rafmacalaba/data-use-mentions-extended huggingface dataset:
,636|5,649|6.5%|61.7%|
|Financial<br>intermediation|3,448|796|5,674|2,974|17,779|26.4%|52.4%|
|Other|5,795|3,114|18,833|8,315|22,141|21.1%|44.2%|

_Source_ : Rosstat, including tabulations in Kuznetsov (2010) for 2005 and 2009. _Note_ : Industry categories have been harmonized as feasible. "Other manufacturing" includes textiles and apparel, leather, and miscellaneous manufacturing. "Other services" includes real estate, rental, and business activities; education; health and social work; and other activities. "Quality FDI stock" is defined as total FDI minus FDI from the three main round-tripping FDI economies (Cyprus, the British Virgin Islands, and the Bahamas). Some FDI registered from other locations may also be round-tripping.

2
and Reinikka(2006)|Survey data|Uganda|Tax expenses|Turnover|Number of employees|Inverse U-shape|
|Gauthier and Gersovitz(1997)|Survey data|Cameroon|Tax expenses|Turnover|Number of employees|Inverse U-shape|
|Mascagni and Mengistu(2019)|Tax return data|Ethiopia|Income tax expense|Gross profts|Turnover|U-shape|
|Carreras et al.(2017)|Tax return data|South Africa|Tax expenses|Gross profts|Turnover|U-shape|
|Mascagni et al.(2016)|Tax return data|Rwanda|<br>Income tax expense|<br>Gross profts|Turnover|Negative|
|<br>Halleux and Valenduc(2007)|Tax return data<br>Financial data|Belgium|<br>Income tax expense|<br>Net income|Total assets<br>Value added<br>Number of employees|Depends on size measure|
|Clark(2004)|Tax return data|Canada<br>Belgium|Income tax liability|Gross profts<br>Net income|Total assets<br>Gross taxable income|Inverse U-Shape|

Note: This table summarizes the key papers studying the relationship between effective tax rates and firm size. We do not include papers that estimate effective tax rates but do not relate them to firm size. The denominators used to estimate effective tax rates in the original studies were relabeled to match one of the four concepts used in this paper: net profit (our preferred measure), operating profit, gross profit, and revenue. This table is discussed in Section 1.1.
household size. UN-DESA (2019a) estimates that countries range from slightly more than 2 people to slightly more than 8 people. These differences have clear geographic patterns with the average person living in a household with 6.9 people in Sub-Saharan Africa, compared to 3.1 in Europe (Kramer, 2020). For detailed breakdown by age, see UN-DESA (2019b). 5 The proportion of the population 0-14 years in a country is monotonically decreasing in income classification (i.e., low-income, lower middle-income, upper middle-income, high-income). World Development Indicators, <u>https://data.worldbank.org/indicator/SP.POP.0014.TO.ZS?most_recent_value _desc=false [Last accessed July 23, 2022.]</u>

5
9 Figure 3: Microeconomic channels of transmission from global food prices (A) Share of food in total consumption expenditure (B) Consumption expenditure by product of the poorest households <!-- Start of picture text --> Percent Percent EMDEs LICs<br>60 24<br>20<br>16<br>12<br>40<br>8<br>4<br>0<br>20<br>0<br>EMDEs LICs<br>(C) Share of net food sellers (D) Share of income generated by food<br>Percent Below poverty line Percent Below poverty line<br>22.5 Around poverty line 35 Around poverty line<br>30<br>22.0<br>25<br>21.5 20<br>15<br>21.0<br>10<br>20.5<br>5<br>20.0 0<br>EMDEs LICs EMDEs LICs<br>Cereals Vegetables Meat Oils and fats Seafood Other foods Sugar Fruits Bread products Milk products<br><!-- End of picture text --> Source: IFPRI, World Bank.

A. B. Based on data from the Global Consumption Database on the share of products in total household consumption expenditure. Data is available for 63 non-LIC EMDEs and 25 LICs. The base year of the household surveys differs but the data has been converted to a common reference year, 2010. The share of income spent on food is likely to be different. C.D. Averages weighted by the number of poor for a sample of 22 non-LIC EMDEs and 7 LICs. Poverty line is defined as $1.90/day.
of the traffic. Table 1 below shows data for the period from 2005 - 2009 for container ports in the region, highlighting Durban’s dominance in the container market.

**Table 1: Port Container Traffic - Eastern and Southern Africa (000 TEU’s)**<sup>**3**</sup>

|**Port**|**2005**|**2006**|**2007**|**2008**|**2009**|
|---|---|---|---|---|---|
|**Durban**|1 899.0|2 334.9|2 479.2|2 642.1|2 395.0|
|**Cape Town**|690.8|764.7|764.0|767.5|694.5|
|**Mombasa**|436.7|479.4|585.4|615.7|618.8|
|**Port Elizabeth**|369.7|497.2|422.8|423.8|441.4|
|**Dar-es Salaam**|258.4|272.7|334.0|373.5|473.7|
|**Walvis Bay**|71.4|83.2|144.9|170.5|Data|
|**Maputo**|54.0|62.5|80.3|92.2|107.0|
|**East London**|49.3|41.8|41.9|57.4|52.5|

_Sources: (TPT, MCLI, TNPA, OSC)_ The critical location of Durban on the North South Freight Corridor, an important route for transit traffic bound for
s ability to counter a deteriorating security situation is challenged by its weak fiscal position (World Bank, 2016).

The goal of this paper is to gain a better understanding of the relationship between conflict, aid and living standards in Afghanistan. In doing so, the paper aims to explain the paradox of lower poverty in areas more severely affected by conflict. It also intends to address current concerns about the transition, shedding light on its possible impact on the dynamics of conflict and living standards in the medium term.

The analysis relies on provincial level data on conflict over period 2007-14, and on cross-sectional household data in 2011. The former is used to estimate a dynamic model of conflict intensity at the provincial level, depending on current and lagged presence of troops. The latter allows to identify the determinants of consumption per capita, and in particular to assess the role played by conflict, aid and the presence of troops. These two models are combined to predict consumption per capita from 2012 onwards, and conflict intensity at the provincial level from 2015 onwards. Comparing these out-of-sample simulations with actual figures on economic activity available until 2015 allows validating the proposed empirical approach.

Admittedly the quantitative model used to forecast the dynamics of conflict cannot incorporate strategic shifts in the insurgency and watershed political developments, such as a potential agreement between the government of Afghanistan and the Taliban, or possible tension between rival groups within the Taliban, or between the Taliban and the Islamic State. Therefore, the forecasts of conflict intensity should be interpreted with extreme caution. However, by suggesting 4
less than the average wage for _s+1_ graduates at each age throughout their work lives. In other words, when calculating the difference between wages of girls with _s_ level of education and those with _s+1_ level of education, we adjust downward by 10% the _s+1_ wages. Second, based on scarcer research from developing countries, we assume that the less educated girls, when gaining an additional level of education, would earn 20% less than the currently observed average wage for girls with _s+1_ level of education.

We make yet another adjustment to account for the possibility that the influx of more educated girls into the labor force will reduce the equilibrium wage for that skillssegment of the labor market. Following on Behar (2004), we use labor demand elasticities of -0.56 to -0.8 for sub-Saharan Africa. Thus, for each 1% increase in the share of the labor force with a higher level of education, we decrease the average wage by 0.56% and 0.8%. For countries in Asia, we draw on Goldar (2008) and apply an elasticity of -0.4. In Latin America and the Caribbean the elasticity is -0.2, as in Fajnzylber and Maloney (2005).

The data come from various sources. The returns to education that are estimated in Mincer equations are drawn from country studies. The average wage is taken from the ILO’s KILM database. The number of girls dropping out of each grade level is from the World Bank’s EDStats.

To allow for some degree of comparison across countries, we state the final cost as a share of the country’s most recent GDP. This also allows us to generate all calculations in local currency but still have comparability ex-post. It adjusts for the size of the economy and, to some degree population, allowing for a better sense of the magnitude of the gain in the context of the country’s output. Finally, it
Capital and the Life Cycle of Earnings,” _Journal of Political Economy_ , 1967, _75_ , 352–352.

Bils, Mark and Peter J. Klenow, “Does Schooling Cause Growth?,” _American Economic Review_ , December 2000, _90_ (5), 1160–1183.

Card, David and Thomas Lemieux, “Can Falling Supply Explain the Rising Return to College for Younger Men? A Cohort-Based Analysis,” _The Quarterly Journal of Economics_ , 2001, _116_ (2), 705–746.

Caselli, Francesco, “Accounting for Cross-Country Income Differences,” in Philippe Aghion and Steven Durlauf, eds., _Handbook of Economic Growth_ , Vol. 1 of _Handbook of Economic Growth_ , Elsevier, 2005, chapter 9, pp. 679–741.

and Antonio Ciccone, “The contribution of schooling in development accounting: Results from a nonparametric upper bound,” _Journal of Development Economics_ , 2013, _104_ , 199 – 211.

and , “The Human Capital Stock: A Generalized Approach: Comment,” _American Economic Review_ , March 2019, _109_ (3), 1155–74.

- , Jacopo Ponticelli, and Federico Rossi, “A New Dataset on Mincerian Returns,” in Francesco Caselli, ed., _Technology Differences over Space and Time_ , Princeton University Press, 2016.

, , and , _A New Data Set on Mincerian Returns Technology Differences across Space and Time_ , Princeton University Press, 2016. Dauth, Wolfgang, Sang Yoon Lee, Sebastian Findeisen, and Tommaso Porzio, “Labor Reallocation and Convergence: Evidence from East Germany,” Technical Report 2019.

- de Hoyos, Rafael, John Kennan, and Rebecca Lessem, “Returns to Investments in Human Capital, within and across Countries,” 2015. Mimeo.
# **Table A6. Descriptive Statistics** **_Using Program-Level Data from the WBSCPS and Administrative Sources_**

|**Variable**|**Mean**|**Std.**<br>**Dev.**|**Min.**|**Median**|**Max.**|**Obs.**|
|---|---|---|---|---|---|---|
|**Panel A. Programquality determinants**|||||||
|_Infrastructure_|||||||
|Maintenance of largest lab: every year|0.57|0.49|0|1|1|<br>2,103|
|Has enough equipment or tools for practice|0.72|0.45|0|1|1|<br>2,103|
|Number of workshops or labs available for practice|6.12|6.15|0|4|65|<br>2,031|
|>30% classes can be taken online|0.15|0.36|0|0|1|<br>2,103|
|Program offers at least one online class|0.35|0.48|0|0|1|<br>2,103|
|_Training and curriculum_|||||||
|Teaches numerical competencies|0.80|0.40|0|1|1|<br>2,103|
|Teaches a foreign language|0.60|0.49|0|1|1|<br>2,103|
|Teaches persistence in complex tasks|0.76|0.42|0|1|1|<br>2,103|
|Promotes work under hardship or
Different from traditional pseudo-panel methods that require multiple rounds of cross-sectional surveys to study poverty mobility at the cohort level, the method that we apply works with as few as two survey rounds and provides poverty estimates at the more disaggregated household level. This method essentially exploits the time-invariant variables across the cross-sectional surveys to link different cohorts, in combination with additional cohort-based assumptions about the error terms, to construct the synthetic panels. Further discussion of this method and detailed estimates are provided in Appendix C.

# **3. Data and Descriptive Statistics**

# **3.1. Data**

We analyze 20 survey rounds from six countries: Egypt, Iraq, Jordan, Mauritania, the West Bank and Gaza, and Tunisia. For Egypt, we use the Household Income, Expenditure and Consumption Surveys (HIECs) for 2012-2013, 2015, 2017-2018, and 2019-2020; for Iraq, the Household Socio-Economic Survey (IHSESs) for 2007 and 2012; for Jordan the Household Expenditure and Income Surveys (HIESs) for 2010-2011 and 2013-2014; for Mauritania, the Permanent Survey of Living Conditions of Households (EPCVs) for 2004, 2008, 2014, and 2019; for the West Bank and Gaza, the Expenditure and Consumption Survey (PECSs) for 2007, 2009, 2011, and 2016-2017; and for Tunisia, the National Survey on Household Budget, Consumption and Standard of Living (NSHBCs) for 2005, 2010, 2015, and 2021. These surveys provide rich information on household expenditures and various household and individual characteristics for the different household types.

of economic status (Ferreira _et al._ , 2012; Beegle _et al._ , 2016; UNDP, 2016; OECD, 2018; Salvuci and Tarp, 2021; Ghomi, 2022).

15
Policy Research Working Paper 7979

# **Abstract**

Stylized facts drive research agendas and policy debates. Yet robust stylized facts are hard to come by, and when available, often outdated. In a special issue of Food Policy, 12 papers revisit conventional wisdom on African agriculture and its farmers’ livelihoods using nationally representative surveys from the Living Standards Measurement Study-Integrated Surveys on Agriculture Initiative in six African countries. At times, the findings simply confirm the common understanding of the topic. But the studies also throw up several surprises, redirecting some policy debates while fine-tuning others. Overall, the project calls for more attention to checking and updating the common wisdom. This requires nationally representative data, and sufficient incentives among researchers and policy makers alike. Without well-grounded stylized facts, they can easily be profoundly misguided.

This paper is a product of the Jobs Cross Cutting Solution Area. 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 author may be contacted at lchristiaensen@ 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
exports will need to skew more towards high value-added sectors, and this may require policy measures to strengthen particular features of the legal and governance framework in addition to more financial sector development. The analysis in this paper contributes to the FSD and governance policy reform discussion.

The next sections are organized as follows. Section 2 benchmarks trade dynamics and two areas of MENA’s business environment focused in this study, financial sector development and governance. Sections 3 and 4 discuss the estimation strategy and data employed. Results are discussed in Section 5, and Section 6 concludes.

# **2. EXPORTS, FINANCIAL SECTOR DEVELOPMENT AND GOVERNANCE IN MENA**

# **2.1. EXPORT DYNAMICS**

MENA’s export-to-GDP ratio outpaced the world average until the mid-1970s, subsequently took a downward turn and has continued to decline since then. By 2008, only 1.8 percent of global exports originated from MENA. Empirical studies repeatedly find that exporters in the MENA region export at rates below their potential. Behar and Freund (2011) find the MENA region undertraded by 60-70 percent in the 2000s. Bhattacharya and Wolde (2010) estimate MENA export levels are 86 percent lower than expected given the characteristics of their economies. Iqbal and Nabli (2007) find that non-oil exports in MENA are only one-third of their expected levels based on country characteristics. Between 2005 and 2010, MENA countries exhibited lower mean exporter size, and exported fewer products and to fewer destinations than other countries<sup>11</sup> (World Bank’s Export Dynamics Database (EDD)).

Using data from the EDD for the 2005-2010 period, summary statistics corroborate previous empirical studies showing that MENA countries export less and are less diversified than comparable countries in the rest of the world.<sup>12</sup> Table 1 summarizes the sample statistics for 6 MENA countries and 26 non-MENA countries. The principal differences in export dynamics between these MENA and non-MENA countries lie in the number of exporters and the value of exports
subsidies|0.39|0.20|0.36|n.a.|
|B2. Budget Investment Grants|n.a.|0.05|0.05|0.09|
|- Public Transportation|n.a.|0.05|0.05|0.09|
|Memo: Tax arrears and deferrals annual fl|ows n.a.|0.76|0.74|1.14|

Memo: Tax arrears and deferrals, annual flows n.a.

# Source: The World Bank

While regional transfers are mainly targeted at compensating producers affected by locally imposed price controls, there is some evidence that enterprises outside of the three sectors mentioned above also frequently benefit from regional govermment support. However, these kinds of transfers are quite badly documented. Despite the growing role of regionally funded subsidies, there are no reliable public finance statistics to clarify the structure of local spending on the enterprise sector. The growing share of regional subsidies to enterprises is either reflected in the budget items 'loans" and 'bthers" or provided from outside of the regional budget altogether. The latter includes transfers from regional extrabudgetary funds<sup>5</sup> , various local tax benefits, and 5 Serova and Melyukhina (1995) describe the Ulyanovsk model of extensive regional government price control and food rationing. The distinctive feature of the model is the existence of an informal agreement between local enterprises and the administration to comply with regional price and trade controls and provide transfers to the regional extrabudgetary fumd used primarily for price subsidization.
**a. Government Revenue**

||Amount in HBS,<br>million TL|Ratio HBS /<br>Administrative data, %|
|---|---|---|
|**Total Revenue (I+II+III)**|**342,692**|**46.4**|
|**I. Tax**|**207,057**|**45.1**|
|**1.Taxes on Income, Profits and Capital Gains**|**69,272**|**49.6**|
|a) Corporate Tax|0|0.0|
|b) Personal Income Tax|69,272|71.7|
|Withholding Tax|65,627|85.2|
|Based on Declaration|3,644|100.0|
|Other|0|0.0|
|**2. Taxes on Property**|**5,259**|**49.6**|
|**3. Taxes on Goods and Services**|**132,526**|**49.4**|
|a) Domestic VAT + VAT on Imports|76,149|58.2|
|b) Special Consumption Tax|52,767|43.8|
|c) Gambling Tax|64|7.2|
|d) Special Communication Tax|3,545|71.2|
|e) Others|0|0.0|
|**4. Other Taxes**|0|0.0|
|**II. Social Contributions**|**135,635**|**73.5**|
|**III. Other Revenue**|0|0.0|

# **b. Government
use a baseline. A baseline can be used to stratify the randomization on key variables, improving power and providing a basis for examination of treatment effect heterogeneity. It is often used to examine what determines take-up of some intervention. It can be useful for verification of randomization in cases where there is a risk of the randomization not being implemented perfectly, and can also be used to test whether attrition is non-random in terms of baseline characteristics. Finally a baseline, and especially multiple pre-treatment surveys, offer the possibility of matched difference-in-differences as a back-up evaluation strategy in cases

> 11 See e.g. Bloom et al. (2010) who collect daily and weekly production data from plants with over 100 workers each once a month.

24
security decades after their establishment in Malawi, a country that underwent a burst of estate creation in the 1980s and is thus ideal for such an exploration. We use the 2006/07 National Census of Agriculture and Livestock (NACAL) that contains data on smallholders and estates for descriptive and analytical evidence. Descriptively, we find that, although estates occupy more than 20% of Malawi’s agricultural area, a large part of their land is left unutilized and, for most crops, their productivity remains well below that of smallholders. Data from digitizing estate leases suggest that legal uncertainty created by the expiration of most leases and failure to collect realistic lease fees lead to large loss of public revenue, beyond weakening incentives for more effective land use and land market operation: Even assuming that 40% of the area registered under agricultural estates is non-existent or unsuitable for agriculture, leasing the remainder at 2
for Jordan in panel a and Kuwait in panel b.

The refugees also boosted the demand side of the economy. Figure S1.1 in the supplementary on- line appendix S1 shows that the largely Transjordanian-owned real-estate sector boomed, housing starts doubled, and construction employment expanded (Troquer and al Oudat 1999; van Hear 1995, 1998; De Bel-Air 2007).

In terms of Jordan’s EFW scores, improvements in Jordanian monetary policy translated into substantial gains in Jordan’s sound money EFW subscore—rising 15.3 percent from 1990 to 1995 and by 35.2 percent from 1995 to 2000. Reforms to tariffs and the removal of trade barriers also saw a rise in Jordan’s trade liberalization subscore by nearly 38 percent over 1990 to 1995, increasing from 4.8 to over 6.6 (table 5).

Acemoglu and Robinson (2008) note that often it is necessary that there be a shock to the distribution of power in order to overcome institutional persistence. Accordingly, these changes are mirrored by significant changes in Jordan’s legal system sub-score within the EFW index, rising from 3.1 in 1990 to 4.63 in 1995—a change of nearly 49 percent. This was a substantial improvement in Jordanian legal institutions, the most rigid economic institution (Acemoglu and Robinson 2008).

Jordan liberalized its economy to settle the Kuwaiti-Palestinians, and they, in turn, were a promarket cohort that tipped the political balance in favor of further free market reforms after being included in the governing coalition (Brynen 1992). The resulting economic boom caused by the refugees and continued by the reforms reduced opposition to liberalization and the risk of policy backtracking as had happened often before.

# **8. Conclusion**

The Kuwaiti-Palestinian refugee surge to Jordan in 1990 and 1991 is a natural experiment that allows us to understand how a government changed its economic policy after experiencing mass