

- Gelman, A., and G. Imbens. 2014. “Why High-Order Polynomials should not be used in Regression Discontinuity Designs.” NBER, Working Paper No. 20405.

- Heckman, J., H. Ichimura, J. Smith, and P. Tod. 1998. “Characterizing Selection Bias Using Experimental Data.” _Econometrica_ , 66: 1017–98.
34 Table 1. Descriptive statistics: main variables

|Variable|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|**Panel **<br>|**A: Scho**|**ol Level**<br>||||
|Enrollment|119,061|470.661|651.445|1.000|10320.000|
|Promotion Rate|119,061|94.290|10.241|0.000|100.000|
|Dropout Rate|119,061|3.214|8.874|0.000|100.000|
|Saber 11 Math STD. Scores|60,348|0.097|1.003|-3.946|7.803|
|Saber 11 Language STD. Scores|60,348|0.099|0.997|-4.027|5.013|
|Public Schools|119,061|0.602|0.489|0.000|1.000|
|N. of Teachers|44,890|32.852|32.940|1.000|347.000|
|Pupil/Teachers Ratio|44,890|21.260|39.266|0.120|2332.000|
|Temporal Teachers (%)|44,890|21.787|25.812|0.000|100.000|
|Capacity Gap at 2016|16769|60.644|130.314|0.000|2875.000|
|**Panel B: **|**Munici**|**pality Le**|**vel**|||
|Predicted Cum. Venezuelans Infow|6,874|6.746|22.891|0|643.313|
|Indicator for Urban Municipality|6,874|0.494|0.500|0|1.000|
<!-- Start of picture text --> Figure 9a: 1976-1990 Figure 9b: 1991-2005<br>-10 -5 0 5 10 15 -10 -5 0 5<br>GDP growth 1976 GDP growth 1991<br>95% CI Fitted values 95% CI Fitted values<br>Mean growth 1976-1990 Mean growth 1991 - 2005<br>8 5<br>6<br>4<br>0<br>2<br>0<br>-2<br>Mean GDP per capita growth 1976-1990 Mean GDP per capita growth 1991-2005 -5<br><!-- End of picture text --> We also calculated the correlation coefficients of growth over time. Statistically significant (nonsignificant) coefficients suggest that country growth rates follow predictable (unpredictable) patterns. The results, presented in table 3.4, indicate that the large majority of coefficients is not statistically significant, including the vectors of 1991 and 2005. This suggests that growth is erratic, even when, on average, it shows some persistence, as seems to be the case in the second period under analysis. Also, it confirms the finding in table 3.3 that most growth variation comes from within individual countries rather than across countries, as suggested in the decomposition of SD in table 3.3.<sup>9</sup> Even though there is some persistence in more recent years, the results imply that past growth does not help predict future growth and that the growth process in Africa may be erratic.

> 9 Easterly et al. (1993) found for a worldwide sample that correlation of growth across decades is also very low, averaging 0.3.

14
a principal component analysis. The first principal component of the Big 5 indicators obtained from this procedure is used as our index of non-cognitive skills.

The Pakistan LSS surveys a total of 11,533 individuals in 2,239 households. Among those, 2,045 males age 15-64 were administered the detailed labor questionnaire. Since income data are non-zero only for individuals who work and who receive payment or for those respondents who report earning a profit, our final sample consists of 1,419 working male individuals that were identified as either self-employed (including agriculture), or paid employees. Given the small number of paid working females in the sample, the paper restricts the analysis to a sample of male workers. We classify individuals who report earnings from their own activity as self-employed, and individuals who receive payment from their employer as wage earners. Self-employed workers were asked the amount of net profits generated over the last period of work, defined as sales minus expenses to the individual. We use this amount converted into monthly profits as our measure of earnings for the self-employed. Table 1 reports summary statistics on this sample. There is a legitimate concern that land owners could conflate rental earnings with crop production minus expenses. However, this is not an obvious issue with the data. Land owners - 20% of the entire category of self-employed and 44% of all individuals who reported cultivation as their main employment - reported agricultural profits that were not significantly different than those reported by renters. The median monthly profit for land-owners in the sample is PK Rps 7,000, against PK Rps 7,500 for tenants.

# <u>Results</u>

Table 2 presents OLS regressions of the logarithm of earnings from both wage and own employment. The first three columns focus on the impacts of stature, cognitive ability and non-cognitive skills 7
# Figure 2.  Country correlations for commodity exporters versus non-commodity exporters, 1980-2020

<!-- Start of picture text -->
1.0 Commodity exp Non-Commodity exp<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Commodity Average = 0.43***<br>-0.2<br>Non-Commodity Average = 0.22***<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>AzerbaijanChinaYemenNepalCôte d'IvoireBangladeshUkraine<br>GreeceNicaraguaEcuadorUruguayGuyanaLiberiaArgentina<br>Antigua and BarbudaBrazilNamibiaEquatorial GuineaLebanonNigerPakistanIndiaBoliviaHungaryJordanKenyaBelarusZimbabweFijiRomaniaSerbiaAngolaQatarCyprusRwandaOmanRepublic of CongoTimor-LesteAfghanistanTrinidad and Tobago<br>BahrainDemocratic Republic of the CongoBelizeJamaicaPolandBhutanLithuaniaKyrgyz RepublicRussiaPapua New GuineaLao P.D.R.VanuatuSyriaCabo VerdeGeorgiaUgandaGrenadaCameroonIndonesiaHondurasMoroccoSurinameTogoSeychellesEstoniaMaltaSri LankaSt. Kitts and NevisDjiboutiSt. Vincent and the GrenadinesIslamic Republic of IranMadagascarPhilippinesAlgeriaUzbekistanMyanmarBarbadosSouth AfricaParaguayTajikistanLesothoMicronesiaGhanaBurundiSaudi ArabiaBeninTurkmenistanMontenegroPanamaMozambiqueMarshall IslandsBosnia and HerzegovinaMoldovaChadComorosThe GambiaCambodiaColombiaTanzaniaLibyaCentral African RepublicCroatiaTurkeyGabonIcelandEthiopiaKiribatiMongoliaSudanTongaSenegalMaliLatviaTuvaluBurkina Faso<br>NigeriaPortugalIsraelPeruSouth SudanBulgariaEritreaSolomon IslandsCzech RepublicIraqAlbania<br>Haiti<br>BotswanaVietnamEswatiniSlovak RepublicTaiwan Province of ChinaSan MarinoArmeniaMaldives<br>SamoaMexicoSt. LuciaSierra LeoneKazakhstanGuinea-BissauMauritaniaCosta Rica<br>SloveniaDominican Republic<br>Chile ZambiaItalyNauruSpainMauritiusTunisiaGuinea<br>PalauKuwait DominicaThailandLuxembourg New Zealand<br>SwedenMalaysiaFranceNorwayBelgiumJapanMalawiGermany NetherlandsEl Salvador The Bahamas<br>KoreaIrelandAustriaAustralia Guatemala Brunei Darussalam United Arab Emirates<br>Finland<br>North Macedonia<br>EgyptSingapore United States United Kingdom<br>Denmark<br>Switzerland<br>Canada São Tomé and Príncipe<br>Correlation between cyclical components of real  government spending and real GDP 1980-2020 Hong Kong SAR<br>Somalia<br><!-- End of picture text -->

Note: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for commodity exporters versus non-commodity exporters. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively.

Sources: Authors’ calculations based on WEO (IMF) data. Commodity classification from World Bank.
# Appendix I.A.12 Historical Demographic Data, Finland 1850-1980

|Year|1850|1865|1870|1880|1890|1900|1910|
|---|---|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over65 years old|13.89|13.28|15.27|15.02|12.15|11.43|10.52|
|Population aged 20 to 59/<br>population over 60 years old|7.06|6.40|7.76|6.95|5.86|5.79|5.45|
|Percentage of population<br>over 60 years old|7.03|7.53|6.44|7.04|7.95|8.21|8.79|
|Percentage of population<br>under 15 years old|34.54|34.98|33.8|34.64|35.81|34.49|34.21|
|Women over 60 years old/<br>Men over 60 years old|1.40|1.32|1.33|1.34|1.28|1.25|1.26|
|**Year**<br>co|**1920**|**1930**|**1940**|**1950**|**1960**|**1970**|**1980**|
|Population aged 15 to 64/<br>population over 65 years old|10.09|10.38|10.45|9.60|8.49|7.81|5.91|
|Population aged 20 to 59/<br>population over 60 years old|5.62|5.43|5.47|5.16|4.40|3.84|3.49|
|Percentage of population<br>over60yearsold|9.43|9.10|9.84|10.10|11.37|13.70|15.99|
|Percentage of population<br>under 15 years old|28.01|31.70|26.93|30.00|30.20|24.51|20.28|
|Women over 60 years old/||||||||
|Men over 60 years old|1.29|1.27|1.42|1.57|1.57|1.41|1.82|

Ratios calculated using data found in Mitchell, B. R. 1990 "International Historical Statistics: Europe 1750-1988".