rafmacalaba/datause-encoder-data huggingface dataset:
- 34 -

# Table 3.8. Values of All Private Infrastructures as Percent of Total Value of Capital by Region, Nigeria, Indonesia, and Thailand **h**

(Percent)

||**Nigeria**|||Indonesi|a||**Thailoran**|**d**|
|---|---|---|---|---|---|---|---|---|
|Stats|Firm<br>Size|Percent|Region|Firm<br>Size|Percent|Reglon|Firm<br>Size|Percent|
|**Lagos**|**Small**<br>t|34.62|Jakarta|small|9.32|Bangkok|Small|22.43|
||Large|15.89||Large|8.88||Large|8.35|
||ALl|16.19||All|8.91||All|8.94|
|**AnoSbra**|<br>**small**|42.82|Metropol<br>|- small|19.72|Netropol-<br>|small|10.11|
||Large|9.67|ft n<br>|Botabek)|Large|20.62|i tan<br>(SMA)|Large|6.06|
||All|11.73||ALL|20.60||Att|6.13|
|IUo|Smell|34.82|Samarang|Smalt|12.74|Chiang|SmaLL|51.21|
||Large|12.02||Large|17.39||Large|18.26|
||IAll|12.09||1All|16.75||All|21.89|
|Koduna|Small|31.83|||||||
||Large|13.03|||||||
|_________|All|13.36|_<br>_<br>_<br>__|||_<br>_<br>_<br>_|||
|**_Kw-_**<br>_w_<br>noSall||16.90|||||||
||Large|~<br>8.24|||||||
||All|8.61|||||||
||SmalL|30.52||Small|12.47||Small|23.08|
|five<br>State|Large|13.27|Three<br>Regions|Large|15.90|Three<br>Regions|Large|7.32|
||ALL|13.67||Alt|15.73||All|7.79|

NOTE: (1) The vaLues **are** for the firms in each cell **as** a whole (i.e., ratios of the sus).

(2) From Appendix Tables E8, FS, and G8.

IV gtll firm **are** estabLishments with **less** than 50 employees.

_hi_ The imiwrator lncludes all private Infrastructure provisions (generatorsr boreholes, radio eipmant, vehicles and motorcycLes); the dernminator Includes machirnry and equipment, alt vehicies *n **motorcycLes.** soureos Yorld **lan*** Nigeria Infrastructure Project Estabilshment Survey 1988. World Bank Research Project tRPO 676-71) EstabLishment Survey for Irdonensa and Thaland 1992.
16 is often viewed as a better test of Wagner's "Law".18) Table 10 reproduces Ram's results.

According to these data, Wagner's "Law" is rejected in about 40% of the cases, i.e., elasticity (I) or (II) being less than 1 or 0, respectively. Ram emphasizes the large variance in elasticity estimates and explains this outcome, as evidence of the lack of any "universality or inevitability" in the trend toward government expansion within nations.

But such a conclusion may be a bit hasty. For one, Ram's data do not include transfer payments, which seem to be growing faster than any other component of total Ge and would, therefore, increase the elasticity estimates in most cases. 19 Second, some bias may be imposed by the particular constant price series employed. This latter point is an issue beyond that of whether real or nominal values of government spending are what should be measured in assessing the growth in government expenditures.

The potential measurement problem appears as follows. Ram's results provide elasticities based on constant prices for OECD countries for 1960 to 1980 which can be compared with the current price series from Saunders and Klau(1985) covering 1960 to 1982. Ram's OECD elasticities for Gc yield 8 out of 21 values below 1.00, in fact, 4 estimates between .51-.75. In comparison, Saunders and Klau's 23 elasticities for Gc based on current prices yield no value less than 1.07. If, as has been argued, the input prices facing government increase faster than other prices, constant price elasticities of Gc will be lower than the value based on current prices. However, the degree
31

# Table 1

# CounterYaiHng duty cam

|_Number of cou_|_nterailbin duty_<br>_S_|_caes, 19846_<br>_una_<br>|
|---|---|---|
|_By_|_Toal_|_Exclw_<br>_Chii_<br>_case_|
|1. CountrIes in which at least one|||
|countervallg duty casew<br>nitiated|||
|United SaLes<br>281|1|1|
|chIe<br>140|1|0|
|Ausraia<br>20|2|2|
|camda<br>11|11|11|
|European Community polices *<br>7|a|6|
|<br>Japan<br>1|2|1|
|Summary, Part1<br>460|28|21|
|Asa percentage of all cases<br>100|6|7|
|IL.<br>CountriesInwhichno CVD|||
|caseshave beeninidazed|||
|Braazl<br>0|88|32|
|Spain<br>0|38|22|
|<br>Argetins<br>0|32|6|
|<br>Mexico<br>0|28|27|
|P--u<br>0|22|7|
|France<br>0|21|21|
|Korea<br>0|21|13|
|OtherEECmembersm*<br>0|126|117|
|Other countries (40)|||
|Summary, PartII<br>0|432|299|
|<br>As apercenugofall<br>cas|94|93|

_Sow AMhW,_ $Iow I Md 2. .L In th 3C. th CYD _uuuit _cbm_ o r the iammuNir. mesr mi do not ha CVD m od i meber sumnd the Community pn ude whedie du h b the ob9an ft CVD lt. paws otrdw counmnea Source: Finger (1987), p. 157.
Bulletin of Economics and Statistics, forthcoming 4. Elbers, Chris, Peter Lanjouw, Johan Mistiaen, Berk Özler, and Kenneth R. Simler. 2004. "On the Unequal Inequality of Poor Communities,"World Bank Economic Review, World Bank Group, vol. 18(3), pages 401-421.

5. Escobal, Javier, and Maximo Torero. 2005. “Adverse Geography and Di¤erences in Welfare in Peru.”In Spatial Inequality and Development, eds. R. Kanbur and A.J. Venables. Oxford: Oxford University Press.

6. Kanbur, Ravi, and Anthony J. Venables. 2005. “Spatial Inequality and Development.”In Spatial Inequality and Development, eds. R. Kanbur and A.J. Venables. Oxford: Oxford University Press.

7. Kim, Sukkoo. 2008. “Spatial Inequality and Economic Development: Theories, Facts and Policies,”Commission on Growth and Development Working paper no.16. World Bank 8. Page, M. E. and Solon, G. 2003. “Correlations between brothers and neighboring boys in their adult earnings: the importance of being urban,” Journal of Labor Economics, vol. 21(4), pp. 831–55.

9. Puga, Diego. 1999. “The Rise and Fall of Regional Inequalities.” European Economic Review, 43: 303–34.

10. Raaum, Oddbjörn, Kjell Salvanes and Erik Sørensen. 2006. “The Neighborhood Is Not What It Used to Be,”Economic Journal 116(1), 200-222.

11. Ravallion, M. and Q. Wodon. 1999. "Poor Areas or Poor People?" Journal of Regional Sciences, Vol. 39(4), p. 689-711.

30
sectors that received these incentives, agglomeration forces led to employment gains in sectors that were not directly incentivized by the policy, and the indirect effects from externalities were more significant through the labor-pooling mechanism than through the input-output linkages.

We caution, however, that our findings do not fully rule out the displacement effect and cannot speak to the national-level welfare impact of the place-based industrial promotion scheme. This will be a useful topic for future research. Additionally, more research is required to assess if agglomeration economies were strong enough for industries to continue co-locating in these areas, once the incentive policies were rolled back. The availability of new data in the coming years makes this a promising research direction.

# References

- Abeberese, A.B. and Chaurey, R., 2019. An unintended consequence of place-based policies: A fall in informality. Economics Letters, 176, pp.23-27.

- Alder, S., L. Shao, and F. Zilibotti. 2016. “Economic Reforms and Industrial Policy in a Panel of Chinese Cities.” Journal of Economic Growth 21 (4): 305–49.

- Amirapu, A., Hasan, R., Jiang, Y. and Klein, A., 2019. Geographic concentration in Indian manufacturing and service industries: Evidence from 1998 to 2013. Asian Economic Policy Review, 14(1), pp.148168.

- Asher, S., Matsuura, R., Lunt, T. and Novosad, P., 2019. The Socioeconomic High-resolution Rural-Urban Geographic Dataset on India (SHRUG).

- Austin, B., E. Glaeser, and L. Summers. 2018. “Saving the Heartland: Place-Based Policies in 21st Century America.” In Brookings Papers on Economic Activity. Conference draft, March.

- Baldwin, R.E., Forslid, R., Martin, P., Ottaviano, G.I.P. and Robert-Nicoud, F. 2003. Economic Geography and Public Policy, Princeton and Oxford: Princeton University Press.

- Baldwin, R.E. and Krugman, P., 2004. Agglomeration, integration and tax harmonisation. European economic review, 48(1), pp.1-23.

- Bartelsman, E., J. Haltiwanger and S. Scarpetta. 2013. “Cross–country Differences in Productivity: The Role of Allocation and Selection.” American Economic Review 103 (1): 305–34.

- Bartik, T.J., 1991. Who benefits from state and local economic development policies?. Upjohn Institute.

- Baum-Snow, N. and Ferreira, F., 2015. Causal inference in urban and regional economics. In Handbook of regional and urban economics (Vol. 5, pp. 3-68). Elsevier.

- Busso, M., Gregory, J. and Kline, P., 2013. Assessing the incidence and efficiency of a prominent place based policy. American Economic Review, 103(2), pp.897-947.

- Brülhart, M., Jametti, M. and Schmidheiny, K., 2012. Do agglomeration economies reduce the sensitivity of firm location to tax differentials?. The Economic Journal, 122(563), pp.1069-1093.

- Burbidge, J. and Cuff, K. 2005. “Capital tax competition and returns to scale.” Regional Science and Urban Economics, vol. 35(4), pp. 353–73.

35
37 unconditional job accession rates for training participants, although lower than those of the total pool of the unemployed, are higher than the rates for participants in other ALMPs. This just says that individuals targeted for ALMPs have a higher incidence of belonging to problem groups than individuals from the general pool of the unemployed. It also seems to say that those targeted for training measures are the best among ALMP participants. It does not say anything about the effectiveness of Polish training measures as the authors do not control for observable and unobservable characteristics.

# _3.3. The Czech Republic36_

Unemployment in what is after the Velvet Revolution the Czech Republic (CR) has remained well below 5%. This unemployment rate is not only one of the lowest in the former communist countries, but also in the world. Many potential explanations of this phenomenon are discussed in the literature, but it has not yet been clearly determined what actually has kept unemployment so moderate in the CR. The explanation certainly lies in a combination of factors that are specific to the Czech economic situation before the beginning of the transformation process, and to the process of transformation itself. It is our aim to embed the analysis of Czech ALMPs into the general discussion of the performance of the Czech labour market.

Considering the flow approach to labour markets, the unemployment rate is a result of inflow into unemployment, oufflow out of unemployment and oufflow out of the labour force. The factors that explain these three flows also explain the level of unemployment. The following exposition of reasons for the low Czech unemployment rate is therefore structured around these three flows.

# 3.3.1 General macroeconomic performance in the Czech Republic

Before beginning with the description of the Czech labour market it should be noted that the general macroeconomic situation of the CR is relatively favourable. Many of the below quoted figures can be found in table 12.

The CR has had good initial conditions: it is a small, relatively open economy and, unlike Slovakia, Poland or Hungary, it has long common borders with Austria and Germany, two of the most advanced economies in the world. The level of net

> 36This section is partially based on preliminary results from the research project **Czech** **_Unemployment and German Labour Demand_** which the author is undertaking jointly with Donata Hoesch and Mark Schaffer.
US dollar prices. The large difference between the two regions reflects not only that Caribbean SIDS suffered from more tropical cyclone disasters than SIDS in Oceania, but also that the former have economic assets exposed to tropical cyclone damage that are much greater in value than those of the latter, because the combined GDP of Caribbean SIDS is eight times larger than that of SIDS in Oceania.

Table 2 shows all the climate-related natural disasters affecting SIDS in the Caribbean and Oceania from 1995 to 2022, for which recorded total damages exceeded 10 percent of GDP. Although the larger economies in the Caribbean suffered the greatest total damages, some of the smaller islands suffered the largest losses relative to their GDP, notably Antigua and Barbuda, Dominica, Grenada, St Lucia, St. Kitts and Nevis and Vanuatu, all of which incurred losses greater than 50 percent of GDP from a single tropical cyclone disaster.

**Table 2 Climate related disasters causing damages exceeding 10 percent of GDP in the Caribbean and Oceania, 1995-2022**

|**Year**|**Country**|**Type of Disaster Event &**<br>**Name**|**Damages as % of**<br>**GDP**|
|---|---|---|---|
||**The Caribbean**|||
|1995|Antigua and Barbuda|Hurricane Luis|61|
|1995|St Lucia|Hurricane Luis|67|
|1995|Dominica|Hurricane Marylin|64|
|1998|Antigua and Barbuda|Hurricane Georges|14|
|1998|St. Kitts and Nevis|Hurricane Georges|109|
|1999|St. Kitts and Nevis|Hurricane Lenny|11|
|2000|Belize|Hurricane Keith|25|
|2001|Belize|Hurricane Iris|22|
|2004|The Bahamas|Hurricane Frances|11|
|2004|Grenada|Hurricane Ivan|148|
|2005|Guyana|Riverine flood|27|
|2013|St Vincent and Grenadines|Riverine flood|14|
|2015|Dominica|Hurricane Erika|89|
|2016|Haiti|Hurricane Mathew|14|
|2017|Antigua and Barbuda|Hurricane Irma|17|
|2017|Dominica|Hurricane Maria|279|
|2019|The Bahamas|Hurricane Dorian|26|
||**Oceania**|||
|2001|Tonga|Cyclone Waka|28|
|2012|Samoa|Cyclone Evan|17|
|2014|Vanuatu|<br>Cyclone Pam|60|
|2016|Fiji|<br>Cyclone Winston|12|
|2020<br>_Source: _|Tonga<br>_Em-Dat_|Cyclone Harold|23|

6
been slow.<sup>2</sup> Further regulatory impediments include high taxation of wages and profits and high employer contributions, difficult access and high cost of credit, and lack of systemic trust in the regulatory and financial environment (World Bank, 2005a). As shown by the World Bank’s Doing Business (2005) indicators, BiH’s “Ease of Doing Business” ranking is only higher than the ranking of Belarus and the Ukraine among ECE transition countries. In a total of 154 countries, the country ranks particularly low, with respect to ease of starting a business, dealing with licenses, registering property and trading across borders. Given this challenging macroeconomic and institutional environment, in the next sections we examine determinants of self-employment transitions.

# **3. Measures of Entrepreneurship**

We use household panel data from four waves of the World Bank Living Standards Measurement Study (LSMS) for the years 2001-2004. The first wave of the LSMS survey was carried out in 2001, covering a sample of 5,400 households, 3,000 from FBiH and 2,400 from RS. The sample is representative at the country level, the entity level, and for urban, rural and mixed municipalities. Wave 2 was conducted in 2002 and 50% of original LSMS respondents were interviewed a second time. These respondents were followed in Waves 3 and 4.<sup>3</sup> This unique panel data provides rich demographic and socioeconomic information, and importantly an insight into labor market dynamics in the two entities.

We employ the labor force population, aged 16-65, and classify them into mutually exclusive groups according to their employment status every year. Following the design of the questionnaire, we define self-employed individuals as individuals describing their status as owner/co-owner of: (i) an enterprise/small business which employs workers (“employer”), (ii) an enterprise/small business which does not employ workers (“own account”). We identify “formal” self-employment, as individuals who fall

> 2 For a description of the SME sector in Bosnia and Herzegovina, see: World Bank (2002).

> 3 For more information on the survey design, LSMS sampling and the creation of the panel and the contents, see World Bank (2003). Survey response rates were satisfying both at the household and the individual level, comparable to those of most well-established household surveys. The truncation of the sample after Wave 1 was conducted ensuring the continuation of representation at the 3 levels mentioned.

4
# **Biofuels and Climate Change Mitigation: A CGE Analysis Incorporating Land-Use Change**<sup>**1**</sup>

Govinda R. Timilsina and Simon Mevel World Bank, Washington, DC

**Key Words:** Biofuels, Climate Change Mitigation, Deforestation, CGE Analysis **JEL Classification** : H41, Q15, Q43,

> 1 The views and findings presented in the paper are of authors and should not be attributed to the World Bank, its management or member countries. We thank David Zilberman, Christophe de Gouvello, Todd Johnson and Mike Toman for their valuable comments. We acknowledge the Knowledge for Change Program (KCP) Trust Fund for the financial support.
> - 18 -

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Our results confirm that the negative effect of conflict on non-resource FDI is observed in both the manufacturing and service industries (Table 4, columns 1 and 2). The coefficients of the conflict variable and the scope-of-conflict moderator are similar across the two specifications and a Chow test shows that the coefficients of the BRD variable are not systematically different for the two industries. This indicates that it is valid to group manufacturing and services into one non-resource industry. The results in columns (3) and (4) suggest that the effect of conflict on hydrocarbon and non-hydrocarbon FDI is comparable in size and positive but insignificant in both cases. In addition, a Chow-test shows that the difference between the BRD coefficients in Column 3 and 4 is not statistically significant. Hence, there is no evidence that the insensitivity of resource-related FDI to political conflict is driven solely by the hydrocarbon sector.

**Table 4 Effect of Political Conflict on Manufacturing, Services, Hydrocarbon, and Nonhydrocarbon Greenfield FDI**

||Dependent Varia<br>|ble: Log greenfield F<br>|DI(in USD millions),LSD<br>|VC Estimation<br>|
|---|---|---|---|---|
||Manufacturing FDI<br>(1)|Service FDI<br>(2)|Hydrocarbon FDI<br>(3)|Non-hydrocarbon<br>FDI<br>(4)|
|BRD (ln)|-0.132+<br>(0.071)|-0.209**<br>(0.067)|0.055<br>(0.105)|0.091<br>(0.091)|
|BRD (ln)*localizedt-1|0.123<br>(0.101)|0.168*<br>(0.096)|0.020<br>(0.142)|-0.082<br>(0.123)|
|Observations|707|707|707|707|
|Number of countries|90|90|90|90|
|Economic Controls|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|

Note: Political conflict is proxied by battle-related deaths. Bootstrapped standard errors are in parentheses. *** p<0.001, ** p<0.01, * p<0.05, + p<0.10

# **Conflict and war onset**

As an additional robustness check, we use a dummy variable for conflict onset instead of our continuous BRD variable to measure whether a country experiences a conflict or a war according to the definitions of UCDP/PRIO. We code an observation as a conflict if there were at least 25 BRD but not more than 1,000 BRD in a year and as a war (or a large conflict) if there are at least 1,000 BRD per year. Thus, we measure the effect of conflict and war onset rather than the intensity of the disputes. Figure 4 shows 29
(World Bank, 2014). This study in Jharkhand expands on this evidence base by including social-emotional variables in a youth survey alongside of more conventional aspirations, education, and employment modules to enable more in-depth analysis of the relationships between these factors. We find that individual psychological factors like self-efficacy and mental health are important determinants of girls’ educational and employment aspirations. This suggests that such hidden forms of human capital may constitute critical targets for interventions aimed at altering girls’ educational and economic trajectories.

We also identify factors that correlate with girls’ level of self-efficacy. We find that individual and household level factors like age, previous training experience, and an “enabling” environment—i.e., one where girls enjoy family/social support, feel connected and have empowered parents and other successful role models to look up to, appear to be important in boosting their sense of self-efficacy. We argue that these findings could usefully inform the design of programs aimed to boost female educational and labor market outcomes.

# **References**

Angrist, J. and V. Lavy (1999) “Using Maimonides’ Rule to Estimate the Effect of Class Size on Scholastic Achievement”, _Quarterly Journal of Economics_ , 114(2), pp. 533-575 Baldacci, E., B. Clements, et al. (2008). "Social Spending, Human Capital, and Growth in Developing Countries." World Development 36(8): 1317-1341.

Bandura, A. (1977) “Self-efficacy: Toward a Unifying Theory of Behavioral Change”, _Psychological Review,_ 84, 191-215 Barry, M. M., Clarke, A. M., Jenkins, R., & Patel, V. (2013). A systematic review of the effectiveness of mental health promotion interventions for young people in low and middle income countries. BMC Public Health, 13(1), 1.

Becker, B. E., and Luthar, S. S. (2002) “Social-Emotional Factors Affecting Achievement Outcomes Among Disadvantaged Students: Closing the Achievement Gap.” _Educational Psychologist, 37_ (4), pp. 197–214 Blattman, C., J. C. Jamison and M. Sheridan (2015) “Reducing Crime and Violence: Experimental Evidence on Adult Non-Cognitive Investments in Liberia”, _SSRN Working Paper No. 2594868_ Blum, R. and M. Ireland (2004) “Reducing Risk, Increasing Protective Factors: Findings from the Caribbean Youth Health Survey”, _Journal of Adolescent Health_ , 35, pp. 493-500 Bradley, D., P. Noonan, H. Nugent and B. Scales (2008) _Review of Australian Higher Education: Final Report_ , Department of Education, Employment and Workplace Relations, Canberra 13
# **APPENDIX B: HDDS AND IDDS FOOD GROUP CLASSIFICATIONS**

## **Table B: Food group classification for Household Dietary Diversity Score (HDDS) and Individual Dietary Diversity Score (IDDS)**

||**HDDS**|**Individual Dietary Diversity Score **|**(IDDS)**|
|---|---|---|---|
||**Food Groups**<br>**(Score: 0-12)**|**Child DDS**<br>**Food Groups (Score: 0-8)**|**Mother’s DDS**<br>**Food Groups (Score: 0-9)**|
|1|Cereals|1<br>Grains,roots or tubers|1<br>Grain,roots or tubers|
|2|Roots and tubers|2<br>Vitamin A – rich plant foods|2<br>Dark green leafy<br>vegetables|
|3|Vegetables|3<br>Other fruits or vegetables|3<br>Vitamin A rich fruits and<br>vegetables|
|4|Fruits|4<br>Meat, poultry, fish seafood|4<br>Other fruits and<br>vegetables|
|5|Meat, poultry|5<br>Eggs|5<br>Meat and fish|
|6|Eggs|6<br>Pulses/legumes/nuts|6<br>Organ meat|
|7|Fish and Seafood|7<br>Milk and milkproducts|7<br>Eggs|
|8|Pulses/legume/nuts|8<br>Foods cooked in oil/fat|8<br>Milk and milkproducts|
|9|Milk and milkproducts||9<br>Foods cooked in oil/fat|
|10|Oils/fats|||
|11|Sugar/honey|||
|12|Misc.|||

Source: Swindale and Blinsky (2006)/FAO 39