rafmacalaba/data-use-mentions-extended huggingface dataset:
Tashkent|300|4%|1.2%|111|6%|1.1%|
|City of Tashkent|281|2%|0.5%|106|4%|0.8%|
|Fergana|359|21%|4.5%|135|15%|2.6%|
|Khorezm|280|40%|9.5%|106|36%|7.5%|
|Total|4013|19%|4.2%|1503|18%|3.5%|

After completion of the face-to-face baseline, interviewers began regularly calling a randomly selected panel of 1,503 households over the phone to conduct short interviews, following a set monthly schedule agreed to by the participating household. The questionnaire for these phone interviews was designed to monitor trends in migration, subjective well-being, measures of income, employment, service disruptions, and related indicators. Phone-based interviews began on September 5, 2018, and the first eight rounds of the survey are used in the analysis that follows, covering the entire period to the end of April 2019. A total of 12,137 unique observations are available for analysis.

Attrition is one potential concern using panel data of this type. To ensure that non-take-up in the first round (and attrition in subsequent rounds) did not seriously affect the required sample size for survey representativeness, households that refused to participate were replaced with other households drawn from the same sample cluster. However, any systematic difference in the household characteristics due to refusal to participate could lead to bias if the replacement households were different on average (with respect to observable characteristics) from the households that refused. Among the random sample of 1,503 households originally drawn from the baseline, about 25% refused to participate in the first round (i.e. initial take-up in the first phone round totaled 1,122 randomly sampled households, and 381 randomly selected replacement households to make up
10.15|53.45|80.43|18.62|
|2010|27.99|48.98|7.94|22.11|39.22|6.23|35.13|60.22|10.16|
|2011|32.62|55.73|10.81|28.98|49.04|10.29|35.54|61.03|11.24|
|2012|40.57|67.41|14.01|40.06|64.33|14.10|40.90|69.48|13.95|
|2013|44.14|72.93|14.85|36.17|64.10|9.54|48.56|77.49|18.18|
|2014|41.54|71.61|12.96|35.43|63.33|10.73|45.25|76.30|14.44|
|2015|43.11|71.85|13.54|37.30|63.60|11.38|46.94|77.11|15.05|
|2016|48.92|75.77|16.06|40.71|67.93|10.21|53.70|80.03|19.77|

Source: RUMiC Migrant Survey 2008 and 2016.

A-2
particular country by computing the ratio of net total imports over total food consumption of the four food groups. Dependency ratios across country groupings are then aggregated using simple averages. The analysis looks at the most important food products for MENA countries which are grains, edible oils, sugar, beef, and poultry.

In all developing MENA countries, except Lebanon, grains account for the largest share in total food consumption, while in high-income GCC meat has the highest weight in the consumption basket (Table 1A). Dependence on food imports in general is high across MENA except in Iran, and nearly complete (around or above 90 percent) in all GCC economies, except Saudi Arabia where it is 80 percent. Thus, in the absence of policies to soften the impact of world food price fluctuations, the passthrough effects from world to domestic prices should be largest in the GCC economies, as well as in some other countries, such as Yemen, Iraq, and Jordan.

To gauge the first-round effects of a sharp increase in key food prices we also compute the corresponding increase in the import bill as a share of the 2010 GDP and as a share of international reserves, excluding gold.<sup>9</sup> The analysis relies on USDA data for supply and demand of individual food commodities, World Bank international commodity prices, International Monetary Fund (IMF) data on international reserves, and World Bank data for the 2010 GDP estimates by country.

The assessment of vulnerability considers increases in international grains, oils, meat, and sugar prices from June 2010 to July 2011 relative to the same period in the previous year. Over this period wheat prices surged nearly 30 percent, corn prices surged 53 percent, sorghum rose 32 percent, and barley prices rose 27 percent. The increase in rice prices was more modest, at 7 percent. The increase in edible oil prices was also significant, with sunflower seed oil prices up 54 percent, rapeseed oil prices up 50 percent, soybean oil prices up 40 percent,
Trade Agreements and countries, our focus on the effect of RTA on inflows to and outflows from the region, and on our account of the role of Global Value Chains.

Also related is Baier and Bergstrand (2007) and Baier and Bergstrand (2015) who address the problem of endogeneity related to free trade agreements and trade flows by using panel data and average treatment effects (ATEs). They find positive estimates and conclude that free trade agreements increase members’ international trade.

Anderson and Yotov (2016) refer to a gravity model to estimate the effects of trade agreements on terms of trade and global efficiency. They face two main problems: heteroskedasticity in trade flows data, and endogeneity due to the two way causality. To address the first one, they use the Poisson pseudo maximum likelihood (PPML). To address the second one, they introduce two variables, one for trade agreements between countries with low most-favored-nation tariffs (MFN), the other for trade agreements between countries with high MFN tariffs. They find an increase in the global efficiency of manufactures trade over the period 1990-2002.

The behavior of trade flows following a regional trade agreement is also impacted by the depth of the agreement, as analyzed in Mattoo et al. (2019). Using a sample of 96 countries for the period 2002-2014, they show that deep agreements lead to more trade creation and less trade diversion than shallow agreements.

Additionally, beyond complementarity in trade policy, GVCs also create strong interdependence of GDP fluctuation at business cycle frequency and are associated with network propagation effects of any reform aiming at reducing the cost of crosscountry trade.<sup>2</sup> Moreover, the segmentation of production across countries also has significant implications for the magnitude of estimated trade elasticities (Amiti et al. (2014), de Soyres et al. (2018)).

Finally, Estevadeordal et al. (2008) study the impact of regional trade agreements on trade liberalization toawrds non-members. Focusing on Latin American countries, they found “
# **Findings**

## **Women often underreported their economic activity**

The key finding is that most women in the visited communities reported engaging in market activities for pay or in production of goods for own-consumption that fit within the definition of employment. Yet most did not respond affirmatively to the questions posed by the household survey. That is, there is a clear disconnect between the definition of employment as meant to be captured in the labor force survey and the definition used by women (and men) in the rural communities visited for this research.

The interviews began with the two key questions included in the 2015 EPHPM used to measure employment.<sup>15</sup> Women who answered yes to the question of paid work typically reported paid employment outside of the home or working in a part-time paid activity from home such as grinding corn or washing clothes for others. At the same time, the qualitative study found that, overwhelmingly, women in both types of communities (those with and those without the presence of cooperatives) were likely to answer ‘no’ to the two questions that aim to capture economic activity - despite working in market activities. Significant discrepancies arose between women’s initial response to either one of the EPHPM survey questions measuring labor force participation and their later reports of actual tasks performed by them suggesting underreporting of work among women. Specifically, in response to the question "During the past week, did you dedicate an hour or more to any work or activity with payment in cash or in kind, or did you get any income? (except housework)", more than half of individually interviewed women responded ‘no’ even though they later described having realized activities for pay.

With regards to the second survey question applied to measure employment (During the past week, did you perform or help carry out any work, with relatives or individuals, without pay? (Except household work)”), a discrepancy could also be noted among those who initially
Policy Research Working Paper 9069

# **Abstract**

This paper examines the effect of Basel III implementation on the access to finance of small and medium-size enterprises in 32 emerging markets and developing economies. Analyzing rich, repeated cross-sectional data and a panel of matched firm-bank data in a difference-in-differences setting with sample selection adjustment, the authors find a short-term, moderately negative effect of Basel III on small and medium-size enterprises’ access to financing. The results suggest that firms with access to bank credit prior to Basel III implementation could have been affected less than firms that were initially on the fringes of financial inclusion—firms with only a bank account. The paper fails to find any additional heterogeneous effects across firm size or age, bank capitalization or liquidity, or across countries that transitioned to Basel III from Basel II versus Basel 2.5. Overall, the initial conditions of the banking system as well as of complementary business and financial regulation can co-determine the size of short-term costs from the newly implemented global financial regulation in emerging markets and developing economies.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mmelecky@worldbank.org, boris.fisera@euba.sk, and roman.horvath@fsv.cuni.cz.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International
</sup> With the increasing availability of CAPI/CATI paradata, there is now an opportunity to undertake similar analyses in low- and middle-income countries.

In what follows, we use the CAPI survey paradata at our disposal and obtain multi-level model estimations to discern interviewer effects on survey duration in Cambodia, Ethiopia and Tanzania. Our analysis differs from the previous paradata-powered research in the sense that we do not model question-level duration but rather focus on module duration and conduct separate analysis for each module. This allows us to contrast results across various modules, currently a significant gap in the literature.

Specifically, we estimate a range of multi-level models that are specific to each individual and household questionnaire module. The dependent variable is always the module duration in minutes. To model duration for individual questionnaire modules, we first estimate **Equation 1** as

> 10 Couper and Kreuter (2013), for example, found that the interviewer variable contributes to less than 2 percent of the variation in interview-length while the respondent variable contributes about 3.8 to 6.3 percent, depending on the model. Most of the variation is at the question-level - about 96 percent, again depending on the model. The authors noted that this result is consistent in the literature and thus one should expect that interviewer and respondent contribution to not impact survey time much when conducting fieldwork.

18
used food access metrics are capturing different aspects of poor food access (e.g., Maxwell et al. 2014). However, these findings build upon previous work and demonstrate that some of these coping strategies do not have the expected relationship with other key welfare indicators, suggesting that it might be unclear exactly what some of these coping strategies are capturing. Therefore, future work needs to be done to unpack the exact causes of these discrepancies and how generalizable the results are to additional contexts.

The rest of the paper is structured as follows. Section 2 presents a brief background on food access indicators and the FIES; Section 3 presents a simple model of consumption to illustrate the relationship between monetary poverty and poor food access; Section 4 describes the data; Section 5 describes the empirical strategy; Section 6 reports the empirical results; and Section 7 concludes.

# **Section 2. Background on Food Access Measures and the Food Insecurity Experience Scale**

Food access is one of the four pillars defining food security agreed upon by stakeholders at the 1996 World Food Summit, with the original formulation ensuring that all individuals have access to ”sufficient, safe and nutritious food.” Given the many different components of food access alone, including consumption of macro-nutrients, food safety, and consumption of micro-nutrients, it is difficult for any single measure to capture all information relevant to policy makers (e.g., Barrett 2010).

Moreover, many dimensions of food access are difficult to measure precisely. Although there are a number of ways to try and estimate the quantity of calories consumed and the nutritional content of consumption, the methods thought to produce the most precise estimates involve individual-level surveys that are complex, expensive, difficult to analyze, and nearly impossible to perform on a large scale (e.g., Gibson 2005; Fiedler et al. 2013; etc.).<sup>7</sup> In the absence of these difficult-to-collect and expensive data, researchers often turn to household consumption and
civic duties. Where relevant data are available, topics are analyzed through the lens of government performance, rather than through the citizen’s perspective and experience of the process. For example, the ACE Project’s comparative data set analyzes registration systems adopted worldwide and relates them to government performance under three criteria: currency, accurateness, and completeness (ACE: The Electoral Knowledge Network 2013). It does not address the challenges faced by individuals in the context of each registration system, the requirements with which they need to comply, and the eventual burden of participating in democracy. This is a gap that _Living Life_ aims to close.

# **2.3. Building the Pilot Methodology**

The methodology for _Living Life_ was developed in several steps to incorporate as much feedback as possible from experts across the World Bank and from other teams who have worked on similar initiatives. The steps can be summarized into:

1. Development of the conceptual framework;

2. Questionnaire development; 3. Data collection; and 4. Data validation and coding.

## **2.3.1. Development of the conceptual framework**

The first step identified the key interactions between citizens and governments for which data should be collected. As discussed in the next section, these key interactions were selected through consultation with experts across the World Bank Group as well as a thorough review of the literature. The selected interactions are seen as the interactions that the majority of citizens need to go through in order to execute their civic responsibilities and to access basic services as defined by the 2004 WDR. A fundamental aspect that was defined in the conceptual phase was the specific scope of each area included in the study. It was decided that only the bureaucratic aspects of each representative process would be studied without addressing issues of service quality as this would require a much more complex approach to data collection. For example, for access to education, _Living Life_ observes how burdensome it can be to enroll a child in school, the required
# Figure 2.  Country correlations for commodity exporters versus non-commodity exporters, 1980-2020

<!-- Start of picture text -->
1.0 Commodity exp Non-Commodity exp<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Commodity Average = 0.43***<br>-0.2<br>Non-Commodity Average = 0.22***<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>AzerbaijanChinaYemenNepalCôte d'IvoireBangladeshUkraine<br>GreeceNicaraguaEcuadorUruguayGuyanaLiberiaArgentina<br>Antigua and BarbudaBrazilNamibiaEquatorial GuineaLebanonNigerPakistanIndiaBoliviaHungaryJordanKenyaBelarusZimbabweFijiRomaniaSerbiaAngolaQatarCyprusRwandaOmanRepublic of CongoTimor-LesteAfghanistanTrinidad and Tobago<br>BahrainDemocratic Republic of the CongoBelizeJamaicaPolandBhutanLithuaniaKyrgyz RepublicRussiaPapua New GuineaLao P.D.R.VanuatuSyriaCabo VerdeGeorgiaUgandaGrenadaCameroonIndonesiaHondurasMoroccoSurinameTogoSeychellesEstoniaMaltaSri LankaSt. Kitts and NevisDjiboutiSt. Vincent and the GrenadinesIslamic Republic of IranMadagascarPhilippinesAlgeriaUzbekistanMyanmarBarbadosSouth AfricaParaguayTajikistanLesothoMicronesiaGhanaBurundiSaudi ArabiaBeninTurkmenistanMontenegroPanamaMozambiqueMarshall IslandsBosnia and HerzegovinaMoldovaChadComorosThe GambiaCambodiaColombiaTanzaniaLibyaCentral African RepublicCroatiaTurkeyGabonIcelandEthiopiaKiribatiMongoliaSudanTongaSenegalMaliLatviaTuvaluBurkina Faso<br>NigeriaPortugalIsraelPeruSouth SudanBulgariaEritreaSolomon IslandsCzech RepublicIraqAlbania<br>Haiti<br>BotswanaVietnamEswatiniSlovak RepublicTaiwan Province of ChinaSan MarinoArmeniaMaldives<br>SamoaMexicoSt. LuciaSierra LeoneKazakhstanGuinea-BissauMauritaniaCosta Rica<br>SloveniaDominican Republic<br>Chile ZambiaItalyNauruSpainMauritiusTunisiaGuinea<br>PalauKuwait DominicaThailandLuxembourg New Zealand<br>SwedenMalaysiaFranceNorwayBelgiumJapanMalawiGermany NetherlandsEl Salvador The Bahamas<br>KoreaIrelandAustriaAustralia Guatemala Brunei Darussalam United Arab Emirates<br>Finland<br>North Macedonia<br>EgyptSingapore United States United Kingdom<br>Denmark<br>Switzerland<br>Canada São Tomé and Príncipe<br>Correlation between cyclical components of real  government spending and real GDP 1980-2020 Hong Kong SAR<br>Somalia<br><!-- End of picture text -->

Note: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for commodity exporters versus non-commodity exporters. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively.

Sources: Authors’ calculations based on WEO (IMF) data. Commodity classification from
000)<br>Supply driven Demand driven<br>Source: ECB; World Bank analysis.<br>2005Q1 2005Q3 2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3 2013Q1<br>2005Q1 2005Q3 2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3<br>2005Q1 2005Q2 2005Q3 2005Q4 2006Q1 2006Q2 2006Q3 2006Q4 2007Q1 2007Q2 2007Q3 2007Q4 2008Q1 2008Q2 2008Q3 2008Q4 2009Q1 2009Q2 2009Q3 2009Q4 2010Q1 2010Q2 2010Q3 2010Q4 2011Q1 2011Q2 2011Q3 2011Q4 2012Q1 2012Q2 2012Q3<br><!-- End of picture text -->

# **_<u>Emerging and developing economies</u>_**

**As regards foreign lending activities, international banks have scaled back longer-term finance in favor of shorter maturities—it is likely this has also been the case for European banks.** There is no direct information available on the maturity structure of European bank claims in emerging markets. However, the BIS provides data on maturities of international

9
Sergio Firpo and Alysson Portella. Decline in Wage Inequality in Brazil: A Survey of educated workers. Nonetheless, we caution that there is a small number of studies on this topic, which should deserve more attention, both in Brazil and Latin America in general.

# **4 Changes in Institutions: Minimum Wages and Formality**

Apart from changes in the educational composition of labor supply, Brazil’s labor markets experienced important institutional changes in the first decade of the 21st century. As we discussed in the introduction, the minimum wage more than doubled in real terms between 1994 and 2013, with a real increase of almost 60% after 2003. At the same time there was a considerable formalization in labor markets, with the share of informal jobs decreasing from half the working force to 38% between 2000 and 2012. Simultaneously, differences in wages between workers employed under these circumstances also decreased. These movements were followed by changes in enforcement of labor legislation and a significant decrease in unemployment, which raised workers’ bargaining power. Finally, although the dispersion of wages for both formal and informal workers decreased in the 2000s, the dispersion of wages of workers in the formal sector remained smaller than that of workers in informal sectors. These changes are all expected to affect the earnings distribution, reducing wage inequalities.

## **4.1 Minimum wage**

Overall, studies in Brazil have not found negative employment responses to the minimum wage, nor large displacement to the informal sector, but they do find compression in wages (Lemos, 2009; Corseuil et al., 2015; Saltiel and Urz ́ua, 2018). These results may be due to concomitant economic growth. For example, Saltiel and Urz ́ua (2018) find larger negative effects of the minimum wage on employment in regions less exposed to commodities booms. Besides the overall economic situation, the effect of the minimum wage may also depend on its bite, as measured for instance by its size relative to median income. Messina
behavior (the same was true of original letters used by the GDT in past campaigns). For example, in a study in Poland summarized by Hernandez et al. (2017), enforcement toned language was much more concrete, and thus potentially more credible (e.g., referring to Enforcement Orders and freezing of salaries and bank accounts). In contrast, given a national tragedy, businesses may have been particularly receptive to messages appealing to their social duty to contribute to revenue mobilization to fight the pandemic.

Fourth, given the role of the tax authority in delivering wage subsidies during the early stages of the pandemic, its communications may have had greater than usual weight in terms of influencing business declaration behavior, and for some types of firms more than others. Notably, during the lockdowns in the initial phase of the pandemic, the tax authority was responsible for administering wage subsidies paid out on basis of the declared payroll of businesses. This meant that letters sent out by the tax authorities were possibly read more carefully than in normal times, particularly for businesses that were disproportionately impacted by the pandemic (e.g., professional services compared to wholesale and retail trade, where we saw some heterogeneity in impacts, discussed earlier). Finally, the crisis has likely increased noise in the tax data, making it more difficult to identify a statistically significant impact of the letters on payroll declaration behavior. For all these reasons, further research is needed to fully determine how contextdependent the results of the RCT presented in this study are.

# VI. Discussion

This study provides a unique opportunity to evaluate the impact of strategic notifications to taxpayers in the context of a global pandemic. Letters were delivered at a time when businesses were forced to drastically cut their operations and likely downsize (both in terms of employees and production overall), and business (and thus accounting) practices likely shifted dramatically. This may have led to a stronger than usual response of payroll declarations to the soft-toned employer letters (and weaker than usual