rafmacalaba/data-use-mentions-extended huggingface dataset:
25 km resolution.<sup>4</sup> We compute population at 25 km resolution by aggregating data from CIESIN (2021) at 5 km resolution. Monthly estimates are interpolated from data provided for 2010, 2015, and 2020. We use two sources to construct our georeferenced measure of income per capita. From the G-Econ database (Nordhaus et al. 2006), we obtain GDP per capita in 2005 purchasing power parity for a global grid with 100 km resolution. Each grid cell is assigned to its geographically dominant country by G-Econ. For each cell in a country, we compute the ratio of cell GDP per capita to the national mean for all cells. We merge the results with annual UN estimates of GDP per capita in constant $US 2015 (UN 2021), and use the cell ratios to estimate annual GDP per capita for each cell. We resample these cells to 25 km for compatibility with the rest of our database.

We draw our subway data from two sources. The first is a global subway survey by Turner and Gonzalez-Navarro (2018), which includes 137 systems installed prior to 2011.<sup>5</sup> The survey includes digital subway maps at five-year intervals from 1930 to 2010. The second source is our own survey of 55 subway systems installed since 2010. We have constructed digital maps for these systems using

> 3 CO2 measurement during the full daily activity cycle will improve as systems like OCO-3 observe each area at more widely varying times.

> 4 Mistry’s data terminate in December 2019. We extend the domain for regression analysis by computing monthly means for each 25 km cell using the data for 2014–19.

> 5  Gendron-Carrier et al. (2020) provide the following definition: “These data define a ‘subway’ as an electric powered urban rail system isolated from interactions with automobile traffic and pedestrians. This excludes most streetcars because they interact with vehicle and pedestrian traffic at stoplights and crossings, but underground streetcar segments are counted as subways
volatility characteristics of commodity prices have attracted less attention. In this paper, we therefore address the following question: has commodity price volatility increased? We do this by exploiting a newly compiled unbalanced panel dataset on 45 individual commodity prices spanning the 1784-2009 period at a monthly frequency.

# **2. Literature Review**

Ever since Prebisch (1950) and Singer (1950), a vast literature has grown around the issue of identifying a secular trend in commodity prices. This research has been mainly concerned that commodity prices tend to decline relative to the price of manufactures with obvious implications for the primary producers.<sup>5</sup> In contrast, the literature on the volatility of commodity prices and its public policy implications is relatively small. This research is mainly focused on understanding how commodity price volatility affects income volatility, especially in poor countries (Koren and Tenreyro (2007)), among others). Other authors have been concerned with how commodity price volatility generates instability in international markets (Blanford (1983), Heifner and Kinoshita (1994) among others).

While mainly concerned with identifying price cycles – booms and slumps – Cashin and McDermott (2002) also test to see if variability in prices is higher or lower across cycles. Using The Economist’s index of industrial commodity prices over the period 1862 – 1999, they find evidence of a ratcheting up in the variability of commodity price movements around 1899 and then again in the early 1970s.<sup>6</sup> The finding of higher variability in commodity prices after the end of the Bretton Woods era is in line with hypotheses of a link between nominal (and real) exchange rates and the volatility of dollar denominated commodity prices Chu and Morrison (1984), Reinhart and Wickham (1994), Cuddington and Liang (1999). Comparing three different datasets<sup>7</sup>

> 5 Interest about a possible negative long-term trend in commodity prices has occupied development economic literature since the late 1940s.
<!-- Start of picture text --> Polay nning, and Rewh<br>Soc10oocnonW10 Data<br><!-- End of picture text --> Many cross-country studies have been conducted over the last 20 years to explain how various factors affect economic growth rates in the developing economies. The data in these studies - which underlie intemational economic comparisons and serve as the basis for - economic policy recommendations give researchers the systematic and scientific information required for their investigations. But the conclusions are often fragile and sometimes contradictory.

Research results are ser.sitive to the choices of components, the aim of the investigation, and the type of model used. In general, researchers need to have better statistical data, particularly on economic policy indicators, and must subject the selected sample to careful tests.

Cross-country studies are particularly unreliable when it comes to estimating the economic impact of government budgetary and regulatory policies. These studies thus provide only a weak basis for developing country economic policies.

This paper is a product of the Socioeconomic Data Division, Intemational Economics Department. Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Estela Zamora, room S7136 extension 33706.

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these rmdings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Copyright / 1988 by the International Bank for Reconstruction and i>elopmentmThe World Bank
Third, these data cast some doubt on the notion that physical availability of school facilities at the primary or secondary level is the key issue in many countries. In South America typically over 90 percent of the shortfall from primary completion is from children that complete grade 1 (hence likely _could_ attend a school) but fail to complete primary school. In South Asia and Western/Central Africa a larger fraction is due to children that never enroll, but in those countries the wealth gap suggests that even poor children had physical access to schools. A companion paper examining differences within Indian states has estimates of school effects, which are quite small relative to household wealth impacts (Filmer and Pritchett, 1998b). This suggests that in many cases issues of the access to _quality_ schooling and maintaining household demand are as important as the number of schools.

At the secondary level the smooth patterns of attainment do not suggest that high dropout across the transition from primary to secondary is a major issue except in a small number of cases (e. g. Turkey, Indonesia, Tanzania).

In many ways this analysis confirms findings of previous studies. There are many country specific studies which look at the enrollment rates by wealth groups. In the context of benefit incidence analysis there is even some cross national compilation of those results (Castro-Leal, Dayton, Demery, and Mehra, 1997). The main value-added of this paper is the direct comparability across countries of educational data, the focus on not just enrollment but the entire attainment pattern (showing the importance of drop-out within levels), and a comparable methodology for documenting attainment differences due to household wealth.

4
In order to assess also the short run effects of liberalization, Table 4 reports an estimated error **correction model for saving.19 The** reported estimates are OLS; unreported GMM estimates lead to the same conclusions conceming the effect offli, and r,.

Despite the fact that we have here corrected the omission of other dimensions of financial liberalization, there is -- except for the OLS estimate for Mexico -- no evidence from the countryby-country estimates of a significant distinct positive effect of the real interest rate on savings. In most cases the long run point relationship is negative, and significantly so in the case of Ghana and Indonesia. The evidence based on the time series for individual developing countries confirms, therefore, the general conclusion derived from previous studies using pooled time series-cross country data that it is not possible to pin down a systematic positive effect of increases in the interest rate on savings.

So far as the effect of financial liberalization itself is concerned, the picture is mixed. For Korea and Mexico (and Zimbabwe when rt, and O] are excluded) the coefficient of the index of financial liberalization is negative and significant in the long run (using the Dynamic GLS estimates). For Korea, there is also evidence of a significant negative short run effect. On the other hand, for Turkey and Ghana (only Turkey, when r,, and O, are excluded) there is evidence of a positive and significant long-run effect.<sup>20</sup> The estimated impacts of the index on private savings are sizable; for example, the results of Table 3 Part II imply that liberalization in Korea and Mexico has permanently lowered savings by 12% and 6% of GNDI respectively. On the other hand we estimnate that liberalization has raised the savings rate in Turkey by 13% and in Ghana by 6%.

So far as the other variables are concerned, the _income_ variable is significant in most cases (both in the long and short run). The sign of
Figure **III:** Bank-Based vs. Market-Based Financial Systems <!-- Start of picture text --> Debt-Equity Ratios*<br>3.5.<br>3.0-<br>2.5.<br>2.0-<br>1.0<br>0.5<br>88 89 90 91 92 93 94 95 96 97 98<br><!-- End of picture text --> <!-- Start of picture text --> Short-Term Debt/Equity*<br>2.5<br>2.0.<br>1.5.<br>1.0.<br>0.5<br>0.0<br>88 89 90 91 92 93 94 95 96 97 98<br><!-- End of picture text -->

# Long-Term Debt/Equity*

<!-- Start of picture text -->
1.2.<br>1.0.<br>0.8.<br>0.60<br>88  89  90  91  92  93  94  95  96  97  98<br><!-- End of picture text -->

<!-- Start of picture text --> Short-Term Debt/Total Debt*<br><!-- End of picture text --> <!-- Start of picture text --> 0.74-<br>0.72<br>0.70.<br>0.68<br>0.66<br>0.6Z<br>88 89 90 91 92 93 94 95 96 97 98<br><!-- End of picture text --> Firms in Bank-Based System Firns in Market-Based System Source: **IFC** Corporate Financial Database and WorldScope ***** Ratios are averages across firms
data resulting from this research are just as important as the analysis. Providing others useful tools to address accessibility quantitatively was an explicit aim of this research given its foundational importance in Nepal. To this end, we are publishing all output spatial data on the Nepal GeoNode, the code and ArcGIS toolboxes used for analysis on Github, and the resulting maps and charts via a series of provincial dashboards. We can also provide the charts and maps in bulk on request; interested parties are encouraged to contact the authors. We therefore hope to advance the state of understanding accessibility in Nepal and enable more precise analysis of its impacts on economic and developmental challenges. It is our sincere wish that other researchers, policy makers and development partners use, adapt, improve and offer feedback on the model data outputs. We look forward to the results of these efforts.
of collateral) there are borrowing constraints, then the individual will reduce his/her ordinary saving to zero. Note that the individual would like to reduce his/her ordinary saving even further (i.e., to borrow) so as to restore his/her desired level of future consumption but binding borrowing constraints do not allow such response. The bottom line is that binding borrowing constraints result in ordinary saving falling by less than the amount of pension contributions, hence total saving increases.

Second, suppose that the mandatory pension program induces the individual to retire earlier than they would have as suggested by Feldstein (1974). Saving during working years will have to be higher to ...nance a longer retirement. Again, ordinary saving will fall by less than the amount of pension contributions, therefore, his/her total saving increases.

Third, imagine that workers save not only for consumption smoothing motives but also for precautionary motives (e.g., extraordinary health expenditures).<sup>2</sup> Since pension wealth is highly illiquid, then it is a poor substitute of precautionary wealth. In those circumstances, ordinary saving will fall by less than the amount of pension contributions, therefore, total saving will increase.

Fourth, suppose that an important driver of saving is that people want to leave an inheritance to their children.<sup>3</sup> Again, due to low substitutability, ordinary saving will fall by less than the amount of pension contributions, capital market imperfections (e.g., borrowing constraints).

> 2Carrol (1997), for example, presents survey evidence in which people predominantly say that being prepared for emergencies is the most important reason for saving.

> 3In a famous study, Kotliko¤ and Summers (1981) show that bequests account for a substantial fraction of U.S. aggregate capital accumulation.

6
<!-- Start of picture text --> CEB; Hetero UC-CEB.<br>4 +—_{_T. +“ 1 -—{_T }<br>2 +——{lJ*_ ewe e 2 +———_{ijiag}#———<br>3 t+—_{_T } 4H e 3 © [+#' —“<br>4 +—fil— “ HH —__] }#———4<br>5 ee—{ |} 5 e +—_{ijia#—__<br>6 e |—{jj} toe 6 ee |+—{f— e<br>7 e'—ii 7 -—_fiia—<br>8 +—{jjwee 8 eo ol e<br>9 e:_i Ve 9 eos—}—_<br>10 e |{j= 10 e eee He<br>-40 -20 0 20 40 -40 -20 0 20 40<br>Design bias at municipal level (x100)<br><!-- End of picture text -->

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L. P.-R. G. P. e. a. (2013): “Genetic Evidence for Recent Population Mixture in India,” _American Journal of Human Genetics_ , 93, 422–438.

- Morduch, J. (1995): “Income Smoothing and Consumption Smoothing,” _Journal of Economic Perspectives_ , 9, 103–114.

- Rao, V. (1993): “The Rising Price of Husbands: A Hedonic Analysis of Dowry Increases in Rural India,” _Journal of Political Economy_ , 101, 666–77.

- REDS (2006): “Rural Economic and Demographic Survey,” National Council of Applied Economic Research.

- Rose, E. (2000): “Gender Bias, Credit Constraints and Time Allocation in Rural India,” _Economic Journal_ , 110, 738–758.

- Rosenblum, D. (2013): “The effect of fertility decisions on excess female mortality in India,” _Journal of Population Economics_ , 26, 147–180.

- Roy, S. (2015): “Empowering Women? Inheritance Rights, Female Education and Dowry Payments in India,” _Journal of Development Economics_ , 114, 233–251.

- Sekhri, S. and A. Storeygard (2014): “Dowry Deaths: Response toWeather Variability in India,” _Journal of Development Economics_ , 111, 212–223.

- Strathern, P. (2015): _Death in Florence: The Medici, Savonarola, and the Battle for the Soul of a Renaissance City_ , Pegasus Books.

- Tertilt, M. (2005): “Polygyny, Fertility, and Savings,” _Journal of Political Economy_ , 113, 1341–1371.

- Visaria, L. (2005): “Female Deficit in India: Role of Prevention of Sex Selective Abortion Act,” _mimeo_ .

- Wei, S.-J. and X. Zhang (2011): “The Competitive Saving Motive: Evidence from Rising Sex Ratios and Savings Rates
majority of Americans in the YouGov survey. Similarly, in the case of electric batteries, the growing dependence on China is evident in the figure. However, import shares for some goods appear responsive to policy. The share of crude oil sourced from ‘non-friendly’ countries has fallen from 60 percent to about 20 percent, as the shale boom made the US a petroleum exporter. Though in 2020 about 80 percent of semiconductor chips were imported from non-friendly countries, in 2022, during which the U.S. articulated a friendshoring policy with regard to the sector, this share had fallen dramatically to 55%. This reflects growth in the import share of Taiwan (China) from 5 percent to 10 percent; Ireland, from 2 to 8; and Israel, from 1 percent to 4 percent.

Dependence on non-friendly countries is only one among several possible factors affecting supply chain resilience. High concentration in the market of a specific product will in general imply high dependence of buyers on suppliers making this market less resilient to supplier-side risk – be it risk associated with exogenous shocks (e.g., natural disasters) or risk 30
# **1  THE WB-WTO SERVICES TRADE POLICY DATABASE**

The Services Trade Policy Database (STPD) builds on a number of prior initiatives, notably by the WTO, the World Bank and the OECD.<sup>5</sup> The STPD contains information on policies and regulations that affect international trade in services on a cross-border basis, through a commercial presence, or through the presence of a natural person.<sup>6</sup> We consider policies and regulations applied by the importing/'host' economy (i.e. the economy where the services consumer is located), which affect the ability of foreign suppliers to supply services to consumers in that economy. Certain domestic regulations that apply to both domestic and foreign agents are also covered insofar as they affect trade. The database focuses on economies’ Most-Favoured Nation (MFN) policies.

The database contains information on services policy and regulation in 68 economies – 39 highincome, 16 upper-middle and 13 lower-middle income economies (see Annex 1).<sup>7</sup> The sectoral coverage is identical to that in the World Bank survey of 2008-2011. Apart from the continued importance of these sectors and sub-sectors for any economy – a factor that justified the World Bank's approach at the time – sticking to the same sectoral coverage in 2016 has allowed us to have a 'time' dimension in our data, and hence to analyze policy developments in the last decade. That being said, three subsectors were added to the 2016 data to ensure greater comparability with the OECD Services Trade Restrictiveness Regulatory (OECD STRI) database. The database therefore covers 5 major service sectors that are further disaggregated into a total of 23 subsectors, namely financial services (commercial banking, life insurance, non-life insurance, reinsurance), telecommunications services (fixed-line, mobile, Internet), distribution (retail, wholesale), transportation (air freight domestic, air freight international, passenger domestic, passenger international, rail freight, road freight, maritime
0.03|0.18|0.00|1.00|
|Number of Obs.|8,475,013||||

_Notes_ : Authors’ calculations using SEDLAC data. Summary statistics include all observations, covering different years for countries as described in Table 1.

ables listed in the summary statistics are used as controls in the later regressions. It is also important to mention that 75% of the sample is located within urban areas. This is particularly the case since data for Argentina and most of the data for Uruguay are taken from surveys representative only for urban areas. Over a third of the workers have formal jobs, but over two-thirds of the workers are in small firms. The SEDLAC data defines a small firm as having less than five employees for most countries. For Bolivia (2001-2002), Chile (1996), the Dominican Republic, Panama, and Uruguay, the cutoff for a small firm is less than four employees. Note that formal jobs are highly correlated with large or public firms. While many workers with a formal job also have a job contract, and vice versa, the two concepts are not perfectly correlated. Also, _Edu − High_ , the indicator for completing high school, includes individuals that have completed college as well.

9
<br>Other countries-products foreign export<br>Country-product-year world imports _×_ # EPZ<br>Country-product-year world imports _×_ # High-Tech zones<br>Control for Macro export<br>Control for Macro export lags<br>Control for domestic presence<br>Control for GDPs<br>Observations<br>Province-product(HS4)-country fxed efects<br>Year fxed efects<br>Province-product-year fxed efects<br>Country-product-year fxed efects<br>Province-country-year fxed efects<br>R-squared (%)<br>F-test of excluded instruments<br>Kleibergen-Paap F-stat<br>Weak Cragg Donald F-test<br>Underid test Kleibergen-Paap<br>Hansen overid test<br>p-value<br>Endogeneity<br>p-value<br>Standard errors are clustered at the province level.<br>_a_, _b_ and _c_<br>instrument the product-country specifc spillovers indicator (Sa<br>total import value with the number of EPZs and the number of|

21
>2</sup> What has been less studied is whether poverty has a causal effect on who agents are matched with in risk-sharing networks. Previous empirical studies have exploited cross-sectional variation or lab experiments to shed light on this.<sup>3</sup> This paper contributes to the literature by showing that an ex-

> 2Theoretical reasons for this have been studied by La Ferrara (2003), Genicot and Ray (2003), Bramoullé and Kranton (2007), Bloch, Genicot and Ray (2008) and Ambrus, Mobius and Szeidl (2014). Empirically, this point has been demonstrated by Fafchamps and Lund (2003), La Ferrara (2003), Dercon et al. (2006), Fafchamps and Gubert (2007), Attanasio et al. (2012), Barr et al. (2012), Munshi and Rosenzweig (2016), Angelucci et al. (forthcoming), Chandrasekhar et al. (forthcoming).

> 3Jalan and Ravallion (1999) show that poorer households in rural China are less well insured against fluctuations in their income. Fafchamps and Gubert (2007) show that the wealth difference between two households is negatively correlated with having a risk-sharing connection in the Phillipines, while De Weerdt (2004) finds the opposite relationship in Tanzania. Schechter and Yuskavage (2011a, 2011b) highlight that the failure to distinguish between reciprocated and unreciprocated links could be one reason for these conflicting findings. They show, using survey data form Paraguay, that reciprocated links are more likely to exist between households who have similar wealth or live in close proximity, while unreciprocated transfers links are more likely between households of different wealth or education levels. Attanasio et al. (2012) show experimentally that among close friends and relatives, individuals with

3
The transition matrix concept gives support to the LUC module in the model. These matrices, elaborated by state and biome, make use of information obtained through satellite imagery for land use changes (LUC) observed between 2002 and 2010 (MCT, 2015). This information was processed to distinguish three major types of land use, Crops (CROP), Pastures (PASTURE) and Forestry (planted forests, FORESTRY), and a residual type identified in the model as UNUSED, which refers to native forests. These transition matrices are detailed by state and, within each state, by six distinct biomes: Amazon, Cerrado, Caatinga, Atlantic Forest, Pampa and Pantanal. An example can be seen in Table 1 , which displays the observed original transitions for the Mato Grosso state.

_Table 1. Transition matrix for the state of Mato Grosso, Cerrado biome. Millions of hectares._

||1 Crop|2 Pasture|3 PlantForest|4 Unused|Total 2002|
|---|---|---|---|---|---|
|1 Crop|8.32|0.82|0.02|0.05|9.20|
|2 Pasture|2.23|37.57|0.06|0.53|40.38|
|3 PlantForest|0.00|0.00|0.02|0.00|0.03|
|4 Unused|0.47|0.98|0.00|13.16|14.61|
|Total 2010|11.01|39.37|0.10|13.73|64.22|

Source: model database.

The transition matrix in Table 1 shows, for example, that 0.53 million hectares (Mha) of physical units of land of the Cerrado biome in the state of Mato Grosso, which was natural vegetation in 2002, became Pasture in 2010,