rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9130

# **Abstract**

This paper analyzes the welfare and distributional impacts of increasing taxes on cigarettes in Georgia. Increasing taxes on tobacco is an effective measure to reduce smoking. According to some estimates, increasing tobacco taxes could save more than GEL 3.6 billion and 53 thousand lives over a 15-year period. However, concerns over potentially regressive effects on the poor are often raised. An Extended Cost Benefit Analysis (ECBA) is applied to simulate the welfare and distributional impacts of raising prices on cigarettes. Decile-specific price elasticities of demand are estimated to account for heterogenous behavioral responses of different income groups. Empirical estimations confirm that poorer households in Georgia tend to reduce consumption more intensely when faced with higher tobacco prices. The estimated magnitude and distribution of elasticities are comparable to peer countries. The simulations based on household survey data suggest that the long-term net distributional effects of increasing taxes on cigarettes in Georgia are likely progressive. Incorporating the indirect benefits of reduced smoking—reductions in medical expenses and earnings from adverted premature deaths—could bring small, albeit positive, income gains for large sectors of the population. The magnitude of those benefits is most significant among lower-income households, potentially contributing to lift them out of poverty.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at afuchs@worldbank.org and mgonzalezicaza@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,
# **V. Conclusion**

We present a unique cross-country database on the contribution of SMEs to total employment, job creation, and growth across 104 developing economies. We find that while small firms (<20 employees) have the smallest contribution to aggregate employment compared to medium (20-99 employees) and large firms (100+ employees), SMEs (<99 employees) are comparable to large firms in their contribution to aggregate employment. We also find that small firms are important contributors to total job creation. The relationship between size and job creation exists even when we control for firm age. However small firms also have lower productivity growth than large firms, which explains why job creation does not translate into faster growth.

With countries all around the world struggling to recover from the crisis, job creation policies are at the top of the agenda for policymakers. Our results caution that the challenge for policymakers is not only to create more jobs, but also to create better quality jobs to promote growth. Overall, our results show that while small firms create more jobs than large firms, their contribution to productivity growth is not as high as that of large firms. Growth and increases in productivity require a policy focus on the potential obstacles, which range from lack of access to finance, the need for business training and literacy programs, as well as addressing other constraints such as taxes, regulations and corruption, which are the focus of an active research agenda. In addition, policies to improve entrepreneurship and innovation are likely to be important, since lack of dynamism is a distinguishing feature of developing countries and young firms tend to be productive and among the fastest growing. Finally, our results also suggest a 21
Moor, G.M. and Nieuwoudt, W.L. 1998 Tenure security and productivity in small-scale agriculture in Zimbabwe: Implications for South Africa _Development Southern Africa_ 15(4), 609-620.

Moyo, Sam 1998, The Land Acquisition Process in Zimbabwe (1997/8), UNDP Harare Persson, Torsten; Tabellini, Guido, Is Inequality Harmful for Growth?, _American Economic Review;_ 84(3), June 1994, pages 600-621.

Piketty, Thomas 1999, Theories of Persistent Inequality and Intergenerational Mobility, Chapter 6 of Handbook of Income Distribution (A. Atkinson, and F. Bourguignon, Eds.) PIR, 1998: _Poverty and Inequality in South Africa,_ Poverty and Inequality Report to the Office of the Deputy President, Praxis Publishing, Durban.

SA-PPA 1997: _The Experience and Perceptions of Poverty: The South African Participatory Poverty Assessment,_ Praxis Publishing, Durban.

South Africa Labor and Development Research Unit (SALDRU) 1994, "South Africans Rich and Poor: Baseline Household Statistics." Cape Town: University of Cape Town.

SPP, 1983: _Forced Removals in South Africa, Vol. 4, Natal,_ The Surplus People's Project, Cape Town.

The Economist, July 17, 1999, Land reform in Brazil A plot of our own Tomlinson, Mary R. 1999, From Rejection to Resignation: Beneficiaries' Views on the South African Government's New Housing Subsidy System, _Urban Studies_ 36:8, 1349-59 van Rooyen, Johan, Jan Groenewald, Simphiwe Ngqangweni, and Tamas Fenyes Eds. 1998, Agricultural policy reform in South Africa, Africa Institute for Policy Analysis and Economic Integration, Cape Town van Zyl, **J.,** J. Kirsten, and H.P. Binswanger 1996, _Agricultural Land Reform in South Africa: Policies, Markets, and Mechanisms,_ Oxford University Press, Cape Town Wilson, F., and M. Ramphele, _Uprooting Poverty: The South African Challenge_ Cape Town: David Philip
valid also after several years, that is, when the poverty mapping is further updated with a third, more recently updated HBS. Otherwise, if a more recent HBS or LSMS is not available but, instead, there is a recent survey not covering consumption expenditure information, the proposed methodology could be complemented by the two methods proposed by Azzarri _et al._ (2006), or Dang _et al._ (2014, 2017).

The third issue covered in this discussion consists in the fact that the per capita consumption expenditure estimated at PSU level from the poverty mapping exercise is affected by (small) standard errors. Such variability should be taken into account in order to refine the original contribution proposed in this paper. We aim to develop this piece of new theoretical research and present it in a future paper.

# **References**

Azzarri, C., Carletto, G., Davis, B., Zezza, A. (2006), Monitoring poverty without consumption data: An application using the Albania panel survey, _Eastern European Economics_ , **44(1)** , pp. 59-82.

- Battese, G.E., Harter, R.M., Fuller, W.A. (1988), An error-components model for prediction of county crop areas using survey and satellite data, _Journal of the American Statistical Association_ , **83(401)** , pp. 28–36.

- Betti, G., Bici, R., Neri, L., Thomo, L., Sohnesen, T.P. (2018), Local poverty and inequality in Albania, _Eastern European Economics_ , **56(3)** , pp. 223-245.

- Betti, G., Dabalen, A., Ferré, C., Neri, L. (2013), Updating poverty maps between Censuses: a case study of Albania, in Laderchi C.R., Savastano S. (eds.)
In the large firm sample of the census scheme there are virtually no significant coefficients on any outcome variables in the aggregate (Table A.6), although breaking apart by initial conditions offers some evidence of similar patterns – higher skill and capital intensity are correlated with high output and plant size responsiveness for young and mature plants, and with higher TFPR for mature and old plants (Annex Table A.7).

These results support the expected heightened effect of increased competition and the opportunity tapped by more capable firms. For younger plants, the effect is to expand size, while for the older ones, it is to defend the existing size. The less capable older plants are eventually likely to exit. The relative rise in labor productivity of older plants, despite lower output gains suggests some relative downsizing, but also in line with district-level pseudo panel results, a possible de Loecker et al. (2016) effect.

# **V. Conclusion**

The natural experiment offered by the GQ upgrades allows examining the adjustment process across plants of different age cohorts and characteristics to an increase in competition and access to markets. Young plants drive the previously documented aggregate positive effects of proximity to the GQ network, while older plants show insignificant or negative effects. Younger plants tend to be more capital intensive and engaged in value chains, suggesting an important domestic “offshoring” dynamic along the GQ. Though in the short-run, exit falls for all plant cohorts in districts along the GQ network, the diverging responsiveness over time between young and older plants suggests that in the long-run older plants will contract and possibly exit. Strikingly, the evidence suggests that this process may take decades.

It is also clear that building roads is not sufficient for local industrial development. The responsiveness of young plants is blunted where factor markets are incomplete, or the business climate is distorted. With the exception of the capabilities of plants and the work force, older plants are less sensitive to misallocation in input and output markets, suggesting that over time they
- Duflo, E., P. Dupas, and M. Kremer. 2011. "Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya ." _American Economic Review_ 10 (5): 1739-74.

- Duncan, G.J., and K. Magnuson. 2013. "Investing in preschool programs." _Journal of Economic Perspectives_ 27 (2): 109-32.

- Evans, D.K., and A., Popova. 2016. "What really works to improve learning in developing countries? An analysis of divergent findings in systematic reviews." _The World Bank Research Observer_ 31 (2): 242-270.

- Evans, D.K., and F. Yuan. 2021. "What We Learn about Girls' Education from Interventions that Do Not Focus on Girls." _The World Bank Economic Review._

- Evans, D.K., P. Jakiela, and H.A. Knauer. 2021. "The impact of early childhood interventions on mothers." _Science_ 372 (6544): 794-796.

- Fernald, L.C., A. Weber, E. Galasso, and L. Ratsifandrihamanana. 2011. "Socioeconomic gradients and child development in a very low income population: evidence from Madagascar." _Developmental Science_ 14 (4): 832-847.

- Fernald, L.C., E. Prado, and Kariger, P. Raikes, A. 2017. _A toolkit for measuring early childhood development in low and middle-income countries._ Washington D.C.: The World Bank.

- Galasso, E., and A. Wagstaff. 2019. "The aggregate income losses from childhood stunting and the returns to a nutrition Intervention aimed at reducing stunting." _Economics & Human Biology_ (Policy Research Note World Bank) 34: 225-238.

- Gallego, F.A., E. Näslund-Hadley, and M. Alfonso. 2021. "Changing Pedagogy to Improve Skills in Preschools
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24 : They record whether the same or different parties control the executive and legislature in presidential systems, and, in parliamentary systems, whether there is a one, two, or three or more party coalition controlling government, or a minority government. The DPI has information on governing and opposition parties for over 150 countries, allowing the Index of Political Cohesion to be compiled for a much larger sample of countries.

There are significant coding differences for the 14 overlapping countries, however, so that for these common observations there is a correlation of only .53 between the two. For example, we differ from the original coding by Roubini and Sachs of the Index in calling Japan a minority government for particular years, in labeling Finland a parliamentary system, and in coding the CDU and CSU in Germany as two parties rather than one (XXsee annex xx for a complete identification of differences). This has a significant impact on their empirical findings, at least over the ten years that the two versions overlap. Table 2 reports results from Roubini and Sachs, replicated for the years 1975 - 1985, using their original coding of the Index and the coding in the database.2- They argue, reasonably, that coalition governments should have greater difficulty responding to the sudden emergence of crisis-driven budget deficits, a common occurrence in this decade.

~¥hey confirm this conjecture in their empirical work, replicated in Column 1, Table 2 for the eleven year period for which the two databases overlap. The IPC coefficient is large, positive and statistically significant. However, using the coding of the Index of Political Cohesion from DPI, we find no such relationship (see Column 2, Table 2). What explains these divergent results? As Table 3 demonstrates, there are several reasons for the differences

> 2 Our data on unemployment and inflation differ slightly from theirs, but despite this, in our replication using their own coding of IPC we find as they do that JPC has a large and positive association with public debt.
- Di Tella, R. and R. MacCulloch, 2005. “The Consequences of Labor Market Flexibility: Panel Evidence Based on Survey Data.” _European Economic Review_ , 49(5): 1225–1259.

- Gimpelson, V.E., R. Kapeliushnikov and A. Lukiyanova, 2010. “Employment Protection Legislation in Russia: Regional Enforcement and Labor Market Outcomes.” _Comparative Economic Studies_ , 52(4):611–36.

- Gimpelson V. and R. Kapeliushnikov, 2008. “Applying Labor Law in Russia: Role of Judiciary.” CLMS‐HSE, (mimeo)

- Hallward‐Driemeier, M. and L. Pritchett, 2011. “How Business is Done and the ‘Doing Business’ <u>Indicators: The Investment Climate When Firms Have Climate Control.” Policy Research Working</u> Paper Series 5563, The World Bank.

Haltiwanger, J.C., S. Scarpetta, and H. Schweiger, 2010. “Cross‐county Differences in Job Reallocation: <u>The Role of Industry, Firm Size and Regulations.” EBRD Working Paper No. 116.</u> Hamermesh, D. 1993. Labor Demand, Princeton University Press, Princeton NJ.

Helpman, E., O. Itskhoki and S. Redding, 2011. “Trade and Labor Market Outcomes.” in D. Acemoğlu, M. Arellano and E. Dekel (eds.), Advances in Economics and Econometrics: Theory and Applications, Tenth World Congress of the Econometric Society.

- Hsieh, Chang‐Tai, and Benjamin A. Olken 2014. "The Missing ‘Missing Middle’." _The Journal of Economic Perspectives_ , 28(3): 89‐108.

- Hsieh, Chang‐Tai, and Peter J. Klenow 2014. "The Life Cycle of Plants in India and Mexico." _The Quarterly Journal of Economics_ , 129(3):1035‐1084.

- Hsieh, C‐T
example, the volume of credit (Koetter and Wedow 2010).

# II DATA

We use the data set from a seminal paper on long-term economic growth determinants and BMA by Fernandez et al. (2001). The data set contains 41 explanatory variables that might be important for growth in 72 countries. We update the dependent variable (average real economic growth per capita in 1960– 2011). The regressors in the data set comprise various measures of economic, political, geographic, demographic, social, and cultural factors. As many of these factors may be determined simultaneously with growth, the regressors typically come from 1960 or even before to alleviate endogeneity concerns. We describe this data set in greater detail in the appendix.

To this data set, we add selected financial indicators from the World Bank’s GFDD (September 2013 version), which collects information on various aspects of financial sectors around the globe. Cihak et al. (2013) describe this data set’s content in detail and offer a 4×2 dimensional classification of financial indicators that reflects their utility in representing the depth, breadth, efficiency, and stability (4) of both the banking sector and the stock market (2). We choose to employ several indicators for which the database provides the richest data. Specifically, we select five different indicators representing various aspects of the financial system:

- **Private sector credit to GDP:** domestic private credit to the real sector to

- GDP; a measure of the depth of the banking sector.

- **Stock market capitalization to GDP:** value of listed shares to GDP; a

- measure of the depth of stock markets.

• **Net interest margin:** accounting value of banks’ net interest revenue as a share of average interest-bearing assets; a measure of the efficiency of the banking sector.

- **Stock market turnover ratio:** stock market value traded to total market

11
organizational structure of the firm, we also consider the impact of organizational innovation on employment. Third, we disentangle the effects of process innovation from product innovation on employment when both are implemented simultaneously by using a two stage methodology with a random coefficients specification in the first stage. Finally, we study how the degree of novelty of innovation and the level of a country’s development affect the elasticity of innovation to employment. Given the nature of the data we use, our results focus on average effects

> 1Lentz and Mortensen (2008), using Danish firm-level data, find that up to 75% of productivity growth comes from reallocation of inputs to innovating firms, of which 25% is entry and exist of firms and 50% reallocation to growing innovative firms.

3
2 KENYA AND CHINA’S TRADE RELATIONSHIP <!-- Start of picture text --> 200<br>Mineral, petroleum oils<br>150 Plastics and polyethers<br>Rubber products, pneumatic tyres for buses and lorries<br>Leather, saddlery, handbags and suitcases<br>Cotton and woven fabrics of cotton<br>100 Woven fabrics of synthetic filament<br>Rubber of plastic footwear<br>Ceramic products<br>Television, image and sound recorders, telephone sets<br>50<br>Motorcycles and mopeds<br>0<br>Commodity<br>Import Value (USD Millions)<br><!-- End of picture text --> Figure 5: Kenya imports rubber footwear, tires, and fabrics from China (2012-2014) Source: UN Comtrade database 2015

# **2.3 China is still a small export market for Kenya**

As shown in Figure 6, Kenya only sends one percent of its exports to China, exporting US $63 million in 2012, US $48 in 2013, and US $70 million in 2014. Kenya exports little to China because it is an oil importer and relatively resource-scarce. With fewer natural resources, Kenya has been unable to take advantage of the commodity boom from China’s growth (Zafar 2007). What’s more, the growth does nothing for Kenya’s agricultural sector because it lacks a comparative advantage in China’s main food imports (wheat, corn, beef, soybeans), making it difficult for Kenya to increase its exports of agricultural products.

<!-- Start of picture text --> 6000<br>4000<br>China, P.R.: Mainland<br>World<br>2000<br>0<br>2012 2013 2014<br>Year<br>Exports (USD millions)<br><!-- End of picture text --> Figure 6:
capital city.|
|**ODB.2013.D10**|Trade|International trade data|Details of the import and export of specific commodities and/or balance of<br>trade data against other countries.|
|**ODB.2013.D11**|Health|Health sector<br>performance data|Statistics generated from administrative data that could be used to indicate<br>performance of specific services, or the healthcare system as a whole. The<br>performance of health services in a country has a significant impact on the<br>welfare of citizens. Look for ongoing statistics generated from<br>administrative data that could be used to indicate performance of specific<br>services, or the healthcare system as a whole. Health performance data<br>might include: Levels of vaccination; Levels of access to health care; Health<br>care outcomes for particular groups; Patient satisfaction with health<br>services|
|**ODB.2013.D12**|Education|Primary and secondary<br>education performance<br>data|.<br>The performance of education services in a country has a significant impact<br>on the welfare of citizens. Look for ongoing statistics generated from<br>administrative data that could be used to indicate performance of specific<br>services, or the education system as a whole. Performance data might<br>include: Test scores for pupils in national examinations; School attendance<br>rates; Teacher attendance rates. Simple lists of schools do not qualify as<br>educationperformance data.|

44
clearing forested areas, polluting and endangering natural environments, and sometimes construction works involve the resettlement of families or communities. The IPF directly considers relevant social and environmental benefits and costs via the social and environmental index (SEI), whose sub‐components are dependent on the evaluating government’s selected criteria.

In Vietnam, the SEI consisted of five indicators: Direct Jobs Created (DJ); Number of Direct Beneficiaries (NB); People Affected by Repurposing of Land Use (PA); Cultural and Environmental Risks (CER), and Pollution, in terms of CO2 equivalent emissions (CO2). The data required to compute each variable were primarily sourced from existing project feasibility studies. Additional variables on projected indirect effects were estimated using data routinely gathered by the National Statistic Office of Vietnam (Marcelo, Mandri‐Perrott, & Haddon, 2015).<sup>13</sup> PCA was used to synthetize social and environmental variables into a composite social‐environmental index. Since many social and environmental data were

> 13. The information collected was used to populate a Microsoft Excel form, which in turn populated a database with embedded macros, programmed to transform the data using transformation functions and weights required to calculate the total SEI score for a project.

12
**Appendix A:** Pre-Analysis Plan for Round 4 Schooling, Income and Health Risk in Malawi (SIHR) Data **(** which can also be found at https://www.socialscienceregistry.org/trials/36)

# **<u>Principal Investigators:</u>**

Sarah Baird, University of Otago Ephraim Chirwa, Chancellor College Craig McIntosh, UCSD Berk Özler, World Bank and University of Otago

# **<u>Analysis plan:</u>**

The core analysis will compare the impact of the Conditional Cash Transfer (CCT) and Unconditional Cash Transfer (UCT) treatment to control EAs for the baseline schoolgirl stratum, and will compare the CCT treatment to the control for the baseline dropout stratum. Most of the analysis will consist of Round 4 cross-sectional regression (using OLS unless not appropriate), although where possible we will also pursue panel difference-in-differences analysis for variables that have been consistently collected in multiple rounds. For consistency, the analysis will include the full set of controls used in the paper by Baird, McIntosh and Özler (2011). These controls include baseline values of the following: a household asset index, highest grade attended, a dummy variable for having started sexual activity, dummy variables for age, and strata dummies. Standard errors will be clustered at the EA level, and results will be weighted to make them representative of the target population in the study EAs.

Only if a significant impact is found in the core analysis will further heterogeneity of impact be explored. Heterogeneity will be explored along both experimental dimensions including the amount of the transfer and the split between the parent and the girl, as well as based on the age at which you got the program and differences between rural and urban.

# **<u>Note on construction of indexes:</u>**

To construct indexes for classes of variables, we will adhere to the following rubric:

- a) For each sub-question in