rafmacalaba/data-use-mentions-extended huggingface dataset:
surrounding grounds. This can be described as a sort of shadow public rental sector despite the legal privatization of individual apartments. It indicates that the mass privatization policy produced high, unintended inefficiencies, by failing to produce homeowners willing and able to undertake necessary maintenance and capital repairs of common property in the M-F buildings that still dominate the post-Soviet urban areas.

Considering the conceptual and practical difficulties of implementing efficient common-property management systems in M-F building - difficulties not limited to the transition countries - the question arises whether the close correlations in mature market economies are not in fact a market rather than a policy outcome. The subsequent question is whether the observed deviations in transition countries can be called policy failures.

# **_Rental Housing Demand and Rent Levels_**

The analysis of the household survey dataset suggests that tenure patterns in the region follow broadly those observed in mature economies for age structure and incomes. However, the markets seem far too small to accommodate young and mobile households in quantitative terms. Conditions for young migrants in Romania and Russia seem most severe.<sup>101</sup> This allows for the conclusion that low availability of urban rental housing reduces labor market adjustment at least in some transition countries.

As far as income is concerned, demand patterns in transition countries are skewed by the fact that young and mobile households, which show particularly high demand for rental tenure, are among the economic winners of transition. Hence the finding of both low- and high-income households in the private rental sector in some studied countries.

Standard predictions of filtering theories, which suggest low willingness of social stock tenants to move as incomes rise, are partly confirmed by the empirical findings. This result holds for both Poland and Russia; in the marginalized public rental systems of the remaining countries, higher-income households have privatized their units. Filtering in the remaining public sector stock in the region is often promoted by the particular incentive structure imposed by “slumification,” which leads to an adjustment of building quality downward to rent levels.

There is evidence
. 2020). The GHG footprint of a given diet varies largely by country.<sup>1</sup> This paper looks at how diets have changed in their nutritional value and their GHGe over time, and what an aspirational diet would look like for Bangladesh— one which improves nutritional value and limits environmental costs. It particularly assesses the environmental costs and nutritional value if people consumed as per the national food based dietary guidelines, or if people consumed as per the planetary health diet. This paper also assesses whether a dietary shift towards some aspirational diet is feasible given the trajectory of change in diets in the past.

# **2 Methodology and data**

This work reviewed Bangladeshi household-level consumption data in 2000 and 2016 and, based on those, modeled two benchmark diets — 1) the human health diet recommended by Dietary Guidelines for Bangladesh (FBDG) and 2) the human and planetary health diet recommended by the EAT-Lancet report (hereinafter referred to as EAT-Lancet) (Loken and DeClerck 2020). We analyzed each of the three types of diet from the aspects of average per adult male equivalent (AME) food consumption (by 7 and 10 food groups), macro- and micronutrient intake, and GHGe. Food consumption is derived from household consumption and expenditure data.

> 1 Bangladesh was not included in this cross-country study.

3
focus of this paper is on relative outcome differences between the finance and marketing treatment groups, both of which were compared to the same control group.

# **6. Conclusion**

This paper develops and studies four distinct hypotheses on pathways to profits for small businesses exposed to finance and marketing training in South Africa. Through a randomized control study of 852 firms, the analysis finds significant improvements in profitability from both types of business training interventions. However, the pathways to achieve these gains differ substantially between the two groups. The marketing group achieves greater profitability by adopting a growth focus of significantly higher sales, improvements in inventory, and hiring more employees. In contrast, the finance group adopts an efficiency focus of significantly lower costs. To precisely identify pathways, the analysis finds that these profitability gains are achieved by both groups adopting business practices related to their respective training.

To test further hypotheses on benefits of each type of training, the analysis shows that marketing and sales training is significantly more beneficial to firms that ex-ante have less exposure to different business contexts; while entrepreneurs who have been running more established businesses prior to training benefit significantly more from finance and accounting skills.

These results have important implications for practitioners and policy makers. In terms of practice, for multinationals a better understanding of the pathways to profitability and the heterogeneity in managerial capital across small businesses can enhance the success of market expansion and customer segmentation strategies. In addition, our results provide useful benchmarks that multinational firms can use to learn how best to develop business skills among their distributors and suppliers in emerging markets.

This research is also important to policy makers wishing to promote business skills training in emerging markets. The World Bank alone invests almost $1 billion per year on skills training programs (Twose 2015), yet the evidence on their effectiveness lags far behind the policy and funding interest. “Vast armies” (de Mel, McKenzie, and Woodruff 2010) of micro businesses continue to populate the poor parts of the world, and very few appear
of the area in delegation _d_ covered by moun-<br>tains. We calculate the fraction of mountains within a<br>delegation as the share of grid cells within the adminis-<br>trative division that are covered by one of the following<br>three categories of mountains: (1) the elevation is be-<br>tween 1000 and 1500 meters with either a slope of at least<br>5 degrees or a local (i.e. within a 7 km radius) elevation<br>range exceeding 300 meters, (2) elevation exceeding 300<br>but lower than 1000 meters and a local (7 km radius) ele-<br>vation range exceeding 300 meters, and (3) Inner isolated<br>areas (_≤_25 sq.km in size) that do not meet criteria (1)<br>or (2) but are surrounded by mountains.|Author’s calculations based on<br>Mountains of the World 2002<br>(UNEP-WCMC, 2002)<sup>[</sup><sup>_f_]</sup>|
|Nighttime lights|Total sum of the average annual delegation _d_ level night-<br>time light intensities for the 1995-2010 period and for the<br>year of 2014. Li et al. (2020)’s dataset contains annual<br>time series data. Intensities are expressed in digital num-<br>bers.|Author’s calculations based on<br>the Harmonized Global Night-<br>time Light dataset 1992-2018 (Li<br>et al., 2020)|
|Number of<br>Daesh recruits|Number of individuals in delegation _d_ who joined Daesh<br>in Syria or Iraq between 2013 and 2014. Note that the<br>personnel records only present information on male re-<br>cruits.|Daesh personnel records|
|Population|Number of
The trade data are reported in current US dollars, and are deflated by the US CPI. We recognize that ideally we would use bilateral price indices to deflate trade between different country pairs but such bilateral deflators are not available. However, the presence of the very general fixed effects has the consequence also of implicitly deflating the trade data. The data are implicitly deflated by prices that vary by importer, product and time; by importer, product and exporter; and by exporter, product and time. They are, however, not deflated by prices that vary along all four dimensions (importer, exporter, product, and time).

Exchange rate data are from the IMF’s International Financial Statistics (IFS) database. In the theoretical framework, the key price that determines the transmission mechanism of exchange rate changes is the price in the importing market charged by Chinese exporters. Equation (6) suggests that this price is determined by the domestic price of the good in China, the Chinaimporter bilateral exchange rate and the passthrough. Since we parameterize the passthrough, the relevant changes to focus on are those stemming from changes in domestic prices in China and the exchange rate. Hence, our bilateral exchange rate is deflated by China’s CPI.

Before we present the econometric results, it is worth looking at some basic data. Figure 2 plots China’s average index of competition (where the average is over all exporters and products). The index is measured in two ways consistent with the discussion in the analytical section. Both the VBI and the CBI rise over time, consistent with China becoming a bigger and more diverse exporter. The CBI shows in particular that by 2008, on average, China occupies nearly all the product space of other developing country exporters. Figures 3a and 3b plot the same indices but disaggregated by region. These charts show that China’s overlap with all regions has risen steadily over time, with the level of the overlap greatest with other exporters in Asia (over 95 percent in
areas very near refugee settlements.<sup>11</sup> Most relevant to our study, Kreibaum (2016) reports that access to private primary schools (but not public schools or health services) has increased at a greater rate as a function of refugee presence. They measure refugee presence at the district level (refugee share of the district population) and focus on the south-western districts between 2002 and 2010. Thus, by including all of Uganda at the parish-year level as well as the post-2014 influx, we extend their analysis. In sum, our study is the first to measure the effects of refugee presence on service delivery outcomes in Ugandan host communities.

> 10See Gianvenuti, Jalal and Kirule (2020) for more details.

> 11Note that both Zhu et al. (2016) and d’Errico et al. (2021) use original cross-sectional surveys. Without pre-treatment data, it is harder for them to make causal claims.

12
show that a transfer recipient’s _perception_ of a donor’s wealth affects the value of the transfer between them, conditional on the donor’s actual wealth. Social pressure is clearly a factor in determining patterns of redistribution, and buying small quantities may be a useful way to deter requests from one’s social network.

A particular feature of the data allows us to estimate a proxy for household-specific social tax rates and test whether buying in bulk is associated with higher social taxes. In addition to recording purchases, diary keepers also recorded the item description, quantity, unit, and estimated value of items sold or given away.<sup>21</sup> These disposal events are recorded on the household’s daily transaction log, in the same manner as the purchases. To estimate a proxy for the household-level social tax rate, we sum the total value of items outgoing over the two-week study period, and divide by the total value of items incoming. Because we are calculating a proxy for a household-level variable, we use every row of the transaction diaries for this calculation, not just the rows associated with the 19 items that are the focus of the rest of this paper. To our knowledge, this is the first accounting measure of social taxes—one based on diary records from a large sample of households—in the literature.

Table 10 shows descriptive statistics for the social tax estimates. The uppermost panel shows the value of resources outgoing in the form of sales or gifts, divided into subcategories. The most important group is “meals and snacks,” which accounts for 40% of outgoing resource flows. The largest share of entries under “meals and snacks” are those for “full meals”—guests at the household table (see Appendix section S6 for details about the item categorization). The lower part of the table shows the value of incoming resources. The mean tax rate is 14.4% (s.d. = 26.5%).<
the foreign-owned sector is especially high with three quarters of migrant workers being women. In 2004, the proportion of individuals who were unemployed (defined as “those who are not working but sought employment”) is relatively low for both migrants and non-migrants registering 0.9 percent and 1.1 percent respectively. For migrants, the proportion of those who were unemployed in origin areas<sup>20</sup> is 2.6 percent, which is a much higher rate than the average proportion of the unemployed after migration.

The average monthly earnings reported in the VMS,<sup>21</sup> are 1.25 million dong, 1.11 million dong and .91 million dong for “State”, “Private” and “Foreign” workers respectively,<sup>22</sup> whereas the migrants who went to the Central Highlands earned .50 million dong on average. Although the income levels of migrants before migration are not reported in the VMS _,_ the survey questionnaire includes a question about migrants’ self-assessments of the change in income before vs. after migration. Table 1 demonstrates that approximately 84.7 percent of migrants feel that their incomes are better whereas few migrants (3.5 percent) reported that their incomes are worse. For those who work for foreign firms, the proportion of migrants who responded that their incomes are better is slightly higher than the average of migrants at 87.9 percent. Migrants who went to the Central Highlands did not improve their incomes as much: the proportion of those who feel that their incomes are better is lower than average at around 74.1 percent whereas that of those who think that their incomes are worse is higher than average (9.7 percent).

> 20 In the survey, each migrant is asked about the main activity in the last six months before migrating. The proportion of those who responded as “unemployed and looking for work” is defined as the proportion of unemployed in the origin
the past four decades: in 1982, 1991, and 2009. Also, the decline is largest during the 2007-09 global financial crisis, followed by the global recessions of 1982 and 1991, respectively. In this way, it accurately captures the relative severity of the three recessions, with the 2007-09 global financial crisis being the deepest and the 1991 global recession being the mildest. Moreover, it also captures periods of global expansions such as post-1991 and post-2009.<sup>18</sup> The factor registers a decline in 2001 when the U.S. economy went through a recession, while global economic activity experienced slow growth. Because the U.S. economy has a relatively large influence on global economy activity, it is no surprise that the global GDP factor picks up changes in U.S. GDP. However, is the estimated factor merely a stand in for fluctuations in U.S. economic activity? Figure 3 (top) plots the global GDP factor alongside a common factor estimated using GDP growth rates for 32 countries that exclude U.S. GDP data. The plot shows that the two common factors, estimated with and without U.S. data, move closely together; the correlation is 0.94. Moreover, the respective median factor loading for the Unites States is 0.46, indicating that the U.S. contribution to the global GDP factor is sizeable but not dominant.<sup>19</sup> Moreover, in

> 18See Kose, Sugawara, & Terrones (2020) for identification of global economic recessions and expansions since early 1950s.

> 19Note that the common factor estimated without U.S. GDP data may still pick up changes in U.S. economic activity that spillover to other countries. A more detailed assessment of whether the fluctuations in the global GDP factor capture broad-based changes in economic activity across countries can be seen in Figure 13, Appendix VII.1. Factor loadings are in Figure 12, Appendix VII.1.
9.2|2.4|7.0|11.9|7.0|
|Europe, Middle East & North Africa|3.3|6.0|15.1|8.0|-3.5|9.4|12.1|5.8|
|Sub-Saharan Africa|5.2|4.3|13.0|7.4|3.0|2.7|12.3|5.9|
|**Memo:**|||||||||
|BRICS 5 countries|11.6|10.9|18.3|13.6|8.3|3.9|18.7|10.1|
|OPEC 12 members|1.8|5.7|15.9|7.7|-2.8|8.9|11.5|5.7|

Note: The classification of manufacturing products in exports and imports is defined as SITC 5+6+7+8-68 in revision 3.

Source: Based on UN COMTRADE Statistics.
population sampling frame, and generate enumeration areas in an effort to overcome the lack of a recent population census (section 2). The SHFS sampling strategy bears resemblance to the strategy proposed by Muñoz and Langeraar (2013), which relies on satellite imagery and grid cells to build a sampling frame in Myanmar. Wardrop et al. (2018) review various efforts to produce spatially disaggregated population estimates based on satellite imagery, in contexts where census data are absent or inaccurate. Barry and Rüther (2005) and Turkstra and Raithelhuber (2004) employ satellite imagery to study informal urban settlements in South Africa and Kenya, respectively, while Aminipouri et al. (2009) estimate various slum populations in Dar‐es‐Salaam, Tanzania. Himelein et al. (2016) compare the viability of various satellite and area‐based sampling methods in second‐stage sample selection in Mogadishu, Somalia.

Second, risks to the safety of field staff required spending as little time in enumeration areas as possible. One strategy to address this issue is to call or message respondents on their mobile phones and not visit dangerous areas at all. A growing body of literature explores the use of mobile technology in this context (e.g. Demobynes and Sofia, 2016; Dillon, 2012; Firchow and Mac Ginty, 2016). However, administration of necessary consumption modules to estimate poverty is not feasible via phone surveys.

To address security concerns, the SHFS adapted logistical arrangements, sampling strategy, and questionnaire design to limit time on the ground. In logistical arrangements, a detailed and timely security assessment ensured that the enumeration areas to‐be‐visited were safe on the day of fieldwork. The fieldwork protocol was designed such that teams would spend as little time as possible in any given region and draw little attention, ensuring enumerator and respondent safety (section 3.2). Concerning sampling strategy, it was not feasible to conduct a full listing of all households in an enumeration area, as this was too time‐intensive
changes at the provincial spatial scale. Further, Türkiye’s economy is rapidly urbanizing. This suggests that an analysis of economic activity within and across urban agglomerations may complement the analysis of traditional administrative units (i.e., provinces) conducted above. Such an analysis is presented below.

# **3. Booms and Busts: Analysis of Nighttime Lights**

This section complements the previous one by using remote-sensed nighttime lights data to analyze Türkiye’s business cycles. We do this for five reasons. First, as mentioned above, and particularly relevant to rapidly urbanizing economies like Türkiye, there is a misalignment between administrative delineations and economically meaningful units like systems of cities and urban corridors. Second, the analysis of province-level data does not capture changes at more granular spatial scales. Third, as Henderson, Storeygard, and Weil (2012) highlight, administrative data may be measured with error or may have other biases. Fourth, we are interested in understanding changes before 2004 (when our province level data begins) — particularly during the 2000-2002 recession and during the following 2002-2004 rebound. Fifth, data on nighttime light luminosity and other imagery is available in near-real time, which would allow us to more accurately monitor economies at meso-level scales and inform policy responses when they are most needed.

We use two different sets of measures (based on different satellites and sensors) that estimate nighttime light emissions. For the 2000-2012 period we use a time-consistent version of the DMSP-OLS data (Zhang, Pandey, and Seto (2016)), while for 2013-2019 we use a time-consistent 10
UCM also generates margins of error around the estimate of governance that reflect this imperfection. These estimates of governance and the associated margins of error are the core output of the WGI. In the remainder of this section, we spell out the methodology in further detail.

# 3.1 An Unobserved Components Model for Governance

For each of the six dimensions of governance discussed in Section 2, we assume that the relationship between the observed data and the unobserved level of governance is as follows:

## (1)

## y(c, k) = α(k) + β(k)(g(c) + ε(c, k))

where y(c, k) represents the observed data for country c on data source k ; g(c) is the unobserved level of governance in country c which we assume has zero mean and unit standard deviation across countries; ε(c, k) is the measurement error in data source k for country c which we assume has zero mean and variance σ<sup>2</sup> (k) ; and α(k) and β(k) are source-specific parameters that map the unobserved level of governance in the country into the observed data. To avoid notational clutter, we omit time subscripts, and our notation does not distinguish between the six dimensions of governance. In practice, we estimate the model in Equation (1) separately for each of the six dimensions of governance, in each of the time periods covered by the WGI.

The parameters α(k) and β(k) capture differences in the units used by different data sources to measure governance. For example, one data source might measure governance on a six-point scale, while another data source might measure governance on a ten-point scale. More subtly, one data source might adopt the convention of using its entire range of units to measure governance, while another data source might have its scores clustered in a narrower band than its underlying units
Khemani, Stuti. 2016. _Making Politics Work for Development: Harnessing Transparency and Citizen Engagement_ . World Bank Policy Research Report.

McKinsey Global Institute. “OGD: Unlocking Innovation and Performance with Liquid Information.” McKinsey Center for Government (October 2013).

Misra, V. “Pilot Study 1, Andhra Pradesh, India: Improving Health Services through Community Score Cards: Learning Notes,” Social Accountability Series, World Bank, Washington, DC (2007). Mungiu-Pippidi, Alina. 2014. “Quantitative Report on Causes of Performance and Stagnation in the Global Fight against Corruption.” Anti-Corruption Policies Revisited. ANTICORRP. Pillar 4, WP3.

ODAC (Open Democracy Advice Centre). “The Right to Know, the Right to Live: Turning the Right to Information into a Living Reality,” Cape Town (2010).

OGD Institute. “Benchmarking OGD Automatically,” Technical Report 000 (2015).

———. “OGD in Government: How to Bring About Change,” White Paper 001 (2015).

- ———. “Supporting Sustainable Development with OGD,” White Paper 000 (2015).

OSJI (Open Society Justice Initiative). _Transparency and Silence: A Survey of Access to Information Laws and Practices in 14 Countries_ , Open Society Institute, New York (2006).

Peixoto, Tiago and Jonathan Fox. 2016. “When does ICT-Enabled Citizen Voice Lead to Government Responsiveness,” Institute of Development Studies, Vol. 47, Issue 1.

Taylor, Frederick Winslow. _The Principles of Scientific Management_ , New York and London: Harper & Brothers (1911).

Tinholt, Dinand. _Characterization Study of the Infomediary Sector_ , Capgemini, Spanish Open Data Portal Annual Report (2013).

United Nations. “A World that Counts: Mobilizing the Data Revolution for Sustainable Development,” Independent Expert Advisory Group on Data Revolution for Sustainable Development (November 2014).

United Nations, Department of Economic and Social Affairs. “United Nations E-Government Survey E- Government for