rafmacalaba/data-use-mentions-extended huggingface dataset:
>Government Revenue Dataset|
|**_Panel B: Economic f_**|**_undamentals (DEA input index)_**||
|Per-capita income|GDP per capita, PPP (constant 2017 international $)|World Bank|
|Share of<br>agriculture in GDP|Agriculture, forestry, and fishing, value added (% of GDP)|World Bank|
|Trade openness|Trade (% of GDP): Sum of exports and imports of goods and<br>services measured as a share of gross domestic product|World Bank|
|Age dependency<br>ratio|Age dependency ratio (% of working-age population): Ratio of<br>dependents (people younger than 15 or older than 64) to the<br>working-age population (ages 15-64)|World Bank|
|**_Panel C: Other fact_**|**_ors (used in regression analysis)_**||
|Control of|Score on ‘Control of corruption’ indicator|Worldwide Governance|
|corruption|(values from -2.5=weak to +2.5=good governance)|Indicators|
|Informal sector|Informal employment (% of total non-agricultural employment)<br>harmonized series|;<br>International Labour<br>Organization|
|Natural resources|Total natural resources rents (% of GDP): Sum of oil rents,<br>natural gas rents, coal rents (hard and soft), mineral rents, and<br>forest rents|World Bank|
|Tax policy|Score on ‘Efficiency of revenue mobilization’ indicator<br>(values from 1=low to 6=high efficiency)|World Bank, Country Policy and<br>Institutional Assessment|
|Technological|Score on ‘Technological Readiness Index (9th Pillar)’|World Economic Forum,|
|readiness|(values from 1=low to 7=high readiness)|Global Competitiveness Report|

>
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE evaluate Kenya’s situation. Detailed comparisons will also be made with immediate regional neighbors in the East African Community.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most 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. Second, in order to make comparisons across countries, we had to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

During the five years from 2003 to 2007, Kenya’s economy grew at an average annual rate of 5.3 percent, much better than the 2.3 percent recorded in the previous decade. Notwithstanding this improvement, current growth levels still fall short of the sustained 7 percent per annum needed to meet the Millenium Development Goals. Less than half of 1 percent of East Africa’s improved per capita growth performance during the 2000s can be credited to improved structural and stabilization policies (Calderon 2008); by contrast, almost 1 percent is related to improvements in the country’s infrastructure platform. Most of the boost was due to Kenya’s ICT revolution, while poor roads proved to be a drag on growth. Simulations suggest that if Kenya’s infrastructure could be improved to the level of the African leader—Mauritius—annual per capita growth rates would be 3.3 percent higher than they are at present. A substantial share of that impact would come from improvements in the power sector alone (figure 1).

**Figure 1. Infrastructure has contributed much to economic growth—but could contribute much more**

a. Infrastructure’s
access in the property; (5) _IT items_ , which refer to the number of computers and cellphones; and (6) _IT services_ , which sums the home phone, internet and pay-TV; (7) _ln(imp cons)_ , is the imputed measure of non-durable expenditure on food personal products, and clothing. Data employed comes from 1997 ENCASEH and 2017-18 ENCEL. The table shows the results from an ANCOVA estimate, which controls for the pre-existing level of the dependent variable (denoted as Y(97)). Controls include (from ENCASEH 1997): HH head age, education, speaking a dialect, HH size, % members under 15, dummies for home, productive land and draft animals ownership, and locality marginalization index. Standard errors clustered at the locality level. Asterisks indicate significance at the ***1%, **5%, and *10% level.

26
# The Effects of Innovation on Employment in Developing Countries: Evidence from Enterprise Surveys

Xavier Cirera<sup>∗</sup> Leonard Sabetti<sup>†</sup>

> ∗We are grateful for helpful comments from seminar participants at the 10th IZA/World Bank conference on Technological Change and Jobs (Bonn, Germany) and the 6th ZEW/MaCCI Conference on the Economics of Innovation and Patenting (Mannheim, Germany). We would like to thank Jordi Jaumeandreu for extensive comments to an earlier version of this paper, and CIIP for financial assistance. Corresponding Author: The World Bank, Innovation and Entrepreneurship, Trade and Competitiveness Global Practice, Washington, D.C. Email: xcirera@worldbank.org

> †The World Bank, Innovation and Entrepreneurship, Trade and Competitiveness Global Practice, Washington, D.C.
agricultural productivity has a larger poverty-reduction effect than increases in other sectors.

Although the potential productivity gains from sectoral reallocation have become more challenging to achieve, there would still be important payoffs from policies that supported diversification, including developing human capital, including at the tertiary level; promoting good governance and easing the cost of doing business; strengthening institutional capabilities; reducing distortions such as uncompetitive regulations and subsidies; supporting R&D; and promoting exports and developing stronger managerial capabilities. Removing barriers to migration can also help to facilitate structural transformation.<sup>11</sup> Given the low level of productivity in EMDE agricultural sectors and its role as the primary employer in LICs, policies to raise agricultural productivity would pay significant dividends. These polices would include improving infrastructure and land property rights.

Future research could investigate the endogeneity of sector allocation. As the shift-share decomposition is an accounting exercise, it should be noted that within sector growth could also directly affect sector reallocation. An improvement in agricultural productivity increases incomes and the demand for other goods, encouraging a shift of labor into other sectors (Eberhardt and Vollrath (2018), Gollin et al. (2007), and Diao et al. (2018)). It could result in facilitating between-sector productivity growth. Hence, the contribution of agricultural productivity could be larger and that of sectoral reallocation could be smaller.

> 11Artuc et al. (2015) show the estimated labor mobility costs caused by labor market frictions in EMDEs are a larger burden than those in advanced economies, using the data with eight major sectors. Bryan and Morten (2019), using Indonesia data show that reducing migration costs to the US level, a high-mobility benchmark, leads to a 7 percentage point increase in productivity growth. Empirically, regress the structural-term on employment rigidity index and raw materials’ share in exports and so on and find that there is a statistically significant impact of the employment rigidity.

11
the post-Suharto era. The negative correlation between broad connectedness and the summary competition index also attenuates slightly after Suharto’s fall, but not significantly so.

> 16 Note the contrast with the results from the firm level descriptive statistics, which show that politically connected firms have higher foreign ownership shares (see Table 3).

17 The natural rate of entry is based on US data, which assuages concerns about entry rates being endogenous to political connections. In principle, entry rates in the U.S. could also be endogenous to political connections and correlated with Suharto connectedness. Existing literature, however, suggests that this is not very likely. Fisman et al. (2012) estimate the value of being connected to former vice president Dick Cheney to be zero. Lobbying and fostering effective state-business relationships can still be an important determinant of corporate success in the U.S., but business-politics relationships are less personalized, and arguably more institutionalized in more advanced countries. Consistent with this argument, Faccio (2006) documents that political connections are less prevalent in countries which are more democratic and in which politicians are required to disclose their assets.

19
controlling for more country- and firm-level factors, and employing a resampling technique to ensure that the sample accurately reflects the population.

Wagle (2010) investigates the effects of regulation on FDI and concludes that FDI-increasing regulations prompt beneficiary firms to grow more quickly, through either selection effects or knowledge transfers.<sup>11</sup> We test for these effects using business environment variables.

Burda and Hunt (2001) take a different approach, investigating the effects on productivity when countries integrate their economies. They find evidence that less productive members of economic unions benefit from productivity transfers from their partners.<sup>12</sup> Winston (1993), Harrison (1994), and Meyer and Vickers (1997) suggest that integration improves productivity growth by increasing competition. This competition leads to the expectation of convergence. This paper considers both integration and international affiliations.

In this paper, we test for the effects of firm size, age, and ownership structure on laborproductivity growth.<sup>13</sup> Dunne, Roberts, and Samuelson (1989), using a dataset including 200,000 U.S. manufacturing firms from 1967–1977, find that size is negatively correlated with growth, and that the expected growth rate of a firm declines with size for firms owned by single-plant firms, but increases with size for firms owned by multi-plant firms, suggesting synergies from FDI. The importance of FDI for growth is a persistent result throughout the literature,<sup>14</sup> and one further supported by our findings.

The research most similar to that presented in this paper is the work of Anos Casero and Udomsaph (2009). The authors show a direct correlation between productivity growth and the quality of institutions and government policies. They also use the Amadeus dataset and employ principal component analysis to determine the business environment. However, their analysis covers only eight European countries, and their sample is somehow biased by the data availability in Amadeus.<sup>15<
.2 Inflation inequality and observed inequality_

As noted earlier, when inflation rates differ across individuals the distributions of nominal and real consumption may follow different paths. Observed changes in standard inequality indices (which are typically computed from cross-sectional nominal income or expenditure data) may offer a misleading picture of the trends in _real_ income or consumption inequality, which are the relevant ones from the welfare perspective. For example, it would be perfectly possible for real inequality increases to be offset by price inequality declines, so that by just observing nominal quantities one may get the mistaken impression that no change in inequality has taken place.

Following Ruiz-Castillo, et al. (2002), let x( _xt_ ) denote some inequality index (which is assumed to be an increasing function in the level of inequality) in period t, where 1 _H_ ' 1 ' _H xt_ = ( _xt_ ,...., _xt_ )'= ( _pt ct_ ,...., _pt ct_ )' . Note that x( _xt_ ) measures the inequality of nominal ' 1 ' _H_ quantities. Further, let _xt_ , _s_ ≡ ( _pt cs_ ,...., _pt cs_ )' , i.e. the vector of household consumptions at _s h h h_ evaluated at the prices of period _t_ (from (1) this implies _xt_ ,0<sup>=</sup> _x_ ,00 _cpit_ ). Then we can write: 4
using similar research designs in other time periods and contexts.

# **Appendix A: Variable definitions**

The key dependent variable we analyze is _change in sooner allocation upon revisiting (MK)_ , which is respondent’s allocation to later period (t=91) in revisit survey minus allocation to later period (t=91) in baseline survey. All other variables are from either the baseline survey, the revisit survey, or from administrative (project) data.

# A.1 Variables collected in baseline survey

_Present-biased ratio_ is fraction of choices where allocation to sooner in near period is more than 100MK higher than allocation to sooner in far period (comparing choices in near and far periods for same interest rate). In all regressions this variable excludes the implemented interest 32
by Jordan’s Department of Statistics. It contains detailed data on household expenditure and income, as well as on direct transfers and household use of education services. There data are also the base for the country’s most recent official poverty estimates. The HEIS is representative of Jordanian households with close to 16,000 households interviewed over the course of a year, from August 2017 to July 2018. We also use administrative and national accounts data from 2018, which broadly coincide with the timeframe of the household survey.

Income concepts and the values of taxes and transfers are reported in per capita Jordanian dinars per year. Per capita values are obtained by dividing total value by the total number of permanent household members. To calculate the indirect burden of indirect consumption taxes and of subsidies we use a 23 sector Input / Output (IO) table from 2010 uprated to 2016.<sup>16</sup>

## Income concepts before and after fiscal interventions

A CEQ assessment uses eight different income concepts starting from ‘pre-fiscal’ or ‘market income’; that is, the income before all fiscal interventions (see Lustig, 2018). This includes all incomes from work (salaries and self-employed income), capital, self-provision of goods and services, remittances and other

> 15 Government employees recruited before 1995 are in the Civil Pension system and armed forces members recruited before 2002 are in the Military Pension system, both of which are being phased out.

> 16 Constructed in Refaqat _et al_ . (2020).
# **2. Data and Methods**

# _2a. Data_

This analysis describes the levels of vaccine hesitancy and its reasons in 53 developing countries between - October 2020 August 2021 using data from the World Bank’s COVID-19 high <u>frequency phone surveys</u> (HFPS) which were implemented to monitor the impact of COVID-19 on households around the world (World Bank 2020). Data are available for one survey round from 39 countries and two or more survey rounds for 14 countries. The countries in the pooled database represent five out of six regions defined by the World Bank: Latin America and the Caribbean (24 countries), Sub-Saharan Africa (14), East Asia and Pacific (7), Europe and Central Asia (6) and the Middle East and North Africa (2).<sup>3</sup> By income group, the sample includes 12 low-income countries, 13 lower-middle income countries, 23 upper middle-income countries and five high-income countries (Appendix Table 1 provides a list of countries with survey month, sample size, region and country income group).

A global core questionnaire served as the basis for each country’s survey but was then customized to fit the local context. As a result, the exact questions may vary across countries, but topics typically included knowledge and concerns about COVID-19, access to food, health care and education, employment and income loss, and safety nets and coping strategies. The survey data was then harmonized ex-post to enable cross-country comparability. The questionnaire was flexible and adapted over time to the pandemic context. The vaccine module, for example, was included more recently.

The sampling frame was drawn from pre-existing nationally representative household surveys in 19 countries, random digit dialing (RDD) in 29 countries, and a list of phone numbers typically obtained from mobile phone operators in 5 countries. The RDD design samples from all active mobile and landline phone numbers, such that RDD surveys are representative of the population aged 18 and over
# **References**

Arndt, C., Pauw, K., and Thurlow, J. 2015. “The Economy-Wide Impacts and Risks of Malawi’s Farm Input Subsidy Program.” _American Journal of Agricultural Economics_ 98(3):962-980.

Bationo, A., Christianson, C.B., Baethgen, W.E., Mokwunye, A.U., 1992. A farm-level level evaluation of nitrogen and phosphorus fertilizer use and planting density for pearl millet production in Niger. _Fertilizer Research_ 31 (2), 175–184.

Benson, T. 1999. Area-specific Fertilizer Recommendations for Hybrid Maize Grown by Malawian Smallholders: A Manual for Field Assistants. Report prepared for the Maize Productivity Task Force by Chitedze Agricultural Research Station, Malawi.

Carletto, C., S. Savastano, S., A. Zezza. 2013. “Fact or artefact: The impact of measurement errors on the farm size -productivity relationship”. _Journal of Development Economics_ , 103, 254-261 Chamberlain, G., 1984. Panel data. In: Griliches, Z., Intriligator, M.D., (Eds.), Handbook of Econometrics, vol. 2. North Holland, Amsterdam, pp. 1247–1318.

Corrado, L. and B. Fingleton. 2011. “Multilevel modelling with Spatial Effects.” Working Paper No. 11-05, Department of Economics, University of Strathclyde.

Dorward, A., 2006. “Markets and Pro-Poor Agricultural Growth: Insights from Livelihood and Informal Rural Economy Models in Malawi.” _Agricultural Economics,_ 35: 157-169.

Dorward, A., E. Chirwa, V. Kelly, T. Jayne, R. Slater, and D. Boughton. 2008. Evaluation of the 2006/07 Agricultural Input Subsidy Programme, Malawi. Final Report submitted to the Ministry of Agriculture and Food Security, Government of Malawi, Lilongwe.

Elhorst, J.P. 2014. Spatial Econometrics from Cross-Sectional Data to Spatial Panels
countries, and ignores the multidimensional aspects ( _e.g._ health, education, living conditions) that result in people living in a state of deprivation. Third, the H2016 operates using national household survey data, which is generally not available on a sufficiently granular level to provide insights into the spatial distribution of welfare losses on a subnational scale, nor the impacts to individual households. Although this has been partly resolved in the follow-up case studies (Philippines, San Francisco, Bangladesh), where regional household survey data was used and the impacts to individual households was evaluated, the survey data generally only includes a small subset of the population.

Hence the H2016 model and its extensions could be supplemented with a complementary analysis that (1) includes a larger representative sample of households on a local scale, (2) models how disasters affect specific assets, services and employment that households depend upon, and (3) quantifies the resulting impacts to the wider development potential of poor or otherwise vulnerable households. Such a model also allows evaluating, and better targeting, adaptation options. While the H2016 made a significant contribution in the sense that it allowed evaluating both hard (e.g. preventive measures) and soft (e.g. cash transfers) interventions in a single modeling framework, it did not allow the evaluation of spatial targeting of these interventions, nor of implementing interventions that make critical infrastructure more climateresilient.

7
since then. In addition, some developed countries with more recent surveys do not have ISCO 08 information at the 4-digit level (e.g. United States). In contrast, New Zealand and Slovenia’s surveys were collected during the second round (2014-2015), which is the same timeframe of Chile’s survey, and closer to the timeframe of Ecuador, Mexico and Peru (2017).

> 19 There are 38 countries with publicly available PIAAC microdata, see <u>https://www.oecd.org/skills/piaac/data/.</u> 20 A similar exercise was implemented by Garrote et al., (2021) to adjust working-from-home measures by internet access rates.

> 21 While there are 43 ISCO08 2-digit level categories, we drop from the sample the categories 01 (Commissioned Armed Forces Officers), 02 (Non-Commissioned Armed Forces Officers) y 03 (Armed Forces Occupations, Other Ranks) since they are not available in PIAAC.

> 22 Table A2 in the appendix contains the estimated coefficients.

> 23 The logic is the same when the measures of GenAI exposure are imputed at the 2-digit ISCO08 level, which are not binary but continuous measures between 0 and 1. That is, the exposure to GenAI augmentation within a group of workers would be the average of the continuous exposure measure.

16
**Table 1: Sectoral Share of Total Export Revenue, selected transition economies)**

||**Agriculture/Food/ Mining**|**Manufactures**|**Services**|
|---|---|---|---|
|Albania|19|35|46|
|Croatia|9|49|42|
|Czech Rep.|5|80|15|
|Hungary|7|76|17|
|Poland|10|73|17|
|Romania|4|85|10|
|Slovakia|4|86|10|
|Slovenia|4|81|15|
|Estonia|11|66|22|
|Latvia|13|64|24|
|Lithuania|13|63|24|
|Russia|40|52|8|

_Source:_ GTAP Input-Output data derived from SAMs for 2001.

**Table 2:  Total Export Related Activity (direct plus indirect linkages), 2001**

||**Total**<br>||**Sh**|**ares**||
|---|---|---|---|---|---|
||**“Multiplier”**|**Agriculture/Food**|**Mining**|**Manufactures**|**Services**|
|Albania|4.8|20|4|24|52|
|Croatia|2.9|18|1|36|45|
|Czech Rep.|3.0|10|2|61|27|
|Hungary|2.8|10|2|51|37|
|Poland|4.2|17|3|43|38|
|Romania|6.6|27|3|39|30|
|Slovakia|2.9
with father, whether currently working, household ownership of DVD, television, a MP3 player, cellular phone, computer, calculator, whether English is spoken and whether the student aspires to study in a university. Preprogram variables measuring school quality from the Ministry of Education 911 forms (formats filled out by schools) include: student-teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade and average teacher schooling levels. These variables correspond to characteristics of the primary school attended in 2007-2008. In addition, we merge the locality-level margination index and its components to our data file. Community-level control variables include the margination index, the proportion of the population that is illiterate, the proportion of households without electricity, the proportion of households without sewage drainage, and the proportion of households without access to piped water. This set of control variables is used to estimate the propensity score model, additionally exact matching on gender and state is carried out. The type of matching is nearest neighbor matching with 1 match.<sup>8</sup>

# **V. Results**

We present results based on two main samples: 1) sixth grade students in the school year 20072008 who attend primary schools in localities of medium, high or very high levels of margination<sup>9</sup> and 2) sixth grade students in the school year 2007-2008 who are enrolled in indigenous primary schools. For each sample we present results on enrollment, type of school and achievement tests in mathematics and Spanish.

We present three sets of results for each indicator:

- 1) State fixed-effects models with individual, school and locality level control variables

- 2) School fixed-effects models with individual control variables and

- 3) Nearest-neighbor matching estimates using individual, school and locality variables for the propensity score and exact matching on gender and state.<sup>10</sup>

We begin by presenting descriptive statistics for our samples.

8 We also carried out matching estimations using 2 and