rafmacalaba/data-use-mentions-extended huggingface dataset:
were either in hiding to avoid a mandatory draft, migrated or dead (Tadjbakhsh, 1996). Gomart (2003) notes though that families that were more prone to poverty were families with few working age men or female-headed households with many small children.

# **4. Data, Main Variables and Identification Strategy**

# **4.1 Data**

This analysis in the study uses data for the 2003 and the 2007 Living Standards Measurement Studies for Tajikistan (henceforth, TLSS). The surveys are nationally representative surveys of households and communities. The sample frame used a two-stage method based on the 2000 Census of Tajikistan. More information about the surveys can be obtained from the World Bank web-site dedicated to Living Standards Measurement Studies (LSMS). The 2007 survey was prepared by the World Bank in collaboration with UNICEF and carried out by the National Committee for Statistics (Goskomstat).

The surveys include data on household consumption of a wide range of food and non-food items; the socio-demographic composition of the household; labor market activities, such as participation in the labor force during the last 14 days and number of hours worked<sup>10</sup> ; the health and education of household members; sources of household income such as individual wages, both cash and in-kind; and transfers to the household from various sources. The 2007 data contain extensive information on the migration of individual household members, including those who are currently present or away, as well as remittances and transfers, such as inter-household and government transfers. The survey differentiates between main and secondary jobs held by individuals.

The analysis of education employs data from the 2003 and 2007 surveys to get an understanding of the long-term impact of armed conflict on education of the cohorts who were of school age during the

> 10 While studies quoted earlier in the paper emphasize the differences between the long and short-run employment outcomes and AWE (e.g. Fernandes and de Felicio 2005), such studies were based on the panel data which allows for
# 1. Introduction

The issue of food insecurity among households in developing countries, where individuals or families struggle to access or afford nutritious food to meet their basic dietary needs, has garnered additional attention since the launch of the Sustainable Development Goals. This has led donors and philanthropic organizations to increase their effort to combat food insecurity, especially in poor countries where malnutrition is assumed to be high. Surprisingly, this problem persists even among households that are not considered monetarily poor. This phenomenon, known as hidden hunger, affects approximately 2 billion people worldwide, according to a study conducted by the World Food Programme (WFP, 2020). Hidden hunger is characterized by an inconsistency between adequate calorie intake and intake of essential vitamins and minerals, leading to various health problems, primarily manifested as stunted growth.

Identifying sub-regions or localities afflicted by hidden hunger, or micronutrient deficiencies, is crucial for targeted interventions and effective allocation of resources. Public health experts employ methods such as randomly collecting blood samples from representative population groups, analyzing them in laboratories to determine micronutrient levels, and comparing them against established reference values – nutritional assessment. This approach, combined with geographical mapping, helps identify areas in need, enabling experts to focus their interventions and resources accordingly. Additionally, dietary surveys and administrative data from ad hoc clinical examinations when patients happen to visit local health centers provide further insights into the nutritional status of the population.

Conducting expensive surveys, such as nutritional assessments and dietary surveys, to identify areas with a high prevalence of hidden hunger is not financially sustainable. Health experts instead resort to using poverty maps or standardized health surveys like the Demographic and Health Survey (DHS) or the Multiple Indicator Cluster Survey (MICS) to pinpoint malnourished individuals. However, relying solely on poverty maps assumes that malnourished individuals exclusively live in impoverished households. This may not always be accurate, since typical poverty measures do not measure differences in welfare levels among members of the same household. On the other hand, DHS or MICS surveys may miss households with average
possible design change would be to combine the product with a short-term loan or, equivalently, originate loans with interest rates that are explicitly state-contingent, based on rainfall outcomes, to help alleviate credit constraints.

As another example of ongoing financial innovation, insurance companies are experimenting with alternative indexes, like the NDVI, for areas that lack good rainfall data. The NDVI index measures vegetation greenness corresponding to the level of photosynthesis in a prespecified grid using satellite imagery. These satellite data exist for several years, are difficult to tamper with and can be accessed online in real time; this approach may also have less basis risk than a rainfall index. In 2005 AIC introduced an NDVI-based insurance product for wheat in Haryana and Punjab, but it faced problems due to cloud cover during critical growth periods. The Centre for Insurance and Risk Management in Chennai is currently involved in another pilot program in Andhra Pradesh.

In some cases where household financial literacy is low or other barriers to take-up are too high, insurance policies could be targeted to a group, such as an entire village, a producer group, or a cooperative.<sup>10</sup> The group could then decide or prearrange how best to allocate funds among its members in case of a payout. Policies could also be sold to input companies. For example, during the 2005 monsoon season, Monsanto bought a bulk weather insurance policy so that it could attach free weather insurance coupons for a minimal level of drought coverage to its cottonseed packets, which were sold to 100,000 farmers in Maharashtra.<sup>11</sup> The 11th Indian five-year plan (2007–2012) asks the government to earmark US$7 billion for insurance, so that 40% of farmers will be insured by 2011/12. Developments in rainfall insurance and other microinsurance markets are already contributing toward this
pressure to innovate in order to remain competitive in local markets. Our results reflect within-cluster spillover effects of exporting and foreign competition on local firms. We also find productivity to be lower in clusters with a higher proportion of establishments with public ownership. Clusters with 100 percent government ownership would have 44% lower output per worker than a cluster with 0 percent public ownership, all else equal. Measures of crime and conflict drastically cut into industrial performance: for each one percent of lost sales due to crime, theft and/or disorder, firms can expect to experience nearly a ten percent loss in productivity. We presume that this quantitatively large result reflects the effects of crime and conflict both on local populations (whom firms depend on as buyers) and on the deterioration or destruction of local infrastructure typically seen in conflict-affected areas.

Regarding access to skills, clusters with higher rates of employee training show the highest estimated potential gain in productivity. We posit that the large coefficient on worker training may be due to local labor markets amplifying spillover effects: worker training and skills such business environment averages from a representative survey to industrial metrics based on a comprehensive and separate data collection instrument. See Aterido et al (2011).

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more than one product to the same destination (up to a maximum of 17 products to the same market).

It is also important to study a variable that captures a firm’s _experience with importing_ . This variable is constructed from Customs import data, in which several of the firms have been matched (only a third of firms have no import experience, and there are three times as many importers as there are exporters). This categorical variable identifies transactions of the firm which have been preceded at any point in time by an import. It further defines if this prior import experience was with the same destination to which the FPD is exporting or with another. The expectation is that exporters that are also engaged in importing would have established trade connections outside the country, acquiring savoir-faire with international transactions and international quality standards, and possibly building a reputation for reliability. Additionally, it can be argued that imported inputs can be cheaper and more technologically advanced than domestic products (J. Wagner 2011). In the particular case of Laos, a small and nonsophisticated economy, domestically produced or traded inputs might simply not exist. Nevertheless, there is still ample scope for such an effect. Anecdotal evidence attests this: for example, a very successful exporter chose to import cardboard boxes for packaging directly instead of buying local to avoid certain taxes (personal interview by Stirbat, Vientiane, Laos, 2012).

The research hypothesis is therefore that having _import experience_ is beneficial for the odds of survival. Furthermore, in cases where the imports come from the same country as the export destination of a firm’s product, this experience should be more relevant for survival, at least to the extent that this would entail a better knowledge of that particular market.

Lastly, the firm’s _prior experience with the product in Thailand_ is a dummy variable which identifies FPDs for which the firm has sold the product in Thailand before anywhere else. Some theoretical models assume that firms learn fully about their profitability in export markets by entering a
- 2 at baseline, ratio of number of sampled clients to village size. Strata and interviewer fixed effects included. Data source: household survey. Sanitation loan uptake comes from administrative data.

The second conclusion we can draw from this table is that on average, households do not report a switch away from any funding source for toilets (we note that the range of funding sources is in line with that reported at baseline). All estimated coefficients are positive, and except for one (’Other sources’), significantly so. This finding provides evidence that the loans did not displace funding for toilets households would have anyways built even in the absence of the intervention. Combined with evidence presented earlier, this implies that the remaining 50% of sanitation loans were for the most part likely used for non-sanitation investments and expenditures. This is consistent with the theoretical predictions from the model, which indicate that two features of the sanitation loan – additional credit supply and lower interest rate – would have made it attractive to finance investments beyond sanitation, and other consumption expenditures. The next section thus considers the intervention impacts on the household’s borrowing behaviour, and impacts on non-sanitation investments, specifically business investments.

# **6.3 Borrowing behaviour**

Next, we study impacts of the intervention on household borrowing. The theoretical model indicated that impacts on overall borrowing and the composition of household borrowing sources, combined with the impacts on sanitation loan uptake, sanitation investments and business investments, could be informative about whether the intervention impacts are driven by the loan label, or by a relaxation of credit constraints and/or the lower interest rate. In particular, the model predicts that overall borrowing should increase in all cases, _except_ when the label matters, and the credit constraint is not fully relaxed. In this case, households may substitute across loan products and investments following the introduction an impact estimate in line with increase in toilet ownership. Alternatively, we can run the regression on the sample for which we have reported data
of each detailed occupation mapped to that occupational group to total hours of work in the previous week. In order to be able to compare the skill content across occupational groups, we standardize the skill measures using the mean and standard deviation taken across the six occupational groups. These normalized skill measures are the input we use to generate skill measures for each province-industry-year in the Sakernas survey using the following calculation: � <mark>where ω</mark> ��� <mark>is the share of occupational group</mark> _<mark>i</mark>_ <mark>in province</mark> _<mark>p</mark>_ <mark>, industry</mark> _<mark>j</mark>_ <mark>, and year</mark> _<mark>t</mark>_ <mark>total</mark> �� <mark>employment obtained from Sakernas, and s is the value of the normalized skill measure</mark> _<mark>k</mark>_ <mark>for occupational group</mark> _<mark>i</mark>_ <mark>from the Philippines STEP survey. When constructing skills measures by demographical groups –women, men, high-skilled workers and low-skilled workers, we transform previous expression using the shares of each occupation in each province, industry and year total employment for each specific demographical group:</mark> �� where <mark>ω</mark> ��� is the share of occupational group _i_ in province _p_ , industry _j_ , and year _t_ total <mark>employment of demographic group</mark> _<mark>g</mark>_ <mark>. Demographic groups include female workers, male workers, high-skilled and low-skilled workers.</mark>

# **B) Skills measures using the U.S. O*Net database**

To check
in rainfall of 10 percent results in a decline of 4.8 percent in per capita consumption for the average household.

The significance of external forces in driving income and consumption growth among the poorest households during this period of poverty reduction underscores the role that macro conditions and stability play in determining the fortunes of households. The government’s success in securing peace in the north of the country and in allowing the development of agricultural markets for farmers to take advantage of new regional trade opportunities ensured widespread welfare gains. However, it also underscores that these gains are vulnerable and can be easily reversed by events beyond a government’s control such as bad weather or price drops.

We also document that despite seemingly favorable conditions for agricultural investment during this period, there were very few changes in the way agricultural households produce. Specifically, few households increased adoption of improved inputs. Although it is not possible to calculate the returns to the use of inputs from the observational data available, we do observe that increased household input use is correlated with crop income growth. It is possible that these findings can be explained by the fact that the expected returns from input use remained negative for many farmers that chose not to use inputs (such as found in Ghana in Rosenzweig and Udry 2016). Indeed, the widespread presence of low-quality inputs reduces the returns to many inputs available on the market (Bold et al. 2015).

It is also possible that barriers to the adoption of inputs reduce their uptake. Bandiera et al. (2015) document that providing extension and access to credit to households increases their adoption of improved inputs, which suggests that lack of knowledge and credit constraints limit the degree to which households can take advantage of a favorable environment for investment. Regardless of the cause, the limited modernization of agricultural production observed during this period highlights that 3
since in both Eastern and Western Europe, all of the HH are above the 1800 threshold. As mentioned earlier, the liberalization of the sector has not yet gone very far. In Eastern Europe, the issue is that few countries have actually privatized their distribution and E.ON enjoys a significant share of the market by itself. Latin America and Asia & Oceania are both inconclusive but are in the range in which it may make sense to take a closer look at the situation. For Latin America, this is once more somewhat surprising. For all of its reforms, the dominating presence of Endesa, EDF and AES in several of the national markets of the region explain the high HH max.

# **3.2 Telecoms**

The market indicator is the number of lines in working conditions controlled by an operator and the information is collected from the 2003 annual report of each firm. We consider fixed and mobile technologies separately.<sup>12</sup> For each sub sector, we consider the 10

> 12 Even if an obvious question in the “real world” would be to consider that the extent to which a product market that combines fixed and mobile should not also be considered since most of the top firms are the same in the two markets.

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# **Abstract**

We estimate the impact of global merchandise trade distortions and services regulations on agricultural value added in various countries. Using the latest versions of the GTAP database and the GTAP-AGR model of the global economy, our results suggest real net farm incomes would rise in developing countries with a move to free trade, thereby alleviating rural poverty – despite a terms of trade deterioration for developing countries that are net food importers or are enjoying preferential access to agricultural markets of high-income countries. We also show, for several large developing countries, the contribution of their own versus other countries’ trade policies.

## **JEL codes: C68, D58, F17, Q17**

**Key words:** Trade policy reform, CGE modeling, agricultural markets, economic welfare

## **Contact author:**

Kym Anderson Development Research Group The World Bank Mailstop MC3-303 1818 H Street NW Washington DC 20433 USA Phone +1 202 473 3387 Fax +1 202 522 1159 kanderson@worldbank.org
accounts and a 3 percent APY on savings accounts at the time of the baseline. While SSP offered an attractive rate of return relative to NVRB’s other offerings, the real return on savings was still negative, since annual inflation ranged from 10-19 percent over the course of our study period (Bank of Ghana, 2014, 2017).

Half the sample in each overdrafter _×_ branch _×_ gender stratum was assigned to the treatment group, which was offered the opportunity to sign up for SSP. Appendix Table A1 confirms that baseline characteristics are balanced across treatment and control groups, both overall and when the sample is split by overdrafter status.

Appendix Figure A2 illustrates the timeline of all study activities. SSP debits began in

> 8 Overdraft scores were constructed for each person using their current account transaction records available at the time of the randomization. The score was calculated as the fraction of balance entries that were negative over the life of the account. The sample was sorted from lowest score (never had a negative balance) to highest (negative balance 66 percent of the time).

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the Nobel prize. Yet, her cognitively empathetic approach to understanding how communities managed common resources led to deep insights on human behavior that have had a transformative impact on the social sciences. I would venture to argue that she would never have been able to do this work had she had a job in an economics department.

Younger economists are increasingly spending time in the field to collect data and test interventions, and some display a great deal of cognitive empathy in how they approach their work. MR Sharan (2021) has written a marvelous account of ten years of travel in rural areas of the Indian state of Bihar, interacting closely with local activists and trying to understand the inner workings of village government. The book, which is meant for a general audience, is less an ethnography than a memoir but it brings into sharp focus the challenges faced by citizens, particularly those from disadvantaged groups in accessing government programs. This embedded, empathetic approach is apparent, albeit implicitly, in Sharan’s research (Sharan and Kumar, 2021) where he and his co-author Chinmay Kumar analyze data from 100,000 village-level politicians in Bihar to examine the provision of public goods to lower castes. They find that leaders matter – when a lower-level lower caste representative reports to a high-caste representative, lower castes receive poorer access to public goods. However, a grievance redressal system instituted by the government is accessed more by lower-caste representatives to counter this discriminatory provision.

It is clear from this overview of field-oriented economics research, that Srinivasan was partially correct. Participant observation can be an extremely valuable tool for economists and can result in quantitative findings that are cognitively empathetic. It does not however, follow that this is true of all field-based research. Cognitive empathy requires a process of listening and learning from the people one is researching, and reflecting their interests, their insights, and their perspectives in the research through a process of iterative learning. Most work in field work in economics tends to be much more top-down, focused on testing an intervention or
_Note:_ Based on logistic regression of whether a household reports continued learning among children on several covariates with country dummies; sample limited to households with children going to school before the pandemic _Source:_ Based on Bundervoet et al (2021), updated by the authors with April 2021 vintage of harmonized HFPS data Children in rural areas and from households with lower levels of education were significantly less likely to continue learning during school closures, widening the pre-existing learning gap with urban children and children from better-off households (education of household head is a good proxy for socioeconomic status of a household) within countries (Figure 9). Children living in households with secondary- and tertiaryeducated respondents were nearly 8 and 11 percentage points more likely to continue learning during school closures relative to children in households with primary-educated respondents. Children in households with job loss during the pandemic, which were typically higher among the less-educated, were also less likely to continue learning. Interestingly, there is a small significant negative association of a household head (respondent) being a man and likelihood of continued learning among children, which might indicate the positive influence of mothers on their children’s education when they are also the main decision-maker of the household. Notably, all the gaps were larger for children in low and lower middle-income countries, where pre-existing inequalities are already larger, than for the full sample of countries.<sup>14</sup>

# 4.4. Adequacy of policy response

By September 2020, advanced economies had on average spent 7.4 percent of GDP on “above the line” measures in the form of budgetary fiscal support to people and firms in response to the pandemic (Bundervoet et al, 2021). These measures, which include cash transfers, expanded unemployment insurance, wage subsidies, and deferral of tax obligations, utility payments and social security contributions, have helped to mitigate the socio-economic impacts of the crisis on households and workers. Governments in EMDEs, however, have been able to spend far less on relief measures
mirrored records. We observe a welcoming trend in which the divergence in recorded values, as indicated by the weighted discrepancy index, is declining. Thus, we note not only an increase in dual recording but also a reduction in the discrepancy level between recorded values. Like the percentage of mirrored records and verifiable trade value, significant differences between reporters remain. Appendix Figure 6 hints at the differences in the quality of reporting from different reporters in the UNSD Comtrade database. Therefore, these differences are explored in detail in the later subsection.

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