rafmacalaba/datause-encoder-data huggingface dataset:
|Author (Year)|Study Type &<br>Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key Findings|
|---|---|---|---|---|---|
|Vanderhout et al.<br>(2020) †<br>Other health outcomes|SRM of 28 studies<br>(20 cross-<br>sectional, 8<br>cohort), 7 countries<br>(6 HICs, 1 in Brazil)<br>meta-analysis n=14<br>(11 cross-<br>sectional, 3 cohort)<br>|Children 1-18 y<br>(n=20,897 in meta-<br>analysis)|Milk consumption,<br>whole (3.25% fat) vs<br>reduced fat (0.1-2%)<br>[assessed using FFQ,<br>24HR, multiday food<br>record, other] Regular<br>consumption defined as<br>typically, daily, or≥4<br>timesper week|Overweight and obesity<br>[assessed by BMI z-score<br>(n=19), body fat<br>percentage (n=4), other<br>(n=5)]|Regularly consuming whole (3.25%<br>fat) vs reduced-fat milk associated<br>with lower risk of OWOB (OR 0.61;<br>95% CI 0.52, 0.72)|
|Horta and de Lima<br>(2019)|SRM of 14 studies<br>(3 case-control, 4<br>cross-sectional, 7<br>cohort), 2 in LMICs|Breastfed subjects<br>(mean age at<br>assessment ranged<br>from 13-71 y)|Breastfeeding|T2DMin later life|Risk of T2DM in later life is lower<br>among subjects who had been<br>breastfed vs. non-breastfed (pooled<br>OR0.67; 95% CI 0.56, 0.80);<br>subgroup analysis found higher<br>protective effect among adolescents<br>10-19 y (OR 0.49; 0.38, 0.63)<br>compared to adults 20+ y (OR 0.77;<br>0.66,0.90)|

Abbreviations: 24HR 24-hour recall; ASF animal source foods; BFP body fat percentage; BMI body mass index; BP blood pressure; CI confidence interval; FFQ food frequency questionnaire; F&V fruit and vegetable; HIC high-income country; LMIC low and middle-income country; OR odds ratio; OWOB overweight and obesity; RR relative risk; SSB sugar-sweetened beverages; SRM systematic review and meta-analysis; T2DM type 2 diabetes mellitus; UPF ultra processed food; WC waist circumference; WMD weighted mean difference † Study also included in Table 6 for children 5+ y due to age range of participants.

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# **2.2.1 Key sources of NTMdata**

Data on non-tariff measures come from three types of sources. First, there are efforts by international organizations, as well as other non-government institutions, to directly collect data on NTMs. As discussed above, a widely used data source is TRAINS, which has information on non-tariff measures by country and sector for HS6 product lines (i.e. a six digit sub-heading in the Harmonized System (HS) classification) and for selected years.<sup>5</sup> These data are the most complete source of comparable information on NTMs across countries. However, they do have some well recognized limitations. For instance, these data do not provide an assessment of stringency or distinguish measures that are discriminatory from those that are not. Moreover, the coverage is partial and focuses only on a subset of the chapters comprising the classification presented in Table 1, mostly chapters A to F. Another widely used institutional source is the Temporary Trade Barriers (TTB) database by the World Bank, which contains information on a number of contingent trade measures for approximately 30 countries that can date back to 1980 and be at the product level.<sup>6</sup> The obvious limitation is that the database only contains information on a narrow set of NTMs.<sup>7</sup> A second source of information is through various forms of government notifications, which are mostly collected by the WTO. These notifications can be direct or indirect. First of all, WTO members are subject to several notification requirements of trade-related measures under the GATT and the TBT and SPS Agreements (direct notifications). In addition, governments can raise Specific Trade Concerns (STC) on TBT and SPS measures that are adopted by other WTO members (indirect notifications). Both sources of information have pros and cons. Direct and indirect notifications are more consistently available for a longer time period relative to data collected by international organizations. However, direct notifications are often incomplete, as they are self-reported by governments that do not necessarily fully abide to their notification requirements. Moreover, this information is not coded and is not readily available for researchers.

> 5 The data are available through the World Integrated Trade Solution (WITS) at: <u>http://wits.worldbank.org/</u>

> 6 The TTB database is available at: http://go.worldbank.org/W5AGKE6DH0. 7 An example of a non-government institution collecting data on NTMs is The Global Trade Alert (GTA), which was set up by the Centre for Economic Policy Research (CEPR) to monitor the protectionist response of governments in the aftermath of the global financial crisis.
conform to our definition of productive expenditure. Furthermore, since ours is a pooled, crosssection/time-series data set, we are able to capture some of the lags involved in translating productive public expenditures into economic growth. Finally, our study is unique inasmuch as it focuses exclusively on developing countries. Other studies use a mixed sample of developed and developing counties, or examine developed countries only. As we will show, the results change dramatically when the sample is restricted to developing countries.

# **3.1** Data and Choice of Variables

Disaggregated spending figures at the level of consolidated general government (including public sector enterprises) are required to examine the full impact of public expenditures on economic growth. Unfortunately, such data do not exist in sufficient quantity for the majority of developing countries. For this reason, the data used in this paper are confined to central government expenditures. The operations of state and local governments as well as expenditures of government owned or controlled public sector enterprises are not included<sup>7</sup> The empirical analysis uses annual data on **69** countries (see the data appendix for a list of countries included) from **1970** through **1990** to examine the link between components of government expenditure and economic growth. The primary source of data on public expenditure variables is the _Government Finance Statistics_ **_(GFS),_** published annually **by** the International Monetary Fund. Not all observations are available for all the countries. The pooled data include total government expenditures (including the **GFS** classification of current and capital), expenditures for defense, education, health, and transport and communication. The

> 7 As a check on our results, we repeat our analysis for the sub-sample of countries for which there are data on consolidated government expenditures (see below).

**10**
13 mainly industrial raw materials. Imports from the FCSK and Poland increased quite dramaticaily in the 1988-91 period, although they were very low in the base year. Since these increases were probably triggered by contraction in industrial activity during the first year of the transformation program, their future growth potential may be limited. This group accounts for a small share of total CEE-5 industrial exports to the EC (in 1991 their share was below 2 percent).

However, the fast move to duty-free access is a more significant concession than the share of these imports might indicate. First, average NTB ratios on EC imports of this group from CEE-5 countries (except for Poland) are significandy higher than on industial products in general (see Table 3). For EC imports from Bulgaria, the rado is by 11.8 percentage points higher, from the FCSK 16 percentage points higher, from Hungary 28.4 percentage points higher, and from Romania 24.4 pernnage points higher (see Table 2 and 3). The average NTB ratio for EC imports of ail industrial products from Poland is 8.2 percentage points lower than for imports falling within the one-year-delayed free trade group.

Second, with the exception of the Balkan countries, CEE-5 exports of this group expanded rapidly in the 1988-91 period. The average growth rate was substantially higber than the average for CEE-5 all industrial exports, especially in the case of exports from the FCSK and Poland. Moreover, exports from these countries crowded out other suppliers, as their share of these EC markets increased from 0.1 percent (both for the FCSK and Poland) in 1988 to 0.4 percent for the FCSK and 0-6 percent for Poland in 1991. While it is unlikely that such a rapid expansion is sustanable over the medium-term, the elimination of trade barriers has significantly improved their competitive position.

Table 3: The "one-year-delayed' Free Trade Group: Exports to the EC an d Pre-ITAs Market Access

||Exports<br>1991|Share in Industri<br>Exporis, 1991|al Index, 1991<br>NTB Coverage<br>1988=100<br>Ratio|Simpb Average<br>Tariff Rate|Range of<br>tariff mres<br>Max-NIin|
|---|---|---|---|---|---|
||(US$ mi|n)|(in percent)|||
|Bulgaria|10|1.4|93<br>33.3|_5.1_|6.0-0.0|
|FCSK|53|1.1|400<br>40.0|_4A_|18.3-0.9|
|Hungary|23|0.7|126<br>52.6|3.0|7.04.0|
|Poland|27|OA|_500_<br>_15A_|6-2|18.3-0.0|
|Romania|1|0.0|60<br>40.0|2.0|3.240|

Sources: See Table 2.
# Tablc 2: THE COMPARISON OF CIL'NA AND THE UNITED STATES BY VARIOUS ESTIMATES

|Country|<br>Source|Agggate G|DP|Pcr Capita G|DP|
|---|---|---|---|---|---|
|||(MillionS)|US = 100|-US$)|US=100|
|U.S.|World Bank|4.223,168 (1986)|100.00|17,480|100.00|
|China|Pcnn (Mark 4)|3.409,700 (1985)|80.70|3,248|18.57|
||Penn (Mark 5)|2.614,200 (1988)|61.90|2,368|13.55|
||Kravis|2.056,000 (1986)|48.68|1,930|11.04|
||Gordon|1,582,000 (1986|37.46|1,485|8.50|
||Ahmoad|1,064,200 (1986)|25.20|999|5.70|
||Ccnsus|836,253 (1986)|19.80|_785_|4.50|
||ACDA|416,528 (1986)|9.86|391|2.30|
||World Bank (Atlas)|330,239 (1986)|7.81|310|1.80|
||WEFA|324,913 (1986)|7.69|505|1.70|

Source: U.S. dat fiom _World Development Report, 1989,_ and other numbers fxon the cited studies.

7. World Bank Atlas approach is an exchange-rate-based conversion method. The conversion factor for any year is the average of the exchange rate for that year and the exchange rates for the two preceding years, after adjusting them for differences in relative inflation between the country and the United States (World Bank, 1991c). Usually, this approach gives lower estimates of real income for the developing countries which can be explained with the findings in ICP and other theoretical studies on the relationship between PPP and exchange rate in low-income countries based on the productivity diferential or factor proportion differential model (Kravis, Heston and Summers 1982; Kravis and Lipsey 1983; Bhagwati 1984).

8. Gordon,et al. (1990) constructed PPPs in each sector of the economy based on some strong assuptions and estimated the size of China's economy in dollar temrs. Ahmad (1983) compared the stnucture of China's prices with those of other countries in a standardized format to construct retail PPPs and to estinate real income for 1981. An overall PPP (2.23 vuan/dollar) reported in Taylor's paper (1986) is used in the Arms Control and Disamament Agency (ACDA) estimates (1988). This 4
3% compared to Honduras where it is less than 1%.<sup>21</sup> Since the quality of the final product is also directly influenced by raw materials and inputs used in production, we could look at the quality of “purchased inputs that is of lower than agreed upon quality” as an indicator of overall quality. Figure 10 shows that this indicator is much higher for firms in Ethiopia, Tanzania and Uganda compared to South Africa, India and China. It is also interesting to note that this issue is more serious for export-oriented firms in these countries, compared to the firms which mainly produce for domestic markets.

<!-- Start of picture text --> Figure 10: Quality Reputation (% of purchased products that is of lower<br>than agreed upon quality)<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Ethiopia UgandaTanzania South Honduras India China<br>Africa<br>Exporters Non-Exporters<br><!-- End of picture text --> <u>Source: World Bank Group,</u> _Enterprise Survey_ , 2002

# **4. Value Chain Analyses**

The above-mentioned competitive factors are driven not only by industry-level efficiency but are directly affected by underlying government policies, institutions and physical infrastructure in the exporting countries (such as a trade facilitating environment, quality control systems, licensing). Value chain analyses can shed some light and provide clear insights on where the opportunities for Africa lie. Trade-enabling policies and regulations need to be addressed _directly_ by the government in a comprehensive, coherent and integrated manner.

While many firms have had international operations and trading relationships for decades, in recent years we have seen the formation of global-scale economic systems which are tightly integrated and often managed on a day-to-day basis. It is therefore hard to imagine that today’s process of economic development could be isolated from these global production systems.

Gereffi (1999) makes a distinction between buyer-driven and producer-driven global supply chains.<sup>22</sup> The former denotes the case of global buyers creating a supply base upon which production and distribution systems are built without

> 21  World Bank Group, Value Chain Studies of Honduras and Kenya, 2005

> 22 Gary Gereffi, “International Trade and Industrial Upgrading in the Apparel Commodity Chain,” _Journal of International Economics_ 48, no. 1, June 1999, pp. 37-70

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We estimate equation (1) using the _system-GMM estimator_ of Arellano and Bover (1995) and Blundell and Bond (1998), which addresses the potential biases that arise from the correlation between the firm fixed effects and the lagged dependent variable, as well as the endogeneity of sales, and cash flow in short panels. We treat all of the firm specific variables as endogenous, and use lagged values dated _t_ -2 and _t_ -3 as GMM-type instruments.<sup>5</sup> Reported standard errors are robust and they are clustered at the five-digit industry level.

# _3. DATA_

The firm level variables are from Prowess, a panel data set of Indian firms, collected by the Centre for Monitoring of the Indian Economy. The data account for about 70% of the organized industrial activity. Firms are classified into industries based on the 2008 National Industrial Classification (NIC).<sup>6</sup> We use data on manufacturing firms for the 1989-2000 period. We drop the observations in the top and bottom 1% of the sample based on investment rate, sales and cash-flow in order to eliminate outliers. To construct firmlevel investment expenditures, we take the annual difference in the gross fixed assets, which measures the value of the firm's capital.

The data on licenses, which represent the fraction of products in an industry subject to licensing requirements, are from Topalova and Khandelwal (2010). The data vary across four- or five-digit industries and over time. Delicensing in India during the 1990s was accompanied by other industrial reforms that included trade liberalization (reductions in tariffs), and lowering of entry barriers to foreign investment. In order to identify the distinct effects of delicensing, we also control for these concurrent reforms. The data on tariffs and the share of products which have approval for foreign investment (FDI) are also from Topalova and Khandelwal (2010). Additionally, we control for the logarithm of real net state product (source: Reserve Bank of India) to account for income differences across states that might affect firm

> 5 We report the Sargan-Hansen tests of overidentification to verify the validity of our instruments.

> 6  The 2008 National Industrial Classification is based on the International Standard Industrial Classification (ISIC) Rev.4.

5
**Table 2: Mahalla Sample and Descriptive Statistics**

||First-|Stage Samp|le|Admin|Data|
|---|---|---|---|---|---|
|Province|Total<br>PSUs|Urban|Non-<br>Urban|Migrant<br>PopShare|Male Share<br>Migrant|
|Andijan|15|2|13|4.6%|85%|
|Bukhara|18|3|15|4.5%|84%|
|Jizzakh|8|1|7|2.8%|80%|
|Kashkadarya|19|2|17|2.0%|87%|
|Karakalpakstan|22|4|18|2.4%|79%|
|Navoi|10|2|8|1.7%|79%|
|Namangan|16|3|13|2.8%|85%|
|Samarkand|16|2|14|3.9%|84%|
|Syrdarya|5|1|4|1.9%|78%|
|Surkhandarya|10|1|9|3.8%|91%|
|Tashkent|15|4|11|1.7%|55%|
|City of Tashkent|14|14|0|1.8%|54%|
|Fergana|18|4|14|3.0%|86%|
|Khorezm|14|2|12|5.6%|80%|
|Total|200|45|155|3.1%|80%|

The administrative data collection approach was simple data entry from official mahalla records in selected PSUs. Each mahalla in Uzbekistan is legally obligated to maintain such records for every person and family residing in that location. These data cover the total population size, demographics, social protection programs, local amenities, and migrant populations. Following the conclusion of the survey data collection fieldwork, the survey data design weights were adjusted on observed population totals in the administration records within the mahallas. These estimates were assumed to be more accurate as they had been updated within three months of the time of the survey fieldwork. Although not identical, there is a strong correlation between the migration descriptive statistics derived from the mahalla records, and those observed in the baseline survey data (as reported in Table 2).

The second stage procedure was conducted using simple random selection of households with equal probability within selected mahallas. A separate stratification level for households that receive social assistance was included, totaling 4 households per mahalla. The final target sample included 20 households per mahalla, 800 of which were social protection recipients by design. The baseline survey included a full consumption and expenditure module using a list/recall approach. The resulting estimates are representative for 12 regions (referred to as provinces in Uzbekistan), 1 autonomous republic, and 1 independent city (Tashkent), crossed with their urban areas (except for the City of Tashkent, which is entirely urban). The baseline survey was conducted entirely on tablet devices (CAPI), enabling validation using cross-referencing, confirmation using geo-coordinates, and other techniques to ensure accuracy. The survey was conducted over the course of a 1.5-month period in May/June 2018. Survey descriptive statistics from the baseline and from the first wave of the panel survey are included in table (3).

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Given the diversity of results in both theoretical and empirical analyses, one would be hard pressed to generalize from the existing literature. The relationship between trade and regional inequalities thus remains wide open, both from a theoretical and empirical perspective.

# **3. Overall trade and regional inequality: Empirical evidence**

This paper revisits the question of the link between trade and regional inequality, using an unbalanced panel dataset comprising 28 countries over the period 19752005. The 28 countries included in the analysis are presented in Table 1, which groups them according to whether they have experienced increasing, stable, or decreasing spatial disparities, using the evolution of the population-weighted coefficient of variation, over the time span covered by the data.

## **Insert Table 1 around here**

As can be seen, the majority of the countries included in the sample have experienced a rise in regional disparities over the period of analysis. In 18 out of the 28 countries spatial inequalities have increased, while seven countries witnessed relative stability<sup>4</sup> , and only three – Belgium, Brazil, and South Africa – saw a reduction in disparities. The rate of change varies enormously across countries (Figure 1). Countries such as Bulgaria, China, Hungary, India, Poland, Romania or the Slovak Republic have witnessed a very rapid rise in disparities, while the rate of increase has been more moderate in places such as Australia, Spain, the UK, or the US. Rates of decline in inequalities have also varied hugely, with Belgium and Brazil experiencing the strongest decline in territorial inequalities. There is also no apparent difference between the trajectories of developed and of emerging countries. Some of the low and medium income countries included in the sample have seen spatial disparities increase – e.g. Bulgaria, China, India, Indonesia, Mexico, and Thailand – while this has not been the case in Brazil and South Africa (Figure 1). However, it is worth noting that the level of territorial inequalities differs widely among countries and especially

> 4 It is often the case that overall stability trends during the period of analysis hide significant variations in the evolution of regional inequality. Two such cases are Canada and China. In both countries, albeit for very different reasons, regional disparities decreased during the 1980s, but have tended to grow – and in the case of China, particularly rapidly – since the early 1990s.

8
this approach in the academic literature see Bohn (1998). At the World Bank the IMF’s emphasis on debt dynamics has been combined with AW’s integration of public sector and CB accounts to re-introduce seigniorage in the analysis, but without the long-run consistency approach and detailed financial sector modeling characterizing the AW models (see Burnside (2005)).

The approach presented here combines all these strands into a simple template usable for country economists, yet sufficiently rich to reflect the results that have come out the research sketched above. The model follows AW in tightly integrating the CB into the public sector, which allows us to bring seigniorage income and the inflation tax back into the analysis. This will allow analysis of the consistency and sustainability of inflation targets within a given set of fiscal plans, a crucial point given that structural reform packages are often embedded in a macroeconomic stabilization program. But we go beyond the steady state analysis of AW by also introducing debt dynamics, like in IMF (2002, 2003) and Burnside (2005). This first of all allows us to analyze how serious deviations from consistency are, and what the trade off is between adjusting now versus adjusting later, if that buys time, but at the expense of a larger required adjustment.

Possibly even more important is that the explicit introduction of debt dynamics allows us to introduce debt structure and the exposure to risk introduced by the structure of the debt into the analysis. Vulnerability to sudden stops is often related to debt structure, especially if there are high peaks in refinancing needs. This occurs often when a substantial part of the debt is issued at short maturity and/or indexed to foreign exchange. In addition, even if the public sector has its house in order, large private sector imbalances and widening current account deficits may trigger expectations of exchange rate depreciation, especially under pegged or managed float exchange rate regimes. And devaluation leads to large capital losses on foreign currency denominated debt and possibly to pressures to bail out failed banking institutions to avoid a systemic banking crisis. Analysis of these issues requires explicit introduction of the structure and composition of public sector debt.

Moreover, the vulnerability of market access countries to sudden stops and reversals of capital inflows makes it critical to incorporate uncertainty in the analysis of fiscal sustainability. Uncertainty surrounding public debt dynamics is often related to uncertainty about medium term projections of the economic growth rate, the primary balance, the cost of public sector borrowing and the existence of either explicit or implicit guarantees of debt or bank deposits.

4