rafmacalaba/data-use-mentions-extended huggingface dataset:
to crowding-in effects of firms in sectors (Farole and Winkler, 2015) or to technology transfer or domestic competition (Alfaro and Chen, 2019). Our results suggest that the spillover happens because--to survive in the fierce domestic competition fueled by FDI faced by a firm—other firms in the sector have a greater incentive to improve productivity and thus to become more competitive. To establish this argument, we leverage firm-level data from the World Bank Enterprise Survey. We first show that sectoral FDI has a positive impact on firm-level productivity, and this impact is ameliorated by the GVC participation of a firm. Second, we show that the effect of FDI on a firm’s propensity to do research and development (R&D) is intensified if the firm has RELATIVELY lower GVC participation.

We use a similar empirical specification for the firm-level regression, except we can distinguish the locality further, from country to city using WBES. We use _i_ to denote city, _j_ firm, _s_ sector, and _t_ year. The regression specification follows lnyijst = α0 + α1lnFDIist × GVCijt + α2lnFDIist + α3GVCijst + γi + θj + CX + τt+εijt(3) where the outcome variables yijst represent labor productivity; GVCijt is GVC participation of city _i_ firm _j_ sector _s_ year _t_ ; _FDIist_ is FDI at time t at the sector-city level.<sup>5</sup> The control variables’ vector **_X_** include the number of skilled workers of a firm in the previous year, foreign ownership share,<sup>6</sup> foreign technology adoption, firm size, sector type, and years of experience of top managers. γi represents city fixed effects; θjdenotes sector fixed effects; τt controls for year fixed effects, while εijt is the error term. Firm’s GVC participation here is defined as the average of its intermediates’ import share and the exporting intensity. The export intensity, in turn, is defined as the ratio of exports over sales.

Table
along the outlines of quasi vertical structures such as buildings, walls, trees, or hedgerows. A given pixel in TDX-DEM is considered as a VE candidate if the ratio between the height of the target pixel (above sea level) and the median height (ditto) in its immediate neighborhood (within a 5 x 5 moving window) is _>_ 1.0 (Esch et al., 2022). Since the natural slope of the local terrain also leads to height differences, the slope-induced height differences are factored out by normalizing the measured local edge height VE with the local slope of the terrain. The terrain slope is measured by first removing all local maxima and vertical edges (TDX-DEM pixels where VE _>_ 0.0) before filling the resulting local gaps in the TDX-DEM elevation data with a four-direction conic search distance weighting (Esch et al., 2022). This generates an edge-smoothed version of the TDXDEM that only shows height differences due to the local terrain, whereas previously included height structures in the original TDX-DEM that arose from local vertical objects are now leveled in. By subtracting the edge-smoothed version of TDX-DEM from the original TDX-DEM, the effect of slope-induced height differences is minimized so that the resulting VE layer only shows

> 1Note that all analyses are exclusively carried out for areas assigned as settlement by the World Settlement Footprint 2019 (WSF2019) human settlements mask (Marconcini et al., 2021).

> 2 Empirical tests with a WSF settlement mask and WSF-IMP layer generated on the basis of 30 m Landsat data collected in 2012 (meaning the exact time of the TDXDEM data acquisition) clearly indicated the superiority of the WSF mask and WSF-IMP information derived from the 10 m S2 data (not yet available in 2012), although the imagery was collected in 2019. Indeed, the WSF2019 settlement mask, although including additional settlement areas that arose between 2012 and 2019, still shows a higher accuracy compared to the settlement mask derived from the coarse 30 m Landsat imagery. Moreover, the imperviousness estimation
**Table 12: Extreme child poverty by Household Age Composition**

||**Extremely**<br>**poor**<br>**children**<br>**(millions)**|**Children**<br>**headcount**<br>**poverty rate**<br>**(%)**|**Adults**<br>**headcount**<br>**poverty rate**<br>**(%)**|**Share of**<br>**extremely**<br>**poor**<br>**children (%)**|**Share of**<br>**sample child**<br>**population**<br>**(%)**|
|---|---|---|---|---|---|
|2 generation<br>( people aged 18-59 and<br>children0-17)|257.9|19.7|12.5|78.5|77.5|
|Elderly and children<br>(people aged >=60 and 0-17)|3.0|21.0|13.4|0.9|0.9|
|Three generation<br>(people aged >=60, 18-59, and<br>0-17)|67.1|18.8|13.0|20.4|21.1|
|Only children|0.6|7.4|.|0.2|0.5|
|Total|328.7|19.5|9.2|100.0|100.0|

Source: GMD, UNDESA, WDI, PovcalNet

# **4. Sensitivity to
**<u>Table 1. Labor value added in exports (LVAX), 2011, $ million, ratio and share of gross exports</u>**

|||||LVAXbr|eakdown||LVAX|Share<br>||
|---|---|---|---|---|---|---|---|---|---|
||||||R|atio|<br>h|of|I|
|Rank|Country|LVAX|Dir|ect|direct|/indirect|sare<br>in<br>|LVAX<br>in|ncome<br>group|
||||_skilled_|_unskiled_|_skilled_|_unskilled_|exports|LVAD||
|1|United States|1,000,205|109,347|380,793|0.68|1.09|0.58|0.10|HOECD|
|2|China|795,419|69,004|177,323|0.39|0.48|0.42|0.21|LM|
|3|Germany|667,647|163,843|228,359|1.25|1.59|0.43|0.35|HOECD|
|4|Japan|451,779|65,360|114,491|0.49|0.83|0.49|0.15|HOECD|
|5|United<br>Kingdom|276,966|55,780|113,022|1.47|1.61|0.43|0.21|HOECD|
|6|<br>France|270,351|58,713|70,809|0.91|0.93|0.38|0.20|
26 worker wage arrears) tend to show inverse relations, as evidenced by the negative value of the 13 coefficients. This is the logical complement of the direct relationship of the supply-side factors: the presence of greater need in a region tends to lead to a reduction in national economy expenditures, not the increase one might expect to meet those needs.

_Table 14. National Economy Expenditures, Multivariable Regression Results, 1992-95_

||**1992***<br>**(N=86)**|**1993**<br>(N=78)|1994<br>(N=78)|1995<br>(N=86)|
|---|---|---|---|---|
|Pre-transfer**regional**|.367|.351|.386|.391|
|**revenue**|(28.4)|(10.6)|(16.3)|(22.4)|
|**Federal transfers**|.140|.269|.293|.244|
||(8.1)|(8.5)|(16.2)|(8.9)|
|**Household incomes**||.030|.030|.022|
|||(2.5)|(4.1)|(4.4)|
|**Doctors per 10,000**|3.32||3.66||
||(2.2)||(3.2)||
|**Wage arrears (1993 data)**||-.311|-.449||
|||(-2.0)|(-3.4)||
|**Constant**|-104.6|4.44|-
# **The Demographic Correlates of Support for Prioritizing Environmental Protection**

While developing nations feature a growing middle class who value their increased consumption of durables that often operate using a carbon intensive grid, these same individuals are growing richer because they are urbanizing and obtaining more education. More educated people are more likely to prioritize the environment, understand their own health production function and be more likely to make long run trade-offs even if they incur costs today (Becker and Mulligan 1998, Costa and Kahn 2004, Kahn 2002).

We turn to the World Values Survey Wave 7 and use these microdata to study selfreported environmentalism. We study how such prioritization of protecting the environment is associated with a person’s age, education, and income. We document that more educated people (who are richer) are more likely to be pro-environment.

Table 7 lists the nations included in the WVS wave from 2020. We use these data to estimate a linear probability model for person i in nation j.

Table 8 reports three estimates of equation (2). In columns (1) and (2), we report linear probability models. In column (1), the survey question focuses on whether protecting the environment is the respondent’s priority rather than economic growth.

In the regression, we include nation fixed effects. We find that a respondent’s education is monotonically associated with greater support for protecting the environment. Relative to a person with very little education, a college graduate is 13 percentage points more likely to prioritize environmental protection. The mean of the dependent variable is 0.55. Younger people and rural people are more likely to prioritize environmental protection. More liberal people are also more likely to prioritize environmental protection. We fail to reject the hypothesis that selfreported personal income has no effect on support for environmental protection. From the coefficients on these survey responses, we view the education results and the age effects to be 13
_The Cultural Trade Index_ Page | **1** of **14**

# **The Cultural Trade Index: An Introduction**<sup>*****</sup>

## **Patrick Kabanda**

Keywords: Culture in Development, Creative Economy, Cultural Policy, Cultural Statistics, Cultural Trade JEL Classification Codes: F1, O1, O2, Z18, Z19

* The idea of creating a Cultural Trade Index first emerged when the author was a writing paper on creative trade for UNDP’s _Human Development Report 2015: Work for Human Development_ .  The author wishes to acknowledge the Human Development Report Office for commissioning that research.  The author also wishes to thank the following individuals for their comments and discussions: Kaushik Basu, Moussa P. Blimpo, Diane Coyle, J. Michael Finger, Bodel A. Gnintedem, Laurent Kemoe, Shiva S. Makki, Anthony F. Mveyange, Matias Costa Navajas, Giuliano Resce, David Rosenblatt, Albert G. Zeufack; Sandra Gain for help with editing; and to Roula I. Yazigi for production assistance.

Patrick Kabanda is a consultant for the Office of the Senior Vice President and Chief Economist at the World Bank. He received a Master of Arts in Law and Diplomacy from The Fletcher School at Tufts University and his Bachelor and Masters of Music degrees from The Juilliard School. He is currently writing a book based on his working paper called _The Creative Wealth of Nations_ (with a Foreword by Amartya Sen). The author welcomes your feedback. Contact: pkabanda@musikaba.net; www.musikaba.net.
_World Development_ , _53_ , 2–13. 5

- Fergusson, L., Molina, C. A., & Robinson, J. (2020). The Weak State Trap. SSRN Scholarly Paper ID 3651454, Social Science Research Network, Rochester, NY. 1, 4

- Ferraz, C., Finan, F., & Martinez-Bravo, M. (2020). Political Power, Elite Control, and LongRun Development: Evidence from Brazil. Tech. Rep. w27456, National Bureau of Economic Research, Cambridge, MA. 6

- Fukuyama, F. (2004). The Imperative of State-Building. _Journal of Democracy_ , _15_ , 17–31. 6, 28

- Gennaioli, N., & Rainer, I. (2007). The Modern Impact of Precolonial Centralization in Africa. _Journal of Economic Growth_ , _12_ (3), 185–234. Publisher: Springer. 1, 5

- Gibson, J., Olivia, S., Boe-Gibson, G., & Li, C. (2021). Which night lights data should we use in economics, and where? _Journal of Development Economics_ , _149_ , 102602. 19

- Government of the United Republic of Tanzania (2013). A Study on Local Government Authorities’ Own Source Revenues. 12

- Hanson, J. K., & Sigman, R. (2019). State Capacity and World Bank Project Success. _unpublished working paper_ . 1

31
outpatients times 20 expected working days per month for each provider.<sup>12</sup> We winsorized the final number of daily outpatients per provider for the top 2.5% of facilities within each country.

SDI surveys measured absenteeism in health facilities via unannounced visits where enumerators compared the actual presence of providers to the roster of listed providers. We provide an upper-bound estimate of the per-provider caseload by counting only those providers who were present at the facility on the day of the unannounced provider survey and we assumed that this count was representative across an estimated 20 working days at the facility. This implicitly assumed that providers who were observed on the survey day across the sample of facilities were representative of the availability of providers at each facility on any given day and representative of the workload of others not observed. If, for instance, the providers who were not observed were at home and not seeing patients, we would over-estimate the average patient load in the system. We were not able to match the identity of providers who worked at multiple facilities, either inside or outside the sample. For our purposes, the representative “provider-day” workload at the sampled facilities is accurately estimated and is the quantity of interest: It is the number of patients a typical provider sees during a typical working day.

We caution that the differences under other assumptions can be large, as the mean facility reported 4.3 providers on staff, of whom only 2.6 were present during the unannounced provider survey visit. If we spread patients across all providers on the roster (include those not present), or assumed that each facility would be open more days each month, the number of patients per average provider-day would decrease proportionally. For the 5% of facilities with over 10 staff on the roster, we did not have a true sample of provider availability from the vignette survey. In these cases, we estimated the number of providers present. To do so, we calculated the average share of providers present across all facilities
factors are important in both rural and urban areas, accounting for more than 60 percent of sibling correlations for men and 65 percent for women. The contribution of common neighborhood factors to variance in education is larger for rural women whereas parental education is relatively more important for both men and women in urban areas. Overall, common family backgrounds are perhaps most important factor for rural women for whom less than 30 percent of variations in education can be explained by individual effort, choices and other unobserved idiosyncratic factors.

To summarize, though the influences of parental education and common family background are smaller in magnitude in rural areas, there has been little or no progress in mobility in education. The largest improvement in educational mobility has been experienced by urban women while men experienced effectively no improvement regardless of their location. We find common neighborhood environment and parental education as the most important source of sibling correlations in both urban and rural areas. The influences of common neighborhood factors are particularly important for rural women.

# **(4.2.2) Less Developed vs. Developed Regions**

Living standards in India vary widely across states. The incidence of poverty among poorer states in India is amongst the highest among developing countries. On the other side of the spectrum, many states such as Punjab have low poverty rates that are comparable to richer countries (e.g. Turkey) (World Bank (2011)). The NFHS 1992/93 identifies the ―backward‖ districts.<sup>35</sup> The NFHS 2006 does not identify any district because of confidentiality considerations with respect to AID/HIV testing results. The states where the most backward districts are located can be matched between the two surveys. The backward districts in 1992/93 are located in five states: Bihar, Madhya Pradesh, Rajasthan, Uttar Pradesh and West Bengal. These states are among the poorest in terms of income in 1993/94 (four of them belong to the so-called BIMARU), and also suffer from poor educational
# TABLE OF CONTENTS

|1<br>INTRODUCTION ..................................<br>1|
|---|
|2<br>ANALYTICAL FRAMEWORK ..................................<br>4|
|3<br>CONTEXT ..................................<br>7|
|EVOLUTION OF LAND TENURE SYSTEMS ..................................<br>7|
|POLITICAL ECONOMY OF LAND REFORMS..................................<br>9|
|ANoTEONDATA..................................<br>12|
|SIZE DISTRIBUTION OF RURAL LAND HOLDINGS..................................<br>13|
|LANDLESSNESS AND ABSENTEE LAND OWNERSHIP<br>..................................<br>16|
|FARM SIZE AND PRODUCTIVITY<br>......
000)|
|Secondary enrollment rate|||0.003<sup>***</sup><br>0.008<sup>***</sup>|
|_(initial level, logs)_|||(0.001)<br>(0.000)|
|ICRG Political Risk Index|||0.005<sup>***</sup><br>0.006<sup>***</sup>|
|_(principal components)_|||(0.000)<br>(0.000)|
|Credit to private sector|||-0.013<sup>***</sup>|
|_(% of GDP)_|||(0.000)|
|Government consumption|||-0.002|
|_(% of GDP)_|||(0.862)|
|Growth volatility|||0.222***|
|_(S.D. growth per worker)_|||(0.000)|
|Constant|0.044<sup>*</sup>|0.083<sup>***</sup>|0.142<sup>***</sup><br>0.130<sup>***</sup>|
||(0.024)|(0.000)|(0.000)<br>(0.000)|
|Observations|870|840|613<br>601|
|_AR(1)_|0.000|0.000|0.015<br>0.001|
|_AR(2)_|0.281|0.238|0.330<br>0.527|
|_Hansen_|0
and trends in child malnutrition: key findings of the 2018 Edition of the Joint Child Malnutrition Estimates. Geneva: World Health Organization; 2018 Licence: CC BY-NC-SA 3.0 IGO. https://www.who.int/nutgrowthdb/2018-jme-brochure.pdf?ua=1

> 14 This visual builds off UNICEF’s original conceptual framework, later adapted by the Lancet series for maternal and child health and nutrition in 2008 and 2013, and maps out potential, evidence-based mechanisms that can lead to stunting in children.

> 15 Small-for-gestational age is defined as lower than the 10th centile for fetal growth from the 1991 US national reference population.

> 16 National and regional estimates of term and preterm babies born small for gestational age in 138 lowincome and middle-income countries in 2010. https://doi.org/10.1016/S2214-109X(13)70006-8

> 17 Philippines National Demographic and Health Survey 2017. Quezon City, Philippines, and Rockville, Maryland, USA: PSA and ICF.

> 18 The INTERGROWTH-21st project established the first international, multiethnic standard including well-dated pregnancies from eight geographically defined populations, and enables a common single standard to describe optimal and aspirational fetal growth around the world.

> 19 Lee Anne CC, Kozuki Naoko, Cousens Simon, Stevens Gretchen A, Blencowe Hannah, SilveiraMariangela F et al. Estimates of burden and consequences of infants born small for gestational age in low and middle income countries with INTERGROWTH-21st standard: analysis of CHERG datasets BMJ 2017; 358 :j3677.

> 20 Chawanpaiboon S, Vogel JP[1], Moller AB, Lumbiganon P, Petzold M, Hogan D, Landoulsi S, Jampathong N, Kongwattanakul K, Laopaiboon M, Lewis C, Rattanakanokchai S, Teng DN, Thikhamthrop J, Watananirun K, Zhang J, Zhou W, Gülmezoglu AM. Global, regional and national estimates of levels of preterm birth in 2014: a systematic review and modelling nalysis. Lancet Glob Health. 2018. 30 Oct. doi.org/10.1016/S2214-109X
# “an organized demand for enhanced services and government accountability”.

At this point it is worth noting that, even if we present here different indicators of civic participation, mainly drawing from opinion surveys such as Americas Barometer and Latinobarómetro, it does not necessarily mean that these indicators are a good proxy for collective action: social capital and participation in associations does not necessarily mean effective production of demands directed to the government, rather they indicate strong interdependences within homogenous groups.

First, in what regards civic participation, the Dominicans rank fourth (out of 24) in Latin America in participation in religious groups (60.5%), sixth in attendance to parents association meetings (27.1%), first in involvement in community improvement groups (25.0%) and fourth in participation in women ́s associations (13.3%). Thus, it may be argued that the Dominican Republic present a very high level of civic participation if we compare with the regional average.

Second, it seems quite difficult to portrait political participation at the different levels in the DR, since survey results are mixed. On the one hand, interest in politics is among the highest in Latin America (44.5% of respondents) and 19.9% of the population affirms to have done campaign work for candidates (the highest). However, this may not be a good proxy for social capital; conversely, it may rather reflect political clientelism. On the other hand, the average number of people that participates in protest is just of 5.4% (among the lowest) and the percentage of people that votes in the elections (76.1%) is just around the regional average.

Third, Dominican citizens seem to be very involved in <u>local politics. The DR enjoys the</u> highest level of participation in municipal meetings (27.3%) and also ranks high in number of formal demands presented to the municipal authorities. At the same
- 52 -

For the stock of infrastructure, offsetting forces were hypothesized. The results from the consolidated budget expenditure regressions indicate that diminishing returns are offset by the expenditure required to offset depreciation of the existing stock of infrastructure or cross country differences in the degree of complementarity between infrastructure and the existing productive structure of the economy. The result is not, however, robust across all definitions of the stock of infrastructure. In particular, no significant relationship is found when the stock is measured as the road and rail kilometers relative to the GDP, or more broadly as the total capital stock (private plus public) of the country.

The level of development was hypothesized to positively, but possibly nonlinearly, influence the level of infrastructure expenditures, since government services and goods are expected to be normal goods, and structural changes accompanying development are expected to increase the return to infrastructure investments. The linear and positive relationship found for consolidated budget per capita infrastructure expenditures is robust and is not sensitive to how the level of development is measured.

The size of the foreign sector was also hypothesized to be positively related to the expenditures on infrastructure given the link between the size of the foreign sector and taxable capacity. Support for this hypothesis is robust. Similarly, the level of foreign savings flows was hypothesized to be positively related to government expenditures on infrastructure since these flows can either directly be used for infrastructure expenditures or reduce budgetary outlays for other items, freeing up funds for infrastructure investment. This hypothesis receives robust confirmation from the consolidated budget infrastructure expenditure regressions.

We speculated that, holding the total level of foreign savings flows constant, countries receiving a higher proportion of foreign funding in the form of commercial bank flows might allocate a different amount for infrastructure expenditures, although no a _prior_ hypothesis was made concerning the direction of the impact. The results here robustly indicate that countries receiving a high proportion of foreign flows in the form of commercial bank loans spend more on infrastructure out of the consolidated budget. In fact, the estimated relationship indicates infrastructure expenditures increase