rafmacalaba/data-use-mentions-extended huggingface dataset:
Corn products are the core of basic food consumption for a large portion of the population (especially among the poor) above other cereals such as wheat or rice (see Table 2). In developing countries, the production of corn products typically escapes monopolistic controls in the first chain of the production since several small farms produce maize. However, the products of these small farms tend to converge into a small number of specialized firms for the stages of storage, processing, and commercialization. At this level, corn product manufacturers can exert market power.

**Figure 4. Consumer price index of selected corn products**

<!-- Start of picture text --> 150<br>140<br>130<br>120 Total<br>Maize<br>110<br>Maize Tortilla<br>100<br>90<br>Jan-10 May-10 Sep-10 Jan-11 May-11 Sep-11 Jan-12 May-12 Sep-12 Jan-13 May-13 Sep-13 Jan-14 May-14 Sep-14 Jan-15 May-15 Sep-15 Jan-16 May-16 Sep-16 Jan-17 May-17 Sep-17 Jan-18 May-18<br><!-- End of picture text --> <!-- Start of picture text --> Source: INEGI 2018.<br><!-- End of picture text --> Table 2 shows the expenditure shares on different cereal products in Mexico. The expenditure shares on corn products are relatively high among the poorest households, where they range from about 13.4 percent for the poorest to about 1.0 percent for the richest decile. Among cereal products, corn products are the most highly consumed, followed by wheat products.

**Table 2. Expenditure shares on cereal products**

||**All**|Poo|rest||Househ|old inc|ome dec|iles|||Richest|
|---|---|---|---|---|---|---|---|---|---|
# **6.1 Silencing Political Opposition through Forced Displacement**

Forcibly displaced individuals are likely to oppose the governments they hold responsible for the crises underlying their flight. Their exit, however, removes them from the electorate, depleting opposition support and diminishing the collective will to demand change from within. In Hirschman’s framework, exit and voice are substitutes. When the most dissatisfied citizens leave, those who remain face higher costs of political engagement and weaker incentives to mobilize against incumbents.

We explore this idea by examining the effects of forced migration on electoral turnout and support for the political opposition. Presidential turnout is defined as the total votes in each election divided by the electoral turnout of 2000, the earliest available before the 2013 oil price shock. We keep the electoral turnout fixed as it may be affected by displacement flows; hence, we only measure changes in the numerator. Opposition support is defined as the ratio of opposition votes (votes for candidates besides Ch ́avez or Maduro) to the electoral turnout of 2000. The year 2000 is a natural baseline because forced displacement was negligible at this time, when Venezuela was benefiting from surging oil prices. Importantly, these results are not mechanically driven by population loss, as the estimates are unchanged when using contemporaneous electoral censuses, which are available for the whole period of study (Table E.14). In our view, however, fixing the denominator in 2000 both simplifies interpretation and yields more conservative estimates, as it provides a lower bound on the effects.

Table 2 presents these results, revealing consistent negative effects of forced displacement in both electoral outcomes. The OLS estimates are again smaller in magnitude than the 2SLS estimates, consistent with the pattern documented in the development results. The Kleibergen-Paap Wald F-statistic is approximately 15, above the conventional threshold of 10, though weaker than in the development specifications, reflecting the smaller sample of five presidential election years. The 2SLS estimates in Panel B indicate reductions of 2.81 percentage points for turnout and 1.
a binary indicator for responding during the first month of the SMS survey on

> 19We calculate weights as follows. Let _wp_ be -1 multiplied by the coefficient in Table A11 Column (1) that corresponds to the page on which an identified woman appears. Let _ws_ be -1 multiplied by the coefficient in Table A11 Column (2) that corresponds to the position on the page where an identified woman appears. We compute the woman’s weight as _w_ = 1 + _wp_ + _ws_ such that women appearing on the first page in the upper left corner have a weight of 1 and women appearing elsewhere in the photo book get up-weighted to adjust for respondent fatigue.

> 20Our confidence intervals account for uncertainty in the traditional intent to treat effect as well as the estimated spillover effect which we compute using the delta method.

> 21See Table A3 for balance in the SMS survey and Table A4 for a description of the correlates of SMS attrition.

19
, 2011; Foster et al., 2016; World Bank, 2020c), the COVID-19 outbreak and the consequent economic collapse may not affect all firms equally. The effect may vary based on external attributes such as size, sector, and the gender of the owner but also by internal characteristics such as productivity, export-orientation and management practices that can potentially make them resilient to specific shocks brought about by the global pandemic. This section assesses the impact of the pandemic across the following dimensions: operation status and sales, financial fragility, and expectation and uncertainty.

## **2.1 Operating status and sales**

The pandemic has already caused massive disruption among firms in the South Asia region (SAR). The top left panel of Figure 1 presents the region fixed effects generated from the Probit regressions on the probability of a firm being open (or partially open), controlling for several key factors - the number of weeks after peak, firm size, subsector, income groups, stringency index (as measured by the Oxford COVID-19 tracker), log of population, and population density.<sup>4</sup> Our estimates show that only about two-thirds (66%) of the firms in SAR were operating at the time of the survey.<sup>5</sup> This

> 4All estimations in this paper control for these factors unless otherwise stated. The estimated fixed effects translates to the average predicted outcome within a particular group. Although India is included in the regional averages, we do not separate out specific figures for the country. Stringency data is available at: https://covidtracker.bsg.ox.ac.uk/ 5The results on operating status should be interpreted with caution because it is measured based on the firms that surveying companies were able to reach at the time of the interview.

4
Treated households also report experiencing increased COVID-19 exposure during the mid2020 period when the survey was collected, which coincided with a peak period of the pandemic (Panel D, Table 5). Treated participants displayed an average of 0.84 additional COVID-19 symptoms compared to a control group mean of 0.47 (q-value _<_ 0.01), and treated households had 0.46 more symptomatic adults and 0.17 more symptomatic children (q-value _<_ 0.01 and q-value=0.01, respectively). And perhaps related to this health shock, there is lower weekly adult income in the treatment group, which decreased by 20 USD PPP (SE 7.19, q-value = 0.02), during Jordan’s first COVID-19 lockdown. This reduction in income, however, did not persist after lockdowns were lifted (see Appendix). These households were also 16 percentage points more likely to report non-adherence to social distancing guidelines (q-value = 0.08). Increased household size, as noted above, may have contributed to increased COVID-19 exposure, especially if individuals from other households were likely to be moving in and out of the home. Another more speculative explanation is that the HSP may have allowed household members to travel outside the home more often, for instance, by improving the security of the home through better window, door and lock installations. As noted above, analysis also shows an increase in women’s physical mobility (Table B.1). Perhaps surprisingly, treated households were significantly less likely to have visited healthcare institutions at midline, with 0.56 fewer visits on average compared to 1.14 in control (q-values _<_ 0.05). A definitive explanation for the overall pattern of health and COVID-19 results remains elusive.

Despite these negative effects, there were also several indications of improved household outcomes due to the HSP. Consistent with the subsidy program relaxing the household’s budget constraint, midline survey data indicate that there were reductions
||||||||0.017|0.019+|
||||||||||(0.011)|(0.011)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State fixed effects|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|Number of observations|1700|1700|1700|1700|1700|1700|1700|1700|1700|1700|
|Adjusted R-squared|0.353|0.381|0.357|0.385|0.358|0.385|0.364|0.391|0.359|0.386|

Notes: Estimations consider changes in district-industry employment shares in urban areas for 1994-2005. Positive values indicate an increase across the period in the urban employment share of the district-industry. Explanatory variables are calculated from the 2001 Census or the 2000 manufacturing surveys as indicated in the text. Change in urban population share is calculated as the change in urban population from the 1991 Census to the 2001 Census. Estimations report standard errors clustered by district, include state and industry fixed effects as indicated, have 1700 observations, and weight observations by the interaction of log district size and log industry size. + significant at 10% level; ++ significant at 5% level; +++ significant at 1% level.
our information experiment.

# **4. Information and Quality Ratings Experiment**

# **4.1 The Sample**

We aimed to conduct our information and quality rating experiment on similar types of firms to those that had been part of the earlier GEM project. To do this, we hired Kantar/TNS RMS Nigeria, the same survey firm that had worked on the GEM project to conduct a baseline survey of firms in Lagos and Abuja operating in the same five sectors as had been the focus of GEM (light 8
.9<br>85.0<br>80 72.0 80 69.1<br>60 60<br>High exports High imports<br>40 Low exports 40 Low imports<br>20 20<br>0 0<br><!-- End of picture text --> Source: Own elaboration based on Population Census, Integrated Public Use Microdata Series and Customs data. Notes: (1) The sample is restricted to the urban and semi-urban municipalities with complete data in the whole period. (2) A municipality has relatively high exports (imports) if its exports (imports) per 2000 worker ratio in 2015 is above the median across municipalities and low exports (imports) if its ratio lies below the median. (3) Labor informality rate is measured as the percentage of employees without pension or retirement benefits at work.

In addition, this study also sheds light on the role of geography, sectors and types of trade which can all explain differences in local labor market outcomes. As for trade, we specifically delve into the role of final versus intermediate imports and GVC spillovers. Drawing on Economic Census data, the analysis 7
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 21 Ahrend, R., Farchy, E., Kaplanis, I., & Lembcke, A. C. (2014). What makes cities more productive? Evidence on the role of urban governance from five OECD countries. OECD Regional Development Working Papers, 2014(5), 0_1.

Barlow, I. M. (1991). Metropolitan Government. London; New York: Routledge.

- Blair, J. P., Staley, S. R., & Zhang, Z. (1996). The central city elasticity hypothesis: A critical appraisal of Rusk's theory of urban development. Journal of the American Planning Association, 62(3), 345-353.

- Bollens, S. A. (1986). A political-ecological analysis of income inequality in the metropolitan area. Urban Affairs Quarterly, 22(2), 221-241.

- Branson, J., Campbell-Sutton, A., Hornby, G. M., Hornby, D. D., & Hill, C. (2016). A geospatial database for Latin America and the Caribbean (draft version 1). Geodata, University of Southampton.

- Cadena, A., Remes, J., Manyika, J., Dobbs, R., Roxburgh, C., Elstrodt, H. P., ... & Restrepo, A. (2011). Building globally competitive cities: The key to Latin American growth. McKinsey Global Institute.

- Cao, Z., Wu, Z., Kuang, Y., Huang, N., & Wang, M. (2016). Coupling an intercalibration of radiancecalibrated nighttime light images and land use/cover data for modeling and analyzing the distribution of GDP in Guangdong, China. Sustainability, 8(2), 108.

Carroll, M. L., Townshend
Our data include information on quantities and values of the products produced, and raw inputs (domestic and imported) used by each plant. These are both defined at a highly granular level, namely nine digits _Klasifikasi Komoditi Indonesia_ (KKI), a more detailed classification based on KBLI. In our sample, each plant produces on average 2 products and 25% of the plants produce more than one product. On average, each plant uses four different varieties of raw inputs. The drawback of the high level of detail in materials’ data is that sometimes there are coding mistakes by the reporters. Appendix B.2 describes in detail the methodology we use to clean the data. After computing unit prices by dividing value with quantities, we use them to construct plant-level price deflators (see Appendix B.5).

One challenge of the _Statistik Industri_ data is the lack of complete series of capital stock. Earlier studies tried to re-construct capital stock series applying the _Perpetual Inventory Method_ (PIM) to the first year of capital stock data reported by the plant (Amiti and Konings, 2007; Javorcik and Poelhekke, 2017). However this imputation method crucially relies on the capital value self-reported by the plant the first year this data is available, which is not necessarily accurate.<sup>13</sup> One potential advantage of using PIM is that purchase and sales data might be more accurate relative to selfreported value of the stock, requiring an appropriate calculation of market values and depreciation. However, PIM needs to rely on measures of capital depreciation, which are difficult to accurately estimate. To mitigate such tradeoff, we have adopted a hybrid strategy, which is described in Appendix Appendix B.3.

# _2.5. Estimating Productivity and Markup Measures_

Our main dependent variables are estimates of plant-level productivity and markup. Unlike most existing studies using industry-level price indexes to deflate nominal variables, the granularity of our data allows us to calculate plant-specific output and input price indexes. This allows us to mitigate the bias deriving
<!-- Start of picture text --> 5000<br>4500<br>4000<br>3500<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>Districts<br>2005 2010 2013 2016<br>Average weekly real wage (takas)<br>Dhaka Gopalganj Magura Jamalpur Rajbari Bhola Nawabganj Rangpur Patuakhali Jhalokathi Rajshahi Mymensingh Bogra Jaipurhat Netrokona Panchagarh Kurigram Habiganj Dinajpur Faridpur Shariatpur Norshingdi Bagerhat Noakhali Kushtia Moulvibazar Madaripur Nilphamari Thakurgaon Gaibandha Jhenaidah Lalmonirhat<br><!-- End of picture text --> Source: Calculated by the authors using data form the Bangladeshi and Indian LFS.

19
<!-- Start of picture text --> 12% 11.3%<br>11% 10.2%<br>y 1% 2% 9.5%<br>Boy g,<br>< 7.8% 7.9%<br>3 8% 7 7.1%<br>8 8.3% \_ .<br>3 7% ~—7.7%<br>= . 7.2%<br>6% 6.8% :<br>5% 5.8% 5.7%<br>5.3%<br>4%<br>2008 2009 2010 2011 2012 2013 2014<br>—®— expenditure/ per capita.<br>—®— expenditure per capita, excluding durables<br>@— expenditure per capita, excluding durables and health<br><!-- End of picture text --> <!-- Start of picture text --> 0.42<br>0.40 40<br>39 0.39<br>0.38<br>. 03<br>= 036 36 ;<br>oO” ; 6 6 0.36<br>35 MR .35<br>0.34<br>0.34<br>33_@ 0.33<br>0.32 . .32<br>0.30<br>2008 2009 2010 2011 2012 2013 2014<br>@—expenditure per capita<br>—®— expenditure per capita, excluding durables<br>—®— expenditure per capita, excluding durables and health<br><!-- End of picture text --> <!-- Start of picture text --> 80%<br>70% 67%<br>6<br>60%<br>s
implementable, replicable, and incentive compatible. Furthermore, from an institutional viewpoint, the SPI is designed to be better aligned with other capacity assessment tools. For example, it is consistent with the Systematic Country Diagnostics (SCD), a new assessment tool that the World Bank recently developed to identify a country’s priority development areas.

On the other hand, the increased numbers of indicators and countries covered for the SPI require more data compared to the SCI. The data for the SPI come from different sources, ranging from established databases with international organizations to national statistical agencies’ websites. Given this heterogeneity of data sources, some data inconsistency may be expected. The complexities with the former are not just simply linearly related to the increase in quantities, but also concern data quality challenges such as missing data. Missing indicators for less-developed countries can represent a quite different data situation from missing indicators for richer countries. While the former may suggest weak statistical capacity, the latter might be explained by the little need of tracking certain indicators. A specific example is child stunting. In the SPI database, we assign maximum scores to richer countries (including the OECD and the EU) for this indicator assuming that richer countries have achieved the desired target of reducing child stunting and provide the benchmark against which progress for developing countries can be measured.

# **5. Some Empirical Illustrations**

# **5.1. Data**

We have constructed the database for the SPI for 2016. Efforts are underway to construct indicators for the SPI after 2016, as well as going backward several years for better comparison. We provide below some empirical illustrations using the available SPI data and various other data sources including the World Bank’s World Development Indicators database (World Bank, 28
<sup>t</sup> i + δZi]+ ηi (4) where E indicates the i-th individual entered into employment, Y<sup>2020</sup> is a dummy indicator which takes the value of 1 for the first quarter of 2020 and 0 for first quarters of earlier years, T is a vector of year dummies (t=2018, 2019, 2020) and X is a vector of individual characteristics, i.e. gender, age, country of birth, education, and region. Both models in (3) and (4) use a probit link function.

# **5. Analysis of impacts on the labor market**

## **5.1. Labor market indicators**

Table 1 shows non-seasonally-adjusted monthly estimates of the main labor market indicators from the LFS since the onset of the pandemic. The February figures show the improvement in the Greek labor market during the period prior to the pandemic.<sup>15</sup> Most notably, the number of workers unemployed in February 2020 was 16.9% lower than in 2019, corresponding to a nearly three percentage point drop in the unemployment rate (from 19.8% to 17%). On the eve of the pandemic, unemployment had been falling compared to the previous year and there had been a modest increase in employment.

The data for March and April present the evolution of labor market conditions while the lockdown was in place. Normally these are months when seasonal factors lead to job creation and an improvement in labor indicators in Greece. In 2020, employment numbers rose very slightly in March and April, less than they had in 2019. The unemployment figures are interesting. Between February and March 2020, the number of unemployed actually decreased by 13.8%, larger than the decrease in 2019.

> 15 Disaggregated trends in the main labor market indicators for 2018Q1, 2019Q1, and 2020Q1 are in Appendix Table A.1.

18
# **Appendix B: Data Definition and Sources**

INFLATION ≡ CPI inflation rate obtained from source b (line 64).

GAP ≡ Deviation of the real GDP growth from its trend, which is computed using Hodrick-Prescott filter with a smoothness parameter of 100. Annual real GDP growth figures are taken from source a.

FP ≡ Percentage change in the index of food production. Data on food production index is taken from source a.

OIL ≡ Percentage change in dollar price of oil obtained from source d.

DEMOC ≡ The democracy indicator. It is an additive eleven-point scale (0-10). The operational indicator of democracy is a weighted average of the scores of the competitiveness of political participation, the openness and competitiveness of executive recruitment and constraints on the chief executive, taken from source c.

DURABLE ≡ Regime durability. The number of years since the most recent regime change. The first year during which a new regime is established is set as baseline year and DURABLE is assigned the value of zero for that year. Each subsequent year adds one to the value of the variable, taken from source c.

BS ≡ Overall government balance expressed as percentage of gross domestic product, taken from source a.

CF ≡ Ratio of net private capital flows to gross domestic product, taken from source a.<sup>26</sup> The data set covering the period 1980-2001 was compiled from the following<sup>27</sup> sources:

- a. World Development Indicators, World Bank; b. International Financial Statistics CD-ROM of the IMF, Version 1.1.54;

- c. Polity IV Database; d. Federal Reserve Economic Data (FRED);

- e. IMF Country Staff Reports.

> 26 For Israel and South Africa the series are constructed by taking the ratio of the financial account—obtained from source b (line 78bjd)—to nominal GDP measured in USD, taken from source a. For South Africa, the financial account is proxied by the Net Errors
source is information collected by World Bank staff, and Iran, where we use food price data compiled by the Central Bank.<sup>7</sup> To our knowledge there is no consistent information on food prices in Libya, which is hence omitted from the analysis.<sup>8</sup> For some countries food prices refer to urban or capital centers. Overall, because of the great emphasis on consistency checks with other than official sources, we believe the food price dataset provides accurate information.

Food inflation rates for eighteen MENA countries are presented in the Annex, Figures 1A-C. We provide data for almost all MENA economies for the period from December 2006 to October 2011. The beginning of this period is commonly regarded as the start of the first global food price shock. In nearly all countries food inflation registered a peak in 2008, yet there are substantial differences in food inflation patterns across the Arab world, reflecting idiosyncratic factors at the domestic level. In general, domestic food inflation in MENA remained lower than global food inflation, and in most Arab economies food inflation remained positive even during the period when international food inflation turned negative. This fact is consistent with asymmetric transmission of international food price fluctuations to domestic markets in the Arab world – a hypothesis investigated in the second part of this paper.

As a first attempt to understand the exposure of the MENA countries to global food shocks, we present ratios of net imports to domestic consumption of key food commodities, together with their respective import and consumption shares, by country (Table 1A) and country groupings (Table 1B). The ratios of net imports to domestic consumption are indicative of the dependency on foreign imports to satisfy domestic demand for grains, edible oils, meat, and sugar. The analysis covers grain and non-grain imports; the latter account for approximately half of all food imports in developing MENA (Table 1B). In the GCC their share is just under 30 percent.

Dependency ratios, presented in Table 1A, are