rafmacalaba/data-use-mentions-extended huggingface dataset:
# **References**

- Abraham, R., & Shrivastava, A. (2022). How Comparable are India’s labour market surveys?. _The Indian Journal of Labour Economics_ , 65(2), 321-346.

- <mark>Ambler, K., Herskowitz, S., & Maredia, M. K. (2021). Are we done yet? Response fatigue and rural livelihoods.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>153</mark>_ <mark>, 102736.</mark>

- <mark>Arthi, V., Beegle, K., De Weerdt, J., & Palacios-López, A. (2018). Not your average job: Measuring farm labor in Tanzania.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>130</mark>_ <mark>, 160-172.</mark>

- <mark>Bardasi, E., Beegle, K., Dillon, A., & Serneels, P. (2011). Do labor statistics depend on how and to whom the questions are asked? Results from a survey experiment in Tanzania.</mark> _<mark>The World Bank Economic Review</mark>_ <mark>,</mark> _<mark>25</mark>_ <mark>(3), 418-447.</mark>

- <mark>Blattman, C., Green, D. P., Ortega, D., & Tobón, S. (2021). Place-based interventions at scale: The direct and spillover effects of policing and city services on crime. Journal of the European Economic Association, 19(4), 2022-2051.</mark>

- <mark>Bruhn, M
vestment in particular health programmes. The other is more general investigations of the processes affectins, and determinants of, health. Both fields of investigation are beset wita methodological problems and will be extremely demanding of the skills and ingenuity of the investigators. It is in the nature of such issues that no complete solutions exist. Nevertheless the WHS will only produce methodologically adequate studies and effective progress if considerable resources are committed to this final stage of the programme. The analysis of many surveys is more or less abandoned bv hard pressed organisations with responsibilities for other work once the initial report has been produced. Thus the central secretariat and outside researchers need to be involved. In addition to directly funding research, a great deal can be accomplished by rapid production of documented files for secondary analysis, organisation of meetings and active prograimes for the publication and dissemination of information. It should be noted, however, that some countries regard health as sensitive and confidential.

# 6. CONCLUSIONS

- (1) A series of coordinated country health studies, producing population-bas **e** stimates d of health and its determinants, has a valuable role to play in some countries as one aspect of efforts being made to imprwve health information systems in the developing world.

- (2) Improvement of methodology for assessing the health impact of interventions is of vital importance but data collection should probably focus on 'ad hoc' enquiries conducted in favourable circumstances. General-purpose health surveys are of limited use for evaluating impact but could provide information required to evaluate the cost effectiveness of sc:vice provision. Moreover a programme of surveys repeated at intervals of several years could form a focus for an overall assessment of progress being made in the health sector.

- (3) The main value of a WHS would arise from the establishment of a central secretariat accumulating expertise in the design, con-

44
## _Heterogeneity across Countries_

Next, we investigate whether the elasticity depends on the income level and the economic structure. To do so, we estimate the elasticity separately for countries with below and above median GDP per capita, share of agriculture, share of industry, and share of services. Table 5 shows the different relationships. First and foremost, we only find modest differences between the relationships among the different groups. However, with fewer observations in the different groups, the elasticity is not statistically significant even at the 10 percent level in some cases. Somewhat surprisingly, the elasticity is statistically significant at the 5 percent level for the poorer countries with a higher share of agriculture (and a lower share of industry and services). This could reflect the better measurement of nighttime lights in low-lit areas with VIIRS compared with DMSP-OLS sensors and suggests that VIIRS data offer great opportunities for economic analysis even in the least developed countries and regions.

Table 5: Elasticities between Nighttime Lights and GDP, by Economic Structure

||||Va|riance of ni|ght light|growth:|var(_z_)|||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||EMDEs|GDP p|er capita|Agricultu|re share|Indust|ry share|Service|s share|
|||Below|Above|Below|Above|Below|Above|Below|Above|
|cov(_y, z_)|1.55***|1.52**|1.61|1.45|1.52**|1.66**|1.36|1.36
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 18a. Electricity is the largest obstacle to doing Figure 18b. High reliance on generators for power business in Sudan**

<!-- Start of picture text --> 70 120<br>60 100<br>50 80<br>40 60<br>30 40<br>20 20<br>10 0<br>0<br>North river Nile Khartoum Gezira Nyala Red Sea<br>Kordofan<br>% of firms that own generator % of power from generator<br>Gazira Khartoum River Nile North Kordofan Red Sea Nyala<br>Perception that electricity is the biggest onstable to doing business (% of firms)<br><!-- End of picture text --> _Source:_ Derived from World Bank (2009b).

**Figure 19. A small share of Sudan’s power consumption is for industrial activities**

<!-- Start of picture text --> 6000<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>2004 2005 2006 2007 2008<br>Agriculture (pumps) consumption Industrial consumption<br>Government consumption Commerical consumption<br>Domestic consumption Electricity Generated<br>GWh<br><!-- End of picture text --> _Source:_ Based on statistical handbooks produced by the Central Bureau of Statistics and on data reported by the NEC, which are assumed to reflect the situation in Sudan.

Power prices are toward the lower-middle end of the range observed in Sub-Saharan Africa. At $0.09 per kilowatt-hour (kWh), the price is close to the average of countries predominantly reliant on hydropower. Nevertheless, given that thermal power has been dominant until quite recently and continues to play a significant role, tariffs look low relative to historic costs of around $0.19/kWh (figure 20).

The
from units that _change_ migration status; households that happen to have a migrant member in all time periods in the data, or no migrant members in any time period, do not contribute directly to the estimates.<sup>7</sup> The fact that identification is now coming from units that change migration status raises three important questions. The first is how many units actually change status? As we have discussed, migration is typically a rare event, and so over a short panel, most households may not change their migration status. For example, Chen et al. (2009) use a panel of 1,358 Chinese households between 2002 and 2006, and find that only 220 (16%) had

> 6Funkhouser (2013) provides an introduction to the use of longitudinal data in studying migration and remittances in the edition of this Handbook.

> 7Households that never have changes in whether they have a migrant member may still affect coefficient estimates _indirectly_ by contributing to other coefficient estimates in a regression equation, such as timeperiod fixed effects.

17
**Table 3. Comparison between the analyzed sample and the Emergency Events database**

|||Proportion<br>in the<br>sample|EMDAT<br>proportion|Difference|P value|
|---|---|---|---|---|---|
|Type of shock|Drought|0.0637|0.0405|0.023***|<0.01|
||Flood|0.0306|0.4060|-0.375***|<0.01|
||Heat|0.1201|0.0194|0.101|0.040|
|Country or region|South Asia|0.1765|0.1055|0.071***|<0.01|
||Sub-Saharan Africa|0.1826|0.1942|-0.012|0.447|
||East Asia and Pacific|0.1409|0.2294|-0.088***|<0.01|
||Europe and Central Asia|0.0380|0.0807|-0.043***|<0.01|
||Latin America and the Caribbean|0.0980|0.1621|-0.064***|<0.01|
||Middle East and North Africa|0.0331|0.0475|-0.014|0.072|

_Note:_ CCDR = _Country Climate and Development Report._ EMDAT = Emergency Events database. It was last accessed on November 28, 2023. Proportion in the sample refers to the share of 70 studies in the sample that analyzed the specific shock or region.

17
# **1. Introduction**

Fiscal policy can be a powerful tool to stabilize the economy, but it represents a double-edged sword. When it operates procyclically, that is, when public expenditure and the fiscal deficit expand during good times or when it cuts public expenditures and reduces the fiscal deficit in bad times (“austerity”), it amplifies the economy’s business cycle, intensifying both economic upswings and downturns. In this way, procyclical fiscal policy can undermine macroeconomic stability as well as long-term growth. Conversely, when fiscal policy runs counter to the business cycle, it helps smooth it, therefore, contributing to macroeconomic stability and long-term growth, which depends on macro stability. The key to implementing countercyclical fiscal policy, however, is understanding its past trends and precise sources of cyclicality so that effective policy reform can be implemented towards greater countercyclicality which contributes to macroeconomic stability and growth.

This paper makes thee contributions to the empirical literature on cyclicality of fiscal policy in general and, specifically, within the Middle East and North Africa (MENA) region. First, using the most recent and largest database covering 184 countries for the period 2000-2022, we reassess the overall direction, intensity, and sources of cyclicality of fiscal policy across the world, with particular focus on the MENA region; we provide a more granular view of subcomponents of expenditures and revenues that contribute to the overall cyclicality than has been the case in the previous literature, which often relied on a single variable of fiscal policy to measure its cyclicality. Second, we pay particular attention to identifying country cases that managed to escape the “fiscal procyclicality trap”, moving fiscal policy management in the desirable direction. Third, we draw implications of the analysis and thoughts on how governments can make their fiscal policies more countercyclical, contributing to macroeconomic stability and growth.

# **2. Related literature and contribution of this paper**

Based on both theoretical insights and empirical evidence, considerable economic policy research advocates for a countercyclical fiscal policy, urging policy
Dupas, P., and J. Robinson. 2009. “Savings Constraint and Microenterprise Development: Evidence from a Field Experiment in Kenya.” NBER Working Paper 14693, National Bureau of Economic Research, Cambridge, MA.

- ———. 2011. “Why Don’t the Poor Save More? Evidence from Health Savings Experiments.” NBER Working Paper 17255, National Bureau of Economic Research, Cambridge, MA.

- EBRD (European Bank for Reconstruction and Development). 2011. _Life in Transition: After the Crisis._ http://www.ebrd.com.

- FATF (Financial Action Task Force). 2011. _FATF Guidance on Anti-Money Laundering and Terrorist Financing Measures and Financial Inclusion._ FATF/OECD: Paris.

- FDIC (Federal Deposit Insurance Corporation). 2009. “FDIC National Survey of Unbanked and Underbanked Households.” http://www.fdic.gov/householdsurvey/.

- Hallward-Driemeier, M., and T. Hasan. Forthcoming. _Empowering Women: Legal Rights and Economic Opportunities in Africa_ . Development Forum Series. Paris: Agence Française de Développement; Washington, DC: World Bank.

- Honohan, P. 2008. “Cross-Country Variation in Household Access to Financial Services.” _Journal of Banking and Finance_ 32: 2493–500.

- IMF (International Monetary Fund). 2011 _. Global Financial Stability Report: Grappling with Crisis Legacies, September 2011._ Washington, DC: IMF.

- Jack, W., and T. Suri. 2011. “Mobile Money: The Economics of M-PESA.” NBER Working Paper 16721, National Bureau of Economic Research, Cambridge, MA.

- Johnston, D., and J. Morduch. 2008. “The Unbanked: Evidence from Indonesia.” _World Bank Economic Review_ 22 (3): 517–37.

- King, R. G., and R. Levine. 1993. “Finance and Growth: Schumpeter Might Be Right.” _Quarterly Journal of
# **Figure 23. SSA: Investment needs**

_Sub-Saharan Africa’s investment needs are high across a wide range of sectors. There has been progress in improving infrastructure in the region, but progress has been slow, especially in energy and transport._

# **A. Total infrastructure spending needs**

# **B. International perspective on Africa's infrastructure deficit**

<!-- Start of picture text --> US$, billions Normalized units SSA middle-income countries<br>Other middle-income countries<br>100<br>SSA low-income countries<br>Operation and maintenance 1,500 Other low-income countries<br>80 Capital expenditure 1,200<br>900<br>600<br>60<br>300<br>40 0<br>20<br>0<br>Irrigation ICT Transport WSS Power Total<br>C. Selected health care indicators D. Selected education indicators<br>Per capita Percent of population Percent of per capita<br>5,000 120 income, ratio<br>4,000 100 50 RangeAE<br>80 40 EMDEs<br>3,000 SSA<br>60<br>2,000 Range 30<br>AE 40<br>1,000 EMDE 20 20<br>SSA<br>0 0 10<br>Health Improved Improved<br>expenditure sanitation water 0<br>(LHS) (RHS) source Government Pupil-teacher ratio<br>(RHS) expenditure<br>Paved-road density Total road density Main-line density Mobile density Internet density Generation capacity Electricity coverage Improved water Improved sanitation<br><!-- End of picture text --> Source: Haver Analytics; Pierce, and Foster 2008; Regional Economic Outlook, International Monetary Fund; World Bank; Yepes (2008).

A. ICT=information and communication technology; WSS=water supply and sanitation. Estimates by Foster and Briceno-Garmendia (
of setting a vulnerability line at some multiple of the poverty line—has been used in World Bank regional reports and poverty assessments in East Asia and the Pacific (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), Uganda, Tanzania, Viet Nam, Mongolia, Cambodia, Brazil, Türkiye and others. The multiples used range from 1.25 to 2.5 times the poverty line.<sup>15</sup> In the context of the United States, several studies have considered the threshold between 1.25 and 2 times the Federal Poverty Guidelines to identify those who are poor or near poor including Montgomery et al. (1996), Heggeness and Hokayem (2013), Hair et al. (2015), Saczewska-Piotrowska (2016), Dube (2019). The appropriate multiple will depend on the context, but most studies fall within the 1.5-2 range. In this paper we use 1.5 times the poverty line. As the next section indicates for many countries this is estimated using data on consumption rather than income, as this is the aggregate available.

The second dimension of inability to cope is educational attainment. Research has consistently shown that households with higher level of education have better understanding and ability to process risk information such as weather forecasts and early warnings (Mileti and Sorensen, 1990; Hoffmann and Muttarak, 2017). More educated individuals are also likely to assess and respond to risks more effectively, thereby being better prepared to cope with natural disasters and weather shocks (Helgeson, Dietz, and Hochrainer-Stigler, 2013; Muttarak and Pothisiri, 2013; Muttarak and Lutz, 2014). Moreover, there is evidence suggesting that even the completion of primary education can enhance the ability to cope with a shock. In the Ugandan context, households with a head who has attained some primary education experience a 2.8% reduction in the negative effect of rainfall on crop income compared to those without

> 15 Recent poverty
liability (=max(output VAT - input VAT, 0)). We use annual data for our main analysis and later show robustness of our results using monthly data. This is because firms outside the large – – taxpayer unit report taxable sales a key outcome variable only annually, and they report output VAT and net liability monthly but retrospectively at the end of each year.<sup>21</sup> In our preferred specifications, we winsorize the outcome variables at the 99th percentile within each treatment group _×_ year, and we confirm robustness of the results to alternative top-coding approaches.

# **5.2 Results**

Our main DiD results are shown in Figure 3. Each column pertains to a different outcome variable. In the top row, we show the normalized trends over time in the treatment and control group, and the DiD point estimate _β_<sup>ˆ</sup> on the _Retaileri · PostReformt_ interaction from equation 2. In the bottom row, we plot the period-specific _βk_ estimates from Equation 3 to confirm that we cannot reject the parallel trends assumption.

If the expansion of electronic transactions triggered an improvement in tax compliance, it should first manifest through an increase in reported taxable sales. However, we observe parallel trends in this outcome and hardly any divergence between the treatment and the control group. We estimate that taxable sales in the treatment group increased only by an additional 3.2 percent after the reform, compared to the control group, an effect which is statistically indistinguishable from zero (Figure 3, column A). The fact that reported sales do not change differentially in the treatment group after the reform, and that the statutory VAT rates did not change, would imply that the output VAT remitted should also be unchanged. Indeed, we find that the DiD point estimate on reported output VAT is also close to zero (-2.6 percent), which is again statistically indistinguishable from zero (column B). The fact that the consumer
constructed from the Penn World Tables as the weighted average of PPP converted log-private consumption, log-gross investment and log-government consumption, using as weights their respective shares of value added in the reference country; as in the within case, we use shares that are averaged across the twenty years in our sample.

To construct the modi...ed log-productivity residual in each country, we subtract from this measure of value added the amount of capital and the amount of labor used to produce it, weighted by their respective share of compensation in the reference country, also in this case kept constant at their average value. The stock of capital in the economy is constructed using the perpetual inventory method on the PPP converted investment time series from the Penn World Tables. The stock of labor is computed in two di¤erent ways using the EU-KLEMS dataset. In our benchmark speci...cation, the amount of labor services used in the economy is approximated by the hours worked. In the alternative speci...cation, it is computed by aggregating over di¤erent types of persons engaged using a translog function, where weights are given by the shares of compensation to each type of labor out of total compensation to labor and are kept constant at their average value in the reference country.

Finally, to correct our welfare calculations for the presence of distortionary taxation, we use data on average tax rates on capital and labor provided by Boscá et al. (2005). The tax rates are computed by combining realized tax revenues, from the OECD Revenue Statistics, with estimates of the associated tax bases derived from the OECD National Accounts. These data update the tax

> 17 The EU-KLEMS data are extensively documented by O’Mahony and Timmer (2009).

25
COVID-19 crisis for El Salvador and Georgia, respectively. Details about the information contained in COV-ES data about government support are given in Section 4.

# **3. Methods**

To estimate the effect of the government support, consider the following specification: ′ oo,ii ii ii ii where is an outcome variable of firm YY = θθSS, is a dummy variable indicating whether the firm + ββXX + εε received any local or national government support, and oo is the set of observed characteristics of YY ii SS ii firm . This specification is not suitable for consistent estimation of for two main reasons. First, XX for most of the support measures, firms need to go through an application process to get it or take ii θθ, some action for this purpose. This creates selection bias. The selection bias will be positive if going through the application process to obtain the support is complex, and only better-performing firms can accomplish it. In this case, the set of firms that obtained support may have inherent features that both made them obtain support and perform better in terms of outcome variable of interest. Consequently, the estimate of would be higher than the true effect of policy (positive selection bias). ii θθ SS In the context of the effectiveness of government support during the crisis, the selection bias can also be negative. It is expected that the firms that were more adversely affected by the pandemic, e.g., lost a larger share of their revenues or experienced deeper drop in liquidity, would try harder to obtain support from the government by working harder to make themselves aware of its existence and trying their best to go through the steps needed to obtain it. While support itself may have eligibility criteria such as firm size, even within the same sized firms, those hit harder are perhaps more likely to obtain it. There is also an element of solidarity contributing to this selection bias. In particular, the firms that are eligible for support may abstain from obtaining it if they do
**Table 1: Definition of the High Technology Aggregate**

|**SITC Codea**|**Description**|
|---|---|
|513|Inorganic Elements|
|514|Other Inorganic Chemicals|
|_515_|Radioactive Materials|
|533.1|Coloring Materials|
|541|Medicinal Products Excluding Pharmaceuticals|
|541.9|Pharmaceutical Goods|
|561.3|Potassic Fertilizers|
|571.2|Fuses and Detonators|
|571.4|Hunting and Sporting Ammunition|
|581.1|Plastics and Products of Condensation|
|581.2|Products of Polymerization|
|651.6|Synthetic Fibers|
|651.7|Yarn and Artificial Fibers|
|711.3|Steam Engines|
|711.4|Aircraft Engines|
|711.5|Internal Combustion Engines|
|711.6|Gas Turbines|
|711.8|Engines, nes|
|714|Office Machinery|
|724|Telecommunications Apparatus|
|729.3|Transistors, Photocell, etc.|
|729.7|Electron Accelerators|
|729.9|Electrical Machinery and Apparatus|
|734|Aircraft|
|861|Scientific Instruments|
|862|Photographic Supplies|
|891.1|Tape Recorders|
|891.2|Recorders of Sound|
|894.3|Nonmilitary Arms|
|899.6|Orthopedic Appliances|

' Based on SITC Revision I classification.

Source: Primo Braga and Yeats (1992).

13
this stage, we do not take these ‡ows into account<sup>5</sup> .

We collected data on the immigration structure by educational attainment and country of birth from all OECD receiving countries. Census and register data are (or will be) available for about 25 countries in 2000 (out of 30 OECD countries). To allow comparison between 1990 and 2000, we consider the same number of receiving countries in 1990 (even if six of them were not members of the OECD). Census data are (or will be) available for about 18 countries in 1990. Survey data and reasonable assumptions can be used to evaluate the structure of immigration in the remaining countries.

In this release of the database, we also use survey data for countries in which the Census is not yet available in the desired format (i.e. for 12 European countries). At this stage, we estimate that we are able to characterize the country of birth and the educational attainment of 92.7 percent of the OECD stock of adult immigrants in 4 OCDE statistics su¤er from various limitations (see OCDE, 2002) . For example, they only provide information on the country of origin for the the main sending countries. In 2000, they are not avalaible for Greece, Iceland, Mexico, Slovakia, Turkey.

> 5According to the United Nations (2002), migration to developed countries represented 53 percent of the world migration in 1990 and 60 percent in 2000. High-skill migration is even more concentrated. In this study, we estimate that about 85 percent of high-skill migrants go to OECD countries.

5