rafmacalaba/data-use-mentions-extended huggingface dataset:
mobility remained. In red zones, industrial establishments in urban areas remained prohibited from operating, except for those in Special Economic Zones and industrial estates/townships with access control. And while private offices could operate again even in red zones, a maximum of a third of the employees could be physically present in the office at the same time. Finally, construction remained mostly prohibited in red zones. On May 17, the lockdown was again extended but new relaxations were announced. For the first time, states were given authority to determine the specifics of the lockdown. In addition, two new zones (containment and buffer) were added to the red, orange, and green zones.

The national lockdown enacted by the Indian authorities was successful in limiting mobility. Figure 1.a uses the Google Mobility Reports for India (Google 2020) to show how mobility declined after the lockdown was enacted. This data is based on tracking smartphones, which in India have a coverage of 27.7 percent (Newzoo 2018). While this means that not everyone 4
to the firm. However, the role of the services sector as one of the external factors in promoting exports of downstream sectors has not been studied. The existing research on the consequences of services sector liberalization is limited mainly to the analysis of the impact of this sector on the on the productivity in downstream industries. (See the above literature summary.) Berulava helps to fill this void. He uses a firm level dataset to explore the impact of services inputs on export performance of manufacturing firms in transition economies.

His principal data source is a firm-level unbalanced panel dataset from the ―BEEPS‖ surveys.<sup>11</sup> The surveys were conducted by the European Bank for Reconstruction and Development (EBRD) and the World Bank in 2002, 2005, 2007, and 2008/09 for firms in 29 countries in the Europe and Central Asian region. Since his objective is the export performance of manufacturing firms, he limits the sample to only the manufacturing sector. He is able to use 11,293 observations on individual firms.

Berulava employs two measures of services sector performance. The first, based on the BEEPS surveys, is the subjective measure of individual manufacturers as to how much of an obstacle for their business they consider the performance of the following three service sectors: electricity, telecommunications and finance. The second measure he used was the EBRD policy reform indices, which reflect the overall liberalization of services sectors.

To deal with selection bias problems, Berulava employed a two-stage estimation process. First, he formulated a model for the probability of exporting and used the predicted individual probabilities as an additional explanatory variable in the second stage panel regression equation. To address the problem of endogeneity of the services input variables, the second stage panel regression for export intensity was estimated by applying the Hausman-Taylor IV estimation

> 11 These are the enterprise survey panels known as the Business Environment and Enterprise Performance Surveys (BEEPS) available at: https://www.enterprisesurveys.org/

15
Pooling multiple years of data allows us to follow each cohort as they age and measure the true rate of net worth accumulation. As an example, the three triangles in Figure 1 identify household heads born in 1960, whose net worth we observe at ages 41, 50 and 58. It appears clearly that when the 1960 cohort reaches age 58 (brown triangle), its average net worth is much higher than that of a 58 year-old in 2001. Therefore, the line that goes through the three data points corresponding to the 1960 cohort is much steeper than the crosssectional profiles: over 20 years, the average net worth for that cohort increases by nearly 40 months of consumption. In other words, considering household net worth by age in a specific year significantly understates the rate at which households accumulate net worth over time. In the example from Figure 1, the rate would look two times slower.

_Figure 1 Household net worth by age in 2001, 2010 and 2018_ <!-- Start of picture text --> 140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65<br>2001 2010 2018<br> Note: triangles denote the average household net worth for the cohort born in 1960<br>months of household consumption<br><!-- End of picture text --> To measure the rate of wealth accumulation experienced by households over their life cycle, net of differences across cohorts and years, we apply the decomposition methodology by Deaton (1997) after pooling 8 rounds of the PSLM spanning 18 years of data (2001-2018). For each of the variables of interest (measures of wealth, consumption, and income), we run a decomposition into age, cohort, and year effects. We estimate the following model using simple OLS regression, after
Policy Research Working Paper 9287

# **Abstract**

Using a new dataset of 207 national elections across 50 democracies, this paper is the first to systematically examine the effects of oil price shocks on incumbents’ political fortunes in developed oil-importing countries. We find that oil price increases systematically lower the odds of reelection for incumbents and increase the likelihood of changes in the ideology of the incoming government. These shocks are found to operate through lowering consumption growth.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rarezki@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 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._ Produced by the Research Support Team
coefficients determining demand for _I G_ different types of investment goods (  _it_ ), and different types of government purchases (  _it_ ), are projected identically.

The import and export elasticities are set to the values in GTAP v4. The base share of exports and imports are taken from the SAM.

> 25 We have chosen to use U.S. patterns in our projections of these exogenous parameters because they seem to be a reasonable anchor.  While it is unlikely that China’s economy in 40 years time will mirror the U.S. economy of 1997, it is also unlikely to closely resemble any other economy.  Other projections, such as those by the World Bank (1994), use the input‐output tables of developed countries including the U.S.

> 26 China Energy Statistics Yearbook 1997‐1999, Tables 4‐5 and 4‐15.

‐82‐
individual level fiscal incidence analysis using a combination of intrahousehold allocation mechanisms.

We combine data from the 2018/19 Ethiopian Socioeconomic Survey (ESS) with administrative data to conduct a gendered fiscal incidence analysis using the Commitment to Equity (CEQ) methodology. We analyze the gender-differentiated distributional impacts of various taxes and transfers focusing on progressivity, inequality, poverty, and pro-poorness.

The results show that, while the fiscal system is progressive, equalizing, and poverty-reducing, some of its elements promote gender equality better than others. On _progressivity_ , we find that PIT, PSNP transfers, kerosene subsidies, primary and secondary education, and health spending are progressive and have no gender differences. Agricultural income tax, informal taxes, wheat subsidies, and tertiary education are regressive but also have no considerable gender differences. In terms of the _pro-poorness of transfers and subsidies_ , only PSNP transfers and primary education are found to be pro-poor. While PSNP is pro-poor for both genders, primary education is pro-poor only for boys. All other transfers, including wheat and kerosene subsidies, secondary and tertiary education, and health spending are not pro-poor.

The impact of fiscal actions on _inequality_ is mixed. For instance, direct taxes are more equalizing for men and direct transfers more equalizing for women. However, agricultural income tax and informal taxes are not equalizing for either men or women; nor are VAT and excise taxes and wheat subsidies equalizing. For both genders, primary education is equalizing, and secondary and tertiary education are not. Health spending is more equalizing for women.

There are gender differences in the _poverty_ effect of some taxes and transfers. Both direct and indirect taxes increase poverty at different rates. For example, the poverty increasing effect of informal taxes and VAT is higher for women than men. Excise tax increases poverty more for men. The poverty reduction effect due to subsidies and transfers is mixed. Spending on PSNP, primary and secondary education, and health reduced poverty more for women than men. There are no gender differences in the poverty-reducing effect of
br>Ln (# of peple in|[0.015]|[0.004]|[0.006]|[0.003]|[0.006]|[0.006]|
|HH)|-0.026***|-0.018***|0.004**|0.003***|0.002|0.020***|
||[0.004]|[0.001]|[0.002]|[0.001]|[0.002]|[0.002]|
|Observations|7,919|7,919|7,919|7,919|7,919|7,919|
|||Fuel and||Food and|Medical||
|Wald test|Food|lighting|Transportation|clothing|care|Education|
|Theta=0|934.7c|108.5c|484.5c|129.1c|51.7c|644.1c|
|Theta=0.2|892.7c|129.4c|473.5c|113.2c|46.4c|608.3c|
|Theta=0.4|963.8c|139.2c|467.6c|82.2c|45.6c|743c|
|Theta=0.5|1067.8c|104.6c|485.6c|99.6c|55.1c|859.5c|
|Theta=0.6|1095.6c|84.3c|496.3c|118.5c|60c|872.5c|
|Theta=0.8|1081.8c|75.9c|501c|132.9c|61.6c|835.6c|
|Theta=1|1065.9c|76c|501c|136.8c|61.3c|809.
& Chalmers, C. (2021). Energy sobriety: a behaviour measurement indicator for fuel poverty using aggregated load readings from smart meters. In Towards Energy Smart Homes: Algorithms, Technologies, and Applications (pp. 21-47). Cham: Springer International Publishing.

Frei et al. (2021). Front. Built Environ., 10 February 2021 Sec. Sustainable Design and Construction Volume 6 - 2020 | https://doi.org/10.3389/fbuil.2020.609877

Freund, C. L., & Wallich, C. I. (1996). The welfare effects of raising household energy prices in Poland. The Energy Journal, 17(1).

Goldblatt, R., Hanson, G., Heilmann, K., & Khandelwal, A. (2016). What's the matter with nightlights? Using Landsat imagery to improve nightlight-based measures of local economic activity with an application to India (pp. 1–18).González-Eguino, M. (2015). Energy poverty: An overview. Renewable and Sustainable Energy Reviews, 47, 377-385. https://doi.org/10.1016/j.rser.2015.03.013 <mark>Gouveia et al. (2022). Energy Poverty. National Indicators. Insights for a more effective measuring. Link:</mark> <u><mark>https://energy-poverty.ec.europa.eu/system/files/202301/EPAH_Energy%20Poverty%20National%20Indicators%20Report_0.pdf</mark></u> <mark>Government of the UK. English Housing Survey. Link:</mark> <u><mark>https://www.gov.uk/government/collections/english-housing-survey</mark></u> Halkos, G. E., & Gkampoura, E. C. (2021). Coping with energy poverty: Measurements, drivers, impacts,
water), and _x_ the set of circumstances (for example, gender of the child, education, gender of the head of the household, and the like). One can fit the logistic regression using survey data: where _xk_ denotes the row vector of variables representing the _k_ dimension of circumstances, hence, _x_ ( _x_ 1,..., _xm_ ) and  (1,..., _m_ ) a corresponding column vector of parameters. From the estimation of this logistic regression, ˆ one obtains estimates of the parameters  _k_ to be denoted by  _k_ , _n_  where _n_ denotes the sample size.

Given the estimated coefficients, one can obtain for each individual in the sample his or her predicted probability of access to the opportunity in consideration: ˆ _p_ ˆ _i_ , _n_  1  _ExExpp_ <u></u> _x_ <u></u> _<u>ix</u>_ <u></u> _i_  _<u>n</u>_ ˆ _n_ <u></u> .

Finally, compute the overall coverage rate, _C_ ; the D-Index; the penalty, _P_ ; and the HOI using the predicted ˆ probability _p_ and sampling weights, _w_ : 8
>International Internet bandwidth (bits per second per person) International Internet bandwidth (bits per second per person)<br>Internet users<br>Internet users<br>International internet bandwidth<br>International internet bandwidth<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Ethiopia Madagascar Rwanda Zambia Sudan Swaziland Uganda Kenya Mauritius<br><!-- End of picture text -->

### _Source:_ AICD.

_Note:_ COMESA = Common Market for Eastern and Southern Africa.

# Financing Uganda’s infrastructure

To meet its most pressing infrastructure needs and catch up with developing countries in other parts of the world, Uganda needs to expand its infrastructure assets in key areas (table 15). The targets outlined below are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country comparisons of the affordability of meeting the targets, which can be modified or delayed as needed to achieve financial balance.

> 3 For example the UCC reported that in June 2009, mobile accounted for 92 percent of all Internet subscriptions. See UCC (2009).

41
Policy Research Working Paper 5620

# **Abstract**

Although both domestic and foreign private banks have gained ground in MENA in recent years, state banks continue to play an important role in many countries. Using a MENA bank-level panel dataset for the period 2001–08, the paper contributes to the empirical literature by documenting recent ownership trends and assessing the role of ownership and bank performance in MENA while accounting for key bank characteristics such as size and balance sheet composition. The paper analyzes headline performance indicators as well as their key drivers and finds that state banks exhibit significantly weaker performance, despite their larger size. This result is mainly driven by a larger holding of government securities, higher costs due to larger staffing numbers, and larger loan loss provisions reflecting weaker asset quality. The results reflect both operational inefficiencies and policy mandates. The paper also provides a detailed performance analysis of foreign and listed banks. Foreign banks are fairly new in MENA, yet perform on par with domestic banks despite their smaller size and higher investment costs. Listed banks exhibit superior performance driven by higher interest margins even in the face of higher costs associated with listing. Taken together, the results do not reject the development role for state banks, but do show that their intervention comes at a cost. As such, there is scope to reduce the share of state banks in some countries and to clarify the mandates, improve the governance, and strengthen the operational efficiency of most state banks in MENA.

This paper is a product of the Financial and Private Sector Development Unit, Middle East and North Africa Region; and the Financial Systems Department, Financial and Private Sector Development. 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 efeijen@worldbank.org, sfarazi@worldbank.org and rrocha@
impact of income per adult on its own, they compute the impact of changing both the labor and nonlabor income per adult. This is done because, in principle, the sum of labor and nonlabor income should be equivalent to changing total income per adult. The results of these two simulations are different, however, and the simulation of labor income is not done explicitly, but rather ends up being a “residual” in step 8 to ensure that the cumulative effect adds up to the total distributional change.

We modify the procedure in three ways: (1) we focus on consumption as a measure of welfare; (2) we compute a cumulative counterfactual distribution by adding one variable at a time; and (3) we compute Shapley-Shorrocks estimates of each component. First, the focus on consumption is because most developing countries use a consumption aggregate to measure poverty. Second, in contrast to the Barros et al. (2006) approach, this method does not separately identify the contribution of the interaction between variables in the observed distributional changes; doing so is partial at best, given that changing any variable can potentially affect all other variables. Instead, the impact of changes in each variable and its interactions with all other variables is calculated as the difference between the cumulative counterfactuals. Box 3 shows an example for one possible path, taking into account the fact that nonlabor income is made up of pensions, transfers, capital income, and other income.

The third methodological change is to address the fact that this methodology suffers from pathdependence, as much of the micro-decomposition literature does. In other words, the order in which the cumulative effects are calculated matters.<sup>6</sup> One of the major contributions of this paper is that we apply the best known remedy for path-dependence, which is to calculate the decomposition across all possible paths, and then take the average between them following the method proposed by Azevedo, Nguyen, and Sanfelice (2012).<sup>7</sup
more precisely the effects of financial liberalization over time, which is attractive since financial reform is often a process over a longer period.

We focus on listed firms, since most firms in the World Scope sample are listed, and because the quality of the accounting data is expected to be higher for listed firms. Focusing on listed firms has the additional advantage that we can compare the performance of the two different measures of marginal profitability of capital, that is Tobin's q versus the sales-to-capital ratio. For each company we need a certain minimum coverage of the data to assess the changes in the financing structure of the firm. We set this coverage to three years and therefore delete firms with less than three consecutive years of observations. It is, however, necessary to delete more firms, because of outliers in the data. Such outliers can be explained by revaluation of assets, divestments, acquisitions, or simply poor data. We impose a number of outlier rules. First of all, we delete observations with negative fixed capital or investment. Such observations might be due to divestments or revaluations of capital. Secondly, we restrict investment ratios from taking high values. Such values might be due to acquisitions or revaluations of capital. Furthermore, we restrict variables to take extreme values in terms of leverage, marginal profitability or cash flow. We also delete firrns in transition economies, because soft budget constraints that have been inherited from the socialistic regime may distort the analysis. Table 6 gives the details of the deletion criteria. After deleting firms according to these criteria we have data on 394 listed firms in 13 countries.<sup>21</sup> Obviously, our sample of firms is non-random. Listed firms, for example, tend to be large in most countries. This non-randomness can be partly controlled for by allowing fixed effects.

For this set of firm-level data we generate the necessary variables to estimate equation (13). We assume that flow variables (such as investment and depreciation) during period t are decided
and tertiary).

## **9.3.2 Health**

**The PECS does not have information on access to public health services.** By using the health-insurance approach (focusing on individuals reporting coverage of government health insurance in the PECS), the model implicitly assumes that eligible individuals do not have significant barriers to access public health services. However, in practice, usage of health services by the eligible population could be lower, particularly in Gaza where most residents use UNRWA-provided health services. Hence, using the healthinsurance approach could overestimate the in-kind health benefits among Gaza residents. We also calculated public health benefits at the national level, assuming that benefits for each health level are the same for residents in the West Bank and Gaza. However, having public health expenditure data disaggregated by health service and region would improve calculation of health benefits.

59
with NTM avoidance schemes. Robustness checks further reveal that NTM-induced trade fraud is more common for homogeneous products, as well as for trade between developed and developing countries, as predicted by the theoretical model.

The rationale of this paper to investigate trade fraud relates to the existing literature on trade discrepancies and tari¤ avoidance. In particular, this paper follows on the “missing trade” approach initially proposed by Fisman and Wei (2004) to explain discrepancies in the statistics between Hong Kong SAR, China, and China. This approach was also used by Javorcik and Narciso (2008) whose examination of the exports statistics of Germany ...nd that discrepancies with the statistics of the importing country tend to be greater when tari¤s are higher. Similar results are found for India by Mishra, Topalova and Subramanian (2008). In addition, focusing on Chinese exports to the United States, Ferrantino, Liu and Wang (2012) show that Chinese exporters may under-report export values in order to avoid paying China’s VAT, thereby helping to explain why reported export values are lower than the corresponding reported import values. Most recently, Demir and Javorcik (2020) also show import duty evasion as a margin through which ...rms adjust to changes in trade policy in Turkey, while Liu, Sheng and Wang (2021) show that trade data over-reporting is positively correlated with the exchange rate spread in China. This paper adds to the literature by looking at NTMs as another source of costs at the borders, which may result in similar schemes employing misdeclaration and misclassi...cation with the purpose to reduce costs. In addition, unlike previous papers that mostly focused on one country’s imports or exports, this paper uses detailed trade data from a wide range of importing and exporting countries, which allow us to detect customs origin fraud and product fraud.

Importantly, this paper also contributes to the literature on assessing the e¤ect of trade policies, particularly non-tari¤ measures, on trade. In