rafmacalaba/data-use-mentions-extended huggingface dataset:
Implicit in our analysis is an East Asian social welfare function which treats persons irrespective of national borders. It is closely related to analyses of the global income distribution, which presumes a cosmopolitan social welfare function.<sup>3</sup> In opinion surveys, East Asia shows some of the highest levels of concern over inequality being too high (Ruggeri Laderchi et al., 2017). Similarly, an informal survey of policy makers in Asia shows a rising concern with inequality (Kanbur and Zhuang, 2012). This appears to be inconsistent with the within-country results presented in Figure 1, which showed a stable Gini index for the average country. However, as Alvaredo and Piketty (2014) argue in their analysis of inequality in the Middle East, inequality at the regional level could be important for explaining these growing concerns. This paper aims to provide the latest evidence on this regional inequality for East Asia.

Over the 25-year period, the East Asia and Pacific Gini index increased by approximately 4.2 points (or 10.8%), reaching 43.3 in 2012. On the one hand, the strong growth in average consumption in China had an equalizing effect on (between-country) East Asian inequality. On the other hand, the increasing region-wide inequality trend is explained mainly by rising within-country inequality in the two most populous countries, China and Indonesia. This is consistent with the trend in the population-weighted average Gini index in Figure 1b (dotted blue line), which captures national inequality for the average person in East Asia. In other words, while inequality has been approximately flat for the average country, it has increased strongly for the average person and, as this paper shows, for the region as a whole. Considering how growth was shared along the distribution, we find that the growth incidence was broadly pro-rich – in line with the observed increase in inequality. The pattern of growth was such that the richest 5% of East Asians received more than 20% of the gain in regional
13 barriers – to agriculture and to processed food – in developed (and even more so in developing) countries are much greater than the supports provided through subsidies; (ii) trade barriers distort both production and consumption whereas domestic support only distorts production (and less so the more those measures are decoupled); and (iii) market access barriers vary much more across countries and commodities, and hence generate larger costs, than do domestic support measures.

These results point to the importance of ensuring that market access is high on the Doha Development Agenda’s agricultural negotiations.<sup>18</sup> Recall, though, that the GTAP database does not include the tariff equivalent of non-tariff import restrictions such as technical barriers to trade. That means market access is even more important than the above model results imply, but it also means improving access requires not only tariff cuts but also stronger disciplines on non-tariff import restrictions. If the DDA can at least result in a lowering of bound agricultural tariffs down to or below applied rates, that will provide a much stronger base from which to seek a lowering of non-tariff barriers in the future.

> 18 This echoes the more-general point made by Snape (1987, 1991) that if multilateral trade negotiators focus just on reducing border measures, domestic subsidies will generally be of minor significance to trade because of their dependence on explicit treasury outlays that are subject to annual budget scrutiny.
data sets have questions on functional difficulties that differ from the WGSS are shown in Table 2.1. Six data sets are censuses that do not cover all six domains: Dominican Republic, Panama, Rwanda, Tanzania, Vanuatu, and Vietnam.

The questions could have wording that differed from the WGSS formulation. If they do, they are shown in Table 2.1. For instance, in South Africa’s 2010 Census, the walking question is worded differently: "Walking _a kilometer_ or climbing a flight of steps" and the cognitive domain is covered by two separate questions for remembering and concentrating. Another example is that of the 2010 Census in Indonesia, which grouped the cognitive and communication domains in one question: "Do you have difficulty remembering, concentrating, or communicating with others due to a physical or mental condition?" Five censuses have a yes/no answer scale: Dominican Republic, Mexico, Panama, Philippines, Rwanda.<sup>1</sup> Meanwhile, Indonesia has only three-level scale: ‘no difficulty’, ‘slight’, and ‘severe’. Tanzania, and Vietnam’s answer scales refer to ‘a little difficulty’ instead of ‘some difficulty’. For the Papua New Guinea HIES, the answer scale is reversed from that in the WGSS as follows: 1. Cannot at all, 2. A lot of difficulty 3. Some difficulty 4. No difficulty.

Only in few data sets (e.g. Ethiopia LSMS) does each individual in the household consistently answer about his/her functional difficulties. In the other countries, it is the household respondent who answers on behalf of every individual in the household.

Three HIES, two LFS, five LSMS, and two general surveys (Afghanistan and South Africa) under study have adopted the WGSS questions to the letter. Event for this subgroup of countries, caution is needed while comparing results across countries. What people may understand from the questionnaire and how they reply could differ given different
standard deviations of idiosyncratic component (i.e. _σ_ Δ _uit_ ) and of the differenced commodity price data (i.e. _σ_ Δ _CPit_ ), would equal one if all variation were idiosyncratic (i.e. _R_<sup>_2_</sup> = _σ_ Δ _uit_ / _σ_ Δ _CPit_ ). The second statistic, equal to the ratio between the standard deviations of the common factor (i.e. _σFt_ ) and that of the idiosyncratic component (i.e. _σuit_ ), would be zero if all variation were idiosyncratic (i.e. _σFt_ / _σuit_ = 0). Finally, we present the factor loadings ( _λi_ ) from equation (1); they should be larger for series that are more related to the common factor.

**Table 2. Cross Sectional Heterogeneity in Commodity Prices**

||_σ_Δ_uit_/_σ_Δ_CPit=R_<sup>_2_[1]</sup>|_σFt_/_σuit_<br>[2]|_λi_<br>[3]|
|---|---|---|---|
|Aluminum|0.915|0.307|-0.301|
|Bananas|0.919|0.198|-0.248|
|Beef|0.910|0.191|-0.235|
|Cocoa|0.975|0.166|-0.215|
|Coffee|1.000|_0.023_|-0.057|
|Copper|0.728|**0.581**|-0.863|
|Cotton|0.879|0.421|-0.342|
|Hides|0.996|_0.036_|0.043|
|Jute|0.975|0.223|-0.281|
|Lamb|0.970|_0.083_|-0.075|
|Lead|
31 using earlier data, for example the 1976-77 Malaysian Family Life Survey. I leave this question as a possible avenue for further research.

# **VI. Success Factors**

The micro-level data on the composition of households with respect to age, type of employment and earnings, together with evidence of differential compensation for citizens vs. non-citizens suggest that immigration policy, possibly in conjunction with complementary measures, can control the quantity and quality of foreign labor. It is likely that additional factors played a role in policy effectiveness in Malaysia and Singapore, in view of their particular country circumstances. In the first place, both countries exhibit tremendous institutional capacity to implement and enforce regulations. Singapore's priority of the state over individual preferences facilitates policy effectiveness, albeit at the expense of individual rights. The permit system, involving issuance of new permits to large numbers of applicants as well as the renewal of expired permits subject to eligibility, requires substantial monitoring, enforcement and effective administration. Singapore's Work Permit and Employment Department of the Ministry of Labor is efficient in processing applications, especially in view of the volume of business; this is reflected by an average processing period of 3 working days for electronic submissions and 7 working days for all others (Singapore Ministry of Labor, 1995). Malaysia is less successful at enforcing and regulating permits, due to greater challenges with respect to the size of its foreign labor force and its vast territory and borders. Nevertheless, Malaysia exhibits significant institutional capacity that has improved during the recent wave of development.

Additional explanatory factors of Singapore's and Malaysia's effective foreign labor management include the structure of their economies and the associated skills mix which accommodates foreign workers. For example, Singapore's production base is highly diversified and labor-intensive in fairly high value-added industries. Moreover, government policy promotes private sector development and encourages investment and foreign participation with a view to competing on international markets. Both countries are outward-oriented and export-driven, in addition to importing intermediate goods, labor and technology.
level of this deficit and the speed of fiscal adjustment. As mentioned earlier, a budget deficit of, say, 4 percent of GDP may be adequate for one country but not for another because fiscal policy is an integral part of the policy framevork. It may be adequate for an economy with single digit-inflation but may be woefully inadequate for a country with 70 percent inflation. Similarly, a target reduction in the budget deficit, (say, 2 percentage points of GDP), may not be nearly enough for an economy where the exchange rate is being used as a nominal anchor but may be sufficient for one in which money supply serves as the anchor.

13. Because of the strong linkages between fiscal, monetary, and other macro-economic policies, the appropriateness of a particular fiscal deficit target depends on other targets for growth, inflation, and extemal and internal debt. Unless these linkages are brought out explicitly, conventional fiscal measures (such as the deficit to GDP or government debt service to revenues) shed little light on the appropriate level of fiscal deficit and consequently

> 3 It is not unusual to find data three years or older in many official or unofficial documents, for example, the IMF publication on budgets, the Government Financial Statistics.

> 4 Even if a consolidated fiscal account were available, the line drawn between public and private enterprises would always be arbitrary. Even within a closed economy, the conventional measure of fiscal deficit (the difference between total revenues and expenditures) has limitations in assessing the impact of fiscal policy on aggregate demand. For developing countries, these problems are compounded by inflation, debt arrears, and quasi-fiscal activities, as well as the temporary nature of some fiscal measures (such as the sale of public assets). Indeed, a recent IMF study has concluded that there is no such thing as the fiscal deficit. See M. Blejer and A. Cheasty, Eds. How _to Measure the F iscal Deficit._ IMF, 1993.
governance within a large state in Brazil has provided evidence useful for policy toward hospital governance throughout that country in ways that are more relevant than if the findings came from comparisons between different countries (see La Forgia and Couttolenc 2008).<sup>6</sup> Comparisons between countries are also useful, but require much more careful consideration of the package of institutional and contextual factors that are likely to support any given governance arrangement. For example, the independence of health insurance institutions can improve performance when it limits undue political interference and can worsen performance when it insulates the institution from accountability for results – the implications of this kind of independence are contingent on interactions with other factors which may vary significantly across countries (Savedoff and Gottret 2008).

Cross-country indicators are easier to identify for governance performance than for governance determinants because the former are more easily defined against an objective standard. For example, an objective standard for unexcused absences might be “below 3%” but a similar objective standard cannot be defined for the degree of decision-making autonomy for health facilities. A recent effort to select health governance indicators (Lewis and Pettersson 2009) implicitly confirms this observation by mostly citing crosscountry studies when proposing performance measures such as informal payments (Lewis 2006) and absenteeism (Chaudhury et al 2006), but citing case studies when discussing determinants such as community monitoring (Bjorkman and Svensson 2007), performance pay (Solon et al 2009), and autonomy (PATHS 2008). The evidence base for assessing governance determinants relies almost entirely on within-country comparisons – either between different governance arrangements or assessing changes over time that can be attributed to a particular governance reform.

# **_Choosing units of analysis_**

Regardless of whether we are looking at within or between country governance measures, it is necessary to identify which units of analysis will be addressed. Health sectors vary so much around the world that it is difficult to make comparisons, let alone conduct research, without defining the organization or system that is being analyzed.

Countries are the
in-kind support to farmers, alleviated credit constraints, increased agricultural investments, and facilitated the procurement of seeds, fertilizers, and pesticides (Varshney et al., 2021). In Ethiopia, households participating in the Productive Social Safety Net Program experienced an increase in food insecurity of only 2.4 percentage points compared to 11.7 percentage points for other households. While previous research has shown that transfers are an effective means to stop individuals from falling into poverty traps (Balboni et al., 2021), these findings provide evidence that social assistance measures have been successful in shielding households from the economic fallout caused by the COVID-19 pandemic.

Second, because the COVID-19 crisis has impacted households and workers across the income distribution and not only the poorest, the social assistance measures implemented can be considered more as mitigation tools rather than purely a poverty alleviation mechanism (Bhorat et al., 2021).

This paper uses the COVID-19 Impact Monitoring Survey (CIMS, 2020) matched to administrative data to analyze a range of outcomes. The analysis is motivated by the question: does an ECT mitigate the microeconomic impact of COVID-19 on poor households that rely on informal self-employment for subsistence? The existing data allows us to draw short-term and medium-term correlations between the ECT and various objective economic security outcomes pertaining to labor market participation, food consumption and children’s human capital and subjective indicators of psychological wellbeing and satisfaction with the government’s response to the pandemic. Round 1 of data collection overlaps with the rollout of the program, allowing to analyze a cohort of beneficiaries as early as June-August 2020. The Round 2 of data collection (November-December 2020) allows to identify a second cohort of late beneficiaries, thus providing information on their outcomes, but also on the outcomes of the first cohort of beneficiaries. We identify correlations between receiving the ECT and the various outcomes for Round 4
Figure 18: Impacts by time spent on Daraz <!-- Start of picture text --> Estimated coefficient of time spent on Daraz<br>with 95% CI, for various outcomes<br>Product management Order management Review<br>Customer messenger Financial statements Promotion tools<br>.1<br>.05<br>0<br>-.05<br><!-- End of picture text --> In Table A.14, we look at the impact of tenure on Daraz on registration status, as a rough test of the hypothesis that participation in digital platforms induces firms to register their businesses. We find that time on Daraz is positively correlated with business registration – that firms on Daraz are less likely to report being unincorporated – but has little impact on VAT registration. This result is robust to the inclusion of controls for firm age and country fixed effects.

# **5 E-commerce Sellers Are a Selected Group of Firms**

The Chaldal and Daraz surveys reveal that while the firms selling on these e-commerce platforms may vary substantially in terms of size and other key characteristics, they are a selective group of firms. Specifically, their owners or senior managers have above average levels of education and are also comparatively young.

Consider the case of Chaldal. Since the average size of Chaldal sellers is quite large, it would be misleading to compare Chaldal sellers to typical small and micro firms in Bangladesh. A recent World Bank survey of small to large-sized Bangladeshi manufacturing firms provides a more useful comparison point (Gu, Nayyar and Sharma (2021)). Note that the sample of the World Bank manufacturing sector survey is tilted towards large firms, compared to the Chaldal seller sample: 86 percent of the firms in the former have more than 20 employees, compared to only 60 percent in the latter. And yet, only 54 percent of the senior managers of the firms in the Bangladesh 25
examine the incidence and distribution of household debt across the region as well as how household debt is correlated to the impact of the crisis at the household level.

# <u>Household Debt towards Banks</u>

Our analysis is based on the 2010 wave of the EBRD-World Bank Life in Transition Survey (LITS).<sup>10</sup> Within this survey at least 1,000 interviews were conducted with randomly selected households in each country. In the 21 countries covered by this report a total of more than 23,525 households were interviewed. The LITS questionnaire gathers information on household composition, housing, and expenses, as well as the current and past economic activity of the respondent. Importantly for our purpose, the 2010 wave of the survey elicits information on the incidence and type of bank debt in the household. Households which own their dwelling are asked whether they have a mortgage. If they do have a mortgage they are asked about the type of mortgage they have, in particular whether it is denominated in local or foreign currency. Further households are asked whether any member of the household has a debit or credit card. The LITS dataset includes sampling weights to account for the differences in the ratio of sample size to population size across countries, as well as for sampling biases within countries. The data

> 10 We thank the EBRD for advanced access to the LITS 2010 survey data.

29
Cochabamba than in La Paz.

Santa Cruz also accounts for the largest share of production of soybeans and sunflower (98% and 100% respectively), the main inputs in production of cooking oil. Figure (6) shows that in Santa Cruz, soybean output per capita reaches 502 kilograms. Despite this strong concentration of output in one department, cooking oil price differences across departments are not significant from a statistical and economical point of view (less than one per cent). With respect to La Paz, in Santa Cruz consumers pay about 0.6% more for cooking oil, and in Cochabamba only 0.1% more. As a processed product, the portion of the final price that is explained by transport costs may be relatively low. In addition to this, there is upward pressure in the price of cooking oil in Santa Cruz as an important portion of exports is canalized through that department.

Similar to the cooking oil case, price differences in wheat flour across cities are not significant. Although half of wheat output is concentrated in Santa Cruz, where output per capita is about 32.5 kilograms per annum (13% is produced in Cochabamba - output per capita is about 10.5 kilograms), it is still likely that all cities are net consumers, as Bolivia relies substantially on imported wheat and wheat flour (mainly from Argentina and from the US). This, together with nature of the product, with a low portion of the final price being explained by transport costs, may explain these low price differences.

Figure (8) shows that output of sugar cane is concentrated in Santa Cruz - about 1781 kg/per capita. In turn, the price of refined sugar there is 7.3% lower than in La Paz (see RHS map).<sup>18</sup> Figure (9) shows output and price differences for potatoes. The lowest price is found in Cochabamba, where output per capita is the highest (among the three regions considered
# **9 Annexes**

## **9.1 Survey Data - Construction of Individual Selection Variables**

_Public Sector Motivation (PSM)_ – In order to measure bureaucrats’ and politicians’ motivation to serve in the public sector, we use Perry’s Public Sector Motivation Index (Perry, 1996). Annex I presents the version used in the survey in Uganda: it is composed of 36 questions measuring motivation in six different dimensions – Attraction to Policy Making, Commitment to Public Interest, Social Justice, Civic Duty, Compassion and Self-Sacrifice. As discussed by Perry (1996), each dimension captures distinct motivations to serve identified previously in the public administration literature. Interviewees are asked how much they agree with each of the questions on a scale of 1 (Strongly Disagree) to 5 (Strongly Agree). We follow the established methodology in the personnel economics literature, as in Dal Bo, Finan and Rossi (2013) and Callen et al. (2016): we first recode all questions so that higher values are associated with normatively positive answers (i.e. higher agreement with “I think there are many public causes which are worth defending” but disagreement with “I hardly think of the welfare of people I do not know personally”), and then create an index for each dimension by taking simple averages of its answers. Finally, we create an aggregate PSM Index for each individual by taking an average of the z-scores of each of the six dimensions.

_Behavioral and Personality Traits_ – The module used to assess behavioral and personality traits is the same as the one used in the World Bank STEP (Skills Towards Employability and Productivity) surveys . Annex II presents the 25 questions used to measure eight distinct dimensions of personality. Taken together, the five first dimensions – Openness, Conscientiousness, Extraversion, Agreeableness and Emotional Stability – are usually referred to as the Big Five Inventory (BFI), which have been shown to approximate the main dimensions used to describe personalities (
2 0 2 4 -6 -4 -2 0 2 4<br>Males Females<br>Kernel densities of standardized scores<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br><!-- End of picture text --> <!-- Start of picture text --> Risk taking (Z score)'<br>Males and females aged 25-54<br>Armenia Bolivia Colombia<br>-2 0 2 4 -2 0 2 4 -2 0 2 4<br>Georgia Ghana Kenya<br>-2 0 2 4 -2 0 2 4 -2 0 2 4<br>Ukraine Vietnam<br>-2 0 2 4 -2 0 2 4<br>Males Females<br>Kernel densities of standardized scores<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<