rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 2. Salary distributions of jobseekers and posted jobs**

_Source_ : Authors’ calculation using the data from Rozee.pk. _Note_ : (1) Salaries are truncated from above at PKR 100,000 in this figure for a presentation purpose. When the average salary is calculated, salaries are not truncated. (2) The average salary of the whole labor force is based on Pakistan Bureau of Statistics (2015).

Smaller and younger businesses are more likely to use the online job portal. Figures 3, 4, and 5 illustrate employers’ distributions by their ownership, number of employees, and business’s age. Sole proprietorship (37%) and private businesses (58%) are dominant while public businesses (4%) and NGOs (2%) are very few. Regarding the business size in terms of the number of employees, the majority are micro (48%) and small businesses (34%). This proportion of micro businesses is higher than the proportion among the entire nonagricultural industries in Pakistan (Figure A1.1).<sup>13</sup> The percentage of the businesses that started operations less than five years ago is 50% in the online job portal but only 11% in the entire nonagricultural industries in Pakistan.

> 13 The statistics of the entire nonagricultural industries are based on the Enterprise Survey 2013. For the details of the survey, see World Bank (2015).

6
margins are not merely the result of cost cutting. Since privatization, the number of branches decreased only slightly, if.at all, for each bank.32 Although some labor shedding has occurred, in no case has the work force been reduced by more than 15%. In summary, the data indicate that remaining employees generate profit far more efficiently than before. This suggests that the banks' loan portfolios became far more commercially oriented after privatization. Credit allocation data presented in the next sub-section provides additional support for this hypothesis.

**Table 8: Ratios of Income to Costs Before and after Privatization**

||Fin<br>Inc/<br>Costs|Serv<br>Inc/<br>Costs|Total<br>Inc/<br>Costs|Assets<br>.|Liabil.|Net<br>Worth|Depo-<br>sits|Loans|#<br>Bra<br>nch|#<br>Em-<br>ploy.|Date|
|---|---|---|---|---|---|---|---|---|---|---|---|
||Mean|Mean|Mean|Pesos<br>000s|Pesos<br>IOOOsS|Pesos<br>OOOs|Pesos<br>IOOOs|Pesos<br>I000s||||
|**Chaco**||||||||||||
|Before|-.379|.173|-.206|42883|34483|8400|10950|140|28|615|11/94|
|After|.683|.683|1.366|220335|207088|13247|130995|63012|28|547|10/96|
|**EntreRios**|||||||
based on the survey data. Specifically, we correlate ranking on land with rank on the sum of land area, ranking on the likelihood of hunger with a nutrition index, and ranking on the need for help with an index constructed based on the survey data about recently experienced shocks. We use Kendall correlations, a special case of standard Pearson correlation evaluating the degree of similarity among sets of ranks (Abdi, 2007).

To check robustness, we use several measures for each of the dimensions of poverty captured in ranking exercise. For land, in addition to the sum of residential and agricultural plots, we create a composite z- score of variables related to land and building ownership, as well as the first principal component of these variables. For the likelihood of being hungry, we use a composite z-score and the first principal component of all nutrition-related questions in the survey. For the likelihood of needing help, we create a composite z-score of all survey variables related to economic shocks, and also use the first principal component of these variables. These variables provide a unique household ranking in nearly all villages. Table 4 presents the results.

Village heads’ rankings of land ownership are weakly but significantly correlated with households’ reported land size. Their rankings of likelihood of hunger are correlated with nutrition measures but the correlation is weaker. Rankings of need for help are not correlated with shocks. The consistent strength of land measures across ranking exercises may be explained by the fact that land is the most directly observable of the three criteria for someone outside the household.

**Table 4: Correlations of rankings and household variables**

||Lowest to highest<br>land holdings|Highest to lowest<br>likelihood to be<br>hungry|Highest to lowest<br>need for help|
|---|---|---|---|
|**Land measures**||||
|Size of all plots (res. and agr.)
# **4.1 Data**

This paper uses equity return indexes of the G7 countries from January 1, 1973 to December 31, 2015. To avoid time difference, I use weekly returns. The weekly frequency avoids market microstructure of using daily returns, yet provides sufficient observations in the tails. The data is acquired from Datastream and includes 2313 observations.

Panel A of Table 2 presents the summary statistics of returns for the whole period. The mean and standard deviation of returns are annualized by multiplying returns by 52. The average returns of all the countries, except Japan, are around 10%. Japan has an average return of only about 7%. Standard deviations of returns vary from 1.14 to 1.69. The row labeled "Skewness" reports the results of the D’Agostino test of skewness (D’Agostino, 1970). It shows that the equity returns in all countries, except the UK and Italy, are skewed to left at 5% significance level and higher. While the equity returns of the UK are significantly skewed to right, the returns of Italy do not exhibit significant skewness. The row labeled "Kurtosis" reports the results of the Anscombe-Glynn test (Anscombe & Glynn, 1983) and implies that all equity returns have acute peaks and tend to be heavy-tailed. The results of the Anderson-Darling test (Stephens (1986) and Thode (2002)) and the Shapiro-Wilk test (Shapiro and Wilk (1965) and Royston (1982)), reported in rows labeled "AD test" and "Shapiro test", indicate that the equity returns do not follow the normal distribution.

To see how the 2008 financial crisis affects the financial market, I divide the whole sample period into two subperiods. The first period includes the first 30 years from January 1, 1973 to December 31, 2002. The second period includes the 2008 financial crisis and extends from January 1, 2003 to December 31, 2015. Panel B and Panel C in Table 2
and 2017, it grew by about 21% between 2017 and 2020. Figure A1<sup>6</sup> shows that such significant discontinuity is not observed in the other northern states, providing evidence for Venezuelan influx to be behind the population shock.

However, total Roraima’s government expenditure data, which includes both the federal and the state level expenditure, fails to show any expenditure increase shock in 2018. Figure 4b shows bimonthly expenditure data after adjusting inflation using national IPCA (Broad National Price Index) of the period 2015-2020 from SICONFI (National Treasury Secretariat), where the y-axis is in millions of Brazilian reais and the x-axis is the distance in 2 months from the second bimonth of 2018. There is no evidence of an increase in government expenditure and government expenditure does not seem to increase before June 2019. In 2018, there seems to be a decrease in total government expenditure in Roraima, which suggests that the influx of Venezuelan refugee and migrants was not accompanied by a positive public expenditure shock. Figure A2 also reveals that other states seem to have experienced no significant change in government expenditure in 2018. The fall in total expenditure in 2018 in Roraima is also evident in the yearly data in Figure A3, which may reflect partially the state’s response to cutback expenditure as a response to the 2015/2016 crisis and shows that total expenditure was not significantly impacted by the Venezuelan influx.

Figure 4: Population and government expenditure time series of Roraima <!-- Start of picture text --> .<br>−15 −10 −5 0 5<br>Years from 2018 −20 −10 0 10 20<br>(a) Population in Roraima (b) Public expenditure in Roraima<br>700000 300<br>600000<br>200<br>500000<br>400000 100<br>300000 0<br><!-- End of picture text --> Note: Panel (a) shows the yearly data on
NL-2/2-1-88

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borrowing requirements will be inflatedS1/ It is for these reasons that the operational deficit is the more appropriate concept for economic analysis.

## 2. Flscal-based Stabilization

We have seen that a sharp cut In the budget deficit (from over 20Z of GDP to 3X), in addition to exchange rate stebilization led to an abrupt halt of the hyperinflationary process in Bolivia. We noted that the precise role of the exchange rate in the stabilization process is controversial. There are those who think that the fiscal policy was the major factor not only in the medium run but also in the initial stage of Bolivian stabilization. In this section we shall compare the above results with some other orthodox stabilizations which relied mainly on fiscal adjustments in a nonhyperinflationary setting. We may recall the argument which claims that in general the fiscal adjustment by Itself may not be sufficient for stabilization.

# _eO_ The Mexican Stabilization of 1983 _/

Let us begin with the Mexican stabilization of 1983 which took place on the background of a swelling public sector deficit (with an operational deficit reaching 101 of GDP In 1981), a record current account deficit (reaching 6.7% of GDP in 1981 compared with 2-3Z in 1977-79), a tremendous increase in the public debt (doubling between 1980 and 1982 in dollar terms) and uncontrolled Increases in nominal wages.

- 1/ With monetary acco-odation by the Central Bank (as in the previous model) the operational deficit may resain constant.

- 2/ This part is based on the excellent account of F. G. Diaz (1983) in 'Macroeconomic policles, adjustment and growth in the long run (Mexico's experience," World Bank sanuscript, and IMF and World Bank data.
|

Notes: 1 <mark>)</mark> Placebo: estimates usin <mark>g</mark> data from 2016 and 2017 <mark>,</mark> when the ENTRE <mark>p</mark> ro <mark>g</mark> ram did not exist. Ex <mark>p</mark> eriment: estimates usin <mark>g</mark> data from 2017 and 2018 (year of the intervention) and the preferred specification in Table 4. 2) Standard errors clustered at schoolyear level in parenthesis; 3) *** p<0.01, ** p<0.05, * p<0.1. 4) The dependent variable is a dummy variable that is equal to 1 if student _s_ in school _j_ dropped out school between year t and year t+1. 5) _ENTREj_ represents the interaction _Entrejt_ x _Postt_ where _Entrejt_ : (1= Student _s_ attends a school _j_ in year _t_ that is in the treatment group) and _Postt_ : (1 = year 2017 in Placebo; = year 2018 in Experiment; 0 otherwise); _ENTRE ij_ with i=1,2,3 captures the same interaction as _ENTREj_ but using assignment to the treatment 26
23

**Fig. 2.4.** Needs for better housing, actual plans to move and structure of demand (% households polled)

<!-- Start of picture text --> 100%<br>90% -<br>80% 9 = 98-2<br>70% _ T___T 7 i__E__9_T E_<br>M3 no plans, no need<br>60%<br>13 no plans, need housing<br>60%<br>o plan to receive tree housing<br>40%-334 27<br>housing Theywerequitereparedtopayt 37.9 37i1 M plan to purchase(exchanget<br>30% - 20an housing<br>20% - 9.2<br>10%<br>0% ", - 144<br>1993 1996 1994 1996 1994 1996<br>Moscow Vladimir Gorodets<br><!-- End of picture text -->

**Sources:** Household **Survey** in **Seven** Cities, Moscow, December 1993; The Moscow Longitudinal Household **Survey,** January 1996; **Vladimir and Gorodeu Longitudinal Household** Survey, **Oct-1994** and Dec.1996.

It is clear from the survey data presented here that market ideas regarding housing search methods are beginning to penetrate the mentality of the Russian public. In our opinion, the widely-held view that most Russian households are convinced that housing should be free and are thus opposed to the market is erroneous. The findings of surveys conducted in the mid-1980s (Kalinina and Kosareva, 1989; Guzanova, 1989; Guzanova, 1990) show that even then, many households did not mind using their own money to buy housing. They were qite prepared to pay to move into larger apartments or to join cooperatives, and this created high demand _for_ cooperative housing. Recent studies show that as soon as the oppohrunity to buy and sell housing appeared, many households
., 2013), and we assume richer households live in and use higher quality assets. What fraction of assets is lost to a disaster also depends on softer measures, such as the existence of early warning systems. In addition to being an effective way of reducing casualties and fatalities, early warning allows households to plan for a disaster, and move some of their assets outside (or above) the affected zone, thereby reducing asset losses (Hallegatte, 2012; Kreibich et al., 2005) **.** Based on previous case studies, we assume that asset losses are reduced by 20 percent when people have access to early warnings, and we use data from the Hyogo Framework for Action (HFA) monitoring system on early warning to estimate the fraction of population with such access.

For income diversification, social protection and financial inclusion, we build on global databases such as the Atlas for Social Protection – Indicators of Resilience and Equity (ASPIRE) and Global Financial Inclusion (FINDEX). Parameters related to the socioeconomic characteristics that modify risks of irreversible human capital losses – employment, healthcare, and education – are from the World Development Indicators (WDI).

Adaptive social protection, where social protection benefits and/or beneficiaries is expanded automatically in the aftermath of a disaster, is an efficient tool to reduce the welfare impact of disasters (Davies et al., 2013; Hallegatte et al., 2016c). It is impossible to predict the support that will be provided after a disaster, so we assume a willingness to share the losses, and proxy for the ability to provide such support, depending on institutional capacity and public financial management. To measure countries’ ability to manage public finance and reallocate resources in times of crisis, we use sovereign credit ratings. We use data from the HFA on contingent finance, the existence of plans for emergency response and 10
. It contrasts the credit terms of politically connected firms within the same industry versus firms in other industries. As with the benchmark specification, we saturate our specification with fixed-effects at the _firm*bank_ , _bank*quarter_ and _industry*quarter_ level.

# **_III_ .C. Summary Statistics**

Table 1 presents the summary statistics of our sample. Panel A shows the composition of the loanlevel data according to loan attributes. We create six dummies for each loan according to whether the loan is: (1) made to politically connected firms (firms from the chairman's state); (2) made to politically connected firms within the same industry as the chairman; (3) made to the politically connected firms located in the municipality in which the chairman lives; (4) provided by large banks; (5) provided by domestic banks; (6) made to large firms. The table displays the mean and number of observations for each dummy variable. The variable _Chairman_ indicates whether a loan is in a state-sector-period with a chairman. On average, 45 percent of loans are extended to politically connected firms. The variables _Chairman*SameInd_ and _Chairman*SameMuni_ represent whether loans are made to politically connected firms in the same industry or municipality as their chairman. 5 percent of the loans in our sample are from firms in the same industry as the chairman, while 6 percent of the loans are obtained by firms from the chairman’s municipality.

> 26 Industries are classified at the three digit 2007 NAICS.

12
# **I. Introduction**

In the 1960s, Africa’s economic growth was similar to South Asia (Collier and Gunning 1999a). However, between 1970 and 2000, the average GDP per capita growth rate was only 0.5 percent per annum, and sub-Saharan Africa is now the poorest continent.<sup>2</sup> In the decade before the recent financial crisis, however, the continent experienced a resurgence in growth. Growth in GDP for the continent averaged 5.9 percent annually (World Economic Forum 2009). Is this trend sustainable? How can Africa keep growing? What are the key policies that facilitate Africa’s economic performance?

This paper sheds light on these questions using micro data. We use recent surveys of the World Bank’s Enterprise Survey for more than 80 countries. We combine these surveys with other cross-country datasets on politics, macro policies, geography and the business environment, to study the determinants of performance of formal manufacturing firms. Our goal is to explore the key factors behind Africa’s disadvantage, if any, relative to other regions.<sup>3</sup> We focus in particular on African firm performance relative to firms in countries from other continents with GDP per capita below 3000 U.S. dollars. We look at a comprehensive set of firm performance outcomes, including static efficiency, dynamic efficiency, export shares, and investment rates.

Our paper adds to the literature explaining Africa’s economic performance (see Collier and Gunning 1999a, 1999b; Bigsten and Soderbom 2006). There are many previous studies examining one aspect of firm performance (such as investment rates, sales growth, exports, or productivity), often using one or several African countries’ firm-level data. However, there is no

> 2 The numbers are based on World Development Indicators of the World Bank.

> 3 In the rest of this paper, we use SSA and Africa interchangeably.  Africa does not include the North Africa region in this
|
|||[.468]|.|(.065)|(.065)|(.098)|(.096)|
||**PHU Functioning Score**|89.6|485|-1.19|-1.02|-1.00|-.342|
|||[19.8]|.|(2.58)|(2.55)|(3.90)|(3.98)|
|**_B. School Polic_**|**_y Component_**|||||||
|**Nearest Second**|**ary School Re-Opened on Time**|.825|132|.085|.106|.072|.080|
|||[.381]|.|(.061)|(.059)|(.079)|(.081)|
|**_C. Index_**||||||||
||**Disruption Index**|0|303|-.096|-.132|.094|.111|
|||[1]|.|(.132)|(.129)|(.235)|(.248)|
||**Disruption Dummy**|.170|.299|-.039|-.042|-.013|.009|
|[=1 if disruption i|ndex greater than 75th percentile]|||(.050)|(.051)|(.103)|(.108)|
||**N (villages)**|200|200|200|200|200|200|

**Notes:** All data comes from the Village Leaders Survey, collected in October 2015. The between group variation reported in Column 2 is computed through a one-way
- 26 -

governments now resort to assembling their own fiscal cadastre, by making periodic field inventories.

The "orthodox" method of conducting such an inventory begins with preparation of a base map of the taxing jurisdiction, followed by a field survey to determine and delineate the boundaries of each parcel. Detailed maps showing parcel boundaries are then prepared and each parcel is assigned an identificaticn code. Data to be used in valuations is obtained during the field survey and incorporated onto the property record. Billing data is obtained from a title or deeds registry which fixes legal ownership.

The standards of surveying and adjudication that would be used in industrialized countries are inappropriate to most developing countries, however. Many have cheaper and simpler approaches which work reasonably well. Accurate parcel boundary demarcation, for example, is not essential for property identification purposes. Some LDC cities dispense with mapping altogether, simply preparing a list of taxable properties identified by street address or (where street addresses are not adequate) painting tax identification numbers directly on each building. Where maps are used, no attempt is made to determine precise legal boundaries; the authorities simply demarcate boundaries sufficiently to distinguish one property from another.

Similar compromises are made in determining liability. The use of title or deed records to identify ownership is impractical in LDC cities. Ownership is disputed on a large proportion of properties. Where not disputed, deed or title records are in such a state as to make retrieval impractical. LDC tax authorities therefore forego any attempt to verify legal title, and instead adopt what is called an "owner of
by comparisons across multiple contexts.

# **4 Research Design**

## **4.1 Data**

### **4.1.1 Refugee Populations**

We use refugee population data provided by UNHCR’s Global Data Service on refugee populations at a sub-national level. These data cover 2001 to 2019 for low- and middle-income countries.<sup>12</sup> We code these locations, which are a mix of regions, cities, camps, and geo-locations, to match the sub-national regions in GWP. We include populations who are displaced outside of their country of birth, which captures people who UNHCR classifies as refugees, asylum-seekers, Venezuelans displaced abroad, and others of concern (all of whom are referred to as “refugees” throughout this paper).<sup>13</sup> While populations are estimated at the country level for every year and displaced nationality, sub-national data is missing for 33% of the total refugee population in LMICs between 2005 and 2018.<sup>14</sup> We therefore develop a methodology to impute the missing population totals at the region-origin-year level. We use the regional proportions of the population for country-origin-years where at least 70% of the refugee population’s location is known and combine this measure with the country-origin-year totals to impute the region-origin’s total population over time.<sup>15</sup> This methodology yields a balanced panel of refugee populations by nationality at the sub-national level. For the analysis presented here, refugee populations are aggregated at the region-year level, combining refugees from all countries of origin.

> 12The population totals are as of December of the reporting year.

> 13This excludes internally displaced people, returnees, and stateleses populations who are also under UNHCR’s mandate. It also excludes Palestinians who are under the mandate of United Nations Relief and Works Agency.

> 14Sub-national data is also missing for almost all of the high-income countries, which also drives our choice to focus on