rafmacalaba/data-use-mentions-extended huggingface dataset:
’Djamena in Chad, and Niamey in Niger) in an attempt to classify the relative proportions of informal and formal buildings. In each city, the team hand-coded several neighborhoods in order to train an algorithm that

> 14We again use mean rather than median because the majority of grid cells in 1985 did not contain any built-up pixels.

> 15The remotely sensed night light metric we use measures average nighttime light emission from the DMSP-OLS Nighttime Lights Time Series Version 4 (Average Visible, Stable Lights, & Cloud Free Coverages). For details, see: https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html.

21
the inclusion of location indicator variables. This improvement leads to one additional correctly identified poor household and the exclusion of one additional correctly identified non-poor households per 100 program applicants at the national level when those in the bottom 20% are targeted. However, in practice, the PMT will likely not be used to administer funds to states, rather the states will administer a program given a pre-determined program budget. Therefore, within a state, the ranking of households based on the PMT score will not change with or without state indicator variables and may not be necessary to include in the PMT.

Table 9. Evaluation of the National PMT with Additional State and Area Variables (OLS Regression)

**Evaluation of Including a Complete Set of Location Indicators in the PMT**

||**OLS Model w**<br>**Poverty Line**<br>|**ith Location In**<br> **/ PMT Cutoff**<br>|**dicators**<br>|**OLS Model w**<br>**Poverty Line**<br>|**ithout Locatio**<br>**/ PMT Cutoff**<br>|**n Indicators**<br> <br>|
|---|---|---|---|---|---|---|
|**Evaluation Criteria**|**Bottom**<br>**20%**|**Bottom**<br>**40%**|**Bottom**<br>**60%**|**Bottom**<br>**20%**|**Bottom**<br>**40%**|**Bottom**<br>**60%**|
|Poverty headcount (observed)|
14<br>5 12<br>2e+05<br>4 10<br>3 1e+05 8<br>0e+00<br><!-- End of picture text -->

**Fig. 10.** Tobias-Li data: log posterior medians of _g_ , number of models visited and posterior mean model size.

Prediction based on prediction subsamples of 178 observations each is summarized in the lower panel of Figure 7. Even though there is some variability in the posterior inclusion probabilities of the regressors over the different priors, we observe virtually identical predictive performance. This is related to the fact that the full model here predicts very similarly to using BMA. This is not surprising as the number of observations _n_ is much larger than the number of potential regressors, so we can hardly overfit and for prediction not much is lost by including all _k_ regressors. In the full model, the only effect that the prior for _g_ has on prediction is the different shrinkage of the coefficients, but as the shrinkage coefficient is very close to one anyway, that will make very little difference. Of course, BMA still has an important role in identifying the important determinants of wages, but is not that critical for prediction. In line with this, the null model does very badly here and never beats BMA or the full model. BMA predicts best in 20% to 46% of the subsamples, with the lowest percentages occurring for the benchmark Beta priors with _c_ = 1 and the fixed- _g_ cases. BMA is beaten by the full model in 42% to 48% of the cases with random _g_ . For the two specifications with fixed _g_ , the full model beats BMA in 52% of the subsamples, while the best model also beats BMA there in over a third of the cases.

Figure 8 tells us that the likelihood marginalized with the prior of everything but _g_ is rather similar to that of the growth datasets. This is perhaps surprising, given that
(2017): “The Next 15 Million: Start and Improve Your Business Global Tracer Study 2011-2015,” International Labour Organization: Geneva.

- Westfall, P. H. and S. S. Young (1993): “Resampling-based multiple testing: Examples and methods for p-value adjustment,” John Wiley & Sons, vol. 279.

- World Bank Group (2019): “Doing Business 2019. Economy Profile of Jamaica,” Tech. rep., The World Bank Group.

29
from transferring knowledge to their local suppliers or clients, spillovers from FDI are more likely to be negative along the horizontal dimension and positive along the vertical dimension. Javorcik uses firm-level data from Lithuania and Blalock and Gertler (2008) use data for Indonesia to show that positive FDI spillovers take place through backward linkages (between foreign affiliates and their local suppliers); however, there is no robust evidence of positive spillovers occurring through either the horizontal or the forward linkage channel.

One recent manuscript that investigates both horizontal and vertical FDI spillovers in China is Lin, Liu, and Zhang (2009). In contrast to Javorcik (2004), Lin, Liu, and Zhang find bigger forward and smaller backward spillovers. Our results will differ from theirs, in part because we focus on total factor productivity and they examine value-added productivity and also use a different estimation method. We also expand the analysis to explore the relationship between trade policies, tax incentives, and externalities from foreign investment. To our knowledge, ours is the first study to explore—in China or elsewhere--how productivity gains from foreign investment vary with tax and tariff policies.

There are also a set of theoretical studies demonstrating that positive FDI spillovers are more likely to operate across industry rather than within an industry. These studies emphasize efforts to minimize the probability of imitation, especially under imperfect intellectual property rights in the host country. As Markusen and Venables (1998) point out, proximity to potential domestic competitors with absorptive capacity to reverse engineer proprietary technology would be detrimental to a multinational, thus motivating it to set up its subsidiaries where potential rivals cannot erode its market share. By contrast, the multinational can benefit from knowledge diffusion when it reaches downstream clients and upstream suppliers, which will encourage vertical flows of generic knowledge that lead to inter-industry spillovers.

This study goes further by investigating the implications of the institutional context for the nature of spillovers. In particular, we examine three institutional features that comprise aspects of China‘s ―special characteristics‖: the
# **5. REFERENCES**

Azevedo, J.P. & Mitra, S. (forthcoming). Global poverty estimation theoretical and empirical validity of parametric Lorenz curve estimates and revisiting nonparametric techniques. World Bank, Mimeo.

Beegle, K., Olinto, P., Sobrado, C., Uematsu, H., Kim, Y. S., & Ashwill, M. (2014). Ending Extreme Poverty and Promoting Shared Prosperity. Could There Be Trade‐offs Between These Two Goals? Inequality in Focus, 3(1). World Bank, Washington, DC.

Cruz, M., Foster, J., Quillin, B. and Schellekens, P. (2015). Ending Extreme Poverty and Sharing Prosperity: Progress and Policies. Policy Research Note 101740. World Bank, Washington, DC.

Datt, G. (1998), “Computational Tools for Poverty Measurement and Analysis,” FCND Discussion Paper No. 50.

Deaton, A. & Zaidi, S. (2002). "Guidelines for Constructing Consumption Aggregates for Welfare Analysis," World Bank Publications, The World Bank, number 14101, November.

Deaton, A. (1997). The Analysis of Household Surveys: A Microeconometric Approach to Development Policy. Baltimore, MD: Johns Hopkins University Press.

Dollar, D. T. Kleineberg, Kraay, A. (2013). “Growth Still is Good for the Poor.” Policy Research Working Paper 6568. World Bank, Washington, DC.

Global Poverty Working Group 2015. “Methodology for computing the indicator on Shared Prosperity for the Global Database of Shared Prosperity (GDSP) circa 2007–2012”, available at: http://www.worldbank.org/content/dam/Worldbank/poverty/GDSP_Methodology_Sep2015%20(2).doc x.

Mitra, S. R. Katayama, and Yoshida, N. (2010). “A Short Note on International Poverty Estimation,” mimeo.
#  Proportion of pharmaceutical sales that consist of counterfeit drugs

One way to calculate these indicators would be to use administrative data on local health facilities that is known to be reliable. This is not the case in most countries and, therefore, a more fruitful approach is to use representative facility surveys. In order to be most useful to public policymakers, these surveys should ideally be expanded beyond the more common exclusive focus on public health care services to include surveys of all health care providers in proportion to their share of clients served.

Fortunately, work on health facility surveys has advanced significantly in recent decades. Two particular initiatives that deserve mention are the Public Expenditure Tracking Surveys (PETS) and Quality of Service Delivery Surveys (QSDS). Together, they can be used to measure three of the five core health governance performance indicators (absenteeism, funds reaching facilities, and stock-out rates).The proportion of informal payments can be calculated from questions asked in household surveys, but more reliable measures can be extracted from patients who have just left a facility. Detecting counterfeit medicines, by contrast, requires special surveys that may include visits to border crossings, warehouses, and pharmacies, as well as health facilities.

_Share of funds reaching facilities and Public Expenditure Tracking Surveys (PETS)_ For measuring the share of funds reaching health facilities, the Public Expenditure Tracking Survey represents the best model. Public Expenditure Tracking Surveys (PETS) follow the flow of government resources through the often-complex system of delivering public services. These studies commonly gather information about the funds that are budgeted, authorized and disbursed through different levels of the public service system and then survey a representative sample of facilities to determine how much of those funds are actually available in the form of services. When these studies are successful, they can measure the share of funds that fail to reach their intended goal and identify how much is lost at different points in the system. This information is useful for focusing investigations of diverted funds but more often is used to improve public policies
0.13|-0.60|
|||(0.16)|(0.15)|(0.10)|(0.14)|(0.41)|
|Socioemotional|Emotions|-0.00|**0.48****|-0.30|0.12|0.81|
|||(0.15)|(0.15)|(0.21)|(0.19)|(0.41)|
||Relationships|**-0.30***|-0.35|0.08|**-0.58***|-0.67|
|||(0.14)|(0.18)|(0.38)|(0.24)|(0.43)|
||Personal growth|-0.16|-0.03|**0.36***|0.34|-0.55|
|||(0.16)|(0.17)|(0.18)|(0.20)|(0.41)|
||Constant|0.44**|1.47***|2.22***|1.39***|0.74|
|||(0.14)|(0.14)|(0.22)|(0.33)|(0.54)|
||N|79|75|47|54|51|
||R-sq|0.36|0.36|0.13|0.15|0.44|

_Source_ : Authors’ calculations based SEAD data.

_Note_ : The regressors for the outcome are dummy variables taking the value of one if the skills count of a skills category of
# **4.2. Direct estimation-based measures**

Finally, we contrast results obtained using the model-based BEER approach against three commonly used direct estimation-based measures of exchange rate misalignment: parallel exchange rate premia, the law of one price condition, and purchasing power parity.

We stress that it is important to consider their assumptions and caveats in evaluating their relevance. For the case of Argentina, where price statistics were allegedly manipulated (under reported), the law of one price and purchasing power parity may considerably underestimate exchange rate overvaluation. Moreover, in the presence of trade restrictions, the law of one price may have the opposite effect of considerably overestimating the degree of overvaluation.

An additional caveat is that direct estimations derived from the LOP and PPP conditions assume that the real exchange rate should be constant and equal to unity in equilibrium, and deviations from unity should eventually die out, exhibiting a stationary or mean-reverting property in the long-run. Empirically, however, the real exchange rate has been found to exhibit persistent deviations and a unit root process, providing weak empirical support for the PPP hypothesis. This speaks in favor of using a modelbased approach such as the BEER, in which we estimate a time-varying equilibrium real exchange rate that allows deviations from PPP to be accounted for by real factors, reconciling the high volatility of the real exchange rate with very slow adjustment of shocks to PPP.

# **_Parallel exchange market premia_**

Parallel exchange rate premia implied by the blue dollar rate and blue chip swap rate (percentage deviations from the official exchange rate) provided two measures of the implied overvaluation of the peso, using data obtained from dolarblue.net and ambito.com.

The published blue dollar rate, or ‘ _paralelo’_ , has been for a long time the bestknown alternative to the official exchange rate and was based on tracking average stock prices of eight major companies quoted on both the Argentine and US exchanges. The blue dollar to official exchange rate spread fluctuated with capital controls, market forces in response to
Another important issue emerging from the conceptual framework of Figure 1 revolves around the exploration of the dynamic relationship between (im)mobility and adaptation outcomes, which has important implications for rural development and local adaptation policies. Exploring this nexus presupposes the availability of both mobility and adaptation outcomes. While we have just illustrated that household surveys are generally well-equipped to look at household-level migration, a key question is whether it is also possible to build a reliable adaptation indicator using their data. The ideal adaptation indicator we have in mind is a household-level indicator of _in situ_ adaptation – which excludes _ex situ_ adaptation via migration – able to capture both on-farm adaptation through the use of modern agricultural practices and investment in inputs and the ability of the household to diversify income away from agriculture. For this purpose, having access to data from an agricultural questionnaire is essential, meaning that building such an index is currently feasible only for household survey collections – such as the LSMSISA – with a specific focus on agriculture. In practice, this index can be built using standard econometric tools such as factor or principal component analysis starting from raw data variables. To illustrate this point, we stick with the Nigeria dataset employed above and show, in Table 2, descriptive statistics for a type of _in situ_ adaptation score built out of the Nigeria Universal Panel Data. We also report descriptive statistics for the raw data variables that enter the estimation model.

**Table 2: Adaptation statistics using the Nigeria Universal Panel Data (2010-2019)**

|**Variable name**|**Mean**|**SD**|**Min**|**Max**|**Obs**|
|---|---|---|---|---|---|
|_In situ_adaptation score|31.371|17.264|0|100|11,262|
|Share of irrigated plots|0.020|0.127|0|
higher number of plots, though they are further away compared to the individual training group. We include all 15 covariates shown in Appendix Table 1 in our vector of covariates Xijs.

Second, we test whether farmers who completed the training differ from those in the overall sampling frame at the time of randomized assignment. Appendix Table 2 shows that the farmers who completed the training have 0.3 more household members on average, are 7 percentage points more likely to be married (rather than just cohabitating), own 0.5 fewer assets and have a 0.3 ha larger plot size on average. However, all these differences go in the same direction for _both_ farmers randomized into the individual training group and those randomized into the couples’ training group, indicating comparability between compilers when analyzing the action plan data comparing couples’ training attendees and individual training attendees.

Table 1 shows summary statistics for the final sample of farmers included in our study. On average, households have six members, evenly split between adults and children. 90 percent of the lead farmers are married (versus merely cohabitating with their partner), while 24 percent of households have a traditionally matrilineal ethnicity (in Côte d’Ivoire, matrilineal ethnicity mainly determines through which channel men inherit assets).

Table 1: Descriptive Statistics

|VARIABLES|Observations|Mean|SD|
|---|---|---|---|
|Age of household head|1,435|45.63|11.21|
|Household size|1,435|6.23|3.44|
|Number of adults|1,435|2.98|1.70|
|Number of children|1,435|3.07|2.35|
|Dependency ratio|1,435|1.18|0.88|
|Married planter|1,435|0.90|0.30|
|Polygamous relationship|1,435|0.12|
provide an overview of strategic CSR and the potential role of information in an international framework. Section 2 outlines the theoretic set up. Subsections (2.1.) and (2.2.) will elaborate the sub game perfect equilibrium of the basic CSR game with imperfect information, then (2.3.-2.6.) will derive the main proposition regarding the interaction between international firm location and regulation as well as analyze and discuss welfare and policy implications. Section 3 concludes.

# _1. A Quick Look at "Trade versus Environment"_

Recent decades have been characterized by unprecedented transnational economic integration and trade liberalization, inducing both firms and the public goods and externalities they produce to expand geographically. Constant technological and political advances such as the internet or the end of the East West Divide in 1990 led to significant reductions in transport and communication costs, a fact that required competitive firms to increasingly decide about international location, outsourcing or relevant markets. According to the United Nations, the number of transnational corporations increased from 37,000 in 1990 to over 60,000 in 2001, with foreign affiliates growing from 170,000 to over 800,000, while OECD data on Foreign Direct Investment (FDI) suggest significant growth in international corporate (investment) activity with worldwide FDI outflows increasing from 241,824 to 1,249,275 million US$ and worldwide FDI outward stocks growing from 1,797,630 to 11,891,825 million US$ between 1990 and 2009<sup>4</sup> . Naturally, the globalization of economic activity opened up the opportunity for firms to stretch supply chains across countries and, in theory, international competition should have acted as a major pull factor for locations endowed with cheap production factors and low regulatory costs. This idea is formalized within the literature on the Pollution Haven Hypothesis (PHH) and Environmental Dumping (ED). In a nutshell, the PHH states that a country's comparative advantage in hosting pollution intensive industries
|27.7|22.3|58.5|76.1|
|LAC|10.5|13.4|12.0|14.3|3.1|2.4|0.9|0.5|28.5|19.5|70.5|56.5|
|MENA|4.5|5.1|15.3|15.3|||2.1|2.1|20.5|12.6|121.5|169.1|
|SAR|3.8|3.3|13.0|14.3|7.7|7.9|5.9|12.7|24.2|7.8|74.1|66.9|
|SSA|14.0|16.0|16.9|15.3|6.0|6.0|2.5|3.8|34.1|18.5|59.5|55.1|

Source: Authors’ calculations using data from Fitch and IMF Financial Soundness Indicators Note: EAP: East-Asia and Pacific; ECA: Europe and Central Asia; LAC: Latin America and the Caribbean; MENA: Middle East and North Africa; SAR: South Asia; SSA: Sub-Saharan Africa. Data for non-performing loans to total gross loans for year 2020 is not available for the MENA countries in the sample.

The results in Table 3 indicate that CDBP correlates positively with liquidity risk (Table 3, Column 4) and negatively with the cost of intermediation (Table 3, Column 5) – a higher relative lending spread is negatively correlated with CDBP. When we condition on all FSIs at the same time, the non-performing loans ratio is negatively associated with CDBP, while the sign on the coefficient for the liquidity risk ratio is still positive and statistically significant (Table 3, Column 6).
is information on the cost of subsidies as a share of GDP in 2007 and 2009. During the period 2007–2009, national food prices increased substantially,<sup>10</sup> but food subsidies mostly declined or stagnated, except in Egypt where they increased.<sup>11</sup> We illustrate this situation on Figure 2 assuming that a country is a price taker in international food markets and faces a perfectly elastic import supply curve.<sup>12</sup> We assume also that the objective of the government is to keep domestic prices and imports of food as stable as possible via price subsidies on imported food.<sup>13</sup> If there is a shock to the world food price, resulting in a jump in the world price from _P_<sup>_W_</sup> up to _P_<sup>_W’_</sup> , the government will have to double the subsidy paid per unit of imported food in order to keep imports at levels prevailing at the old international prices _P_<sup>_W_</sup> .

**Figure 2. Impact of Subsidies on Domestic Food Prices**

<!-- Start of picture text --> P W‟<br>MS‟<br>P W s s<br>MS<br>s<br>P<br>MD<br>M‟ M<br><!-- End of picture text --> As discussed above doubling of subsidies will be difficult in countries with limited fiscal space, implying that domestic prices _P_ might move up. In our example,

> 10 Food CPI increased by 20 percent in Jordan, 44 percent in Egypt, 21 percent in Syria, and 8 percent in Morocco between 2007 and 2009.

> 11 According to World Bank data, during the period 2007–09, food subsidies declined as a share of GDP from 1.8 percent to 0.8 percent in
|Russian|Mature|20%|18%|28%|64%|-|
|---|---|---|---|---|---|---|
|Federation|Young|22%|12%|28%|82%|-|
||All|5%|6%|9%|63%|-|
|Turkey|Mature|6%|7%|9%|65%|-|
||Young|1%|1%|2%|27%|-|

Source: Author’s elaboration from Enterprise Survey (2014).

<u>Differences across trader-status groups: Trader firms i.e., exporter, importer or both, tend to be more</u> innovative in both dimensions (technological and non-technological) than non-trader firms (Table 6), with relatively larger gaps between groups for process innovation. However, these differences are statistically different in few cases. Further, the proportion of innovative firms within the non-trader group in leading countries is larger than the proportion of innovative firms in laggard ones. The overall innovation dynamic of the region is consistent with what is the average behavior in other regions such as ECA and Africa.

<u>Table 6. Firm Innovation by Trade Status (% of all firms in each category)</u>

||**T**|**echnologic**|**al innovation**|**Non-techn**|**ological**|
|---|---|---|---|---|---|
|**Country**|**Product**|**Process**|**Product & Process**|**Organizational**|**Marketing**|
|**Bangladesh**|||||