rafmacalaba/data-use-mentions-extended huggingface dataset:
**Table 1: Survey of Public Investment and Spending Multipliers**

|**Country &**<br>**Region**|**World Bank**<br>**Income**<br>**Group (2020)**|**Author(s)**|**Study**<br>**Period**|**Methodology,**<br>**including**<br>**identification**<br>**strategy**|**Multiplier range**|**Type**|
|---|---|---|---|---|---|---|
|Peru|Upper Middle<br>Income|Central<br>Reserve<br>Bank of<br>Peru (2012)|1992-2012|VAR (output<br>elasticities of<br>expenditure to filter<br>out automatic<br>stabilizers)|0.24; 0.92 (short-<br>term)<br>0.49; 1.42(medium-<br>term)|Current; Capital<br>spending|
|Peru|Upper Middle<br>Income|Rossini_et_<br>_al._(2012)|2005-2011|SVAR (a<br>multivariate, linear<br>representation of a<br>vector of observables<br>on its own lags)|0.78; 1.36 (short-<br>term) 0.52; 2.63<br>(medium-term)|Current; Capital<br>spending|
|Australia|High Income|Hunt_et al._<br>(2009)||DSGE model<br>(simulated impacts<br>on the output)|1.22<br>1.12|Public
<u>Governance. The main measure of institutional quality is a standardized index that summarizes 6 other</u> (also standardized) indices from the Worldwide Governance Indicators, produced by the World Bank (see Kaufmann, Kraay and Mastruzzi, 2010 for methodological details). The indices, namely Control of Corruption, Government Effectiveness, Political Stability and Absence of Violence/Terrorism, Rule of Law, Regulatory Quality and Voice and Accountability, aim to capture different dimensions of institutions. To construct the governance index, we standardize the sum of the 6 standardized indices above. The data are available for 174 countries from 1996 to 2016.

The indices are built based on surveys conducted with a number of respondents worldwide, as well as expert assessments. Following the original definitions in Kaufmann, Kraay and Mastruzzi (2010), Control of Corruption reflects perceptions of the “extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests.” Government Effectiveness measures the “perception of quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies”. Political Stability and Absence of Violence/Terrorism summarizes the perceived “likelihood of political instability and/or politically-motivated violence, including terrorism”. Rule of Law reflects “the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence”. Regulatory Quality captures the “ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development”. And lastly, Voice and Accountability summarizes the “extent to which a country's citizens are able to participate in selecting their government, as well as freedom of
and household-level networks.<sup>1</sup> However, in the _Progresa-Oportunidades_ setting, many beneficiary communities are very close to each other, thus spillovers may occur not only within, but also across, villages.

To investigate the presence of neighborhood effects, we combine data from the experimental evaluation of the program with information on the geo-referenced locations of the villages benefitting from it. We focus our analysis on the secondary school participation decisions of program-eligible children, which is the primary short-run outcome of the intervention and the key requirement associated with the largest component of the in-cash transfer.

We use a simple empirical framework that allows us to disentangle the effects of the incentives resulting from the program eligibility of the household (and the village it resides in) from the indirect effects arising from the local density of program recipients at the level of areas surrounding targeted villages. In particular, we exploit the randomized evaluation design and the clustered spatial distribution of the villages in our sample in order to identify the causal effects of program externalities generated by those neighboring villages selected in the experimental treatment group. Next, we investigate whether spillovers arise in this setting because of social interactions between program beneficiaries or as a result of other changes associated with variations in the local density of the program across areas surrounding villages.

We find evidence of a positive effect of the local frequency of participants in the program over short distances (0–5 km) on secondary school participation decisions, which tend to quickly dissipate at larger distances (5–10 km). With estimated effects of respectively one or two or more treated villages in the neighboring area on secondary school enrollment of 6.1 and 8.0 percent, this spillover effect does not increase linearly with the number of treated villages. But the magnitude of the indirect effect of the program is substantial when compared to the direct effect of own village treatment of 9.7 percent.

Crucially, these spatial externalities appear to exclusively affect children from beneficiary households; there is no evidence of such effects
2002 and 2010<sup>59</sup> , suggesting little changes in the structure of these economic activities. On the other hand, this harmonized pattern is not depicted for the agriculture sector. While welfare returns to the agriculture sector employment have been somehow flat across quantiles in 2002, Figure 4 reveals a U-shape plot in 2010 that is skewed towards the upper ends of the distribution. Thus,

> 55 More precisely these variables reflect whether the household has a at least one member working in a determined sector (education, defense, transport, tourism etc...). The sectors selected are similar to the ones provided officially by the department of statistics in the national accounts’ value added breakdown of GDP.

> 56 As a reminder, the paper uses household per capita expenditure to proxy the income distribution of the Jordanian population.

> 57 The coefficients estimates themselves can be found in Table 9.

> 58 The results on the upper percentiles should be taken with caution. Indeed, as most household surveys, the Jordan HIES have low reporting on expenditure data for richer segments of the population especially those at the upper end of the population income distribution. Therefore the sample distribution might have a bias by design of the survey where the rich are under-represented. However, the paper is forced to work with these constraints as no further data on these households is available to the authors.

> 59 The magnitude of the impact is more strongly observed in 2010 though.

26
only a few minor export taxes remain – see Piermartini (2004) and also Thiele (2004).

> 3 More information on the MAcMaps database is available in Bouët et al. (2004) and at

> <u>http://www.cepii.fr/anglaisgraph/bdd/macmap.htm. For details of its incorporation into the GTAP Version 6</u> dataset, see <u>www.gtap.org.</u>

5
where _distjk_ is the distance in kilometers along the shortest path on the actual road network, ( _distjk >_ 50 _km_ ) takes on 1 if the inequality holds and on zero otherwise, and _ξ_ 0<sup>_T_is calibrated</sup> so that we match the share of the total government budget allocated to roads.

**Calibration of Transport Costs.** Observed transport costs between two neighboring regions _i_ and _j_ are a function of the road quality investment _Iij_ (derived from the DNIT geospatial roads database) and the geographical-barriers parameter _δ_<sup>_T_The road quality along a given</sup> _ij_<sup>.</sup> discretized segment is computed as the average speed at which products can travel (see Appendix E for details). To calibrate _δ_<sup>_T_inturn,weusethedistanceinkilometerstakinginto</sup> _ij_<sup>,</sup> account that one can reach a location through the observed road network or, if there are no roads connecting _i_ and _j_ , by land. More specifically, we compute the geographical barriers between any two locations as the shortest path through the road network or by land weighted by the ruggedness index. We assume, following Allen and Arkolakis (2014), an exponential form for the normalized distance.

**Calibration of Electricity Investment Costs.** To assign values to the marginal cost of investing in quality of electricity, _ξi_<sup>_G_,we assume that</sup><sup>_ξ_</sup> _i_<sup>_G_can be expressed as</sup><sup>_ξ_</sup> _i_<sup>_G_=</sup><sup>_ξ_ ̄</sup><sup>_G_/</sup><sup>_δ_</sup> _i_<sup>_G_,</sup> where _ξ_<sup> ̄</sup><sup>_G_</sup> is calibrated to match the share of investment in electricity
Borjas, George. 1995. “Assimilation and Changes in Cohort Quality Revisited: What Happened to Immigrant Earnings in the 1980s.” Journal of Labor Economics 13(2): 201-245.

Borjas, George. 2000. “The Economic Progress of Immigrants.” In G.J. Borjas (ed.): “Issues in the Economics of Immigration.” Chicago: University of Chicago Press.

Borjas, George J. and Bernt Bratsberg 1996. “Who Leaves? The Outmigration of the Foreign-Born.” The Review of Economics and Statistics, 78(1): 165-176.

Borjas, George J. and Marta Tienda (eds) 1985. “Hispanic in the U.S. Economy.” New York: Academic Press.

Carliner, Geoffrey. 1980. “Wages, Earnings, and Hours of First, Second and Third Generation Males.” Economic Inquiry, 18(1): 87-102.

Chiswick, Barry R. 1978. “The Effect of Americanization of the Earnings of ForeignBorn Men.” Journal of Political Economy 86(5): 897-921.

Chiswick, Barry R. 1986. “Is the New Immigration Less Skilled Than the Old?” Journal of Labor Economics 4(2): 168-192.

Chiswick, Barry R., Yew Liang Lee and Paul W. Miller. 2005. “Longitudinal Analysis of Immigrant Occupational Mobility: A Test of the Immigrant Assimilation Hypothesis.” International Migration Review, 39(2): 332-53.

Deaton, Angus. 1985. “Panel Data from Time Series of Cross-Sections.” Journal of Econometrics, 30: 109-126.

DeFreitas, Gregory. 1980. “The Earnings of Immigrants in the American Labor Market.” Ph.D. dissertation, Columbia University.

Duleep, Harriet O. and Mark C. Regets. 1994 . “The Elusive Concept of Immigrant Quality: Evidence from 1970-1990.” Discussion Paper PRIP-UI-31, The Urban Institute, Washington, DC. Published in 2002 as IZA Discussion Paper No. 631.

Duleep, Harriet
Table 5 focuses on alternative non-financial channels that could be correlated with the impact of stock market liberalization on the long-term export survival. Both stock market liberalization and its interaction with the distance to comparative advantage maintain their significant coefficients with the expected signs throughout Table 5.

Columns (1)-(6) of Table 5 look at the impact of economic development and trade

> 19 In this paper, we use SITC data that are slightly more aggregated than the data used by Jaud, Kukenova, and Strieborny (2018) but allow us to look at a longer time span. Additionally, we look at exports to the whole world market rather than focusing on the US destination market alone.

20
diversified sources of imports can help mitigate the effects of a negative supply shock. Conversely, having diverse destinations for exports can allow for export increases when negative supply shocks affect the partners’ dominant sources. Tanzanian export restrictions are found to suppress maize price responses, either by making price declines larger, or price increases less positive. However, the marginal impact of the maize export restriction on poverty is small.

Considering climate change, analysis of the CMIP3 archive of global climate model simulations indicates that severe dry conditions in Tanzania will most often coincide with non-dry conditions in Tanzania’s key trading partners within Africa in the future. However, there may be decades in the 21<sup>st</sup> century projections when some countries frequently experience coinciding severe dry conditions. Based on future predictions under climate change, Tanzania’s key trading partners will also experience increases in the occurrence of severe dry conditions as greenhouse gas concentrations rise in the 21<sup>st</sup> century. Tanzania is likely to experience non-dry years in most of the years in which key trading partners experience severe dry conditions in the 21<sup>st</sup> century, including 58% of dry years per decade for South Africa and Zimbabwe, and about 90%of dry years per decade for Australia and Mexico, respectively. These results suggest that Tanzania could benefit from exporting grains to countries within and outside of Africa as climate change increases the likelihood of severe precipitation deficits in other countries while simultaneously decreasing the likelihood of severe precipitation deficits in Tanzania.

As demonstrated by the case of maize in this paper, the Tanzanian economy has the potential to capitalize on the increasing heterogeneity of climate impacts on agriculture in the future. However, such benefits will only arise if the trade policy environment changes. In the past, inconsistent application of export bans may have prevented Tanzania from capitalizing on historical export expansion opportunities. The World Bank (2009) also points out that export bans and other trade restrictions negatively affect private sector development
use and physical activity data collection hold promise of significantly elevating the predictive capabilities of the underlying models. Based on these results, we offer a few recommendations for the design of future data collection campaigns and suggest some priorities for future research to advance the agenda toward scalable adoption of these techniques for the integrated use of sensor data and time use survey data.

First, to reduce the possibility of misclassification it will be important to revisit the activity lists in time use modules such that the activities with broad definitions (‘Social, religious’ activities for instance) are split into multiple subcategories. Not only would this avoid ambiguity in reporting social and religious activities, thereby reducing misreporting of activities, but it would also improve the prediction of other activities. Inclusion of more disaggregated activity lists in recall-based time use modules are relatively costless exercises - since the interviewers aim to elicit respondent narratives and classify the activities in accordance with the narrative, rather than asking whether each respondent performed each potential activity in a list for a given time interval. The development of a more disaggregated activity list, however, may involve an initial investment and subsequent refinements, based on qualitative work and additional piloting. For instance, the current International Classification of Activities for TimeUse Statistics (ICATUS) is updated as of 2016 - comprising 165 activity groups, classified into 56 divisions and 9 major divisions – and still represents a manageable number of categories (UN Women, 2021). By comparison, the activity list used in this study included only 26 activity groups.

Second, a key source of misclassification, respondents’ recall, can be averted only by stepping away from recall-based time use surveys. Mobile phone apps that allow respondents to log activities in real time, particularly those with activity illustrations that are fit for low-literacy settings, offer promise to reduce recall bias (Daum et al., 2019; Kilic et al., 2024). If implemented well, mobile phone-based time use diaries, coupled with higher-frequency physical activity tracking data collection (i.e.,
contrasting findings. There is, however, evidence that adjustments happen and more investigation is needed to better explain the different patterns observed across different countries.

This work contributes to the literature on pricing behavior in an effort to analyze how exporters in small Least Developed Countries (LDCs) – normally considered price takers - behave with respect to pricing decisions. This paper’s analysis benefits from a novel, firm-level dataset from Tanzanian exporters<sup>2</sup> . By accessing this disaggregated information, this paper is able to examine diverse aspects of pricing behavior across both firms and destinations. The analysis contained herein benefits from the methodology adopted by Manova and Zhang (2012) and corroborates most of their findings.

Specifically, firms setting a higher export price earn greater revenue and sell lower quantities within a destination for a given product. Moreover, exporters that serve multiple destinations seem to have a higher dispersion of prices across markets, indicating that firms charge different prices if they serve multiple destinations. These results represent preliminary evidence favoring quality competition models. The main difference from Manova and Zhang (2012) relates to the result that homogeneous - rather than differentiated - goods lead price differentiation across destinations. This stands in contrast to the expectations and findings of previous studies. However, most of the studies that work to identify evidence of price differentiation examine middle- to high-income countries and focus mainly on the manufacturing sector. By contrast, Tanzania is a LDC, and the analysis in this paper includes all products, rather than just manufacturing.

In Tanzania, as in most Sub-Saharan African countries, the agriculture sector remains the main source of employment and constitutes one-third of Tanzania’s GDP. Tanzania does, however, have a comparative advantage in agricultural goods and, over the years, some donor-driven projects have been successful in developing technologies to add value in agricultural production<sup>3</sup> . For these reasons, the finding that price differentiation relates to homogeneous goods might indicate that the country’s comparative advantage in agricultural
Policy Research Working Paper 7789

# **Abstract**

Analysis of household expenditure surveys since 2008 in 22 Sub-Saharan African countries shows that one-third of all people use electricity. As expected, users are disproportionately urban and rich. In communities with access to electricity, lack of affordability is the greatest barrier to household connection. Lifeline rates enabling the poor to use grid electricity vary in availability, with six countries allowing 30 kilowatt-hours or less of electricity usage a month at low prices. Affordability challenges are aggravated by sharing of meters by several households—denying them access to lifeline rates—and high connection costs in many countries, made worse by demands from utility staff for bribes in some countries. Collection of detailed information on residential schedules enabled calculation of the percentage of total household expenditures needed for electricity at the subsistence and other levels. Affordability varied across countries, with grid electricity even at the subsistence level being out of reach for the poor in half the countries and even more so once connection charges are considered. Examination of the gender of the head of household shows that female-headed households are not disadvantaged in electricity use once income and the place of residence (urban or rural) are taken into account. However, female-headed - households tend to be poorer, making it all the more impor tant to focus on helping the poor for the goal of achieving universal access. Installing individual meters and subsidizing installation, encouraging prepaid metering so as to avoid disconnection and reconnection charges, reformulating lifeline blocks and rates as appropriate, and stamping out corruption to eliminate bribe-taking can all help the poor.

This paper is a product of the Energy and Extractives Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at mkojima@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of
identify a long-term relation between firm and sovereign ratings, although a short-term relation is still identified. As we will see below, this contrasts with what we find for the bank panel. Although we don't have a fully satisfactory explanation for this lack of a long-term relation between firm and sovereign ratings, we detect that the paucity of observations for NHICs' firms is more pronounced

_Ferri, Liu, and Mcajnoni, "The Role of Rating Agency Assessments_ **_in_** _Less Developed Countries: Impact of the Proposed Basel Guidelines "_
_Table 1.04 Extensive Regression Analysis with Additional Bureaucratic Control Mechanisms (High-Income Democracy Sub-Sample)_

||<br>|**Corruption Perceptions Index**<br>||
|---|---|---|---|
||**(1)**<br>**(GLM)**|**(2)**<br>**(GLM)**|**(3)**<br>**(GLM)**|
||**Additive**|**Additive**|**Factor**|
|**Higher EDD/NEDD***<br>**DEM**|||-0.60*<br>(0.33)|
|**Higher EDD/NEDD Ratio**|-0.50***<br> (0.06)|-0.46***<br>(0.10)|-0.30***<br>(0.09)|
|**High-Income Democracies**|||-0.82**<br>(0.27)|
|**Oversight Index**|-0.30**<br>(0.10)|-0.23*<br>(0.12)||
|**Government Effectiveness**||-1.02***<br>(0.09)|-1.00***<br>(0.05)|
|**Coordinated Market Economies**|-0.33<br>(0.48)|-0.34<br>(0.39)|-0.17<br>(0.35)|
|**Standard**<br>
household expenses, a bad management practice, are disproportionately low productivity, cutting across all three firm types.

**Figure 20: Profile of Urban Informal Firms by Financial Services and Management**

<!-- Start of picture text --> Low-educated Self-employed High-educated Self-employed Employers<br>1 1.0 1.0<br>0.8 0.8 0.8<br>0.6 0.6 0.6<br>0.4 0.4 0.4<br>0.2 0.2 0.2<br>0 0.0 0.0<br>accountBank Mixed w/ HHexpenses Uses bankinginst Mobilemoney accountBank Mixed w/ HHexpenses Uses bankinginst Mobilemoney accountBank Mixed w/ HHexpenses Uses bankinginst Mobilemoney<br>Bottom 40% Bottom 90% Top 10% Bottom 90% Top 10%<br><!-- End of picture text --> Source: Own elaboration based on IES. Notes: Cross-country averages using firm weights. The number of countries used for each variable is the same as the one indicated in previous figures.

# **7. Urban**

To assess how urban spaces, infrastructure, and services interact with the labor market, we consider which urban factors are correlated with different job types. We use the GMD to explore correlations between job type and the urban variables including access to electricity, water, improved sanitation, internet, and mobile phones. The results from the GMD should be interpreted with caution since the survey presents information about public services at the respondent’s home, which is not necessarily the location of the business.<sup>62</sup> The IES, on the other hand, provides insights into which urban goods and services informal firms need and use at their workplace. Finally, we use big data to proxy the correlation between urban spaces and informal job types in urban Mozambique, including poverty, urban density and cell tower density. Again, the urban