rafmacalaba/data-use-mentions-extended huggingface dataset:
Table A2: Food Insecurity Experience Scale (FIES) Questions in Ethiopia

||Pre-COVID-19 Surveys||COVID-19 Surveys||
|---|---|---|---|---|
|FIES|Question|Recall|Question|Recall|
|FS1|Did you worry that your household would not have<br>enough food?|7 day|Was there a time when you or any other adult<br>in your household were worried about not having<br>enough food to eat because of lack of money or<br>other resources?|30 day|
|FS2|How many days have you or someone in your<br>household had to rely on less preferred foods?|7 day|Was there a time when you, or any other adult<br>in your household, were unable to eat healthy<br>and nutritious/preferred foods because of a lack<br>of money or other resources?|30 day|
|FS3|How many days have you or someone in your<br>household had to limit the variety of foods eaten?|7 day|Was there a time when you, or any other adult<br>in your household, ate only a few kinds of foods|30 day|
|39|||because of a lack of money or other resources?||
|FS4<br>FS5<br>|How many days have you or someone in your<br>household had to limit portion size at meal-times?<br>How many days have you or someone in your<br>household had to reduce number of meals eaten<br>in a day?|7 day<br>7 day|Was there a time when you or others in your<br>household had to skip a meal because there was<br>not enough money or other resources to get food?<br>Was there a time
stronger general trust. Individualistic culture (Individualistic Culture) is captured by the individual empowerment index (version 2) in WVS, which measures the extent to which the people in a society are mentally and habitually empowered to make their own choices and pursue them in their actions. Since countries with higher individual empowerment are usually viewed as being more individualistic, this index serves well as a proxy of individualistic culture. For countries having more than one wave of survey covering a period of 1996-2014, we use the data from the latest wave. Trust and Individualistic Culture have significant missing incidence, available in less than half of the number of countries for all other variables. To ensure a reasonable sample size, and to

> 5Data available at World Development Indicators | DataBank (worldbank.org).

> 6 In particular, we measure the distance to Beijing in kilometers. Other functional forms do not make a significant difference.

12
- O’DONNELL, M., U. NWANKWO, A. CALDERON, C. STRICKLAND, ET AL. (2020): “Closing Gender Pay Gaps,” .

- PARSONS, C. A., J. SULAEMAN, M. C. YATES, AND D. S. HAMERMESH (2011): “Strike three: Discrimination, incentives, and evaluation,” _American Economic Review_ , 101, 1410–35.

- ROUSILLE, N. (2021): “The Central Role of the Ask Gap in Gender Pay Inequality,” _Working paper_ .

- SIN, I., S. STILLMAN, AND R. FABLING (2020): “What Drives the Gender Wage Gap? Examining the Roles of Sorting, Productivity Differences, Bargaining and Discrimination,” _The Review of Economics and Statistics_ , 1–44.

- SOFOLUKE, R. AND O. SOFOLUKE (2021): _Twice As Hard: Navigating Black Stereotypes and Creating Space For Success_ , New York, NY: DK.

- STATISTA (2019): “Online Shoppers in Africa by Gender,” https://www.statista.com/statistics/1190608/online-shoppers-in-africa-by-gender/.

- SUBRAMANIAN, N. (2021): “Workplace Attributes and Women’s Labor Supply Decisions: Evidence from a Randomized Experiment,” _Working Paper_ .

- UNCTAD (2018): “UNCTAD B2C E-COMMERCE INDEX 2018: FOCUS ON AFRICA,” Tech. rep.

- USAID (????): “Gender Equality and Women’s Empowerment in Kenya | Kenya | Archive - U.S. Agency for International Development,” https://20122017.usaid.gov/kenya/gender-equality-and-womens-empowerment-kenya.

- VESTERLUND, L. (2018): “Knowing When to Ask: The Cost of Leaning-in,” Working Paper 6382, Department of Economics, University of Pittsburgh.

WORLD BANK, G. (2013): “Gender at Work: A Companion to the World Development Report on Jobs,
42

# **Brazil**

The official position of the Government of Brazil is that the downstream petroleum sector was completely deregulated effective January 2002. The government has nevertheless continued its informal policy of keeping domestic prices of petroleum products artificially low through losses suffered by Petrobras, a semi‐public oil company that controls more than 99 percent of the refining capacity in the country.

A history of producer prices of diesel is shown in Figure 9. Also shown are the spot prices of diesel on the U.S. Gulf Coast—which has the lowest prices of three benchmark markets in the United States, the other two being the West Coast and New York Harbor—during the same period. Brazil is a net importer of diesel. Therefore, import‐parity prices should form the basis for economic pricing, which means that shipping costs should be added to the U.S. Gulf Coast prices. In 2014, according to the annual statistics provided by the regulatory agency, 44 percent of diesel imports was from the United States and another 40 percent was from India. It is clear that, for many months since 2011 and until the recent precipitous drop in world oil prices, producer prices were lower than even FOB prices on the U.S. Gulf Coast and consistently lower than import‐parity prices. Producer prices of gasoline and diesel did not change between February and September 2015.

**Figure 9: Comparison of producer prices of diesel fuel in Brazil with U.S. Gulf Coast FOB diesel prices**

<!-- Start of picture text --> 2.50<br>R$/liter<br>2.00 US$/liter<br>U.S. Gulf Coast FOB<br>1.50<br>1.00<br>0.50<br>0.00<br>Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Jul 14 Jan 15 Jul 15<br>R$
# **Productivity effects of land rental markets in Ethiopia: Evidence from a matched tenant-landlord sample**

# **1. Introduction**

It is well recognized that there is considerable potential for the agricultural sector to help improve labor productivity in rural areas through technology, integration into supply chains, and better functioning of factor markets including those for land and labor to facilitate off-farm employment (World Bank 2007). In the context of economic growth, low-cost and flexible mechanisms to bring land to its most productive use can help in the transformation of rural economies by allowing those with little comparative advantage in agriculture to take up non-agricultural employment and thus lay the basis for a more diversified growth (de Janvry _et al._ 2001). Given their greater flexibility and limited upfront capital requirements, rental markets allow greater flexibility than sales and allow owners working off-farm to continue enjoying the benefits of land ownership (Otsuka and Hayami 1988) while at the same time drawing on non-agricultural income (Deininger and Jin 2006). There is, however, concern that, especially if other markets do not work well, contractual arrangements adopted in such markets may fail to fully realize their potential. However, few empirical studies have explored productivity impacts of different forms of land rental, making it difficult to assess the scope and possible nature of policies to help realize such potential more fully. Doing so for the case of Ethiopia‟s highlands, where rental markets are quite active, is the goal of this paper.

We use a sample of some 1,700 households and their 800 rental partners to address two questions that are critically important for productivity and welfare. First, the extent to which „Marshallian inefficiency‟ affects productive efficiency of sharecropping and whether parties can alter associated impacts. Second, whether landlords‟ choice of partners with greater agricultural ability affects the productivity impact of land rental market and the extent to which policy can help to encourage productivity-enhancing outcomes.

Use of within-household fixed effects estimates for owner-cum-tenants to answer the first question suggests that output
. Also, Big Data are particularly precise when both market share and coverage -i.e., urban areas- are high. Thus, information on geographic regions (e.g., rural areas) where coverage is low, or market shares are small might be taken with caution. Notice that this type of population is hugely mobile and difficult to locate if the information is not frequently updated. This poses significant trade-offs between having the most updated data and the cost of getting it. Lastly, although the confidentiality of users is not an issue in this type of exercise (the number of Venezuelan phones was requested as an aggregated PSU-level figure), our telecommunication partner still censored the precise number when it referred to fewer than five clients.

> 15 Notice that the Government of Ecuador approved Executive Decree 826 on July 25, 2019 for conducting a migratory registry to be finalized by end March 2020.

10
<!-- Start of picture text --> Nigeria Afghanistan<br>- =) =- =<br>se] co « =<br>+ = o<br><0 On <0 w<br>c= Ty] c= Ow<br>8* v= 8 ‘=<br>ml fas ms Ooom<br>1940 1960 1980 2000 2020 1940 1960 1980 2000 2020<br>year year<br>Kernel density estimate<br>Ww<br>o<br>3<br>Fo<br>Bo<br>As<br>5<br>oOo<br>0 50 100 150<br>loss<br>kernel = epanechnikov, bandwidth = 2.5534<br>— Conflict == loss<br><!-- End of picture text -->
(3), where _εij_ is a random error term and all the other terms as explained before. It is the presence of conflict, which we observe, that allows us to detect a discrepancy between desired and actual sovereignty. By construction, conflict enters (3) with a negative sign.

A complication in estimating such a model is that conflict is determined endogenously by the interaction of factors influencing sovereignty demand and supply. In a sense, conflict is the ―price‖ of freedom. The model should thus be estimated via instrumental variables regression to identify the effect of conflict on sovereignty, but we do not believe that a valid instrument can be found. No prior study of the effects of decentralization on any number of outcomes has proposed a valid instrumental variables approach to estimate the effects of conflict on decentralization or vice versa. Thus, we are unable to make causal claims, but still believe that it is informative to present correlations on the correlates of decentralization, and the negative correlation between conflict and actual (observed) sovereignty would serve as a plausibility check on our model.

# **3. Data**

As units of analysis, we use the primary administrative subdivision (provinces, states, republics, departments) of decentralized countries, which we define as all countries with multitiered political systems or unitary systems with significant economic decentralization.<sup>30</sup> Forty eight countries meet our decentralization threshold, yielding 876 regions. We have one observation per region for the period from the mid-1990s to the early 2000’s. The number of regions in each country ranges from 2 in Serbia-Montenegro to 89 in Russia (we correct for this 30 The list includes all countries that are not identified as having centralized systems in Polity III database and we add countries according to other criteria and our own research to expand the period coverage of Polity III. The coding procedure is explained at length in a supplement. Coding notes for this dataset are too lengthy to present here and will be posted online upon publication
individuals are employed for a wage, either as production workers or managers. Managerial roles in the public sector receive a gender-specific premium. In equilibrium, the most skilled women operate businesses in the private sector, mid-high skilled women transition between managerial roles in the public sector and home production, mid-low skilled women engage in production at home, while the least skilled join the production labor force. Regarding men, the lowest-skilled individuals work as production workers, some with mid-skills assume managerial roles in the public sector, and the most skilled run private businesses.

We calibrate the model to match data from the Arab Republic of Egypt. This country is an interesting case study since it exhibits a disproportionate share of workers in the public sector. Public sector employment is close to 22% of total employment (18% for men and 42% for women), about 7 percentage points higher than the average of 108 non-MENA economies.<sup>3</sup> Many of these employees are highly educated: among workers with college education, around 25% of men and two-thirds of women work in the public sector. Even though the model structure is simple, we show that it is capable to match several features of the allocation of talent in the Egyptian economy. Moreover, the tractability of the model allows to easily extend the analysis to several other developing economies.

We compute aggregate productivity gains, in terms of output per worker and total factor productivity (TFP), from adjusting the current public sector premium and the size of the public sector to their optimal levels. We show that reducing the public sector premium from the current to the optimal level would yield aggregate gains of 12% for output per worker and 8% for TFP if Egypt exhibits the elasticity of the private sector to public goods of the average developed economy (0.1, Bom and Ligthart 2014). The lower the value of this elasticity, the higher the productivity gains in our baseline economy from reducing the current public sector premium and the current share of
A. Sanghi and D. Johnson goods originating from China (UN Comtrade 2015).<sup>2</sup>

# **2.2.1 Kenya imports rubber manufactures from China**

As in other Sub-Saharan African countries, Kenya mainly imports manufactured products from China. Figure 4 shows the top four import categories from China: manufactured goods classified chiefly by material made up 35 percent, machinery and transport equipment were 31 percent, miscellaneous manufactured articles were 24 percent, and chemical and related products were 8 percent of total imports from China. In 2012, the top goods from China were rubber products, footwear with outer soles of rubber or plastic and woven fabrics of synthetic filament (5).<sup>3</sup> <!-- Start of picture text --> 5000<br>4000<br>3000 Chemicals and related products<br>Machinery and transport equipment<br>Manufactured goods. Ex: leather, rubber, fabrics<br>2000 Miscellaneous manufactured articles. Ex: apparel, footwear, furniture<br>1000<br>0<br>Commodity<br>Imports (USD millions)<br><!-- End of picture text --> Figure 4: Kenya imports rubber manufactures from China (2010-2014) Source: UN Comtrade database 2015 Kenya imports a large amount of rubber products because China has a comparative advantage in cheap manufactured goods; its manufacturing sector and infrastructure is also much more integrated in global value chains. The special economic zones for manufacturing in Kenya have poor infrastructure and a weak rule of law, inhibiting its sales and growth. The barriers to production will prevent local producers from competing with Chinese goods.

> 2UAE exports almost 60 percent of products originally from China according to the United Arab Emirates Information Guide `http://alluae.ae/uae-imports-exports-re-exports/`

> 3We use the 6 digit Harmonized System (HS) classification from the United Nations

6
# **1. Introduction**

COVID-19 has spread relentlessly across the world, considerably affecting nearly all aspects of livelihood nearly everywhere. The health crisis quickly led to an economic crisis, forcing governments to issue support measures for households and the private sector to help cope. This paper complements the growing research into whether these support measures were useful. In this paper, we study whether the support measures provided during the COVID-19 pandemic by the governments of two rather distinct countries, El Salvador and Georgia, were effective in terms of some measures of the private sector performance. We use firm-level data from the World Bank’s Enterprise Surveys (ES) that were completed pre-pandemic, and two rounds of COVID Followup Surveys (COV-ES) which recontacted all the respondents of the ES to collect data about firms’ sales, employment, finances, and government support.<sup>2</sup> The ES are representative samples of each country’s private sector enabling analysis of patterns affecting a large part of each country’s economy.

We use data from only two countries, El Salvador and Georgia, to help alleviate concerns about selection bias, which are central to the estimation of effectiveness. Unlike in many others, the support measures in these two countries were not conditional on a specific set of firm outcomes during the pandemic. Given that fiscal capacity is not unlimited, governments across the world tried to target the neediest and often created eligibility criteria for support measures. These criteria often included specific parameters from the firms’ experience during the pandemic, such as a drop in revenue, or workforce, etc. While this may be a good way to target the neediest, it greatly complicates estimation of the effectiveness of the support as it becomes nearly impossible to form appropriate groups of firms to compare their performance. The Governments of El Salvador and Georgia also tried to target the neediest, but they used pre-pandemic fixed characteristics such as firm size and sector. In selecting the set of countries, we were also mindful of the
age cohort are calculated using non-parametric locally weighted regression (LOWESS) with a bandwidth of 0.3. The following data sources are used: China: 2009 CHNS and the 2008 CHARLS pilot; Indonesia: 2007 Indonesia Family Life Survey (IFLS); Korea: 2006 Korean Longitudinal Study of Aging (KLoSA); United States: 2008 Health and Retirement Survey (HRS); United Kingdom: 2008/9 English Longitudinal Study of Ageing (ELSA).

30
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Annex Table A2

# Table A2: INDIA **COUNTRY and PUBLIC SECTOR SOLVENCY and SUSTAINABILITY**

||**1995/96**|**1996/97**|**1997/98**|
|---|---|---|---|
|**Country Solvency**||||
|Non-Interest Current Account (NICA) ($ millions)|-1,650|-588|-365|
|NICA/Exports|-3.5%|-1.1%|-0.7%|
|Real Export Growth|31.5%|7.0%|10.1%|
|Difference between 10% and Real Export Growth|-21.5%|3.0%|-0.1%|
|Country Solvency Index|-43.1%|_5.5%_|-0.1%|
|Country Solvency Adjustment (% of Exports)|-39.6%|6.7%|0.5%|
|**Public Sector Solvency**||||
|Primary Balance (LCU billions)|-115|-151|-266|
|Primary Balance/GDP|-0.9%|**-1.1%**|-1.7%|
|Real GDP Growth|8.0%|7.3%|_5.0%_|
|Difference between 10% and Real GDP Growth|2.0%|2.7%|_5.0%_|
|Public Sector Solvency Index|-3.5%|2.5%|3.1%|
|Public Sector Solvency Adjustment (% of GDP)|-2.5%|3.5%|4.8%|
|**Country Sustainability**||||
|Gross
In this section, we present and discuss the estimation results of transaction cost equations. Subsequently, we assess these estimation results on the basis of survey data of traders. We continue with presenting selected regime probabilities computed on the basis of predicted transactions costs. The section is completed with estimations that attempt to explain fluctuations in regime probabilities.

# _<<B>>Estimation of transaction cost equations_

To apply the proposed approach, we use Malawi district-level monthly maize price data complemented with other variables over the period from June 1999 to October 2009 (see appendix for details). The district is the unit of observation: buyer (seller) or deficit (surplus) locations refer to deficit districts (surplus districts), where the difference between district production and district requirements determines whether a district is a seller district (production – requirements > 0) or a buyer district (production – requirements < 0). For the estimation of transaction cost equations, we use the specification proposed in equation (1) conditioned on a selection of observations specified in equation (6). For comparisons over time, unit transaction costs and fuel prices are deflated with the Malawi consumer price index. Additionally, the unit transaction cost, distance, and fuel price are transformed into natural logarithms. Table 1 reports estimated transaction cost equations. Absolute t-statistics are given in parentheses below the coefficient. We have omitted the coefficients and statistics of constant terms and dummy variables. Columns in the table represent estimation results with different values for three parameters: minimum per person per day caloric intake (2200 or 2300 kcal), excess production over requirements above which district exports are triggered (two or four months of maize requirements, respectively; φ in equation (3a)), and the distance above which domestic trade is unlikely to occur (200 or 300 km).

|**Table 1**|**Estimating Transaction **|**Cost Equ**|**ations: Sample Restricted by Distance**<br>|
exports for China, we also assume an additional 5 percent decrease in supply across the board to account for factors such as the negative impact of Covid-19 in trade logistics and border management. Given the substantial uncertainty surrounding the current health crisis, the next section analyzes additional scenarios where the initial shock associated to Covid-19 is smaller and larger than for China.

Under this set of assumptions, results on the initial decrease in global export supply due to Covid-19 are presented in Table 3. Covid-19 is estimated to lower the world’s export supply of food by 12.7 percent, on average. The negative impact ranges between 2.4 percent for cocoa beans to 23.6 percent for rice. Many important staple foods, including most cereals such as barley, oats, rye and wheat and meslin and other products such as eggs, poultry, swine meat and potatoes are estimated to have sizeable drops in export supply of over 15 percent. These estimated effects should be considered as initial impacts, as the longer-term effects of Covid-19 on the export supply of food will largely depend on how long it will take the health crisis to be contained -something that it is difficult to predict at this stage. With this caveat in mind, we next study the impact of changes in export supply on food prices under different trade policy scenarios.

To calculate the impact of an initial shock in export supply on international prices, the percentage change in the export supply of each food product-country is divided by the import elasticity of that product.<sup>15</sup> Elasticities at the HS 4-digit product level are estimated for a panel covering 152 importing countries by Fontagné, Guimbar and Orefice (2019) and are assumed to be constant.<sup>16</sup> Price changes for individual country-products will depend on the concentration of their imports from the top-50 countries most affected by Covid-19 and on the demand elasticities. Aggregate results on the impact on prices of an initial decrease in export supply due to Covid-19 are presented in Table