rafmacalaba/data-use-mentions-extended huggingface dataset:
# **B.2 Descriptive statistics**

## **Table B.1:** General Utilization

|Median|Mean|SD|Min|Max<br>N|BL data|
|---|---|---|---|---|---|
|(1) Number of health episodes in which sought western care<br>1<br>12 _♦_<br>1|0.988|0.382|0|3<br>4496|Yes|

Questions about ante and post natal care, vaccinations and illness/injury episodes are asked for the past one month. Questions about child birth are asked about the past year. †: dummy variable; §: likert scale (1-4); _¶_ : likert scale (1-5); _♦_ : measured across all health episode types (ante and post natal care, child birth, under-2 vaccinations, and illness and injury); w , 1 , 6 , and 12 stand for measures referring to the last week, one month, six months and twelve months respectively.

**Table B.2:** Maternal Utilization

||Median|Mean|SD|Min|Max|N|BL data|
|---|---|---|---|---|---|---|---|
|(1) ANC/PNC visits index<br>12|_−_0.187|_−_0.054|0.956|_−_1.779|5.602|887|Yes|
|(2) Birth in western medicine facility<br>12 †|1|0.862|0.345|0|1|877|Yes|

†: dummy variable; §: likert scale (1-4); _¶_ : likert scale (1-5
function. These responses provide us with a composite index of the quality of government bureaucracy or its capability. The manner of its construction is spelled out in Appendix 1. Second, we summarise governments' policy stance over time again through an index that combines three key indicators; the degree to which an economy is open -- as measured by the share of trade in GDP -- the degree to which a country's exchange rate has been over-valued -- as measured by the black market premium on the exchange rate -- and the degree to which local prices have departed from international prices. Details as to the construction of the index are again given in Appendix 1.

# **3. Growth of Government**

Government size has grown throughout this century but the most dramatic part of this expansion has been since 1945. Aside from greater public provision of infrastructure, utilities, education and health services, there was also a clear shift toward greater direct control over production. In the industrial economies, large-scale transfer programmes -- such as unemployment benefits -- were established. For example, in the major countries of Western Europe, government expenditure which had on average only accounted for around 10% of GDP in 1870, accounted for some 30% in 1960 and over 50% by 1995 **'°** While long run data from the industrial economies suggest that wars have tended to

> **10** These are nominal ratios of total govermnent expenditure to GDP. Government expenditure includes central and local governments and the social security sector. Tanzi and Schuknect (1995); World Bank (1997).

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20 Figure 7: Poverty impact of policies implemented during 2010-11 food price spike (A) Global poverty impact of a 10 percent rice, wheat, and maize price increase (B) Global poverty impact of 10 percent rice, wheat, and maize price increase <!-- Start of picture text --> Percentage point Rice, wheat and maize Millions Percent<br>0.25 Rice 2.5 0.25<br>Wheat<br>0.20 Maize 2.0 0.20<br>0.15 1.5 0.15<br>0.10 1.0 0.10<br>0.05 0.5 0.05<br>0.00 0.0 0.00<br>SSA LAC SAR EAP MNA Number Percent (RHS)<br><!-- End of picture text --> (C) Regional poverty impact of the 2010-11 food price shock (D) Global poverty impact of policy responses to the 2010-11 food price shock <!-- Start of picture text --> Millions Millions Combined action Percent<br>7 10.0 Individual action 1.05<br>6<br>9.6<br>1.00<br>5<br>9.2<br>4<br>0.95<br>3 8.8<br>0.90<br>2<br>8.4<br>1<br>0.85<br>8.0<br>0<br>-1 7.6 0.80<br>SSA SAR EAP MNA LAC Number Percent (RHS)<br><!-- End of picture text --> Source: Authors’ estimates.

Note: Based on estimates using the MIRAGRODEP computable general equilibrium model described in Annex 6.3.

A. Change in the poverty headcount measured at $1.90
relationship between the minimum wage and the poverty line, and the extent of income sharing within the household.

The empirical evidence on the minimum wage-poverty relationship in developing countries largely comes from Latin America. The conclusions vary. Some find that increases in the minimum wage do reduce poverty (e.g., Lustig and McLeod 1997; Morley 1995). Gindling and Terrell (2006), using micro-data for Honduras, estimate that a 10% increase in the minimum wage reduced the likelihood of extreme poverty by 1.8% and poverty by 1% in that country. On the other hand, there are examples of studies that find no connection between minimum wages and poverty reduction (e.g., Neumark, Cunningham, and Siga 2006, for Brazil). Even where researchers do find such a link, they do not necessarily advocate minimum wages as an antipoverty tool. Lustig and McLeod (1997: 81) point out that their results should not be seen “as a flat endorsement of minimum wages as a cost effective policy to reduce poverty” because of potential negative employment and growth effects, particularly in the long run. Nonetheless, they do argue that reducing minimum wages in developing countries will hurt the poor, at least in the short run.

# **_3.2  Impacts on productivity_**

The effects of the minimum wage on productivity have been considered by researchers only infrequently. One systematic analysis to measure the productivity effect was carried out by Bassanini and Venn (2007), using cross-country aggregate data for 18 OECD member countries from 1979-2003.<sup>25</sup> They estimate that a 10 percentage point increase in the minimum wage-tomedian wage ratio was associated with an increase of between 1.7 and 2.0 percentage points in 25 This research is also reported in OECD (2007).
and non-food products increases with development.<sup>1</sup> This is due to the shape of the formal food Engel curve, which is effectively flat in low-income countries but becomes negative in middle-income countries. In some least-developed countries, we find that subsidizing food relative to non-food can neither be justified on equity nor on efficiency grounds.

Our third contribution is to investigate the impact of consumption tax policies on inequality by combining the calibrated optimal rates with the household data. The impact of a tax policy on inequality depends not only on its progressivity, but also on the level of tax rates and size of tax bases which determine the share of taxes in households’ budgets. We find that setting a uniform tax rate on all goods while taking into account

> 1This is true for our baseline value of the elasticity of substitution in consumption between formal and informal varieties, the parameter which determines the size of the efficiency cost of taxation due to the existence of an informal sector. We use a baseline value taken from Faber and Fally (2017) and Atkin et al. (2018b) and discuss the robustness of our results to alternative values.

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safe-sex behavior metric. Condom availability is important for adolescent health given the sporadic nature of adolescent sexual activity. Column 6 in table 5 reports the results of the voucher experiment.

The administrative data from voucher redemption shows statistically significant and important effects. The table shows that 28 percent of treatment students redeem them, compared to 18 percent of control

> 18 Classroom level spillover effects were also statistically undetectable using a dose-response model. Specifically, this was implemented by defining the spillover variable as one over the number of classrooms in grade 9 in the school. Spillover coefficients again followed the same pattern of no significant effects.

> 19 Note that for this table the dependent variables are not an index but a single variable, so the study does not standardize them. This has the benefit of allowing for comparability with other studies. Furthermore, in this table there is no control for baseline value of the dependent variable because it was not available at baseline (except for columns 3-5).
# **8.2  Identifying Critical Road Links**

Since our methodology ties forest clearing impacts to specific road links, we can also use our high-resolution database to grade those links by expected biodiversity losses when upgrading occurs. We compute mean expected losses in corridors extending 10 km on either side of the Delorme-identified secondary road links in moist forest areas. We normalize to the range [0100] for ease of comparison. Figures 21-23 identify the road corridors where a generalized road upgrading program is expected to produce the greatest losses in biodiversity value.<sup>12</sup> The maps highlight the highest four deciles (purple (90-100); red (80-90); orange (70-80); yellow (60-70)), along with primary road links where upgrading does not occur (blue).<sup>13</sup> In Bolivia (Figure 21), the largest clusters of critical road links are in the center-north and far south, with smaller clusters visible in the north, east and west. Their relatively skewed spatial distribution reflects Bolivia’s skewed area distribution of expected losses (Figure 18). In contrast, the distribution of critical corridors in Cameroon (Figure 22) reflects its more evenlydistributed expected losses from road upgrading (Figure 22). Road clusters in the most critical (purple) category are visible in the southwest, west and north, frequently alongside red clusters. Critical links in the lower priority (orange and yellow) categories are more widely scattered.

Like Cameroon, Myanmar (Figure 23) exhibits a more even distribution of critical road corridor clusters than Bolivia. Large clusters of the most critical (purple) corridors are visible in the east, with smaller clusters linked to next-priority (red) corridors in the north, west and south. Again, clusters in the lower priority categories (orange and yellow) are widely scattered.

> 12 This exercise focuses on the impact of generalized secondary road upgrading.  It incorporates a network analysis methodology that can also be used to
to mention the possibility of evading excise duties and the cess on such sales.<sup>28</sup> If data were available that would permit all of these complications to be taken into account, the average ex-factory realization prices would be somewhat higher than the prices we have estimated. According to the Goldar and Gulati study ( p. 93) the extent of the resulting underestimation of sugar protection was probably not large during the period covered by their study (1981-89), but it may have greater in the 1960s and 1970s because of the considerably higher excise tax rates during those years. Some indication of the upper limit to the resulting underestimation of the NPCs for mill sugar is given by the estimated NPCs for khandsari in UP ( see below) since khandsari is not subject to the levy controls, excise taxes are lower, and all of it can be sold on the free market.

When comparing the price of a commodity in a domestic market with prices in international markets, as far as possible the two prices should be for a similar specification, variety and quality. This raises a problem in computing the nominal protection for Indian plantation white sugar, since world trade is mainly in raw and fully refined white sugar, and international price data for plantation white is not consistently reported. Based on some limited comparisons of international prices, Goldar and Gulati assumed that the premium paid for India's plantation sugar over the price of raw sugar is 90 percent of the premium of international prices of fully refined sugar over the international prices of raw sugar. We have made the same assumption. Specifically ,the world price fob Europe of plantation white sugar price has been

**26** Details of the excise tax rebates schemes for early and late crushing are given for each year since 1961 in the _Indian Sugar Year Book_

**27** Details of these schemes are also given in the _Indian Sugar Year Book_

**2S** The 1984 black economy report identifies this as one of several major sources of
). Other external factors are commodity prices such as the international price of oil, the price index of minerals and metals, the price of agricultural commodities, and an index of commodity terms of trade which are gathered from the World Bank’s Commodity Outlook.

# 4. **Empirical Evidence**

# **4.1 IV Estimation**

This section analyzes the empirical results from the instrumental variable method. The dependent variable is gross capital inflows as a ratio of GDP. The different types of capital flows are also included as alternative dependent variables such as gross FDI inflows, gross PI inflows, and gross OI inflows. The set of determinants chosen follows recent empirical literature—for example, Forbes and Warnock (2012), Calderón and Kubota (2019), and Ghosh et al. (2014). Pull factors are domestic factors that attract foreign capital flows, such as domestic economic growth, the level of development (as measured by the GDP per capita), consumer price index inflation, primary balance, exchange rate flexibility, quality of institutions (as proxied by ICRG indicators such as investment profile, government stability, socioeconomic conditions, corruption, bureaucratic quality, among others), trade openness and financial openness. The push or external factors considered in this analysis are the US stock market volatility index (VIX), economic growth of main trading partners, US policy uncertainty, and commodity terms of trade, as well as the international price of oil and the international price index for minerals and metals. The regression analysis compares the behavior of pull and push factors across different time periods: 1980–2017, 1980–99, and 2000–17. This implicitly tests whether financial globalization plays a role in driving changes in the sensitivity of capital inflows to pull and push factors. We run those regressions of the different groups such as all countries, advanced countries, non-SSA countries, Sub-Saharan countries, and natural resource abundant countries. We control the potential endogeneity of domestic GDP growth by using the
offered plot level recommendations and the grant ( _T_ 1) adopted about a third of a practice less than farmers in _T_ 2 so receiving plot level recommendations did not increase, and if anything _lowered_ , compliance. These results reinforce the three patterns documented with take in the section: the of the in-kind program up previous importance grant (consistent with H3); the small negative role of plot level specificity (contra H2), and the fact that autonomy did not decrease adoption (contra H4a). The results for the standardized index of new practices in column 4 are consistent with those in column 3.<sup>35</sup>

> 33Existing practices comprise ploughing (56% in 2015), the use of inorganic fertilizers (97%) and covering the applied fertilizer (85%). New practices included deep tillage or ripping (5% in 2015), using hybrid seeds (5%), sowing with a precision drill (10%), fertilizing at sowing (9%), application of pre-emergent herbicide (2%) and using highquality fertilizers (manufactured by YARA) (0%). We did not ask about covering the fertilizer, using high-quality fertilizers or using pre-emergent herbicide after sowing at baseline, so they are only available in 2015.

> 34Using the GPS coordinates, we also assessed whether control farmers with plots located close to those of treated farmers (controlling for the total number of nearby study plots defined with reference to a 500m or 1000m radius) are more likely to adopt the new practices. The intuition is that while the density of study farmers nearby is endogenous, the share of those farmers that is treated is exogenous by virtue of randomization, and so if spillovers were significant, one should detect larger changes in the adoption of recommended practices among control farmers near treated farmers. We find no evidence of any such spillovers.

> 35We test whether the lower adoption of _T_ 1 (relative to _T_ 2) is due to proximity to other study farmers as disagreement
on these results, we propose a set of best practices for economists seeking to use remote sensing weather in conjunction with household survey data.

This work contributes to an ongoing body of research which explores the implications of measurement error for estimates of agricultural production and agricultural productivity. Previous work has focused on measurement error in self-reported data such as land area, harvest quantity, and seed variety. We contribute to this literature by reporting on the extent of measurement error in remote sensing weather data, inaccuracies introduced by the obfuscation of farm or household GPS coordinates, and inaccuracies introduced by the use of weather metrics that are not fit for the purpose.

The use of remote sensing weather data has become common in economics research. Weather data has been used as an explanatory variable in studying agricultural production and productivity, and also as part of identification strategies to explain everything from migration to human capital development to economic growth to technology adoption. Our results demonstrate that care must be taken when using weather data as measurement error in these data can substantially alter results.

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2020) rely on the 2018 edition of OECD PMR indicators on 35 OECD and 11 non-OECD countries to illustrate the potential of liberalizing countries’ trade and FDI regulations. The authors point out that, compared to the OECD average, countries in Latin America and the Caribbean rank especially restrictive in these dimensions and could profit from opening up more to foreign trade and participation. More generally, they also highlight the particular importance of lowering domestic entry barriers for foreign suppliers, especially concerning the retail trade and professional services.

To what extent can increased openness to trade and FDI positively affect labor market outcomes? A strand of empirical work documents how countries see higher aggregate employment when they increase competition in product markets by widening scope for trade and/or foreign participation. Using panel data from 20 OECD countries, Felbermayr et al. (2011) find that a 10-percentage-point increase in trade openness lowers the equilibrium rate of unemployment by about 0.8 percentage points. Most importantly, however, they find no evidence for an unemployment-increasing effect of trade openness in their panel data set or in a larger cross-sectional one. Similarly, Piton and Rycx (2019) rely on OECD PMR indicators and country–fixed effect regressions to analyze the effects of product market deregulations on employment. Alongside to lowering entry barriers, they find that reducing barriers to trade and FDI is key to explaining the overall impact of product market reforms between 1998–2013. The average trade and FDI reforms over the period are associated with a decline in the predicted unemployment rate by 0.3 percentage points.

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are relatively wealthier than other households, or have systematically different preferences, all else being equal, then their asset purchases may differ from other families—even in the absence of migrating members. To tackle these confounding factors, I added controls for initial levels of wealth and preference proxies by including , which serves as a vector of baseline (pre-migration) household and location characteristics as of 1998. The indicators encompassed within this vector are wide-ranging: whether the household has had at least one member who has ever migrated outside Kerala, the number of all adults in the household (age 25 or older, whose education decisions would mostly all have been made by that age) with their various educational attainments, overall household size, the number of male-headed households, the age of the head of household, the highest educational level of the head of household, the inverse hyperbolic sine transformation (IHST) of landholdings, and district-level dummies.<sup>26</sup> My second reason for adding these controls is that, to the extent that unobserved ex-post shocks are correlated with observed pre-migration characteristics of the household or community, incorporating a set of pre-migration controls would further help to mitigate the influence of time-varying unobservables. Robust (Huber–White) standard errors are reported in all cases.

# <u>B.2. Asset Indices</u>

When one examines changes to a household’s welfare, the primary outcomes of interest are measures of its assets. The KMS data sets do not contain income and consumption information, but they do include asset indicators. While this could be interpreted as a limitation of the data, the use of asset holdings may better reflect a household’s long-term economic status and is also less likely to be subject to recall bias or measurement error.<sup>27</sup> To create the asset indices, I followed Filmer and

> 26 The inverse hyperbolic sine transformation of is . Interpretation is analogous to that of a logarithmic dependent variable, without suffering the problem of
a gravity pattern: countries export more both to markets that are closer geographically, culturally, or linked by trade agreements and to larger markets.

> 1 One may alternatively define W as net exports, equal to total exports minus total imports by country _i_ of product _k_ , to ensure that revealed comparative advantage does not identify products with minimal domestic value kk ii,W WW added. This is a concern especially in entrepôt countries, like Djibouti and Singapore. There are other approaches to xx identifying export value added that rely on inter-country input-output matrices, though these are available only at a high level of product aggregation.

> 2 The latter two databases are most popular because they impute frequently missing export values for developing countries in COMTRADE with so-called mirror data on imports reported by other countries.

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**Table 7: Regressions for the female share of employment in MGNREGS**

||Full<br>sample<br>|Samp<br>headcou<br>avai<br>|le with<br>nt index<br>lable<br>|Sample wit<br>female wag<br>|h male and<br>es available<br>|
|---|---|---|---|---|---|
|Constant|0.676|0.829|0.193|0.131|0.697|
||(8.671)|(5.341)|(0.887)|(0.246)|(3.922)|
|Rationing rate|-0.505|-0.419|-0.307|-0.456|-0.469|
||(-3.758)|(-2.898)|(-2.904)|(-5.108)|(-5.972)|
|Headcount index of rural|n.a.|-0.515|0.241|n.a.|n.a.|
|poverty||(-1.538)|(1.518)|||
|Female casual non-PW|n.a.|n.a.|-0.549|-0.744|n.a.|
|wage (log)|||(-2.056)|(-3.475)||
|Male casual non-PW wage|n.a.|n.a.|0.675|0.806|n.a.|
|(log)|||(5.156)|(3.556)||
|Female wage relative to|n.a
at fine levels of granularity. Digital data therefore may offer new insights for understanding and resolving some development challenges. Recent examples from empirical research include:

- **Measuring poverty, agricultural productivity, and malnutrition.** Influential recent work has used satellite imagery of nighttime lighting to measure poverty by using it as a proxy for wealth.<sup>96</sup> In Rwanda, researchers have mapped personal data from mobile networks to individual phone subscriber wealth. They applied their model to predict wealth throughout the country, showing

> 92 World Bank. 2018. _Data for Development: An Evaluation of World Bank Support for Data and Statistical Capacity._ Independent Evaluation Group, Washington, DC: World Bank.

> 93 Cameron, Grant J., Hai‐Anh H. Dang, Mustafa Dinc, James Foster, and Michael M. Lokshin. 2019. “Measuring the Statistical Capacity of Nations.” World Bank Policy Research Working Paper 8693.

> 94 Kilic, Talip, Umar Serajuddin, Hiroki Uematsu, and Nobuo Yoshida. 2017. “Costing Household Surveys for Monitoring Progress toward Ending Extreme Poverty and Boosting Shared Prosperity.” WPS7951. The World Bank. <u>http://documents.worldbank.org/curated/en/260501485264312208/Costing‐household‐surveys‐for‐monitoring‐ progress‐toward‐ending‐extreme‐poverty‐and‐boosting‐shared‐prosperity.</u>

> 95 Serajuddin, Umar, Hiroki Uematsu, Christina Wieser, Nobuo Yoshida, and Andrew L. Dabalen. 2015. “Data Deprivation : Another Deprivation to End.” WPS7252. The World Bank. <u>http://documents.worldbank.org/curated/en/700611468172787967/Data‐deprivation‐another‐deprivation‐to‐end</u>

> 96 Neal Jean et al. (2016), “Combining Satellite Imagery and Machine Learning to Predict Poverty,” _Science_ l 353 (6301): 790‐794.

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