rafmacalaba/data-use-mentions-extended huggingface dataset:
actual RER on the ratio of net foreign assets to GDP, productivity differentials and terms of trade. The actual RER is proxied by the real effective exchange rate ( _REER_ ), as defined by the domestic price index of country _i_ vis-à-vis the price index of its main trading partners multiplied by the nominal exchange rate of country _i_ , where _eit_ is the nominal exchange rate of country _i_ ( _vis-à-vis_ the US dollar) in period _t_ , _Pit_ is the consumer price index of country _i_ in period _t_ , _dkt_ is the nominal exchange rate of the _k-_ th trading partner of country _k_ in period _t_ (in units of local currency _vis-àvis_ the US dollar), and _Pkt_ 0 is the wholesale price index of the _k-_ th trading partners in period _t_ . The _nominal exchange rate_ , _e_ , is proxied by the average price of the dollar in local currency (line _rf_ of the International Monetary Fund's International Financial Statistics (IFS)). Domestic and foreign prices, _P_ , are proxied by the consumer price index of the country (line _64_ of IFS). According to this definition, an increase in _q_ implies a real appreciation of the domestic currency.

_NFA_ data is drawn from Lane and Milesi-Ferretti (2001, 2007). This database comprises a set of foreign asset and liability stocks for a large group of industrial and developing countries spanning over the 1970-2005 period. The construction of the data is thoroughly documented in Lane and Milesi-Ferretti (2001, 2007), and the NFA position of country _i_ in year _t_ is defined as: where the letters _A_ and _L_ denote assets and liabilities, respectively. Thus, the net foreign asset position is the sum of net holdings of direct foreign investment, _FDIA_ - _FDIL_ , plus net holdings of portfolio equity assets, _EQYA_ - _EQYL_ , and the net position in non-equity related assets ( _i.e._ ''loan assets''). In turn, the net position in non-equity related assets consists of international reserves, _RA_ , and the net
sugarcane compared ...................... 45_|
|EFFECTIVE PROTECTION FOR SUGARCANE, 1981-90 ............. 46|
|EFFECTIVE SUBSIDY FOR SUGARCANE, 1981-90 ..................... 48|
|PRODUCER SUBSIDY EQUIVALENTS ESTIMATES FOR<br>SUGARCANE, 1981-90 ...........................................<br>51|
|REFERENCES ............................................<br>54|
|STATISTICAL DATA SOURCES ............................................<br>56|

**DATA TABLES Si TO S9**

## **ANNEXES 1 TO** 22

iii
# **Appendix I:  Correlation Results**

## **Table I.1 Central Bank Independence and the Broader Governance Environment**

|Variables|Independence of<br>central bank|<br>Corruption<br>Perceptions Index|Governance<br>Pillar Score|Government<br>Effectiveness|
|---|---|---|---|---|
|Independence of central bank|1||||
|Corruption Perceptions Index|0.472***|1|||
|Governance Pillar Score|0.346***|0.690***|1||
|Government Effectiveness|0.440***|0.928***|0.720***|1|
|GDP per capita|0.383***|0.803***|0.609***|0.784***|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.2  Country Risk and the Broader Governance Environment**

|Variables|Independence of<br>central bank|<br>Corruption<br>Perceptions Index|Governance<br>Pillar Score|Government<br>Effectiveness|Country risk<br>score|
|---|---|---|---|---|---|
|Country risk score|-0.484***|-0.828***|-0.638***|-0.880**
This is problematic in an environment where uncertainty about future rules of the game as well as market development is a standard feature. Theories of irreversible investment under uncertainty predict that investment will be slower to respond to demand shocks if uncertainty is relatively high. As mentioned in Section 3.1 testing empirically for the effects of risk is difficult, as risk is not easily measured. In this context, the design of innovative survey questions has been a major step forward. Pattillo (1998) uses panel data on Ghanaian manufacturing firms from 1994 and 1995 to test various hypotheses from models of irreversible investment under uncertainty. Based on data on entrepreneurs’ subjective probability distribution over future demand, Pattillo calculates the variance of demand and uses this as the measure of uncertainty. Empirical results indicate that uncertainty has a negative effect on investment and this effect is more pronounced for firms with more irreversible investment. A similar 26
*Memorandum:**Share of Pan-|Europea|n impor|ts||||||||
|in Bularia's manufactured|||||||||||
|g<br>imports|78|78|77|81|82|83|83|82|104|100|
|in Bulgaria's total imports|23|22|34|47|41|49|49|51|212|126|

Source: Own calculations based on Bulgaria's reporting to UN COMTRADE database.

Another indication of some progress in industrial restructuring is the improvement in Bulgaria’s net exports of machinery between 1999 and 2003. Exports of machinery in terms of their imports increased from 38 percent in 2001 to 42 percent in 2003 (Table 12). Taking into account that the value of these imports in 2003 was 83 percent above their 2000 level, this strikes one as quite an impressive export performance. It appears that the “EU-15-connection” has been crucial not only as a supplier but also as a buyer accounting for more than 50 percent of turnover in machinery and automobiles and parts. Consider that both exports of machinery and automobiles and parts grew fastest in terms of their imports from the EU. This was not because of falling imports from the EU as these increased in 2000-03 by 105 percent (machinery) and 85 percent (automobiles and parts).
0.0001)|
|Experience<sup>2</sup>|-0.101|-0.108|-0.111|-0.082|
|(x 100)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Gender|-1.092|-1.148|-1.131|-0.714|
||(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|______________|0.431|0.434|0.438|0.336|

Source: Authors' calculations based on the "Pesquisa Nacional por Amostra de Domicilios" (PNAD). P-values in parentheses.

Let us now turn to the estimation of the multinomial logit in equation (4). This was estimated separately for household heads and for others, since the set of explanatory variables was slightly different in each case (see the description of vectors Z, and Z1 in Section 2 above). Table A3.1 in Appendix 3 presents the results for household heads, and Table A3.2 for other household members. In both tables, the results are presented as the effects of other choices versus that of remaining outside the labor force ('unoccupied').

For household heads, education was not significantly related to the likelihood of choosing to work in the wage sector vis-a-vis staying out of the labor force, at any time. In addition, the positive effect of education decreased from 1976 to 1996, to the point where it was no longer statistically significant. The dominant effect on the occupational choices of urban household heads over this period, however, was a substantial decline in the constant term affecting the probability of participating in either productive sector, as opposed to remaining outside the
# **B Data**

The data required for the model are:

- _E_ exports (% of GDP)

- _M_ imports (% of GDP)

- _RD_ tax revenue from VATs and sales taxes (%GDP)

- _RE_ tax revenue from export taxes (% GDP)

- _RM_ tax revenue from import taxes (% GDP)

- _RK_ tax revenue from capital taxes (% GDP)

- _RL_ tax revenue from labor taxes (% GDP)

- _TD_ tax rate on domestic goods and services

- _TK_ tax rate on capital (corporate tax rate)

- _TL_ tax rate on labor

- _αi_ labor-output ratio in production of _untaxed_

- _αf_ labor-output ratio in production of _domestic αe_ labor-output ratio in production of _exports_

Country-specific data are set out in Table 9. All country-specific data were obtained from IMF Statistical Annexes to country reports, available on the internet at www.imf.org. Table 9 also reports the year for which the data apply, and whether the country had a VAT in that year.

In the absence of data on labor-output ratios for all countries, the model uses the average ratios for the five countries of Table 10, supposing that the ratios are constant across all countries. These data are derived from social accounting matrices prepared by the International Food Policy Research Institute. Among other data, the SAMs provide for each commodity: the value-added by each factor; exports of each commodity; domestic demand for the commodity; and goods taxes paid. Let _Li_ denote the labor share of value added for each commodity _i_ . Let _Ei_ denote exports of each commodity. The first row in the table, the labor-output ratio <u>�</u> _<u>i</u>_<sup>(</sup><sup>_Li×Ei_)</sup> for exports is then . Commodities are classified as _untaxed_ if the taxes <u>�</u> _i_<sup>_Ei_</sup> paid on the commodity constitute less
|overty<br>ertyline|
|---|---|---|---|---|
||Index|Diff.|Index.|Diff.|
|Pre-fiscal income|0,461||0,173||
|Net market income|0,445|-0,017<sup>***</sup>|0,190|0,018<sup>***</sup>|
|Disposable income|0,429|-0,016<sup>***</sup>|0,158|-0,032<sup>***</sup>|
|Adjusted income|0,383|-0,046<sup>***</sup>|||

Source: Authors’ estimates based on ECH (2017), INE and Fiscal Accounts and CEQ Methodology. Notes: *** p<0.01, ** p<0.05, * p<0.1 for a test of means testing the null hypothesis that the difference between the income concept of the row and the income concept of the row above is zero.

We perform this decomposition and report the results in **Table 4** . The vertical equity measure indicates the change in the Gini coefficient when keeping individuals in the same order, that is, in the absence of reranking. Since reranking means a loss of horizontal equity, the reported measure of reranking effect (RE/VE) has traditionally been called horizontal inequality. The results show 19 Given the unavailability of indirect taxes information for this exercise, it is not straightforward to compare the effects on the Gini index found here (a drop of 0.078) with the results obtained in other countries. Studies that incorporate both direct and indirect taxes, where the latter are typically regressive, will appear to be less inequality reducing than studies that incorporate direct taxes
# **Understanding the Geographical Distribution of Stunting in Tanzania: A Geospatial Analysis of the 2015–16 Demographic and Health Survey**<sup>**1**</sup>

# **George Joseph**<sup>**a**</sup> **, Peter W. Gething**<sup>**i§**</sup> **,  Samir Bhatt**<sup>**iii**</sup> **, Sophie C.E. Ayling**<sup>**ii**</sup>

i. Water Global Practice Group, World Bank Group, 1818 H Street NW, Washington DC, 20433, USA.

ii. Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7LF, UK.

iii. Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK.

§ Corresponding author JEL: O55; I14; C31 Key Words: Stunting; Geo‐statistics; Tanzania; Africa, Health Policy

> 1 This work was made possible by the Swedish International Development Cooperation Agency and benefited from funding from the Government of Japan through the Japan Trust Fund for Scaling Up Nutrition.
respectively. The same negative relationship holds for per capita expenditure with an albeit smaller elasticity of -.22.

# **3.2 Empirical Results**

## **3.2.1 Nighttime Lights**

The nighttime lights serve as the baseline of the study. The analysis is based on regressions of the measures of economic activity (employment, number of enterprises, expenditure) on nightlight data from DMSP-OLS. This exercise focuses on one single year in 2012. Since commune boundaries change, the shapefile does not perfectly match the economic data provided for all years. The highest matching rate is for the year 2012 and therefore the analysis proceeds with this year.

This paper follow the sum of lights (SOL) approach that is commonly applied for indicating light intensity. The sum of lights approach counts up all nightlight sensor values (coded from 0-63) of pixels that fall within the area of a commune. To account for non-linearities in the relationship, log-log models are estimated.<sup>8</sup> In the case of the nightlight approach with a singular regressor, the coefficient _β_ can therefore be interpreted as the elasticity of the economic variable with respect to the total light emitted.

Table 2, Panel A reports the results from the simple linear regression model in (1). In column (1), the estimated elasticity of the number of enterprises with respect to nightlights is 0.393 in the year 2012, indicating a strong relationship between economic activity and nightlights even at small geographies. The coefficient is significant at the 99.9% confidence level and measured with high precision. The R-squared measure of 0.09 however indicates a rather low predictive power of the nightlights in the cross-section. The employment regression

> 8The log-log specification drops communes with zero nightlights of which there are 850. Using a log-linear prediction model does not alter the main results (see robustness checks in the appendix).

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||||problems in wave<br>3.<br>UGA: only for<br>plots with erosion<br>problems from<br>wave 4 onwards|
|---|---|---|---|
|_livestock_|Is the respondent<br>engaged in<br>livestock<br>activities?|||
|_ag_asset_index_|Agricultural assets<br>index|Index based on a PCA of all<br>agricultural assets owned by the<br>household|UGA: missing in<br>wave 1|
|_perennial_crops_hh_|Does this<br>household grow<br>perennial crops?|||
|_nb_fallow_plots_|Number of fallow<br>plots under<br>household<br>management|||
|_nb_plots_|Number of plots<br>under household<br>management|||
|_share_kg_sold_|<br>Share of harvest<br>output (in kg) sold|Set to missing if reported share<br>above 100% percent. Set to<br>missing if household does not<br>harvest any crops.||
|_agro_ecological_zone_|Agro-ecological<br>Zones||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above|
|_dist_popcenter_|HH Distance in<br>(KMs) to Nearest<br>Population Center<br>with +20,000||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above|
|_dist_market_|HH Distance in<br>(KMs) to Nearest<br>Market||From the<br>geovariables files,<br>which are missing<br>in NER w2,
results are in Table 3. There is indeed a bias when calculating the welfare effects with aggregated data. The size and direction of the aggregation bias calculated by comparing measures derived from aggregated data (presented in Table 3) instead of disaggregated data (presented in Table 2), are summarized in Table 4. At the developing world level, for instance, the welfare gains would be 2.39%, instead of the 2.50% obtained when we use disaggregated data; using aggregate data thus leads to an underestimation of the average gains across countries by approximately 4 percentage points.

For individual countries the biases are much larger and can be positive (in 25 countries) or negative (29 countries). The average absolute difference between average gains estimated using aggregated vs disaggregated data is 0.75 percentage points, or approximately 30 percent of the average gains from trade across countries. In the Central African Republic, Pakistan, Indonesia, or the Republic of Yemen, the welfare gains using aggregate data are roughly half of the estimates using disaggregated data. More extreme differences appear when we inspect countries with positive biases. In Cambodia and Ethiopia, the welfare gains are overestimated by a factor of 9 or 4, respectively. There are many instances where the estimates are biased by a factor of close to or above 2 (Burkina Faso, Cambodia, Ethiopia, Guinea-Bissau, Madagascar, Niger).

Using more aggregated data also gives different estimates of the distributional impacts of the gains from trade. Using the aggregated data trade policy is estimated to be pro-rich in 29 of the 54 countries, whereas it is estimated to be pro-rich in 23 of the 54 countries when using disaggregated data. More aggregated data tend to underestimate the gains accruing to rich households slightly more than they underestimate the gains accruing to poor households.

We now turn to examining product heterogeneity in the welfare effects. This is the heterogeneity that arises when we decompose the gains from trade into the consumption and income effects and, fundamentally, into disaggregated effects across goods
interpretation of these results is that, in order to benefit from FDI spillovers, a given firm must be sufficiently far from the technological frontier, i.e. that it has room to grow. Evidence for this is found in Chinese data by Hu et al. (2005) and across OECD countries by Griffith et al. (2004). In addition, we find that, at least for high revenue firms, that higher foreign ownership in the firm itself is linked with higher employment growth.

Finally, columns (8) and (9) split our sample between old firms (those founded before 1990) and young ones. Overall, we find that older firms are more sensitive to FDI spillovers, a finding consistent with Gorodnichenko et al. (2014). Specifically, we provide evidence that more foreign ownership of firm _i_ increases growth, with a slightly greater effect from increases in foreign ownership than from decreases. Foreign ownership of firm _i_ , however, has no significant impact among the younger firms. Older firms are also affected by horizontal

> 31If we control for sector which attract more FDI as compared to sectors where FDI is leaving we find evidence that this effect is driven by sectors where FDI activity is reduced. In sectors where FDI activity is increasing we find in turn evidence that competition spillover matters. Results are available on request.

> 32The F-statistic for this test was 1.33, resulting in a p-value of 0.25.

> 33Gorodnichenko et al. (2014) use a similar threshold. Our threshold separates the top 5% of companies from the remainder.

16
, especially the poor and rural households.<sup>17</sup> Agriculture dependent households are thought to be particularly at risk of suffering the impacts of climate change and adverse shocks. Climate change and natural disasters are expected to be especially severe in rural areas in this region,<sup>18,19</sup> while smallholder agricultural production remains predominantly rainfed and the adoption of drought or heat resistant seeds or other such climate-smart technologies is limited.<sup>20</sup> We document that crop losses due to adverse shocks are common and costly both to individual farm households and to the economy at large, and that farmers often suffer multiple shocks in the same season. Taken together, production losses have a substantial aggregate impact. Importantly, these events and their impacts are underreported or undetected in common macro data sources for disaster reporting, such as EM-DAT.

Our analysis of micro-data offers an important complementary perspective to analyses based on aggregate statistics derived from disaster inventories. Aggregate statistics are critical to the study of disaster impacts, providing annual data at a global scale. They are less well-suited to capture the differential impacts of disasters on different population groups, especially poor and vulnerable people.<sup>21–</sup> 23 This is because they account primarily for damages to assets and losses in agricultural production whose value is greater and better documented among richer households and in richer countries. For instance, according to the most recent estimates of EM-DAT, about 70% of economic losses due to disasters occurred in the Americas, compared to just under 4% in Africa.<sup>24</sup> A recent study using the same data source concluded that disaster impacts do not affect poor people as much as the general population.<sup>25</sup> In contrast, evidence from survey micro-data suggests that poorer households and individuals are more exposed and less resilient to adverse climatic and environmental shocks and suffer disproportionately greater well-being losses than better-off households.<sup>18
approved. The literature before us has examined the determinants of being a program beneficiary (i.e. the final allocations) in great detail, but we know of no studies that decompose targeting performance into applications and, conditional on application, selection.

In this paper, we exploit a unique combination of datasets to examine the application and selection processes at the national and household levels. At the national level, we combine data on the universe of project applications and final funding allocations in Tanzania‟s Social Action Fund (TASAF II) with a poverty map, census data, and voting data at the ward level to distinguish determinants of applications from determinants of funding conditional of having 2
way to help the poor was to increase the size of the nation’s economic pie, and any attention on the small slice of the pie given to the poor would be a distraction from the bigger picture.

If the benefits of economic growth took time to reach the poorest and inequality were to grow, the work of Kuznets (1955) provided reassurance that this widening in income gaps would be only temporary and nothing to worry about. Kuznets, on the basis of US data over the period 19131948, suggested that after an initial period of economic growth in which there could be an increase in inequality, as economies became richer they became also more equal. As Piketty (2015) points out, for Kuznets it was enough to be patient, and before long growth would benefit everyone.

Piketty’s work shows unfortunately a more sobering picture. The sharp reduction in income inequality that was observed in almost all advanced countries between 1914 and 1945 – and that Kuznets picked up – stemmed essentially from the world wars and the violent economic and political shocks they entailed. Since the 1970s, income inequality has increased significantly in rich countries, especially in the United States, despite overall economic growth.

Even worse, not only overall growth may not reduce inequality, but inequality could hamper faster economic growth, as recently pointed out by a number of IMF studies (Berg and Ostry, 2011, Ostry, et al., 2014; Dabla-Norris et al., 2015).<sup>5</sup> Higher inequality could hamper economic growth by making it more difficult for lower-income households to stay healthy and accumulate physical and human capital (Aghion et al., 1999; Galor and Moav, 2004). Poor children may end up in lower-quality schools, leading to underinvestment in education and to lower labor productivity than it would have been in a more equitable world (Stiglitz, 2012). Increasing concentration of incomes could also reduce aggregate demand, undermining economic growth, because the wealthy spend a lower fraction of their
consumer of acquiring a

> 6We also use the average per-capita GDP growth rate to test the robustness of our results.

> 7The database of political SWFs are from Bortolotti _et al._ (2015).

> 8The database are publicly available at `http://www.pewforum.org/2015/04/02/religious-projection-table/2010/number/all/`

> 9The data for financial development depth and bank efficiency come from the Global Financial Development Database (GFDD), publicly available at `http://econ.worldbank.org/` .

> 10 The World Development Indicators are publicly available at `http://www.worldbank.org/` .

13
of nondiscretionary transfers reported by Sorribas-Navarro (2011).<sup>37</sup>

> 37In the data, a government’s fiscal balances (flow) do not necessarily match the change in debt (stock). For example, the central government’s budget equation (in nominal terms) _transfer + other non-interest expenditure + interest expense = revenue + change in debt_ does not hold in the data. One reason for this statistical discrepancy is that we use gross debt instead of net debt to calculate _change in debt_ , as is customary in the literature, due to the lack of accurate public asset data.

26