rafmacalaba/datause-encoder-data huggingface dataset:
Table A.22: Difference in technology sophistication in general business functions with and without outlying enumerators

||All Sample|Sample Without<br>Diferent<br>Enumerators|Diference|
|---|---|---|---|
|Vietnam||||
|Mean|1.934|1.947|-0.013|
|SE|(0.012)|(0.012)|(0.017)|
|Observations|1,499|1,341||
|Senegal||||
|Mean|1.406|1.404|0.002|
|SE|(0.011)|(0.011)|(0.016)|
|Observations|1,786|1,784||
|Bangladesh||||
|Mean|1.482|1.458|0.024|
|SE|(0.015)|(0.015)|(0.021)|
|Observations|903|798||
|Kenya||||
|Mean|1.938|1.936|.002|
|SE|(0.020)|(0.020)|(0.029)|
|Observations|1305|1296||

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Data from the Firm-level Adoption of Technology (FAT) surveys in Vietnam, Senegal, Bangladesh and Kenya. Brazil, Ghana, India and Korea are excluded because they do not include significantly different interviewers. The average of technology sophistication in general business functions ( _MOSTj_ ) is compared between all sample and sample excluding significantly different enumerators. Standard errors in parenthesis.

compare variables in RAIS with variables we collected in FAT for the same firms.

First, we analyze the correlation between sales per worker and our technology measures (GBF) and (SBF) from FAT and average wages from RAIS. Table A.23 reports the point estimates of regressing firm-level FAT variables on the log or average wages per worker from RAIS and a set of firm-level controls. The FAT variables are log of sales per worker (column 1), and average technology sophistication (GBF, column 2, and SSBF, column 3). In all three cases we find strong positive associations between the FAT and the RAIS variables.

Second, we compare the differences between labor-related indicators from a matched employer-employee administrative data for firms in FAT versus the universe of firms. To perform this comparisons we obtained the weighted average for firms in FAT, using the weights we constructed as described in section A3 and compare it with the average for all 110
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48
_Working Paper #9633._ <mark>Nelson, R.R., S.G. Winter. 1982.</mark> _<mark>An Evolutionary Theory of Economic Change.</mark>_ <mark>Cambridge, MA: Belknap.</mark>

- Pagano, M., Wagner, C., & Zechner, J. (2020). Disaster resilience and asset prices. arXiv preprint arXiv:2005.08929.

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Penrose, E.T. (1959). _A theory of the growth of the firm_ . New York: Wiley.

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Schumpeter, J.A. 1942. _Capitalism, Socialism, and Democracy._ Scott, W.R. 2014. _Institutions and Organizations: Ideas, Interests, and Identities._ Thousand Oaks, CA: Sage Publications.

Selznick, P. 1957. _Leadership in Administration._ New York: Harper & Row.

24
country data set and employ pooled probit analysis to estimate the contribution of the key factors to inflation starts.<sup>2</sup> Some earlier research has examined the determinants of inflation in EMEs.<sup>3</sup> To the best of our knowledge, however, there has not been any empirical study investigating the contributing factors to inflation starts in the case of EMEs, which would enhance the ability of policy makers in these countries to pursue appropriate policy actions in a preemptive manner.

The empirical findings suggest that an increase in the output gap—above-trend real economic activity—agricultural shocks, and expansionary fiscal policy raise the probability of inflation starts in EMEs. On the other hand, the results indicate that a more democratic environment and an increase in capital flows relative to GDP reduce the probability of inflation starts in these countries.

The remainder of the paper is structured as follows. The next section provides a brief overview of the literature on the determinants of inflation in EMEs. Section 3 discusses the definition of inflation episodes and inflation starts. Section 4 presents the empirical framework and estimation results. Section 5 highlights the salient features of inflation starts, in view of the empirical findings. Finally, Section 6 concludes the paper by summarizing the policy implications.

# **2. Review of the Literature**

The nature of the mechanisms underlying the dynamics of inflation in EMEs has stimulated much discussion. A quick glance at the literature points to two different

> 2 Our approach is very similar to a recent study by Boschen and Weise (2003), which focuses on the OECD countries.  This study includes the following countries: Argentina, Brazil, Colombia, India, Indonesia, Israel, Korea, Malaysia, Mexico, Peru, the Philippines, South Africa, Thailand, Turkey, and Venezuela.  The selection of the countries is dictated by data availability for the period under consideration.

> 3 See, for instance Loungani and Swagel (2001) and Mohanty and Klau (2001) as well as the references therein.

3
# **Tourism Sector in Panama: Regional Economic Impacts and the Potential to Benefit the Poor**

## **Irina Klytchnikova and Paul Dorosh**<sup>**1**</sup>

The World Bank, 1818 H Street, NW, Washington, DC 20433 and International Food Policy Research Institute (IFPRI), 2033 K St., NW, Washington, DC 20006 <mark>Emails: iklytchnikova@worldbank.org and p.dorosh@cgiar.org</mark> _Keywords_ : regional impacts of tourism, poverty and tourism, multiplier effects of tourism. JEL: R11, R12, R13.

_Sector Board:_ Environment (ENV).

> 1 <mark>Irina Klytchnikova is Senior Economist in the Sustainable Development Department of the Latin America and Caribbean Region of the World Bank. Paul Dorosh is Division Director, Development Strategy and Governance Group at the International Food Policy Research Institute (IFPRI). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent, or of the International Food Policy Research Institute.</mark>
# **2 - Aid effectiveness and vulnerability: from the macro to the micro level**

In cross-country studies, Guillaumont and Chauvet (2001, 2004) provided evidence that a worse external environment (measured by climatic and trade shocks) increases the impact of aid on growth and Collier and Dehn (2001) found that the adverse effects of an extreme negative export price shock can be mitigated through an increase in aid. Nevertheless, at a micro economic level there is no empirical evidence to support this statement yet. On the contrary, people who carry out development projects would state that instability hinders the success of the projects. After stressing the advantages of a micro economic study, we will analyze the reasons for this apparent paradox.

_Benefits from using micro economic data on the success of projects_ Macro economic studies that analyze aid effectiveness through the impact of international aid on growth face some well known difficulties. First, growth is not the only objective of aid. Other objectives, like poverty alleviation or increasing education, could be considered more legitimate, but most of the macro economic studies are limited to the impact of aid on growth because of a lack of other reliable and frequently available indicators of economic development. Now since we have seen that economic instability can increase poverty through growth as well as directly, estimating the cushioning effect of aid only on growth would lead to an underestimation of this effect. Secondly, macro economic studies can usually capture only the short or medium term effects of aid yet we would not expect an education project, for example, to have an impact on growth for many years to come.

To some extent, these difficulties can be avoided using micro economic data. In this paper, our explained variable is the outcome of the projects, based on three criteria: relevance of the objectives, efficacy and efficiency of the projects. The relevance of the projects refers to all the development priorities defined by the receiving country and the World Bank rather than only the impact of aid on economic growth. These data also account for the long term effects of aid since the benefits are actualized for each project. Furthermore the use of a micro economic dependent variable explained by macro-economic variables limits the risk of endogeneity: the risk of reverse causality is reduced if we suppose that a project is usually too small to influence the characteristics of a country; however, because aid could be correlated with some unobserved variables, the endogeneity of aid has to be tested.

4
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- 5 -

remains an issue. Since Tanzania is a large country, infrastructure inequality is significant. Particularly in rural areas, access to electricity is minimal. Only 2,059 villages are electrified out of 10,652 village in Tanzania (Figure 4).

Tanzania’s road network is in relatively good condition by regional standards (Figure 5). The primary road network in particular is maintained well. However, transport connectivity in the country is in general still limited. Road density is low (Figure 6), and as a result only 24 percent of total rural population has access to an all-weather road within 2 km (Figure 7). The estimated transport costs vary significantly across locations. For instance, firms based in Dar es Salaam have easy access to the port and thus international markets, but the transport cost from Mwanza (1,000 km away) to the port is $150 per ton. From Kigoma, it is about $180 per ton (Figure 8). But these inland areas are much closer to major actual and potential agricultural production areas (Figure 9).

Given these infrastructure and economic conditions, firms are, in theory, supposed to decide where to locate. In Tanzania, there are about 45,000 enterprises that are formally registered according to the Central Register of Establishments data in 2010. Firms are currently concentrated in Dar es Salaam and other major cities. The largest firm agglomeration lies in Dar es Salaam, in which 4.4 million people or about 10 percent of total population live, and accounts for about 25 percent of total firms (Table 2). This is followed by other major regional cities, such as Arusha, Moshi and Mbeya. These secondary agglomerations are located dispersedly in the country (Figure 10). The geographic distribution of firms is relatively less skewed in Tanzania than in Kenya (Figure 11). In Kenya, nearly 60 percent of total firms are based in Nairobi. In the East African region, the firm distribution is much more even in Rwanda, though the size of the country is different (Figure 12).

Notably, however, the primary concentration of firms in Dar es Salaam seems to be accelerating. About 2,300 firms were newly created or formally registered in the three districts in Dar es Salaam in 2010 alone. Particularly, other manufacturing sectors than
Another concern was the staggered implementation of quota adoption in some concentrations in the post-2016 period, as discussed in recent literature (Borusyak et al., 2022; Callaway & Sant’Anna, 2021; de Chaisemartin & D’Haultfœuille, 2020; Goodman-Bacon, 2021; Sun & Abraham, 2021), which suggest the conventional two-way fixed effect may fail to provide unbiased estimates. In this study setting, differential treatment timing does not pose a significant threat to the estimates because most concentrations adopted a quota policy in 2016, with a few late adopters, which were mostly newly established concentrations with no variation in the treatment status and absorbed by the concentrationuniversity-academic program fixed effect. Additionally, once a concentration fixes the allocated slots by gender, it remains treated, and the allocation level does not change, reducing the risk posed to the TWFE estimate stated in recent literature. Nevertheless, I report the findings and event study results using the estimators of Borusyak et al. (2022) and Sun & Abraham (2021).

# **6 Results**

## **6.1 Main results**

Table 2 reports the overall impact of gender quotas on women’s access to educational opportunities and score-related outcomes. The quota increased the share of female students at the treated concentrations by nine percentage points, a 32% increase relative to the comparison group mean, as seen in Column 1. Specifically, the quota improved the share of female students from low-SES backgrounds by 3 percentage points, a 30% increase compared to the control group mean, as shown in Column 2. These findings highlight the effectiveness of the quota in closing the gender gap in higher education by amplifying women’s educational opportunities.

Nonetheless, as seen in Columns 3–8, score-related outcomes indicate that such an expansion is asso19
Rausch, Sebastian, Gilbert E. Metcalf and John M. Reilly (2011). Distributional impacts of carbon pricing: A general equilibrium approach with micro-data for households. Energy Economics 33 (Supplement 1): S20-S33.

Steckel, Jan C., Ira I. Dorband, Lorenzo Montrone, Hauke Ward, Leonard Missbach, Fabian Hafner, Michael Jakob and Sebastian Renner (2021). Distributional impacts of carbon pricing in developing Asia. Nature Sustainability 4:1005–1014.

Telaye, Andualem, Pablo Benitez, Seneshaw Tamru, Haileselassie Medhin, Michael Toman (2019). Exploring Carbon Pricing in Developing Countries: A Macroeconomic Analysis in Ethiopia.

World Bank Policy Research Working Paper, WPS 8860. World Bank Washington, DC. Timilsina, Govinda R. (2022). Carbon Taxes. Journal of Economic Literature, Vol. 60 (4), pp. 1456–1502.

Timilsina, Govinda R., Jin Cao and Mun Ho (2018). Carbon Tax for Achieving China's NDC: Simulations of Some Design Features using a CGE Model. Climate Change Economics. Vol.9, No. 3, pp. 1-17.

Timilsina, Govinda R., Jun Pang and Yuanhua Chang (2022). Economic Impacts of Meeting China’s NDC through Carbon Taxes with Alternative Schemes for Recycling Tax Revenues. World Bank Policy Research Working Paper #10087, World Bank, Washington, DC.

Timilsina, Govinda R., Yazid Dissou, Mike Toman and Dirk Heine (2021). Carbon Tax in an Economy with Informality: A Computable General Equilibrium Analysis for Côte d’Ivoire”. World Bank Policy Research Working Paper, WPS 9710. World Bank Washington, DC.

van Heerden, J., R. Gerlagh, J. Blignaut, M. Horridge, S. Hess, R. Mabugu and M. Mabugu (2006). Searching for Triple Dividends in South Africa: Fighting CO2 Pollution and Poverty while Promoting Growth. The Energy Journal, Vol. 27 (2), pp. 113-141.

Williams, Roberton C., III, Hal Gordon, Dallas Burtraw, Jared C. Carbone, and Richard D. Morgenstern (2015). The Initial Incidence of a Carbon Tax across Income Groups. National Tax Journal 68 (1):195-213.

World Bank (2022a). World Development Indicator Database. <u>https://databank.worldbank.org/home Retrieved on August 25, 2022.</u> World Bank (2023). States and Trends of Carbon Pricing 2023. World Bank, Washington, DC.
Table 8. Effect of displacement on gender norms among young women (ages 13-24)

||Repro|ductive health|E|conomic opportunit|y|Mob|ility|
|---|---|---|---|---|---|---|---|
||Attitudes|Behaviors|Attitudes|Behavio|rs|Attitudes|Behaviors|
||Approve<br>contrac|Use & decide<br>contra|D Wcare<br>hh|Works & dec<br>money|Share<br>chores|D rest<br>mobility|Say visits|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Displaced|0.01|-0.05|-0.08*|-0.15*|0.04|0.03|0.08|
||(0.01)|(0.05)|(0.05)|(0.08)|(0.08)|(0.05)|(0.05)|
|**Household characteristic**|**s**|||||||
|Children under 5|-0.02|0.08*|0.13***|0.04|-0.88***|-0.11***|0.16***|
||(0.02)|(0.05)|(0.04)|(0.07)|(0.09)|(0.04)|(0.05)|
|Female headed|0.02*|0.05|-0.05|0.00|0.14**|-0.07|-0.16***|
||(0.01)|(0.05)|(0.04)|(0.06)|(0.07)|(0.04)|(0.05)|
|**Wealth quintile**||||||||
|Poor|0.01|0.05|-0.16**|0.06|0.07|0.08|0.16**|
||(0.02)|(0.06)|(0.07)|(0.06)|(0.08)|(0.07)|(0.07)|
|Middle|0.02*|-0.03|-0.27***|0.24***|0.13|0.08|0.09|
||(0.01)|(0.06)|(0.08)|(0.09)|(0.09)|(0.08)|(0.08)|
|Rich|0.02|0.04|-0.28***|0.34**|0.00|-0.00|0.12|
||(0.01)|(0.08)|(0.09)|(0.15)|(0.12)|(0.09)|(0.09)|
|Richest|0.02|0.15|-0.33***|0.13|0.21|0.06|0.12|
||(0.01)|(0.14)|(0.09)|(0.14)|(0.19)|(0.08)|(0.09)|
|**Context**||||||||
|Reference network|0.18|-0.02|0.32***|0.09|-0.17|0.30**|0.54***|
||(0.11)|(0.07)|(0.11)|(0.14)|(0.10)|(0.15)|(0.17)|
|Royalties (ln)|0.00|0.00|-0.01|0.00|-0.00|-0.00|-0.00|
||(0.00)|(0.00)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Social investment (%||||||||
|<br>exp)|-0.00|-0.01|-0.00|0.02**|-0.01*|-0.00|-0.00|
||(0.00)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Constant|0.84***|0.68|0.56|-1.44**|2.16***|0.61|-0.02|
||(0.12)|(0.49)|(0.62)|(0.72)|(0.63)|(0.54)|(0.89)|
|Observations|1,292|1,267|3,426|605|465|3,389|3,396|
|Number ofgroups|1,019|1,001|1,624|506|391|1,615|1,622|

_Source:_ Author based on DHS 2000/2005/2010/2015. _Note:_ Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Ref. group for wealth is poorest.

27
modest gains in the poverty impacts over other policy-relevant alternatives. Indeed, in a number of cases and depending on the country and the nature of its poverty profile, simpler state-contingent targeting methods or even a “basic-income scheme” (in which everyone is covered) dominate in certain policyrelevant cases, such as when one allows for plausible lags in PMT implementation. However, none of these methods can be considered to perform especially well. Prevailing methods do not reliably reach the poorest. The costs of each method in practice may then be decisive in the choice.

The following section describes the PMT method that we assess, while Section 3 describes the measures we use in assessing econometric targeting. Section 4 studies the basic version of PMT, while Section 5 turns to various extensions and revisions to that version. For stylized transfer programs, Section 6 compares the poverty impacts of econometric targeting to those of less methodologically sophisticated methods, including un-targeted (universal) transfers and simple demographic “scorecard” methods. Section 7 presents our results for (informationally-feasible) differentiated transfers, including optimal transfer schemes for poverty reduction with a given budget but limited information. Section 8 uses the panel surveys to introduce lags in implementation. Section 9 offers some cross-country comparisons of the performance of econometric targeting; here we ask how much the impacts of PMT on poverty for a given budget are explicable in terms of the alternative targeting measures and the predictive ability of the PMT regressions. Section 10 concludes.

# **2. Econometric targeting**

Quite generally, we can think of any PMT as some weighted function of a vector of covariates _xijt_ . The specific form of this function that has become popular and that we focus on uses householdconsumption regression coefficients as the weights. We can write the following empirical regression function for the consumption of household _i_ in country _j_ at date _t_ on a vector of covariates _xijt_ using a survey sample of size _N jt_ : The PMT is then based on: The most common method in practice for estimating  _jt_ and  _jt_ in (1) appears to be Ordinary Least Squares (OLS) using log consumption per capita as the dependent variable. As usual, OLS 8
decline in entry and exit rates by 11 percentage points, and to an increase in the first-year survival rate of entrants by 6 percentage points.<sup>30</sup> In columns (6)-(10) of Table 7 we report results using country-destination-year level data for years 2004 to 2008 and controlling for destination and year fixed effects, along with bilateral gravity variables. The results on the effect of stage of development are qualitatively similar to the results using country-sector-year level data, though the magnitudes are smaller. In this case, there is evidence that country size also matters, with larger countries having lower entry and exit and higher entrant survival rates.

Columns (11)-(15) of Table 7 show results based on the country-sector-destination-year level data for years 2004 to 2008, considering interacted sector-destination fixed effects, along with year fixed effects and bilateral gravity variables. The evidence indicates a strong negative effect of income per capita on exporter turnover and a strong positive effect of income per capita on entrant survival within sector-destination cells for this sample including 34 developing countries and 3 developed countries. Moreover, the findings are maintained in specifications that include more stringent sector-year, destination-year and sector-destination-year fixed effects, as seen in Appendix Table A2.2 (Panel B).

These results show that exporter dynamics change as countries get richer. In less developed countries, turnover is largely a process of entry and exit where many firms enter into export markets and exit almost immediately. In richer countries, fewer but more resilient exporters enter in any given year.

> 30 These magnitudes are obtained as the product of the difference in logs GDP per capita, 1.704, and the corresponding coefficient in columns (1)-(3) of Table 7: -0.063, -0.063, and 0.038.

22