rafmacalaba/data-use-mentions-extended huggingface dataset:
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35 For ffirther details see Cross-National Data on Governument Employment and Wages, on the World Bank Administrative and Civil Service Reformi web site at http://wwwl.worldbank.org/publicsector/test/civilservice/cross.htm. See also Stevens **(1994).**

**11**
May 2012 Midwest Macro Meetings, NYU, the Reserve Bank of Australia, and the Bureau of Labor Statistics (BLS) for helpful comments. This research was conducted with restricted access to BLS data on US import and producer prices. The findings, interpretations, and conclusions expressed in this work are those of the author’s and do not necessarily reflect the views of the BLS or the World Bank (or its Board of Directors, or the governments they represent). I thank my BLS project coordinators, Rozi Ulics, Kelley Khatchadourian and Ryan Ogden, for assistance throughout the project.

> 1In the aggregate data in Figure 1, pass-though of competitors’ RERs to import prices is about 0.32 (t = 4) in a simple univariate regression. After controlling for bilateral RERs (as I do the BLS microdata), aggregate pass-through of competitors’ RERs falls to about 0.19 (t = 2.5) (see partial scatter plot in the online appendix). However, controlling for competitors’ RER’s also reduces aggregate bilateral PT to 0.195 (t = 5) (not reported). In the aggregate data, competitors’ RERs are approximated

1
!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009); Southern Sudan statistical handbooks 2006–08; World Bank (2011).

Improving South Sudan’s electricity situation requires a phased approach, starting with supporting and expanding off-grid electrification schemes. Programs that boost off-grid electrification through commercial means can be a viable option. These options not only enhance access to modern lighting in the absence of grid electrification but also boost private sector development. One such option is through the program Lightning Africa. Given the absence of any grid network in South Sudan, the most efficient and rapid way of providing lighting services to the people (particularly in rural areas) would be through the private sector by promoting the low-cost modern lighting offered by photovoltaic (PV) modules. The incorporation of South Sudan into th _e Lighting Africa program_ deserves consideration as a practical short-term solution to the country’s energy challenges.

Lighting Africa is a joint International Finance Corporation (IFC) and World Bank program that has to accelerated the development of commercial off-grid lighting markets in Sub-Saharan Africa. The program aims to mobilize the private sector to build sustainable markets to provide 2.5 million people with safe, affordable, and modern off-grid lighting by 2012 through a variety of tailor-made products, many of them in the form of PV solar lanterns. The longer-term goal is to eliminate market barriers for the private sector to bring these innovative products to 250 million Africans by 2030, who currently lack electricity that rely on costly and low-quality candles and kerosene lamps. Improved lighting provides 32
# Table A1 Statistical Description

The sample consists of all financial listed firms in the CSMAR database from 2008-2017.

||N|Mean|St. Dev.|Min|Max|
|---|---|---|---|---|---|
|∆(Investment/Total Fixed Asset)|1250|0.03|0.43|-.832|1.25|
|Titling Firm dummy|1250|0.04|0.19|0|1|
|ROA in 2012|1250|6.16|17.12|-13.29|40.18|
|Tobin Q in 2012|1250|2.04|8.69|.11|232.59|
|Δ Tobin's Q|1250|0.83|6.71|-89.14|101.35|
|The relative size of the acquirees in the M&A deals|1250|0.05|1.27|0|44|
|Shenzhen dummy|1250|0.06|0.23|0|1|
|Tenure Unexpired|1250|0.02|0.16|0|1|
|Rights Uncontended|1250|0.02|0.15|0|1|
|SOE dummy|1250|0.69|0.46|0|1|
|Historical Annual Stock Market Return|1250|-0.36|0.12|-0.54|-0.07|
|Δ Historical Annual Stock Market Return|1250|-0.01|0.19|-0.37|0.40|
|Dividend / Total sales|1250|0.02|0.07|0|1.94|
|Incentive Package|1250|2.53|8.48|0|78.04|
|Chairman-CEO duality dummy|1250
<!-- Start of picture text --> Remaining carbon budget Remaining carbon budget<br>at initialization (GtCOz2) at initialization (GtCOz2)<br>300 600 900 1200 300 600 900 1200<br>e Simulated data a<br>Carbon price distribution<br>104 4+ mamm—_Carbon budget distribution<br>ye Average carbon price - uncertainty<br>3S M@ Average carbon price - base<br>2 103<br>#<br>fo)<br>Y<br>a<br>5 1074%%<br>O<br>2 ~~<br>10!a e ————<br>10°<br>300 600 900 1200 0 300 600 900<br>Remaining carbon budget when Remaining carbon budget when<br>information is revealed (GtCO2) information is revealed (GtCO?2)<br><!-- End of picture text -->
**H4 (social contact).** Respondents who report having higher levels of contact with Venezuelans will prefer less restrictive policies across all dimensions.

# **3. Venezuelan Migration to Colombia and Institutional Context**

The Venezuelan humanitarian, economic and social crisis over the last decade, has resulted in resulted in the outflow of over 5.91 million Venezuelan citizens (around 16% of the country’s population) from the country. Over 4.5 million have stayed in the Latin American region, with Colombian being the main host.

Colombia shares a large border with Venezuela spanning 1,378 miles (see Figure A1). In addition, Venezuela and Colombia were part of the same country in the early 19<sup>th</sup> century ( _Gran Colombia_ ). As a result, both countries have a shared history and share important cultural similarities. It is therefore not surprising that Colombia has been a major recipient of Venezuelans with an estimated 1.75 million now living in the country.<sup>3</sup> The official crossing points at the Colombia-Venezuela border have remained open for much of the Venezuelan crisis. The border crossing points were temporarily closed in 2015 but re-opened in 2016, after which a large number of migrants crossed (see Figure 1). It has always been relatively easy for Venezuelans to cross the border at unofficial border-crossing points.

<!-- Start of picture text --> Figure 1 - Number of Venezuelan migrants in Colombia<br>2000000<br>1500000<br>1000000<br>500000<br>0<br>2014 2016 2018 2020<br>Year<br>Number of Venezuelan migrants<br><!-- End of picture text --> _Source_ : Authors' calculations using data published by the Ministry of External Relations 2020. The vertical solid line indicates the border closure in 2015 and the dashed line the re-opening of the border in 2016.

> 3 This represents about 3
# **5. Conclusion**

Theories of corruption and regulation suggest that regulation often leads to corruption. This suggests a simple and practical solution, deregulation, to the vexing problem of corruption faced by the private sector. Empirical evidence on the issue, however, is rather limited. The present paper attempts to fill these gaps in the literature by using firm-level survey data and the actual experience of firms with corruption and regulation. Our results indicate a significantly higher level of overall corruption associated with higher regulatory burden. A similar result holds for petty corruption involving six specific transactions between the private firms and the public officials.

Several issues remain to be explored. We highlight a few here to illustrate the point. First, petty corruption that arises in specific transactions is likely to be better correlated with regulations that are specific to the transactions rather than the overall regulatory burden on the firms. A rigorous analysis of the issue is needed. Second, the measure of regulatory burden we used is a composite measure of the rules on the books and their implementation. However, the effect of rules on the books and their implementation on corruption may be different. This implies the optimal policy to combat corruption will also depend on the role of rules on the books vs. their implementation. Future work in the area can help shed light on the issue. Third, we explored only the overall relationship between corruption and the regulatory burden. It is conceivable that the strength of the relationship may vary depending on the sorts of firms or countries. Future work in the area can help confirm or reject any possible heterogeneity in the corruption-regulation nexus. Fourth, the empirical analysis assumed a linear relationship between corruption and regulation. It is possible that the relationship may be non-linear. Increases in corruption associated with heavier 24
and use bank-year observations from 2004 to 2009 in our study to mitigate the reserve causality problem. Although fixing the deposit insurance measure at year 2003 leads to measurement error in the deposit insurance variable and may bias us against finding any significant results, it alleviates the concern that some unobservable country characteristics drive both changes in deposit insurance and bank risk.<sup>4</sup>

> 4 It might be argued that the deposit insurance scheme in 2003 is positively correlated with government interventions in the recent crisis, which seems to favor our hypothesis that deposit insurance is associated with lower bank risk in the crisis. However, there is no evidence for such a claim and in contrast previous studies have suggested that lack 9
At temporal resolutions of one year or greater, it may be useful to augment the Hakkarainen technique with a second filter based on an econometric model like (1) above. The model relates local CO2 concentration anomalies to local emissions from industry, fires and non-industrial population sources. Model parameters provide estimates of characteristic marginal relationships between local emissions and satellite-recorded concentration anomalies, while model residuals measure the deviations of local anomalies from their expected values. In principle, model estimation could be viewed as a useful postHakkarainen filter, with trends in model residuals used for tracking changes in local CO2 emissions intensities. However, the present case is complicated by the presence of both static and dynamic variables in the model. The industry components are basically fixed effects because they are derived from fixed plant capacities, not variable outputs. Among the non-industrial components, population, income per capita and the subway variables are interpolated from observations over intervals of a year or longer. The only variables with one-month periodicity are fires, heating degree days and cooling degree days. Under these conditions, it is not clear whether the econometric post-Hakkarainen filter adds significant value to an emissions tracking analysis.

We test the filtering utility of econometric model estimation with data for 6,142 Functional Urban Areas (FUAs) with populations greater than 100,000, as defined by Schiavina et al. (2019). Our exercise controls for potential biases introduced by limited sampling within FUAs. Although the OCO-2 satellite platform provides the best available database, its coverage for our 25-km grid cells is limited by its 16-day repeat cycle, relatively narrow observation track, and the frequent occurrence of cloud cover over some areas. Within an FUA, typical concentration anomalies may differ substantially across grid cells. To cite one possible consequence, a naive trend analysis could generate spuriouslypositive results in cases where early-period observations are more numerous in lower-anomaly cells and later observations are more concentrated in higher-anomaly cells. To test for this potential source of bias, our exercise includes regressions with dummy variable controls for grid cells.

9
> The survey and the paper only cover credit and deposit services, and do not include investments, pensions, insurance or other financial products.

Broadly, the results of the survey on financial consumer protection in deposit and loan services reveal that most countries have some form of consumer protection legislation in place, but these do not often address concerns specific to the financial services industry. We also find that enforcement mechanisms are weak, partially due to lack of resources, institutional capacity, and limited enforcement powers of regulators. Although effective third-party dispute resolution mechanisms are essential to implementation of the law, such mechanisms are not common. Additionally, we compile comprehensive information on the set of laws and regulations relevant for financial consumer protection based on the responses to the survey.<sup>4</sup> As part of the analysis we conduct a number of statistical tests and discuss the challenges related to the empirical research in the area of financial consumer protection.

> 2 For an overview, see Rutledge (2010) and Brix and McKee (2010).

> 3 See Ardic _et al._ (2010), CGAP (2009), CGAP (2010) and Kendall _et al._ (2010) for further information on the _Financial Access_ database.

> 4 For a comprehensive listing of all consumer protection legal frameworks, see Annex I.

3
GEP with respect to GDP per capita is significantly lower in SSA relative to other regions while the GEP with respect to survey means is only marginally lower suggests that the passthrough between aggregate economic growth growth and household consumption growth is lower in Africa (that a given growth in per capita GDP results in less growth in survey mean consumption in SSA than elsewhere). This is confirmed by the results in Table 7, which show the results of regressing changes in survey mean consumption/income per capita during a spell on growth in GDP per capita, controlling for initial differences. On average, the passthrough between growth in GDP per capita and growth in survey means is statistically equivalent to unity, indicating that on average a 1 percent growth in GDP per capita is associated with a 1 percent growth in household welfare as measured from surveys. This result is robust to including country dummies (Column (2) of Table 7). The passthrough between economic growth and household consumption growth is however significantly lower in SSA (Column 4 of Table 7), suggesting that, relative to other regions, countries in SSA need higher economic growth rates to achieve a similar growth in household consumption expenditures as measured by surveys. This result holds when regressing household consumption expenditures from surveys on household final consumption expenditure as measured by national accounts (Table 8).<sup>15</sup> Strikingly, the elasticity between

> 12 For literature on the impact of inequality on economic growth, see, for instance,  Galor and Zeira, 1993, Bénabou, 1996; Aghion and Bolton, 1997. Dabla-Norris et al. (2015) for instance, find that rising inequality is associated with a medium-term decline in GDP growth.

> 13 Inequality as measured from surveys tends however to be substantially underestimated (see, for instance,  Chancel et al. (2023) for Africa).

> 14 As a robustness check, we assigned spells to time periods depending on which time-period “dominates” the spell. For instance, a spell that started
Dhanasekaran (2010) examines the causal relationship between gross domestic product and savings using annual data in India between 1950-51 and 2002-03. In his investigation, he adopts cointegration and causality models that allude to the presence of a stable long-run relationship between both variables. Having verified that GDP and savings are cointegrated, the Granger test was implemented in pursuit of indicating the absence of any causal relationship between both variables.

Kim’s (2010) study explores internal and external determinants that could affect personal savings in the United States using time series data between 1950 and 2007. The findings reveal that personal savings is highly dependent on personal income, tax, bank credit and employment, while the dependency ratio, current real estate loan, real interest rate, and status of economic performance are indeterminate.

Agrawal and Sahoo (2009) estimate the long-run total and private savings functions for Bangladesh using annual data during 1975 - 2004. They find that the total savings rate is mainly determined by GDP growth rate, dependency ratio, interest rates, and bank density. Additionally, the private savings rate is also affected by the public savings rate. The Granger Causality test pointed to a bi-directional causality between savings and growth. They also carry out the Forecast Error Variance Decomposition (FEVD) analysis using the Vector Auto Regression framework. The FEVD is consistent with the causality results obtained using the Granger Causality tests as well as the estimated savings functions.

Slami and Sheikhy (2008) examine the behavior of household savings in Algeria from 1970-2005; they tested several models using the Least Squares method. The following models were tested: the classical model, Keynesian model, relative income model, permanent income model, life cycle model, and Taylor model. They find that the permanent income model most accurately explains the saving behavior of an individual in Algeria. According to this model, previous savings and current available income determine savings behavior.

Nwachukwu and Egwaikhide (2007) examine the determinants of private saving in Nigeria. Their study re-estimates Nigeria’s saving function using ECM and
-|---|
|2009|Population|Near the Syrian Border|19.8%|3.3%|4.8%|
|Share||Rest of the Country|80.2%|96.7%|95.2%|
|2013|Population|Near the Syrian Border|20.2%|1.9%|36.9%|
|Share||Rest of the Country|79.8%|98.1%|63.1%|
|2014|Population|Near the Syrian Border|17.9%|2.7%|32.0%|
|Share||Rest of the Country|82.1%|97.3%|68.0%|

_Notes_ : In 2014, the population weights exclude individuals under 15 due to changes in the LFS _Source_ : LFS 2009-2014. World Bank author’s calculations.

Table 8. Households, by Comparison Group

|||Host<br>Community|Settled Migrant<br>HHs (>4 years in<br>Turkey)|Recent Migrant HHs<br>(<=4 years in<br>Turkey)|
|---|---|---|---|---|
|2009|Near the Syrian Border|3,046,843|9,744|399|
|Population|Rest of the Country|15,623,820|315,158|10,433|
|2013|Near the Syrian Border|3,384,455|6,439|5,064|
|Population|Rest of the Country|16,947,974|349,414|14,678|
|2014|Near the Syrian Border|3,176,654|10,917|18,575|
|Population|Rest of the Country|17,047,500|417,846|64,
## **CREATING A POVERTY MAP FOR AZERBAIJAN**

# **Abstract**

‘Poverty maps’, that is graphic representations of spatially disaggregated estimates of welfare, are being increasingly used to geographically target scare resources. The development of detailed poverty maps in many low resource settings is, however, hampered due to data constraints. Data on income or consumption are often unavailable and, where they are, direct survey estimates for small areas are likely to yield unacceptably large standard errors due to limited sample sizes. Census data offer the required level of coverage but do not generally contain the appropriate information. This has led to the development of a range of alternative methods aimed either at combining survey data with unit record data from the Census to produce estimates of income or expenditure for small areas or at developing alternative welfare rankings, such as asset indices, using existing Census data. This report develops a set of poverty maps for Azerbaijan which can be used by different users. Two alternative approaches to the measurement and mapping of welfare are adopted. First a map is derived using imputed household consumption. This involves combining information from the 2002 Household Budget Survey (HBS) with 1999 Census data. Secondly an alternative map is constructed using an asset index based on data from the 1999 Census to produce estimates of welfare at the rayon level. This provides a unique opportunity to compare the welfare rankings obtained at the regional level under the two alternative approaches. In order to visually present the spatially disgaggregated estimates of welfare in Azerbaijan, this project has also involved the production of a digital census map of Azerbaijan. This involved matching the Census enumeration areas to a digital settlement map of Azerbaijan. An important externality of the research is that is now possible for the State Statistical Committee of Azerbaijan to display graphically the results of the 1999 Census of Azerbaijan along with other data.

1
enter their internship. Our sample of medical students represents 49% of the 2003/4 cohort, while our sample of nursing students represents an estimated 16% of the 2003/4 cohort. The statistical analysis is based on data from three sources: (i) a self-administered (supervised) questionnaire which included contingent valuation questions; (ii) a medical knowledge test; and, (iii) data from two behavioral games.<sup>14</sup> As set out in the introduction, two major challenges are to measure the willingness to work

> 8 There are no detailed figures available for the urban – rural distribution, but from partial analysis it is clear that the distribution is biased in favour of urban areas . Data from the survey used in this paper suggests indeed that 67% of nursing students and 92% of doctors prefer to work in an urban area in the long term. In the context of Ethiopia this implies a strong preference for Addis Ababa.

> 9 The role of the public sector, both for wage setting and in the allocation of health workers also motivate the use contingent valuation in the Ethiopian context.

10 Students from private schools formally do not have an obligation. But private for profit schools are a very new phenomon (Only one school is listed to transfer from pre-accredition to full license; the school is part of ths sample- all the others remain pre-accredited), and students from non-profit schools often partcipate in the lottery.

11 Close to sixty percent state that they can get their first posting changed; about seventy percent will swap their posting, and over twenty percent would consider to pay for a swap. None of these activities are legal. 12 Seventy-one percent of hospitals, 94 % of health centers, 82 % percent of health stations and all health posts are currently run by the government. In contrast 80% of pharmacies, drug shops and rural drug vendors are privately owned.

13Details on the sampling strategy are provided in the appendix. We focus on clinical nurses
linked to changes in the country’s trade deficit or, alternatively, to changes in the availability of external finance. Thus, the above identity can be rewritten as: We add a terms of trade windfall term, _ToT_ , calculated as the difference between _EL_ expressed in constant and _EL_ expressed in current dollars. A country’s “growth spectrum” for any given year or period can then be expressed as the vector { _G_ , _EP_ , _DR_ , _EL, ToT_ }. As shown in Appendix II, the growth spectrum underpins a simple identification grid that helps separate supply from demand shocks according to their impact on the different components of the spectrum. We will now apply the identification grid to the effects on the growth spectrum of three basic types of shocks that will appear recurrently in the analysis of LA’s growth history.

12 We use logs of rates of growth and (generally) ten-year backward-looking moving averages to better approximate elasticities (hence the underlying fundamentals), as in Thirwall’s (1989) growth model. Ten-year moving averages provide a smoother, hence more revealing and less noisy perspective on medium-term growth dynamics than yearly observations. At the same time, the ten-year moving averages provide a richer view on cycles than lengthier averages. However, we will occasionally use longer (twenty-year) moving averages when seeking to illustrate longer term growth trends rather than cycles. Appendix II describes in more detail the growth decomposition database and provides a link where the ten-year moving averages version of the database may be downloaded.

11