rafmacalaba/datause-encoder-data huggingface dataset:
||[390]|[390]|
|---|---|---|
|All|0.42|0.70|
||(.40-.45)|(.67-.73)|
||6,435|8,013|

Notes: The table reports the mean access to a set of essential drugs, and minimum set of medical equipment (by country), with all individual country statistics calculated using country-specific sampling weights. 95% confidence intervals in parenthesis and number of observations (facilities) in parenthesis. Drugs take the value 1 if the health facility is stocked with oral rehydration salts, antibiotics (amoxycillin or cotrimoxazole), artemisininbased combination therapy, and oxytocin, 0 otherwise. Medical equipment takes the value 1 if the health facility is equipped with a minimum set of functional medical equipment (thermometer, stethoscope, and sphygmomanometer), 0 otherwise.

59
food consumption are large and that they are majorly driven by climate variability.

The remainder of the paper is organized as follows. Section 2 provides definitions of our outcome of interest and the rainy season quality. Section 3 explains our methodology with an overview of the food insecurity prediction model based on a cat model framework, a description of the approach used to construct RSQ indices, and the empirical strategy for the estimation of vulnerability functions. Results are presented in section 4 and section 5 concludes.

# **2 Defining and measuring food security and rainy season quality**

## **2.1 Food security**

Food security is defined as a situation in which “all people, at all times, have physical and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life.”<sup>4</sup> The concept of food security at the household-level rests on four pillars: (i) the (physical) _availability_ of food that depends on local food production, food stocks and net trade, (ii) the economic and physical _access_ to food, (iii) the _utilization_ of food that reflects the nutritional status of individual through feeding practices, food preparation, diet diversity and intra-household distribution of food and (iv) _stability_ that requires that the first three components be stable over time.

In this paper, we measure food security at the household-level with the Food Consumption Score (FCS). It is calculated as a weighted sum of reported consumption frequencies of various food groups over a period of seven days. The 8 food groups considered are assigned weights according to their nutritional importance. The highest weights are placed on food groups providing proteins such as meat (4), dairy (4) and pulses (3); the complete list of food groups and their associated weights is provided in Table A.1 in the appendix. Consumption frequencies for each food group can vary from 0 to 7 days and FCS values therefore range from 0 to 112. Standard threshold values of 28.5 and 42 are used to define situations of moderate and severe food insecurity respectively, although these food insecurity lines may be adjusted to local contexts. Higher FCS values correspond to more diversified and protein-rich diets while low values may reflect unbalanced diets with low protein intakes. The FCS essentially has the ability to capture differences in food security status across households and disparities in food insecurity rates across space. It also allows for consistent and meaningful comparisons over time, in particular because it does not require food prices in its calculation. However, the FCS does not capture the quality

> 4Source: World Food Programme/FAO.

6
as designed by the World Bank in the 1980s. These surveys allow the measurement of consumption and income sources, and we code the geographical location of PSUs with precision using the locality name. We complement these income surveys with Demographic and Health Surveys (DHS), which do not measure consumption but record anthropometric data as well as housing conditions. All sample designs are two-stage and regionally stratified, and each PSU contains between 12 and 25 households.

We choose to analyze four different welfare outcomes: consumption per capita, children’s height-for-age, access to electricity, and access to a man-made source of water. Despite the multi-topical nature of these surveys, very few other welfare outcomes are usable for comparison.

First, we construct a household expenditure variable that includes all current expenditures, such as food, clothing, transportation, housing, and imputed rents, and expenditures for education. We only exclude overly infrequent or badly measured expenditures such as those in health, durable goods, and transfers. Except for Mali, we also compute the value of the consumption of own food production, which we add to household expenditures to obtain a total consumption variable. We use monthly data on the national consumer price index and express individual household consumption at 1988 or 1993 prices. Comparisons of household consumption levels are made in US dollars at 1988 or 1993 exchange rates and prices.<sup>7</sup> We consider one important component of agricultural income, household production of the three main cash crops produced in the region: cocoa, coffee and cotton. We retain the value of output sold, either as directly reported by the households (Ghana and Guinea borders) or as physical quantities priced at the official 7For more details on the construction of variables, see section S2 of the supplemental appendix.

19
**Figure 11. Service Export Sophistication & Goods Export Sophistication**

<!-- Start of picture text --> LUX<br>IRL<br>IND GBRCHE<br>MLI CIV BEN GHA GABPNG TZACMRJAMGUYBWABLZMNGECUARMHNDLSOGMBCOGPERPRYCHLGTMLKAALBSENSLEMUSMARKGZSLVPAN ISLTUN COLJORMOZ ARGBGRAUSEGYZAFROMGRCLVARUSBRAURYTURVENLTUBOLIDNHRV CRICYPESTNORPRTPOLITATHACHNESPMEXSVNNLDAUTFRAPHLUSAMYSHUNSWESAUKORFINJPNSGP<br>SDN NAMKEN<br>NIC<br>NPL<br>PAK<br>RWA<br>BGD<br>BDI<br>45°<br>8 8.5 9 9.5 10<br>ln Goods EXPY 2005<br>10<br>9.5<br>9<br>8.5<br>8<br>7.5<br><!-- End of picture text --> _Source_ : Authors‘ calculations.

# **5. Empirical Methodology and Results**

We now turn to the analysis of the relationship between the ―dynamic commercial service EXPY‖, referred to as _Service EXPY_ ( _sEXPY_ ) and constructed as described in the previous section, and economic growth. We first present the results of the cross sectional analysis followed by various dynamic panel estimation techniques to capture the time series dimensions in the evolution of service export sophistication and its impact on growth. Since we are interested in investigating the empirical validity of a phenomenon that started in the mid nineties and its nature is changing rapidly, we choose three year panel for our analysis. The data includes up to 103 countries over the time span of 1990-2007. Choice of the time period is guided by two considerations- first, the consistent reporting of service exports in the Balance of Payments only started in the late 1980‘s and second, we are interested in capturing a phenomenon which gained momentum only in the 90s. We control for the standard determinants of growth in our reduced form regressions and include initial level of income per capita to control for the convergence effects.

The choice of explanatory variables in these growth regressions is never straightforward. A survey of empirical methodology by Durlauf et al (2005) acknowledged 43 distinct growth 17
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||**Table 10. Interna**|**tional Investme**<br>**Panel A:**<br>|**nt Positions**<br>**Role of Chin**<br>|**/World GDP:**<br>**a**<br>|**Robustn**<br>|**ess**<br>||
|---|---|---|---|---|---|---|---|
|||**Bank Loans and**|**Deposits**|**Portfolio Inve**|**stment**|**Foreign Direct I**|**nvestment**|
|**Source**|**Destination**|**2001**|**2018**|**2001**|**2018**|**2001**|**2018**|
|North|North|19.8%|19.0%|26.0%|38.0%|18.9%|29.9%|
|North|South (excl. China)|2.9%|3.3%|2.3%|6.0%|4.1%|6.6%|
|North|China|0.1%|0.3%|0.0%|0.5%|0.3%|0.6%|
|South (excl. China)|North|4.2%|3.1%|1.0%|3.5%|2.7%|6.4%|
|South (excl. China)|South (excl. China)|1.5%|2.1%|0.2%|1.0%|0.5%|2.6%|
|South (excl. China)|China|0.1%|0.9%|0.0%|0.6%|0.5%|1.3%|
|China|North|0.1%|0.4%|n/a|0.2%|0.1%|0.6%|
|China|South (excl. China)|0.2%|0.9%|n/a|0.2%|0.6%|2.1%|

||**P**|**Bank Loans and**<br>**anel B: Role of the**|**Deposits**<br>**Richest Sou**|**Portfolio Inve**<br>**th Countries**|**stment**|**Foreign Direct I**|**nvestment**|
|---|---|---|---|---|---|---|---|
|**Source**|**Destination**|**2001**|**2018**|**2001**|**2018**|**2001**|**2018**|
|North|North|19.8%|19.0%|26.0%|38.0%|18.9%|29.9%|
|North|South (excl. Top 20)|1.4%|1.6%|1.1%|3.6%|3.2%|4.6%|
|North|South (Top 20)|1.5%|1.9%|1.2%|2.9%|1.1%|2.6%|
|South (excl. Top 20)|North|1.9%|1.7%|0.2%|1.0%|1.8%|4.6%|
|South (excl. Top 20)|South (excl. Top 20)|0.0%|0.4%|0.0%|0.2%|0.1%|1.4%|
|South (excl. Top 20)|South (Top 20)|0.5%|0.9%|0.0%|0.3%|0.7%|2.6%|
|South (Top 20)|North|2.4%|1.8%|0.9%|2.7%|1.0%|2.4%|
|South (Top 20)|South (excl. Top 20)|0.3%|1.3%|0.1%|0.9%|0.6%|1.5%|
|South (Top 20)|South (Top 20)|1.0%|1.2%|0.2%|0.5%|0.2%|0.5%|

Note: This table shows the year-end value of international investment positions scaled by world gross domestic product (GDP) for alternative groupings of countries. Bilateral data are aggregated for all countries within a source region to all countries within a destination region. For each investment type and year, world GDP includes the GDP of destination countries for which we have data for that year (all source countries with data for a given year also have data as destination countries). Within each investment type and year, world GDP is the same across blocks. Panel A separates China from the rest of the South. For portfolio investment, there are no data available for China as a source of investments for 2001. Panel B separates the top-20 richest South countries from the rest of the South. The top-20 richest South countries are classified based on the International Monetary Fund's current GDP per capita in 2018 and include the following economies: Australia; Bahrain; Brunei Darussalam; Czech Republic; Estonia; Hong Kong SAR, China; Israel; Korea, Rep.; Kuwait; Lithuania; Malta; New Zealand; Oman; Qatar; Saudi Arabia; Singapore; Slovak Republic; Slovenia; Taiwan, China; and United Arab Emirates.
# **1 Introduction**

Citizens in developing countries often distrust state institutions and their ability to provide public services. When citizens distrust public institutions, they may fail to cooperate with – the state unless the state coerces them to cooperate (Besley, 2020a; Levi, 1988). For example, distrustful citizens might avoid paying taxes for public goods or settling disputes – through the judicial system actions that entail individual, immediate costs in exchange for public, long-term benefits. Increasing public trust becomes ever more critical in times of crises such as pandemics or wars when states must act swiftly and rely on its citizens to cooperate with potentially costly state measures.

The Covid-19 pandemic provides a rare opportunity to study the linkages between citizen cooperation, perceptions of state capacity, and trust in the state as a crisis unfolds. Containing the outbreak depends crucially on whether citizens voluntarily comply with government directives that limit civil liberties and thus on whether citizens trust the government and the state. While it is too early to judge whether the Covid-19 pandemic will become a pivotal moment on the scale of a war, Besley (2020b) argues that events similar to the pandemic led to substantial social and economic changes in the past. Importantly, the pandemic underscores trust in state institutions as a key outcome at stake. Governments’ management of the crisis will likely carry a lasting impact on this dimension of trust.<sup>1</sup> Communication offers an important link between public trust in the state and governments’ pandemic response. In this paper, we provide evidence on the extent to which communication designed to _(i)_ encourage support for government policy and _(ii)_ improve perceptions of state capacity can redress deep-rooted mistrust in elected governments and state institutions. Estimating the causal link between such communication and attitudes towards the state from observational data poses challenges as governments strategically adjust their communication in response to public opinion.

To address our research questions, we leverage randomly assigned informational treatments shared with Pakistani citizens through a phone survey. We conducted our experiment during the early stages of the pandemic (in May and June 2020) with 5,771 respondents in Lahore and Faisalabad, the two most-populous cities in Pakistan’s Punjab province. Pakistan provides an excellent setting to explore our research questions since many Pakistanis distrust the state and increasingly rely on non-state actors for basic services (Cheema et al., 2017, Acemoglu et al., 2020).

We designed the informational treatments to increase citizens’ support for the govern-

> 1For instance, see Daron Acemoglu’s presentation on “Unkowns, Challenges and Opportunities in the Time of Covid-19” (April 9, 2020) on the Royal Economic Society Covid-19 Hub.

2
elasticities, that determine the degree of mobility of primary factors across sectors; (iii) the flexibilities of regional investment allocation; (iv) consumer demand elasticities.

The GTAP data base, version 6, includes 87 regions and 57 commodities. For our analysis, the regions are aggregated from 87 regions to 16 regions. The regional aggregation has been selected primarily based on importance in the world production, consumption, international trade, economic development and geographic location. Thus, the regional aggregation includes five donor countries and eleven recipient countries (Table 1).

# **Table 1. Regional aggregation**

|**Acronym **|**Region **|**Type**|
|---|---|---|
|**USA**|**United States**|_Donor_|
|**CAN**|**Canada**|_Donor_|
|**WEU**|**Western Europe**(Austria, Belgium, Denmark, Finland,<br>France,<br>Germany,<br>Greece,<br>Iceland,<br>Ireland,<br>Italy,|_Donor_|
||Liechtenstein,<br>Luxembourg,<br>the<br>Netherlands,<br>Norway,<br>Portugal, Spain, Sweden, Switzerland, the United Kingdom)||
|**JPK**|**Japan and South Korea**|_Donor_|
|**ANZ**|**Australia, New Zealand and Oceania**|_Donor_|
|**EEU**|**Eastern Europe**(Bulgaria, Cyprus, Czech Republic, Estonia,<br>Hungary, Latvia, Lithunia, Malta, Poland, Romania, Slovakia,<br>Slovenia)|_Recipient_|
|**FSU**|**Former Soviet Union**|_Recipient_|
|**MDE**|**Middle East**(Turkey, Rest of Middle East)|_Recipient_|
|**CAM**|**Central America**(Mexico, Central America, Carribean)|_Recipient_|
|**SAM**|**South America**(Colombia, Perù, Venezuela, Rest of Andean<br>Pact, Argentina, Brazil, Chile, Uruguay, Rest of South<br>America)|_Recipient_|
|**SAS**|**South Asia**(Bangladesh, India, Sri Lanka, Rest of South Asia)|_Recipient_|
|**SEA**|**Southeast Asia**(Taiwan, Indonesia, Malaysia, Philippines,<br>Singapore, Thailand, Vietnam)|_Recipient_|
|**CHI**|**China**(China, Hong Kong)|_Recipient_|
|**NAF**|**North Africa**(Morocco, Rest of North Africa)|_Recipient_|
|**SSA**|**Sub-Saharan Africa**(Botswana, Rest of SACU, Malawi,<br>Mozambique, Tanzania, Zambia, Zimbabwe, Other Southern<br>Africa, Uganda, Rest of Sub-Saharan Africa)|_Recipient_|
|**ROW**|**Rest of the world**|_Recipient_|

Source: Authors’ modelling aggregation based on GTAP data base

**13**
growing high-value crops), selling directly products to markets (sell direct) and increased rural roads density.

**Table 3:  Transport Determinants of Income Derived from Agricultural Sales**

|Dependent variable: in|come (‘0,000 S<br>|hillings)<br>||||
|---|---|---|---|---|---|
||Basic<br>(1)|Controls<br>(2)|Density<br>(3)|Tororo<br>(4)|Greater than 2km<br>(5)|
|Selldirect|**150.638*****|**144.447*****|**148.927*****|**124.201*****|**126.209*****|
||(39.679)|(40.170)|(39.805)|(39.235)|(39.326)|
|Crop type|**122.013****|61.243|95.355|**257.234*****|**249.087*****|
||(59.587)|(62.682)|(62.990)|(77.457)|(78.050)|
|Yield|**0.176*****|**0.187*****|**0.207*****|**0.218*****|**0.219*****|
||(0.058)|(0.058)|(0.059)|(0.057)|(0.057)|
|HH size||1.636|2.951|3.239|2.869|
|||(3.625)|(3.658)|(3.543)|(3.570)|
|Secondary||**16.303****|8.304|6.09|5.584|
|||(6.256)|(7.143)|(6.949)|(6.977)|
|Genderof head||61.288|39.307|18.331|11.391|
|||(42.884)|(43.044)|(42.145)|(42.890)|
|# ofbikes owned|||27.646|22.141|21.175|
||||(22.737)|(22.081)|(22.123)|
|Passability|||-0.604|0.001|-0.046|
||||(0.532)|(0.545)|(0.548)|
|Road density|||**440.951***|**680.394*****|**693.383*****|
||||(247.416)|(249.809)|(250.403)|
|Tororo||||**127.105*****|**123.665*****|
|||||(37.474)|(37.699)|
|Greaterthan 2km|||||22.574|
||||||(25.407)|
|Constant|3.078|-74.553|-153.226**|-291.41***|-288.192***|
||(24.811)|(51.275)|(65.691)|(75.549)|(75.686)|
|# ofobs.|173|170|169|169|169|
|R2|0.2209|0.2631|0.3021|0.3494|0.3527|
|Significance:10%*, 5|%**,1%***. N|ote: Standard|deviation inp|arenthesis.||

**_The low impact of living inside the 2 kilometer buffer_** .<sup>21</sup> The apparent paradox lies in

the fact that increased rural roads density has a positive impact on incomes but not the 2 kilometer buffer. Therefore, some minimal road access is needed to impact economically

> 21  As mentioned before, the Rural Access Index measures household remoteness to more than 2 kilometers from an all season road. By 2 kilometer buffer, we consider the 2 kilometer bands to each side of the road.

24
<!-- Start of picture text --> 120<br>a<br>” a<br>s =<br>8 80<br>ae<br>oO<br>a NS<br>40<br>2011 2013 2015 2017 2019<br>—Ghana —SSA, average —LMICs, average<br><!-- End of picture text --> <!-- Start of picture text --> 15<br>10<br>:<br>8<br>(5)<br>5 ——_<br>ao ee<br>0<br>2011 2013 2015 2017 2019<br>—Ghana —LMICs, average<br><!-- End of picture text -->
**Table 2: Skilled and urban premiums in 2005 and 2030 in the baseline, low and high growth scenarios**

||**Skil**|**l prem**<br>**a**|**ium in ur**<br>**reas**|**ban**||**Urban**|**premium**||
|---|---|---|---|---|---|---|---|---|
||2005|Low|Baseline|High|2005|Low|Baseline|High|
|**EUR**|1.5|-2.4|-2.3|-2.4|2.9|0.0|0.0|0.0|
|**USA**|1.5|-1.5|-1.4|-1.4|2.6|0.0|0.0|0.0|
|**XHY**|1.6|-2.1|-1.9|-1.9|3.4|0.0|0.0|0.0|
|**CHN**|5.9|-1.4|-1.3|-1.4|1.9|-1.2|-2.2|-2.7|
|**XEA**|1.7|-1.2|-1.0|-1.0|5.6|-0.6|-1.4|-2.0|
|**SAS**|3.0|-0.9|-0.9|-1.0|3.6|0.9|-0.1|-1.1|
|**ECA**|2.4|-0.2|-0.1|-0.1|2.0|-0.1|-1.5|-2.1|
|**MNA**|1.4|-1.4|-1.4|-1.3|2.9|0.9|0.3|-0.3|
|**SSA**|3.7|-0.9|-0.9|-0.9|5.5|-0.2|-0.7|-1.0|
|**ARG**|2.3|-2.3|-1.5|-1.4|1.5|-0.7|-1.5|-1.6|
|**BRA**|3.1|-0.7|-0.5|-0.4|1.4|0.4|-1.2|-1.7|
|**CHL**|2.0|-2.0|-1.4|-1.1|1.7|0.9|-1.4|-2.8|
|**COL**|2.5|-0.9|-0.6|-0.5|1.3|-0.7|-1.2|-1.3|
|**MEX**|2.6|-0.7|-0.5|-0.3|2.0|-0.5|-1.7|-2.5|
|**PER**|1.8|-0.5|-0.5|-0.6|1.8|0.3|-0.9|-1.6|
|**CAC**|2.5|-0.8|-0.6|-0.7|1.5|0.3|-0.9|-1.5|
|**XLC**|2.4|-3.2|-2.5|-2.3|1.2|2.0|0.0|-1.3|

Source: World Bank staff calculations.

Note: ARG-Argentina, BRA-Brazil, CAC-Central America & Caribbean, CHL-Chile, CHN-China, COL-Colombia, ECA-Europe & Central Asia, EUR-EU27 & EFTA, MEX-Mexico, MNA-Middle East & North Africa, PER-Peru, SAS-South Asia, SSA-Sub-Saharan Africa, USA-United States, XEA-Rest of East Asia, XHY-Rest of high-income, XLC-Rest of Latin America

# **4. What happens to the middle class in Latin America and the Carribean – A global perspective**

## **Which middle class?**

In this paper, members of the middle class are individuals living with more than 10 and less than 50 dollars per day (dollars are expressed in 2005 Purchasing Power Parity terms for international comparison purposes). These bounds do not change from 2005 to 2030 since they measure the ability to purchase a given basket of goods and services.

This 10 to 50 dollars range is appropriate for identifying the middle class’ living standards in Latin America and Caribbean. In fact, the lower bound corresponds to the minimum level of income for individuals who are self-reporting as middle class earner; note also that individuals with incomes below 10 dollars a day usually do not own assets and are vulnerable to poverty. However, this definition may be less relevant for other developing regions. While LAC residents belonging to the 10-50 dollar per day group are earning incomes above the 75<sup>th</sup> percentile of their national distributions, the same 10-50 dollar group is primarily composed of the top 5% richest individuals in China, East Asia, South Asia, and Sub-Saharan Africa. As Ravaillon (2010) points 21