rafmacalaba/data-use-mentions-extended huggingface dataset:
showing the descriptive trends in Figure 2 where firm outcomes are plotted against annual standardized rainfall using a binned scatterplot. We use two rounds of the NSS survey data on unincorporated enterprises for this exercise, conducted in 2010-11 and 2015-16, and restrict our sample to 13,010 enterprises located in rural areas. Based on the unconditional correlations, we discern a strong positive effect of higher annual rainfall incidences on enterprise revenues and value-addition, and weak negative effects on machinery and tools, credit (along the extensive margin) and wage per worker.

We next rigorously examine these descriptive trends using specification (3). All specifications apply enterprise weights and include district and firm level covariates. Standard errors are clustered by district. Table 3 shows our baseline results. Consistent with the results seen in Figure 2, we see that a positive rainfall shock has a large and statistically significant impact on enterprise revenues and value addition, and a limited effect on produc– tive capital (machinery and tools). The coefficients are economically significant compared to average enterprise revenue and value addition when there is neither a positive or negative shock (the reference case), a positive rainfall shock increases monthly enterprise revenues (value-addition ) by an additional INR 9,000 (INR 3,038).<sup>8</sup> The lack of an impact on enter– prise capital is expected precipitation shocks are temporal fluctuations while investments in machinery and tools are lumpy investments which yield returns over the medium and long-term.

Columns (5)-(7) of Table 3 investigate whether positive rainfall shocks affect hiring, wages or wage per worker.<sup>9</sup> We find no effect on either of these outcomes. If anything, there is a negative impact on total workers, which could occur if these enterprises are competing with the farm sector for labor, and positive rainfall shocks increase the demand for farm labor (see Colmer (2019)).

Table 4 shows the impact of precipitation shocks on qualitative measures of enterprise performance
, the bottom 2nd percentile of aggregate food consumption expenditure per capita is winsorized in each round of the survey.

> 16I constructed the ESS wealth index in the same fashion as the wealth index in the Demographic and Health Surveys (DHSs) - ( <mark>Rutstein, 2015).</mark> Specifically for the Ethiopia DHS wealth index construction, see: http://www.dhsprogram.com/topics/wealth <u>index/Wealth-Index-Construction.cfm.</u>

10
</mark> Gort, M., & Klepper, S. (1982). Time paths in the diffusion of product innovations. _The economic journal_ , _92_ (367), 630-653.

Griliches, Z. (1986). Productivity, R and D, and Basic Research at the Firm Level in the 1970s. _The American Economic Review_ , _76_ (1), 141-154.

Hall, B. H. (2011). _Innovation and productivity_ (No. w17178). National bureau of economic research.

<mark>Hardy, M., & McCasland, J. (2016). It takes two: experimental evidence on the determinants of technology diffusion.</mark> _<mark>Unpublished paper, University of British Columbia</mark>_ <mark>.</mark> <mark>Hegde, D., & Shapira, P. (2007). Knowledge, technology trajectories, and innovation in a developing country context: evidence from a survey of Malaysian firms.</mark> _<mark>International Journal of Technology Management</mark>_ <mark>,</mark> _<mark>40</mark>_ <mark>(4), 349-370.</mark> Hendry, D. F., & Krolzig, H. M. (2004). We ran one regression. _Oxford Bulletin of Economics and Statistics_ , _66_ (5), 799-810.

Hoover, K. D., & Perez, S. J. (1999). Data mining reconsidered: encompassing and the general‐ to‐specific approach to specification search. _The econometrics journal_ , _2_ (2), 167-191.

<mark>Hsieh, C. T., & Klenow, P. J. (2009). Misallocation and manufacturing TFP in China and India.</mark> _<mark>The Quarterly journal of economics</mark>_ <mark>,</mark>
# 1 Introduction

The Africa Infrastructure Country Diagnostic (AICD) has conducted extensive data collection and analysis of infrastructure in Africa, including the countries of the Southern African Development Community (SADC). The results have been presented in a variety of continental reports covering different areas of infrastructure—information and communication technology (ICT), irrigation, power, transport, and water and sanitation—and different policy areas, including investment needs, fiscal costs, and sector performance.

The purpose of this regional report is to present the key AICD findings for the SADC community. The main value in doing so is that it makes it possible to benchmark the infrastructure situation in the region against that of other African peers, to identify the main gaps in the regional infrastructure backbones, and to quantify the costs and benefits of regional integration, as well as their distribution across member states.

A number of methodological issues should be borne in mind.

First, owing to the cross-country nature of the data collection, there is inevitably a time lag in the data. The period covered by AICD runs from 2001 to 2006. Most of the technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Given the fast pace of regional integration, the snapshot presented here does not necessarily correspond to today’s situation but rather represents the 2006 baseline against which subsequent progress can be measured.

Second, given the need to make comparisons across countries, indicators and analysis had to be standardized and made consistent. That means that some of the indicators may be slightly different from those routinely reported and discussed at the country level.

Third, the database on which the analysis is based was designed to give a national and continental picture of infrastructure, as opposed to an explicitly regional picture. But national infrastructure provides the basic building blocks for regional integration, and hence can be used to build up a picture of the regional situation. Nevertheless, some
experiments to provide an estimate of how generalizable the findings are likely to be.

Inverse probability weights at the individual level based on the 2011 population census were calculated for both the phone survey and online experiment so as to be able to compare representative findings across the two surveys. Respondents to the phone survey and online experiment were disproportionally more likely to be male and younger than the national population and their geographic location also differed (see Table 2). Inverse probability weights were used to adjust for these differences. For example, weights greater than one were applied to female, older respondents in the Southern region and weights of less than one were applied to younger men in the Momase region (see balance table once weights are applied for the online survey experiment as well as a full list of weights for the two surveys in Appendix A, Tables A1-A3). The weights were calculated at the individual level (as opposed to household) because the questions about vaccine hesitancy focused on an individual’s willingness to get vaccinated and their reasons behind this.

# **5. Findings**

This section presents the descriptive results from the phone survey and the control group in the online experiment. It also highlights what the phone survey reveals in terms of the drivers of vaccine hesitancy. In addition, this section details the efficacy of the treatments used in the online randomized survey experiment.

# <u>5.1 Descriptive results from the phone survey and online experiment</u>

This subsection presents broadly representative, descriptive findings from the phone survey and the control group in the online experiment, using the same approach to weighting (that is, inverse probability weighting at the individual level as discussed in Section 4.3). Both the phone survey and online experiment show there were high levels of vaccine hesitancy in PNG, which respondents stated 14
_Sisben_ data‖) contains the universe of individuals above and below the threshold of eligibility (whether or not they actually participate in the program). This information is then used to construct indicators of program participation and length of exposure. In order to focus on a period of time that is comparable to the one examined in the matching analysis, the final subsample is restricted to treatment municipalities that were covered during the first phase of expansion between 2001 and 2004. This implies that in addition to the comparison group, the samples are comprised of beneficiaries that could have been covered for up to nine years until 2009.<sup>14</sup> The two resulting data sets (―PSM data‖ and ―SIFA + _Sisben_ data‖) are merged with the administrative records on registration and results for the _Icfes_ test.<sup>15</sup> This exam is a nationally recognized and standardized test that is administered prior to graduation from high school and mandatory for entrance to higher education. Over four million students registered and took the test between 2003 and 2009. This database identifies test takes by date of birth, full name and national identification number. This dataset is merged with the cohorts of children identified in the ―PSM data‖ assembled to perform the matching analysis, and with the ―SIFA+SISBEN data‖ to implement the RDD strategy. In order to avoid problems of nonrandom mismatch, strict procedures were followed to merge the datasets including matching based on full name, birth date, national identification number, and a minimization of the phonetic _Levenshtein_ distance.<sup>16</sup> (see Appendix A for more details about the data merging procedures). The final matching rates are around 18 percent for the matching analysis and 24 percent for the RDD approach.<sup>17</sup> The long-term impacts of the program on the human capital of children are estimated on two outcome variables. The first is an indicator of high school completion that is measured through a dummy
**Table A.15:** Public Goods Provision, Heterogeneity by Nepotistic Networks

|||**Public Good**|**s Index**||
|---|---|---|---|---|
|_Villages where..._|Village head has<br>no relative ingovt|Village head has<br>at least 1 relative ingovt|No bureaucrat<br>w/parent ingovt|At least 1 bureaucrat<br>w/parent ingovt|
||(1)|(2)|(3)|(4)|
|New village head|0.773<sup>_∗∗_</sup>|-0.069|0.603<sup>_∗∗_</sup>|0.484|
||(0.359)|(0.388)|(0.282)|(0.526)|
|Observations|191|133|295|81|
|Control mean|0.17|0.36|0.17|0.40|
|Robust p-value|0.020|0.683|0.022|0.309|
|MSE-opt. bandwidth|20.6|16.1|19.2|23.8|
|Effective obs.|86|47|129|38|
|Not nepotistic vs Nepotistic_p_||0.022||0.287|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). The dependent variable is a standardized index of local public service provision constructed using the 2021 Podes data, as in column 1 of Table 7. The sample includes: in column 1, all villages in which the village head reports having no relative in the village government; in column 2, all villages in which
owns them or not), and is the logarithm of material inputs of firm The value of the capital stock is only available for manufacturing firms.ii<sup>7</sup> is the measure of management practices and nn ii. is the measure of skills. For these variables normalized scores (z-indices) are used. ii is a vector MM of all other controls that are predicted to affect firm performance, such as firm age, manager age, ii ii SS ZZ and a set of industry dummies and location variables. In addition, we estimate models with labor productivity as the dependent variable: To assess the importance of firm capabilities, we test the hypothesis that and are equally effective (or ineffective): 4 5 = 0. We use an F test to check whether ii and iivariables are SS MM jointly statistically significant. We see F as a measure of the relative increase in the sum of squared ii ii αα = αα SS MM residuals (SSR) when moving from an unrestricted to a restricted model (Wooldridge, 2008). Nevertheless, this test does not provide information of partial effects on . Thus, we cannot use it to test differences in the effectiveness of the different capabilities’ drivers. YY The overall analysis does not allow to causally relate firm capabilities to performance, but it is an important step before promoting and testing individual policies. It allows to understand how high or low are Mozambique’s levels compared to other countries, which types of firm capabilities seem more relevant for firms that were able to grow and perform in Mozambique, and for which types of enterprises firm capabilities helps to explain further differences in performance.

# _Estimating heterogeneous effects_

To assess the heterogeneity of the findings by firm type (small versus large; young versus old; male versus female-owned; located in Maputo versus in other places; exporters versus nonexporters; foreign vs non-foreigner), we estimate the following model for formal firms with at least five employees:

> 7 In the Enterprise Survey questionnaire, capital
, for example). There are both private – accruing to the individual in the form of better jobs, higher wages, for example – and social – meaning they affect not only the recipient of education but others as well – benefits. Better educated mothers have lower fertility rates while parental education raises children’s schooling level and scholastic achievement. Parental education also improves child health. More schooling in general improves one’s health and life expectancy. There are also a number of social benefits, such as technological change and diffusion, social cohesion and reduced criminal activity (Psacharopoulos and Patrinos 2008).

The market benefits of human capital can be captured in rate-of-return calculations estimated from wages reported by workers in nationally representative surveys. Thus, they are based on the wages more and less educated people command in the labor market. Such estimates also provide proxy indicators of shortages of quality education. Low-income countries tend to yield higher rates of return to schooling reflecting skilled labor shortages. The average rates of return to each additional year of secondary schooling are 20, 18 and 12 percent for low-income,
worker) species remain informal business owners during the following quarters. About 12 percent of individuals in the formal business owner species switch to being formal business owners. This fraction is smaller

> 8 According to COFEMER’s website, by November 2011, 191 municipalities had adopted SARE (http://www.cofemer.gob.mx/contenido.aspx?contenido=122).

> 9 Bruhn (2008 and 2010) also controls for personal background characteristics in the regressions. I do not include these variables here since they were already used to separate the sample into different species.

> 10 Some industries were not eligible for SARE. In this paper, I only study the impact of SARE on informal business owners in eligible industries since individuals in non-eligible industries are not able to register their business through SARE. Informal business owners in eligible industries make up 96.5 percent of informal business owners in the ENE.

> 11 High mobility across occupational sectors has also been observed in earlier work with the Mexican Labor Market Survey (Bosch and Maloney, 2006, and Woodruff, 2007). The labor market is similarly dynamic in Brazil (Bosch and Maloney, 2010).

9
less well de...ned. In addition, in 2001, an increasing number of comparison schools started organizing school meals on their own, with funding from the parents. Prior to the introduction of the breakfast program, only two schools, one treatment and one comparison, organized a school meal program on their own. By September 2001, 14 comparison schools out of 25 were organizing their own meal program. This phenomenon might have been the result of a mere time e¤ect, or it might have been a spillover from the program itself that was contaminating the comparison schools.

It is possible that the implementation of the program in the treatment schools could have contaminated the comparison schools. Since the experiment was not blind, the comparison schools knew that the treatment schools were receiving the school meals. This potentially provided a signal that school meals were a worthwhile intervention, making it more likely that comparison schools would start a meal program on their own. Or there could have been a peer e¤ect or increased competition. Possibly, schools close to the treatment schools learned from the program and incorporated that knowledge into their organization. Or they could have felt the competition from the treatment schools and started a school feeding program to try to compete better. With the data available at the time of writing, it is not possible to determine what the mechanism was. Data are currently being collected on non-subsidized school meals in all schools in the district. Combined with GPS data, these data will allow us to identify the e¤ect of geographic proximity to 24
industrial diversity, business environment includes licensing fees and taxes, and infrastructure includes the costs of transport and electricity.

The coefficients of the regression model could be biased owing to endogeneity concerns. On the one hand, although the regression assumes that the rate of growth of firm employment is a function of different factors, it is possible that employment growth drives agglomeration, availability of infrastructure and the existing business environment within a location. To mitigate reverse causality concerns, the explanatory variables are lagged (i.e. they are defined at year ��1). On the other hand, the effect of unobservable factors could continue to bias the coefficients. The underlying assumption within the model is that is a particular city offers certain inherent features that improve the profitability of firms, this will lead to employment growth. Such features could be related to the factors captured within the model, but they could also have to do with essentially un-measurable factors such as local business cultures or natural endowments. A battery of fixed effects are included in different variations of the model to control for omitted variables bias in the estimations, specifically, industry, city, year and firm fixed effects.

Localization can be measured by own industry employment in the city, which reflects disproportionately high concentration of firms within the same industrial sector within the city. This is measured as the proportion of industry _j_ ’s employment in city _k_ as a share of all of industry _j_ ’s total employment in the country. The higher this value, the higher the expectation of intra-industry concentration benefits in the city.

The Herfindal measure is used as a measure of industrial diversity within the city. The Herfindal index of a city _k_ is the sum of squares of employment shares of all industries in city _k_ . Unlike the measure of localization, which focuses on one industry, the industrial diversity index considers the industry mix of the entire city economy. The largest value for is one when the entire city economy is dominated by a single industry. Thus a higher value signifies lower
7</sup> level through creation of a composite index for the relevant crop basket.

The methodology for the calculation is described in Box 2.1 below. If we consider the methodology in the context of a probabilistic catastrophe risk modelling framework, the key ‘hazard’ input into the process is the rainfall data used in the calculation of Actual Evapotranspiration. It is the high levels of variability around rainfall that are determining the occurrence or otherwise of meteorological drought (translated into crop yield loss), and this is where the large covariate shocks arise that probabilistic catastrophe models seek to capture.

Recorded climate data on temperature, humidity, windspeed and solar radiation are also used in the methodology to determine the evaporating power of the atmosphere at a specific location and time of the year. However, whilst simulating trends in these parameters over the long term could be an interesting exercise on the impact of climate change, the low variability of these parameters over time compared to rainfall make them less interesting candidates for probabilistic simulation in the context of drought.

The LEAP software makes available a combination of satellite and weather station datasets from which historical precipitation values can be taken or estimated. The primary precipitation dataset comes from the National Meteorological Agency of the Republic of Ethiopia (NMA). NMA provides rainfall (and other climate data) on a daily and dekadal<sup>8</sup> basis for its network of weather stations.

> 7 _Woreda_ is an administrative unit in Ethiopia, equivalent to a county in the United States.

> 8 Ten days accumulated

8
Policy Research Working Paper 9147

# **Abstract**

Tourism is an important source of foreign exchange and employment across developing economies. A scant literature has explored the relationship between tourism and the advent of the internet. This paper contributes to the tourism-trade literature and studies the empirical relationship between international tourism and the adoption of digital technologies that facilitate search about tourism opportunities across countries. It links foreign visits with the spread of the use of the internet in sending countries and the level of development of business-to-consumer digital tools in host countries. The paper estimates a well-specified gravity model of tourist arrivals between country pairs with panel data. The results indicate that frictions affecting bilateral tourism flows have been attenuated by the advent of digital tools. The absolute value of the effects of bilateral geographic distance, language differences, and border-contiguity seem to be reduced by the use of the internet by potential tourists and the business sector in host countries. The results are robust to alternative proxies for internet use for tourism search proxied by data from Google trends. The paper also presents simulations of the potential impacts of advances in the adoption of digital tools over time, linking the adoption process to mechanisms of technology adoption that are commonplace in the literature.

This paper is a product of the Finance, Competitiveness and Innovation 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 author may be contacted at jlopezcordova@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, and conclusions expressed in this paper are entirely those of