rafmacalaba/data-use-mentions-extended huggingface dataset:
food security, labor market engagement, and mobility) evolved as the crisis unfolded into the medium and long terms. For hosts, the large population influx was expected to have significant and heterogeneous impacts on multiple dimensions of welfare. However, evidence on this front was very limited due to lack of representative data after the influx.

The Cox’s Bazar Panel Survey (CBPS) was designed in 2018 to produce a representative longitudinal survey to track key socio-economic and health indicators of Rohingya and the host Bangladeshi population in Cox’s Bazar district.<sup>8</sup> The survey’s main objective was to build an evidence base on the medium- and long-term impact of the Rohingya crisis on economic and social conditions among Rohingya and hosts. Due to the localized nature of the shock in the sub-districts of Ukhia and Teknaf, it was important to compare the

> 2 United Nations High Commissioner for Refugees (UNHCR) refugee population factsheet (as of 15 July 2019) <u>http://data2.unhcr.org/en/situations/myanmar_refugees.</u>

> 3 Staff calculation based on UNHCR refugee population factsheet (as of 15 July 2019).

> 4 Staff calculation based on UNHCR refugee population factsheet (as of 15 July 2019) and Bangladesh Bureau of Statistics (BBS) population census 2011.

> 5 Figures account for the total population of displaced Rohingya persons reported by the UNHCR (new and old arrivals).

> 6 This is the density reported for Camp 3 in Ukhia in the UNHCR assessment “Settlement and protection profiling of all camps Ukhia/Teknaf, Cox’s Bazar, Bangladesh, Round 5, July 2019.”

> 7 Host households in Cox’s Bazar live in largely rural areas of the district, where consumption poverty is both high and significantly worse than the national average of 24.5 percent. Poverty rates outside the district capital of Cox’s Bazar Sadar are higher than both the national and district averages, with the primary
answer is yes; a dummy variable in general equals one if there exists (legal) equality between the genders, except for the variable capturing whether women are required by

> 12 For details on methodology of the World Bank’s WBL database see http://wbl.worldbank.org/methodology 13 Many variables in the World Bank’s WBL database are available for both unmarried women (compared to unmarried men) and married women (compared to married men). If both are available, we consider the situation of married women.

15
with digital services trade and various other development variables (section 3.3.3). Given that the econometric analysis uses the gravity model, it also relate the main variable of interest with various gravity determinants such as distance and market size (section 3.3.4), and finally with some variables related to the internet (section 3.3.5).

The analysis is conducted using the trade data available in the TiVA database, in particular the underlying gross exports data. This data source covers bilateral goods and services trade up till the year 2015.<sup>11</sup> The analysis focuses on digital services trade, which covers different sectors. First, it includes the purely digital services such as publishing, audio-visual and broadcasting services, telecommunications, and IT and other information services, which correspond to ISIC Rev. 4 numbers 58-63. Second, it adds a series of business services that have become substantially digitalized in recent years in order to capture the so-called digital-enabled or digitally delivered services trade (see also UNCTAD, 2019; López González and Jouanjean, 2017; and Borga and Koncz-

> 11 To main reasons stand out in preferring the OECD TiVA database over other databases such as the ITPD-E database. One, in the econometric analysis the OECD trade data is used for reasons set out in Section 4. Even though the ITPD-E database provides data for more developing countries, it is preferable to use consistent trade data throughout all sections of the paper. Second, the OECD TiVA trade data distinguishes between more sub-categories of digital sectors, whereas the ITPD-E database lumps up digital services into one aggregate sector, namely ISIC Rev. 4 code “J”, which covers information services, telecommunications, and IT, computer and other information services combined (i.e. ISIC Rev. 4 codes 58-60, 61, 62, 63 respectively). However also data from the ITPD-E database is used for a robustness check and found largely similar results.

11
registration (column 4) even while controlling for the interaction with financial development. The financial development interaction remains significant, despite a drop in the number of observations. That is, countries that have been more affected by the crisis (as indicated by a lower index of turbulence) have experienced sharper declines in their new business registrations. Finally, we run a specification in which we include log GDP per capita, financial development and turbulence with crisis (model 5) and find the results to be unchanged.

To summarize, we find that the current financial crisis has negatively affected new business creation and that this effect was larger in countries with more developed financial markets and countries that have experienced more economic and financial “turbulence” as a result of the crisis. The decline in new limited-liability registrations was more pronounced in 2009 than in 2008 and was more significant in higher income countries.

# **5.  Conclusion**

We use panel data on the number of new firm registrations in 95 countries to study the impact of the 2008 financial crisis on new firm creation. The data show that dynamic business creation occurs in countries that provide entrepreneurs with reduced red tape and a stable investment climate. The data also show that nearly all countries experienced a sharp drop in business entry during the crisis, and that the degree to which the crisis impacted new firm creation is highly correlated with measures of crisis severity. We also find that the crisis had a more negative impact on new business creation in countries with higher levels of financial development. These results can help guide effective policymaking and deliver new capabilities for identifying the impact of reforms.

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instruments that are exogenous to the error term but are correlated with their own contemporaneous values. To choose appropriate lags for the instruments and check their exogeneity and validity, the study applies the Hansen test of overidentifying restrictions and tests for the second- and third-order autocorrelations in the error terms. In addition to applying instruments, both the two-step difference and system GMM methods address the endogeneity stemming from the omitted country fixed effects. The two-step difference GMM estimation approach uses the first-difference for the variables in the regressions and the level indicators for the instruments, while the two-step system GMM approach augments the two-step difference GMM approach with regressions that have level variables and instruments in first-differences. If the Hansen test of overidentifying restrictions and autocorrelation tests verifies the exogeneity of chosen instruments, system GMM would be preferred to difference GMM, as adding level equations increases the amount of information that can be exploited to estimate the sector elasticities in a panel with small T and large N.

# **3. Data and Descriptive Statistics**

This study compiles panel data of annual value added and electricity consumption for 126 countries with sector level observations in agriculture, manufacturing, and services during the period of 1996 to 2014. The empirical estimations also include control variables obtained from various sources that are country or sector specific. The WDI database and the Penn World Table 9 provide data for the sector-level value added and other country- and sector-level control variables, and the IEA has data on sectoral electricity consumption. About a one-third of the data set covers high-income countries, another third includes upper-middle-income countries, and 20 percent and 10 percent of the sample represent, respectively, lower-middle-income and low-income countries.<sup>6</sup> Naturally, the coverage of data from IEA and WDI is better for high-income countries, which have few missing values, including for value added, electricity consumption, and control variables across all sectors. By contrast, low-income countries are missing an average of 12.8 out of 54 observations across all sectors and years. Of the three sectors
046)|(0.047)|(0.047)|(0.047)|(0.040)|
|R-squared|0.025|0.025|0.030|0.024|0.045|0.060|0.036|
|Mean Dep. Variable|0.004|-0.020|0.008|0.000|0.007|0.002|0.016|
|<br>Observations|5,592|5,285|5,535|5,469|5,592|5,592|5,589|
|_Controls in All Panels_||||||||
|Individual Covariates|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Household Size in Afgh.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Enterprise Property in Afgh.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Contract in Afgh.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Dwelling Type in Afgh.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Income Type in Afgh.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of Assets in Afgh.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ The Social Capital Index is constructed using the outcome variables of columns (ii) to (vii) using the methodology of Kling, Liebman and Katz (2007). Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</
f fpcdt_|0.011|0.001|0.039|_−_0.003|_−_0.021|0.003|
||(0.016)|(0.004)|(0.031)|(0.008)|(0.015)|(0.006)|
|Observations|101,379|101,379|39,502|39,502|61,265|61,265|
|Adjusted _R_<sup>2</sup>|0.762|0.823|0.632|0.803|0.786|0.824|

Notes: It is also possible that NTMs induce firm-level product quality upgrading. There is some evidence to that effect even in the agriculture sector (e.g., Curzi et al., 2020). Thus, it is possible that interpreting our price effects in Table 5 as quality upgrading may be misleading if in our setting unit values do not proxy quality perfectly, but instead reflect differences in efficiency, market power or foreign exchange differences. As a result, we also test the effects of NTMs on product quality upgrading for African exporting firms. To confirm the existence of such an effect, we need to introduce a measure of product quality into our analysis. As such, we follow the set up in Khandelwal et al. (2013) to recover estimated product quality directly from observed trade data. For brevity, we relegate the description of the quality estimation A1. procedure to the Appendix We plot a graph of estimated quality and prices in Figure A1 and find that prices and quality are positively correlated with the correlation being stronger in the manufacturing sector compared to agriculture. Regressing quality and quality-adjusted prices on NTMs, we find no evidence that NTMs induce a quality upgrading effect for firmproduct level exports (Table 6<sup>17</sup> ), giving further credence to our findings in Table 5. If
ever there is also a literature looking at firm productivity. The impacts of economic density may be different for firms than for households. Obviously firm productivity directly affects incomes earned, but incomes also reflect labor force participation and matching opportunities for the household, as well as incomes from the informal sector which are not well represented in censuses or typical surveys.

While we found strong neighborhood externalities for different density measures in Table 8, we find no such effects for these types of measures for firms. For firms being in a neighborhood with more people, total employment or Landscan ambient population does nothing to firm productivity measured by value added per worker. The only impact on productivity is from neighborhood localization externalities – having more employment or more firms within a defined neighborhood near a firm within the own industry. And, as we will see even those effects are nuanced, applying to particular sectors and trading off competition versus spillover effects.

# **6.1 Kampala data**

To study firm productivity at the neighborhood level in Kampala, we use data from the Uganda Business Inquiry (UBI) survey conducted in 2002. The UBI is an economic survey which made use of the official Census of Business Establishments (COBE) of 2002 as its sampling frame. The principal objective of the survey is to provide the necessary information and data to measure the contribution of each industry sector to the growth of the economy. So the survey covers a large range of economic variables, including value added and business assets. Coverage is comprehensive, with information on all sectors of the economy - including the informal sector<sup>16</sup> - and coverage of all the officially recognized districts in Uganda. The sector definitions are in line with the International Standard Industrial Classification (ISIC), Revision 4, and cover 15 1-digit sectors.<sup>17</sup> A stratified twostage sample design was used to select the businesses for the UBI, which focused on getting

> 16The informal status of a business is recorded using a
However, targeting is expensive in monetary, administrative, and political terms. The production of data is costly; the collection of data takes time; and the exclusion of some applicants may have political and social consequences. Therefore, targeting always involves a trade-off between errors of inclusion, errors of exclusion, and higher implementation costs (Morley and Coady 2003). Among the strategies in social program targeting, geographical targeting exploits information on variations in poverty rates by location to channel resources toward regions with the highest poverty rates.

Nonetheless, the implementation of geographical targeting is not a trivial endeavor because household surveys alone do not usually provide information to support accurate poverty estimates across small administrative units. Poverty measures derived from survey data are usually either too noisy or lack adequate coverage. Small area estimation techniques have been developed as a way to produce highly disaggregated poverty rates by imputing poverty into a population census.<sup>1</sup> The poverty maps created through these techniques provide relatively precise estimates of poverty and have been used in several countries to channel resources toward localities in which poverty is more concentrated.

There are two main approaches to the application of poverty maps in social program targeting. In the single-step approach, localities with the highest poverty rates are identified and prioritized in the disbursement of resources.<sup>2</sup> Evidence shows that the effectiveness of this process increases in cases where poverty is more spatially concentrated (Elbers et al. 2007). An alternative approach, which is used in the case of the national social registry (RNU) in Senegal, consists of two steps. First, the information supplied through the poverty map is used to develop eligibility quotas by location. These quotas are then processed using additional tools to identify program recipients. Including vulnerability on the targeting raises program’s effectiveness because the share of the population living above the poverty line that may be only a shock away from becoming poor. Moreover, this share is likely to increase if the poverty rate has recently declined.
# **Empirical strategy**

Using the synthetic semi-annual panel data we constructed, we estimate several models for each of the two outcomes: likelihood of being separated from a job from one period to another and the likelihood of finding a job. In all models we include individual and half year fixed effects. Adding individual fixed effects absorbs all time-invariant differences across individuals, including average differences between groups of workers, such as Syrians and Jordanians. We are thus measuring differences across these groups over time as they deviate from the group average. If we do not see significant differences, this does not mean that there are no differences between the two populations, rather that there are no differences in trends over time.

The first model investigates the relationship between Syrian nationality and the likelihood of finding (separating from) a job in each half year from the beginning of 2016 to the end of 2020. (1) iitt ii tt 1tt ii tt iitt yy = αα + γγ + ββ SS ∗HH + εε _Where_ : _equal 1 if individual i finds (separates from) a job between half-year t-1 and half-year t, 0 otherwise_ iitt yy : _Syrian indicator_ ii : _Half year dummy_ SS HHtt: _half year fixed effects_ tt: _individual fixed effects_ γγ ii : _error term_ αα iitt In the second model we use the same specification but, in addition, we control for the type of εε employment of the individual’s last job, that is their current job if they are currently employed and their last job if they are currently non-employed. Workers do not transition frequently between informal and formal jobs. In our sample only 80 individuals have changed their employment type 19
, the global inflation factor accounted for a sizable share of within-country inflation variance in advanced economies and EMDEs (Figure 3; Table 2). In the median country, the global factor accounted for 12 percent of inflation variation, but its role varied widely across and within country groups (from near zero to 70 percent). For example, for the full sample period, the contribution of the global inflation factor was much greater in the median advanced economy (24 percent) than in the median EMDE (10 percent). The group-specific factors have also played

> 8 Long-term trends (proxied by 15-year moving averages) in national inflation rates and output growths are eliminated. In Appendix, robustness of the main results to different data frequencies (e.g., the use of quarterly data) or different de-trending methods are tested.

> 9 For 90 percent of the countries in the sample, the factor loadings on the global factor (coefficients associated with the global factor in the model) are positive (and statistically significant within 90 percent confidence intervals), indicating that national inflation rates generally move in tandem with the global factor. The remaining 10 percent of the countries are mostly, although not exclusively, in Sub-Saharan Africa (Algeria, Cameroon, Central African Republic, Democratic Republic of Congo, Gabon, Gambia, Mali, Niger, Saudi Arabia, and Senegal). In these countries, the factor loadings on the global factor are not statistically significantly different from zero.

7
# **Figure 2.1. Correlation between PIQ and institutional measures**

**A. PIQ-F vs ICRG Investment Profile Index**

## **B. PIQ-D vs ICRG Investment Profile Index**

- **C. PIQ-F vs ICRG Corruption Index**

## **D. PIQ-F vs WGI Regulatory Quality**

- **E. PIQ-F vs CPIA Public Sector Management and Institutions**

## **F. PIQ-F vs Dabla-Norris PIMI index**

_Sources:_ Authors’ estimates; Dabla-Norris et al. (2012); International Country Risk Guide (ICRG); World Bank’s Worldwide Governance Indicators (WGI) and Country Policy and Institutional Assessment (CPIA). _Note:_ The figure shows binned scatterplots with fitted linear regression lines for the 2000-21. A. Sample includes 87 countries. B. Sample includes 78 countries. C. Sample includes 87 countries. D. Sample includes 120 countries. E. Sample includes 76 countries. F. Sample includes 62 countries.

11
...rst two rounds, 730 answered the ...nal wave survey. Appendix 1 details wave by wave attrition rates and shows the robustness of our main treatment e¤ects to corrections for attrition.

Each follow-up round collected data on changes over the quarter in ...xed capital from purchases, sales or repair; the current value of inventories and raw materials, and the value of the last month’s expenses, sales, and pro...ts. The most important ...rm outcome variable measured is ...rm pro...ts. Pro...ts were elicited via a direct question, following the recommendations of de Mel et al. (2009b). Firm owners were asked: “After paying all expenses, what was the income of the business (the pro...ts) during January 2009? (Consider all expenses, including wages of employees but not including any income you paid yourself as an expense)”. This de...nition of pro...t thus includes the return to the entrepreneur’s labor and managerial talent. Nominal pro...ts were converted to October 2008 real pro...ts using the Greater Accra region Consumer Price Index collected by the Ghana Statistical Service.

An innovation in this experiment was the use of computerized cross-sectional and panel consistency checks. Data was collected using PDAs, and a consistency check was triggered whenever reported pro...ts exceeded reported sales in the cross-section, whenever a ...rm reported sales but not pro...ts, and whenever the change in pro...ts from one quarter to the next was less than -33.3 percent or greater than +50 percent (provided the absolute change in pro...ts was at least 20 cedis). We discuss these consistency checks in more detail in Fafchamps et al. (2010), where we show that they lead to some improvements in data
the survey date.<sup>19,20</sup> On average, pregnant women represent approximately 10 percent of all women aged between 15 and 49. The <u>Addendum gives the pregnancy incidence for each country in the DHS</u> data set.

For children, we use the z-scores for height-for-age (stunting) and weight-for-height (wasting).<sup>21</sup> These anthropometric data are measured for all children aged under 5 in the DHS and LSMS surveys. We then create our measure for stunting (low height-for-age) and wasting (low weight-for-height). A child is deemed to be stunted if his height-for-age z-score is two standard deviations below the median of the reference group; wasting is defined similarly using weight-for-height. Stunting and wasting, while both considered indicators of undernutrition, have different causes and effects. Stunting is an indicator of persistent, longer-term, chronic undernutrition from which it is much harder for a child to recover. Compared to wasting, it is known that stunting has adverse longer term consequences for child development.<sup>22</sup> Wasting tends to be more responsive to short-term (possibly seasonal) food deprivations or illnesses.

Tables 2 and 3 give the summary statistics for the nutritional outcomes for women and children using the DHS and LSMS.<sup>23</sup> Focusing on the larger sample of countries available in the DHS and taking population-weighted averages, we find that 11% of adult women are underweight, while 32% of children are stunted and 9% are wasted (similar numbers are found for children in the LSMS). Across countries, a higher incidence of underweight women is associated with a higher incidence of wasted children (r=0.40, significant at the 5% level<sup>24</sup> ). The correlation between women’s and children’s nutritional status is weaker for stunting (r=0.14).<sup>25</sup> This