rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Externalities from Investment in Trade Facilitation**

Trade facilitation improvements benefit not only the countries that implement them, but also their trading partners. The more intense are trade relations between countries, the greater the benefit partner countries will enjoy should one (or more) of the countries improve its trade facilitation regime, all others being equal. Given the importance of intra-regional trade between the EU15 and the new and candidate EU members, the expected gains to the former resulting from the trade facilitation improvements in the latter will likely be significant.

Table 3 shows that if all four dimensions of trade facilitation for each of the new member (EU8) and candidate member countries (Bulgaria, Romania, and Turkey) improve up to half-way the EU15 level, almost $10 billion trade gains will be expected for the EU15, the EU8, and the three candidate members as a whole in 2001.<sup>11</sup> Among the total gains for trading partner countries, $2.7 billion (or 28%) comes from their export gains, and $6.9 billion (or 72%) come from their import gains (Table 3, Table 4). In other words, if the trade facilitation capacity of new and candidate EU members improve measurably, their trade volumes with other European countries will also increase; in particular, partner countries will increase their volumes of imports from the improving countries by a larger margin than partner exports increase to those countries.<sup>12</sup> These findings echo our argument that trade facilitation improvements in new and candidate EU member countries will strongly increase their intra-regional trade with the EU15. The trade gains for the EU15 represent 74% of the total trade gains for all European

> 11 Here, we use the data of bilateral import volume of Bulgaria from EU15, other EU8 countries, Romania and Turkey in 2000 since the data in 2001 is not available. As argued above, the IT infrastructure development level of Estonia is better than the
**Figure 7:** Distributions and yield-input regression coefficients for different measures of yield in one sample split N = 732. _Notes:_ Left top panel shows the kernel density of the different measures of yield. The right middle panel shows the scatter plots of CC vs ML yields. The bottom left/right panel shows the bivariate/multivariate yield-input regression coefficients for different measures of yield.

<!-- Start of picture text --> G CC<br>ML<br>SR<br>0 1000 2000 3000<br>Yield (kg/ha)<br>0.6<br>0.4<br>0.2<br>G<br>G<br>0.0<br>G<br>−0.2<br>−0.4<br>Area Seeds HH Labor<br>0.0015<br>0.0010<br>Density<br>0.0005<br>0.0000<br>β<br><!-- End of picture text --> <!-- Start of picture text --> 0 500 1500 2500<br>ML Yield (kg/ha)<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0 G G G<br>−0.2<br>Area Seeds HH Labor<br>2500<br>1500<br>CC Yield (kg/ha)<br>500<br>0<br>β<br><!-- End of picture text --> 33
’s World Economic Outlook database and authors’ calculations. Notes: Only the observations in the estimation sample for annual data are included in this table.

- <u>Policy variables. This category is self-explanatory and is the main area of focus of this paper. For</u> fiscal policy a measure of counter-cyclicality is used; namely, the difference between the fiscal balance and the cyclically-adjusted fiscal balance. An increase thus represents a tighter fiscal stance. Monetary policy enters through the change in money velocity; thus, an increase represents monetary tightening. The degree of exchange rate flexibility is measured by the index created by Reinhart and Rogoff (2004). This index ranges from 1 to 5 and a larger number represents greater exchange rate flexibility. The extent to which the country relies on capital controls is also part of the estimation through a de jure index that was created by Chinn and Ito (2008) using the IMF’s AREAER report. A larger index implies more openness (i.e., liberalization) of the capital account.

- <u>Global conditions. This is captured by the degree of global risk aversion as measured by the S&P</u> volatility index. It is the only regressor that enters contemporaneously in the estimated regressions. All other regressors are lagged to limit endogeneity concerns with growth/vulnerability outcomes.

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<sup>36</sup> For countries with HCI data, these ILO data are available for 115 countries or 71% of the sample.

The secondary source is the JOIN database microdata. At the time of writing, the public JOIN data set provides data by Status in Employment or Economic Activity, but not both. As such splitting by both (with the latest surveys) needs to be constructed manually from the underlying microdata.<sup>37</sup> These data are used for 28 developing countries.

Finally, there a number of countries/regions that have ILO data on the total number of employees and employers, but are missing data on the non-agricultural share of employees.<sup>38</sup> In these countries I interpolate the agricultural a <u>e y</u> share of employees, ~~.~~ Hong Kong SAR, China, and Macao SAR, China, are city states, so aammaaf AAffa ff eemm effee ye ee we know almost all employees work outside agriculture, so the total number of employees will be virtually identical AANNEE= AAeeAAaaff eemmeeffeeyye ee to the number of non-agricultural employees. For other countries, I utilize ILO data on �= <u>e y</u> (employment not employees) which has wider coverage.<sup>39</sup> Using a sample of countries AAeeAAaaff a aammaaf AAffa ff eemm effee ymmeemmAA � AANNEE with both variables, I estimate a regression of , and find that for high income countries and AAeeAAaaff eemmeeffeeyymmeemmAA for other countries.<sup>40</sup> These estimates of are not surprising as self-employment and family work are AANNEE= θθAANNEE θθ≈1/2 especially common in agriculture. Given these estimates, I then interpolate the SEBJ as: θθ≈1/4 θθ

> 33 Recall the SEBJ is defined as the number of employers plus non-agricultural employees, as fraction of total employment.

> 34 Downloaded from https://ilostat.ilo.
# **6. Conclusion**

Household surveys remain the only source of data for generating official poverty and inequality statistics in most low- and middle-income countries. Differences in food consumption data collection make global and cross-country poverty and inequality comparisons challenging. This study uses nationally representative household survey data from Tanzania that was fielded as a randomized experiment to understand differences in household consumption, poverty, and inequality measures by comparing three alternative methodological designs.

The randomized nature of the study ensured that households were assigned to three treatments: 14-day diary, 7-day recall with short list of food items, and 7-day recall with long list of food items. The paper used both descriptive and regression approaches to explore the differences in consumption and poverty emanating from the three methods. The results show that the number of food items consumed is highest in the diary treatment, compared to the recall designs. Food diversity was similar across the three designs, with relatively common items forming the food consumption basket. To capture more diverse food items consumed in the recall design requires fielding an expanded list of relevant food items in the questionnaire. It is worth noting, however, that a very long item list comes at a cost; respondent fatigue, long interview time, and cost could compromise quality of data collected from such.

This notwithstanding, food consumption expenditure was higher in the recall designs than the diary treatment. Non-food consumption expenditure is relatively similar across the three designs, with very little significant differences. This indicates that differences in total consumption expenditure are highly driven by variations in food consumption. On the poverty measures, the results provide evidence that the poverty line matters; at lower poverty threshold, we observe higher poverty rates in the recall designs, but the reverse is observed at higher poverty thresholds.

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to notice is that the dollarization ratio has been, in general, steadily increasing over time. While in 1990 the average ratio for these countries was 0.23, in 1998 rose to 0.37. A second interesting fact is that this increase comes in a period when the inflation rate has demonstrated a dramatic decrease. In 1990 the average inflation rate for the thirteen countries was 1,353% (315% without counting the hyperinflation episodes of Argentina, Nicaragua and Peru); in 1998 this figure was 10%. This fact supports the idea that the dollarization process in Latin America follows a hysteresis process, since the optimal portfolio theory would predict a return to the domestic currencies once their expected value increases with lower inflation differentials. In fact, Figure 1 shows that the inflation differential

> 5Although information on most monetary and macroeconomic variables is available since 1970 for all countries, we could only obtain data on FCDs for all these countries for the nine years. 6FCD include all dollar denominated bank accounts including domestic and foreign banks. 7These variables were obtained from the IFS.

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5–19 employees (small), 20–99 employees (medium), and 100+ employees (large). These types of firms are also quite distinct from those with fewer than five employees (micro); in particular, it is expected they do not experience the same extent of fluctuations in sales and employment levels. A shortcoming of this approach is that the VAT returns do not include information about the number of employees of each firm. As such we merge the VAT and PAYE return data and can only report on the subset of firms that provided both types of returns in a given month. Once again this is likely to mean the results may not be generalizable beyond firms that share these characteristics.

The final part of the descriptive analysis we conduct is to examine the trends from both data sources based on characteristics other than firm size. The three most notable characteristics are in terms of sector, location, and firm age, as this information is available for all firms in both data sources. The sector analysis is decomposed into three categories that are identical across the survey and tax data (manufacturing, retail and wholesale services, and other). The location analysis in both data sources focuses on a simple binary split between firms in Lusaka and firms everywhere else.

# **3.2.2 Determining the covariates of recovery**

Both data sources, but particularly the survey data, provide the opportunity to explore what firm characteristics are associated with experiencing an initial reduction in sales and employment, as well as a recovery by 2021. This analysis complements exploring the descriptive trends because through conducting econometric analysis we can identify what firm characteristics appear to be most closely correlated with firm performance. It is important to emphasize that this regression analysis is not causal as there is no exogenous variation. We cannot rule out, among other things, reverse causality and omitted variable bias. For example, on the former, if we find a positive relationship between receiving government support and recovering from the pandemic, we
This may be interpreted as constituting a "big push" in complementary infrastructure investments (Rosenstein-Rodan (1943)). However, issues related to the sustainability of such a push remain relevant across a broad group of countries (Kose et al. (2021)). This paper does not address these issues directly, but documents (spatial) patterns in the sectoral changes at meso-level scales. We find significant differences in the patterns of growth when viewing these booms through a spatial lens. These patterns are often ignored when monitoring growth solely at the national level and serve to reveal changes within subnational economies that are relevant to policy design.

The rest of the paper proceeds as follows. Section 2 describes analysis of Türkiye’s booms and busts using official province-level data. It begins by providing the relevant macroeconomic context. It then documents changes in absolute convergence across sectors and time periods. Finally, it describes results that provide a spatial perspective on sectoral growth rates across the business cycle. Section 3 shows that the changes in nighttime light luminosity are useful signals

> 9 Zürcher (2017) and Pamuk (2018) provide geographic, cultural, historical, political, and economic context.

> 10 We do not examine the external causes that may be common to many booms and busts experienced by middle-income countries. These include, for instance, the related shocks to global savings imbalances and liquidity, which in turn, are related to the monetary policies of the United States and the European Union (e.g., Rajan (2015), Kalemli-Ozcan (2019), Bräuning and Ivashina (2020)).

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br>(30.469)|9.964<br>(8.605)|
|Country FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Control Var|Yes|Yes|Yes|Yes|
|R-squared|0.402|0.307|0.380|0.063|
|Observations|152|862|178|882|
|Countries|23|118|22|121|

Note: R-squared refers to pseudo R-squared for the logit models in Column 1 and Column 3. Estimates are obtained with an OLS model. Cluster robust standard errors in parentheses. *** p<0.001, ** p<0.01, * p<0.05, ꝉ p<0.10 We estimate a logistic regression with clustered robust standard errors because nonviolent uprisings are measured as a binary variable in the MEC data set. In this model only those countries for which there is variation in the ongoing uprising variable, capturing either the onset or offset of an uprising, can be included, which limits the set of countries included in the logit model to only 23. In the regression of subjective well-being on uprising, as recorded in the MEC data set (Table 5, Column 1), the effect of _suffering_ remains positive and statistically significant. On average, a one-percentage point increase in the _suffering_ rate increases the probability of a large-scale nonviolent uprising with 1.3 percentage points. Also the coefficient on the _struggling_ variable remains positive and significant with an average 29
# Early-Life Access to a Basic Health Care Program and Adult Outcomes in Indonesia<sup>_∗_</sup>

Md Nazmul Ahsan Rakesh Banerjee Riddhi Maharaj

**Keywords** : Primary Care, Midwife, Early-Life, Adult Human Capital, Indonesia **JEL Classifications** : I15, I18, J13, O15

> _∗_ Md Nazmul Ahsan (corresponding author) is an assistant professor at Saint Louis University; his email address is nazmul.ahsan@slu.edu. Rakesh Banerjee is a lecturer (assistant professor) at the University of Exeter; his email address is r.banerjee@exeter.ac.uk. Riddhi Maharaj is an assistant professor at Ramakrishna Mission Vidyamandira; his email address is riddhimaharaj.rkm@gmail.com. The authors thank Editor Eric Edmonds and three anonymous referees, seminar and conference participants at Saint Louis University and Southern Economic Association for valuable comments and suggestions. A special thanks to Arya Gaduh for sharing the 1983 PODES data. All remaining errors are our own.
on key outcome variables, treatment, and other covariates and controls are reported in table 1. The main object of interest is coefficient 1, which captures the effect of access to digital infrastructure on welfare and poverty. To account for the existence of heteroskedasticity and serial correlation, standard errors are clustered according to the relevant EAs. ββ To identify 1, the analysis assumes no endogeneity or E[ i |D i , i ] = 0. This assumption is violated if there are any unobservable, omitted variables that confound the relationship ii ii−1 ii ii ii between the outcome of interest and the treatment. It is plausible, for instance, that places covered by ββ εε XX , θθ , ττ 3G also tend to have local business environments that are more friendly to firm entry and job creation, which could, in turn, result in improvements in welfare. In this case, it would be incorrect to attribute improvements in welfare to 3G coverage alone.

> 10 The Mobile Coverage Maps database 3G coverage maps are provided as polygon shapefiles, while some of the 3G coverage maps shared by operators are available only as point shapefiles. In the case of the point shapefiles, households were coded as covered if they were in EAs in which the centroids were less than 1 kilometer from the closest coverage points.

11 As a robustness check, the contemporaneous effects of distribution terminals on consumption and poverty are also tested. The conclusions do not change whether the treatment is lagged or not (see appendix A, table A.2).

> 12 See Pop et al. (2018) for more detail . Although 4G is beyond the scope of this paper, 4G coverage maps received from Orange-Sonatel (for December 2017 and December 2018) are used to test the impact of 4G technologies on consumption and poverty. The results are reported in appendix A, Table A.7. The finding is that 4G coverage is positively correlated with consumption and negatively with poverty, but this relationship is not robust to the inclusion of
**Figure 2: Share of working population in different business size classes, versus GDP per capita and Gini coefficient**

<!-- Start of picture text --> 3 β: -0.799 3 β: 0.517<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -2 -1 0 1 2 -2 -1 0 1 2 3<br>Log GDP per capita, PPP (constant 2017) Gini index<br>3 β: -0.598 3 β: 0.280<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -2 -1 0 1 2 -2 -1 0 1 2 3<br>Log GDP per capita, PPP (constant 2017) Gini index<br>3 β: 0.560 3 β: -0.487<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -2 -1 0 1 2 -2 -1 0 1 2 3<br>Log GDP per capita, PPP (constant 2017) Gini index<br>3 β: 0.946 3 β: -0.412<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -2 -1 0 1 2 -2 -1 0 1 2 3<br>Log GDP per capita, PPP (constant 2017) Gini index<br>Latin America Europe and U.S. Asia and Australia Linear fit<br>Share of workers who are<br>self-employed without employees<br
9 trillion of external sovereign bonds (BIS 2018), about the same as total German government debt or about 10% of total gross US government debt.

> 10 Dimson, Marsh, and Staunten (2001) gather annual data on equities, bonds, and bills for 16 countries back to 1900. Their data on government bonds mostly builds on representative domestic currency instruments. Jordá et al. (2017) compile yearly country-level indices of asset returns, 1870-2015, for 16 countries, including housing.

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included support groups, home‐based care, spiritual support, condom distribution, and provision of transport to clinics for PLHIV.

Another review of a subset of faith‐based organizations in a South African database (Birdsall 2005) found that the nature of services provided by FBOs depended on the setting. FBOs in rural areas were less involved in food programs but more involved in condom distribution than FBOs in urban areas, and a higher proportion of FBOs in urban areas reported involvement in care and support. Most FBOs in both rural and urban areas provided services to PLHIV, OVC, and HIV‐positive mothers, but in urban areas FBOs were more likely to also provide services to substance abusers, sex workers, and street children.

Finally, community responses have been driven by peer‐learning facilitated by networks, such as Grassroots Organizations Operation Together in Sisterhood (GROOTS) International, Huairou Commission, Shack/Slum Dwellers International and others.

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3 developed and grow less rapidly than larger states. In this paper, we test this hypothesis using cross-country data ia a large sample that includes many microstates. In light of the grim predictions of theory, the picture of microstates which emerges from this analysis is somewhat surprising. After controlling for a range of factors, we find that microstates have on average higher income and productivity levels than small states, and grow no more slowly than large states. Per capita GDP growth rates are more volatile in microstates, due to their much greater exposure to international trade and fluctuations in their terms of trade. However, any growth disadvantages of this greater volatility are more than outweighed by the growth benefits of trade openness reaped by microstates by virtue of their necessarily large trade volumes. Finally, microstates are well-positioned to take advantage of opportunities international risk sharing, since the correlation of economic fluctuations in microstates with the world business cycles is surprisingly low.

These resulls contribute evidence in support of the growing view in the literature that small size might not be a disadvantage after all. Kuznets (1960) notes that small states also have advantages: primarily that many are lucky to have good natural resources and have a small and more cohesive populations which allows them to adapt better to change. Srinivasan (1986) and Streeten (1993) argue without systematic empirical evidence that small may also be beautiful. Using a sample of 48 countries Millner and Westaway (1993) fail to find evidence that the effect of a number of growth determinants varies with country size. Armstrong et. al. (1998) uses crosssectional regressions covering a large number of small states and independent regions to argue that population size. does not significantly affect growth, controlling for initial income and regional effects.

The remainder of this paper proceeds as follows. In the next section we document that small states are richer and have higher productivity levels than small states. In the following section we observe that small states suffer no obvious growth rate disadvantage, and attribute this to a number of offsetting
# **II. Within-country inequality around the world**

The analysis so far has treated the entire world as a single unit. By contrast, most studies of inequality focus on within-country inequality, which remains the level at which most policies to address inequality operate. There is another reason to emphasize within-country inequality: Some commentators have used the evidence on falling global inequality to dismiss concerns with rising within-country inequality for instance in the US. However, countries remain the relevant level for most concerns over inequality; in fact, studies show that wellbeing can be affected by very localized inequality.<sup>13</sup> As the country decomposition in Figure 2 showed, the majority of the world’s population lived in a country where inequality was increasing between 1988 and 2008. In such an analysis every country is weighted by its population, so this conclusion does not imply that inequality increased in the average country. It is important to clarify this because trends can be different whether or not population weights are used. I will briefly review the trends in within-country inequality for all countries in the world, before discussing the trends in Latin America, East Asia and Sub-Saharan Africa in more detail. At the end, I present the most recent numbers on the developments since the onset of the Great Recession. Throughout this section, I concentrate on within-country inequality (typically measured by the Gini index) without using population weights. I will draw mostly on the reviews by Alvaredo and Gasparini (2015) and Morelli et al. (2015) as well as new up-to-date calculations based on data from World Bank (2016c).<sup>14</sup> In the _average developing country_ , national inequality increased during the 1980s and 1990s, while it declined in the 2000s (largely driven by countries in Latin America). By contrast, the _average person_ in the developing world (i.e. using population weights) lived in a country with steadily increasing inequality between the mid-1980s and 2010 (driven by the surge