rafmacalaba/data-use-mentions-extended huggingface dataset:
between specific policy areas. High-level survey findings are used to validate or question prevailing findings in the literature regarding the effect of countries’ legal and regulatory environments, and investment promotion on FDI attraction. In addition, by gathering data on respondent profiles, survey data allow us to examine heterogeneity in investors’ preferences and decision-making criteria across a wide variety of affiliate dimensions, including affiliate size, age, broad sector, and trading activity. Finally, by asking respondents about specific policy areas, the survey can provide insight into the areas that truly matter to investors beyond simple claims of policies’ general importance as a whole.

# **3. DATA**

The analyses in this paper draw upon the results of the 2019 Global Investment Competitiveness (GIC) Survey (survey), a survey of executives of the affiliates of multinational enterprises (MNEs) in developing countries. The survey data cover more than 2,400 foreign investors with operations in 10 middle-income countries: Brazil, China, India, Indonesia, Malaysia, Mexico, Nigeria, Thailand, Turkey, and Vietnam. The 10 countries covered by the survey account for more than half of the global population, one-quarter of global GDP, and one-fifth of global trade. From an FDI perspective, they accounted for over a third of global inflows (37 percent) and three-fourths (75 percent) of inflows to developing countries in 2018.

The survey was conducted using 30-minute phone interviews in the primary business language(s) of the host economies. The interviews were conducted between June and November 2019. The survey was administered to senior executives of foreign-owned affiliates who possess a broad understanding of 4
13464|13464|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. In Panel A, the dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1 if the respondent reported that her household received any transfers from any ultra-poor, other poor, middle class or upper class households (within her community) respectively. In Panel B, the dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1 if the respondent reported that her household gave any transfers to any ultra-poor, other poor, middle class or upper class households (within her community) respectively.

40
to the banking system is quite short. Consequently, the search for useful early warning indicators should move towards high frequency data, such as market data. To explore how market data performs in crisis prediction, however, requires more work to define and date crisis episodes accurately. Future research should proceed in this direction.

The question of how institutional variables, such as politics and regulation, influence bank fragility has been a fruitful area of exploration, and there are several directions in which this work can continue. For example, it would be interesting to study how compliance with banking regulation and the introduction of the BASEL II capital agreement might affect financial stability, particularly in developing countries (see also Goodhart, 2005, this volume). Another area of focus has been the impact of policy choices such as liberalisation, foreign bank entry, and the resulting market structures on bank fragility. As banking systems around the world are being quickly reshaped by globalisation and consolidation, the study of how these trends affect bank fragility will continue to attract attention.

Finally, the field of banking crises is at the crossroads of open economy macroeconomics and the microeconomics of banking and regulation. These two areas of research have evolved quite separately in the past, but to understand financial crises insights from both fields must be brought together. Exploring more closely how bank level information can be incorporated in cross-country empirical models of banking crises would be a useful direction for future research.

28
of the information that is contained in zero trade flows (Santos Silva and Tenreyro, 2006).

Our primary purpose is to estimate the effects of the CAFTA-DR agreement. For this, we introduce the variables _CAFTAij_ , _t_ to capture the specific impact of this trade

> 11Larch and Yotov (2023) survey the methods and data for estimating the effects of trade agreements. 12We focus on total goods data for our baseline due to data quality issues in the services trade flows.

9
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE This report presents the key AICD findings for Zimbabwe, allowing the country‘s infrastructure situation to be benchmarked against that of its African peers. Given that Zimbabwe is at present a distressed low-income country, but has approached middle-income status in the past, two sets of African benchmarks will be used to evaluate Zimbabwe‘s situation: that of low-income countries and that of middle-income countries. Detailed comparisons will also be made with Zimbabwe‘s immediate regional neighbors in the Southern African Development Community (SADC).

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. In the particular case of Zimbabwe, it is hardly possible to make sense of the financial data for the period 2005–08 due to the spiraling currency crisis: hyperinflation soared to an official figure of over 230 million percentlow-income countrieby February 2008. Therefore the report will focus on the Zimbabwe‘s 2004 pre-currency-crisis and 2009 post-currency-crisis data points. Most technical data presented are for 2006 (or the most recent year available), while financial data for comparator countries are typically averaged over 2001–06 to smooth out the effect of short-term fluctuations.

Second, to make comparisons across countries, indicators are standardized to allow for a consistent cross-country analysis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

In common with the rest of the continent, southern Africa‘s<sup>2</sup> growth performance improved markedly in the 2000s vis-à-vis the 1990s. The overall improvement in per capita growth rates has been estimated at 2.1 percentage points, of which 1.1 points are attributable to better structural policies and 1.0 point to improved infrastructure. Zimbabwe stands out markedly as a sad exception. Zimbabwe‘s broad-based output declined dramatically during 2000–08. Its GDP contracted by roughly 50 percent —possibly the largest peacetime
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Growth Targets.” _Journal of Accounting and Economics_ 66 (1): 318–338.

- Ma, Ben, Guojun Song, Lei Zhang, and David A. Sonnenfeld. 2014. “Explaining Sectoral Discrepancies between National and Provincial Statistics in China.” _China Economic Review_ 30: 353–369.

- MacRae, C. Duncan. 1977. “A Political Model of the Business Cycle.” _Journal of Political Economy_ 85 (2): 239–263.

- Maddison, Angus, and Harry Wu. 2006. “China's Economic Performance: How Fast Has GDP Grown? How Big is It Compared with the USA?" _Working Paper at the University of Queensland_ .

- Magee, Christopher S. P., and John A. Doces. 2015. “Reconsidering Regime Type and Growth: Lies, Dictatorships, and Statistics.” _International Studies Quarterly_ 59 (2): 223–237.

- Manski, Charles F. 1993. “Identification of Endogenous Social Effects: The Reflection Problem.” _Review of Economic Studies_ 60 (3): 531-542.

- Maskin, Eric, Yingyi Qian, and Chenggang Xu. 2000. "Incentives, Information, and Organizational Form." _Review of Economic Studies_ 67 (2): 359-378.

- Martinez, Luis R. 2019. “How Much Should We Trust the Dictator’s GDP Growth Estimates?” _SSRN Working Paper_ .

- Mehrotra, Aaron and Jenni Pääkkönen. 2011. “Comparing China’s GDP Statistics with Coincident Indicators.” _Journal of Comparative Economics_ 39 (3): 406–411.

- Michalopoulos, S. and E. Papaioannou. 2014. “National Institutions and Subnational Development in Africa.” _Quarterly Journal of Economics_ 129 (1): 151–213.

- Nie, Huihua, Minjie Jiang, and Xianghong Wang. 2013. “The Impact of Political Cycle: Evidence from Coalmine Accidents in China.” _Journal of Comparative Economics_ 41 (4): 995– 1011.

- Nordhaus
calculate the share of total consumption expenditure allocated to each component for each household. We also compute aggregate country-level shares. In the few cases in which consumption shares at the country level were missing, we imputed them from previous rounds of the survey.<sup>5</sup>

> 4 We focus on the two-digit COICOP classification to achieve better intercountry comparability and because of the lack of information in most of the ECA household budget surveys on the representativeness of the sample results based on more granular product classification.

> 5 <u>Table A1, in Appendix, indicates the year of the latest available survey and the sample size for each country.</u>

8
global public goods (IEG 2011a: viii). While multi‐donor trust funds could, theoretically, improve donor coordination prior to implementation, reducing excessive and harmful donor fragmentation in the field (Huq 2010, IEG 2011a: ix), its effects on donor harmonization seem ambiguous (IEG 2011a: 43, Barakat et al. 2012: 34f.) as trust funds usually do not replace existing bilateral and multilateral projects (Barakat 2009: 112).

An evaluation of World Bank trust funds (IEG 2011a: 7) highlights their importance for countries in arrears or entities, where IDA is legally forbidden to engage and bilateral donors prefer not to engage alone. Looking at aggregate official aid flows, however, it is still unclear whether, and in what sectors or countries, trust fund aid substitutes for (i.e., “crowds out”) or complements multilateral or bilateral aid, or if it is additional to traditional aid. Using data on donors organized in the OECD’s Development Assistance Committee (DAC), Reinsberg et al. (2015) find some evidence that earmarked or multi‐bi aid is additional to “core” multilateral aid, although their estimations may suffer from potential reverse causality and simultaneity. Based on data for the World Bank through 2009, Huq (2010) finds that the sectoral allocation of trust funds executed by recipients is “aligned” (or positively correlated) with IDA but not with IBRD disbursements.

Donor countries might prefer using trust funds rather than bilateral or multilateral aid for a number of reasons. Donors might seek to supplement IDA funding for particular countries, sectors and/or projects, and trust funds are often used to co‐finance IDA projects. In other cases, bilateral donors may use trust funds to complement their bilateral programming when their aid agencies do not have sufficient presence or expertise in countries to implement programs effectively (OECD 2010: 40, 2011: 29). Donor officials state that the proliferation of trust fund aid is associated with a need
Since, as we explain below, most of our firm-level databases only cover firms with ten or more workers, the model-based counterpart of the average size should is adjusted accordingly. TFP is still defined as in equation 13.

# **5.2 Calibration**

We must choose parameter values for the elasticity of substitution _θ,_ the subjective discount factor of the household _β_ , and the set of parameters governing the process of firm dynamics, entry and exit: entry and fixed operation costs _fe_ and _fc,_ the size of the jump in the binomial process ∆, the exogenous exit rate _δ_ , and the parameters in the innovation cost function, _η_ and _φ_ . For calibration purposes, we consider the economy with no distortions and target moments of the firm size distribution and the life cycle of firms in the US. Table 1 summarizes the parameter values and the matching moments.

Table 1: Parameter Values and Calibration Targets

|Parameter|Value|Target|
|---|---|---|
|_ρ_|3|Hsieh and Klenow (2009), Broda and Weinstein (2006)|
|_β_|1<br>1_._05|Interest rate of 5%|
|_δ_|0.025|Employment-based exit rate of large frms of 2.5%|
|_η_|0.00056|Top 10% employment share = 0.77|
|_φ_|15|Employment age 21–25 relative to age 1|
|∆|0.25|Std dev. of employment growth of large frms|
|_G_(_ω_)|_ω_0 = 0|Size of entrants = 20% of average incumbent|
|_fc_<br>_fe_|0_._1|Exit rate of 5%|
|_fc_|3.165|US average frm size conditional on _L ≥_10 = 118|

The top 10 percent employment share, the average employment ratio between 21–25 and 1-year-old firms, the average employment ratio between entrants and incumbents
storms that cause fatalities in the international data set than cause damages. This is because the international data set has a minimum damage requirement before storm damages are reported. It is not likely that this will bias the results because the bulk of damages done by tropical storms are caused by the most powerful storms.

We rely on the US regressions for the storm intensity coefficients. We rely on the international coefficients for the income and population density coefficients. We then calibrate the damage function for each country so that current baseline and current climate lead to observed national damages: 13
|-0.0191|-0.127***|
||(0.0460)|(0.0487)|(0.0494)|(0.0374)|
|Momase region|0.000459|-0.155***|0.00284|-0.0969**|
||(0.0404)|(0.0419)|(0.0436)|(0.0328)|
|Secondary school or less|-0.0892***|-0.0832**|-0.0723**|-0.0777***|
||(0.0269)|(0.0281)|(0.0280)|(0.0220)|
|Constant|0.161***|0.344***|0.172***|0.282***|
||(0.0412)|(0.0414)|(0.0443)|(0.0351)|
|Observations|915|876|891|1514|

Note: This chart is based on answers to question 34 in Appendix B.***, **, and * indicate significance at the 1, 5, and 10 percent critical level. Standard errors in parentheses.

46
- Trim the database from (a) businesses very close to formalization who would have formalized anyway and (b) businesses very far from formalization which would not be interested by the program

- Remove businesses that ever got a loan from a commercial bank that will most probably not been interested by the program (less than 3% of informal businesses)

-

- Reduce the standard deviation of the main outcomes (profit and turnover)<sup>18</sup>

- Include a sufficient number of businesses in Dantokpa market.<sup>19</sup>

> 18 Outside Dantokpa market we excluded businesses with sales or profit lower than CFAF 12,000 (USD 20), profit greater than CFAF 150,000 (USD 252) or sales greater than CFAF  400,000 (USD 671). In Dantokpa market, we excluded businesses with sales or profit lower than CFAF 10,000 (USD 17), profit greater than CFAF 200,000 (USD 336) or sales greater than CFAF 500,000 (USD 839).

> 19 We choose to take 22% of the total study population from Dantokpa market to have the same share of businesses from the market as in the 2008 firm census (INSEA, 2008).

55
εε</sup> (4) where denotes income, 0 denotes the subsistence income, for which uu(mm) = 0) = 0 , and is the coefficient 1 −εε of relative risk aversion that captures the curvature of utility function . A parametric value for θ also mm mm uu(mm εε requires defining representative incomes for the (monetary) poor and not (monetary) poor status because ) and c ) . Typically, could be mean or median income among the uu (monetary) poor and ooc oo c could be mean or median income among the not (monetary) poor. cc−ooc oo ooc oo uu(PP) = uu(mm uu(NNPP) = uu(mm mm cc−ooc oo Parametric values obtained for θ are sensitive to the values selected for these parameters, and in mm particular for the curvature parameter and the subsistence income 0 . As an illustration we provide the results for the parametric values of 1/θ obtained for India in 2019 as a function of different parameter values for the CES utility function presented in Eq (4). We assume that εε mm and c respectively capture mean consumption among the (monetary) poor and the not (monetary) poor. We estimate these ooc oo cc−ooc oo mm mm two mean consumption values from the estimates provided by the Poverty and Inequality Platform of the World Bank. We check for robustness for different values of curvature parameter and the subsistence consumption parameter 0 . As suggested by the results shown in Table A3, the theoretical predictions for εε 1/θ based on these assumptions depend on the values selected for these two parameters.<sup>23</sup> mm

> 23 We consider a plausible lower bound for the value for the subsistence consumption y0 to be $0.5 a day. It is biologically impossible to survive with zero consumption. In the micro-data reported in PovCalnet, values for reported consumption below $0.5 a day are very infrequent. The value
38.7 percent of children under 59 months were 2 standard deviations below the reference population for height-for-age, 18.6 percent for weight-for-height and 37.7 percent for weight-forage (INSD/ORC Macro, 2004). Similarly, 38 percent of children in that age group experienced some form of fever or respiratory infection in the two weeks preceding the national Demographic and Health Survey. Among children who had been ill, only 30 percent received any care from a health professional (INSD/ORC Macro, 2004).

We find that children under age five in families that received conditional cash transfers have 0.43 more visits to the health clinic for routine preventative care during the previous year compared to children in control households, a 49 percent increase compared to the mean in the control group. We find similar magnitude beneficial impacts for children in families where the mother or father received conditional cash transfers, indicating that at least when the transfers are conditional, the gender of the cash transfer recipient is not a critical factor influencing the frequency of routine health facility visits. In contrast, we do not find beneficial effects from unconditional cash transfers that are given to either mothers or fathers. This highlights the point that at least for this particular outcome of routine preventative health clinic visits, conditionality is more important than transfer recipient gender in increasing health clinic utilization for children.

Our experimental design has several distinctive features differentiating it from other studies. First, we randomly test conditional and unconditional cash transfers in the same environment, which allows us to determine whether the key factor driving impacts is an income effect or conditionality.<sup>3</sup> Second, unlike many cash transfer programs that pay the transfers to

> 3 A recent study by de Brauw and Hoddinott (2007) makes use of administrative errors in Mexico to try to untangle how much of any observed program impact is due to the program’s income transfers and how much is due to the conditionality forcing households to undertake specific behaviors in order to receive the funds.
# **1. Introduction**

Yemen is one of the poorest countries in the Arab region with a per capita GDP of US$1,160 for 2008 (WDI, 2010) and faces a wide range of developmental challenges, amplified in 2011 by deepened domestic conflict. In 2007, the country was ranked 140 out of 182 according to the HDI (Human Development Index) (UNDP, 2009). Ever since reunification in 1990, Yemen’s relative position on the HDI index has remained more or less unchanged, with very slow progress towards attaining the Millennium Development Goals (MDGs). At 3 percent, the country has one of the highest population growth rates globally, with the population expected to double in 23 years to around 40 million. This increases the demand for educational and health services, drinking water and employment opportunities. Yemen faces a severe water shortage, with available ground water being depleted at an alarming rate. Its oil production and reserves are declining with severe budgetary consequences. The Yemeni economy is caught in a jobless slow growth cycle leading to stagnant per capita incomes and rising levels of unemployment, particularly amongst the youth. Unless resolved promptly, the political crisis that erupted in 2011 threatens to make Yemen’s prospects for rapid growth and progress on MDGs even bleaker.

Social development indicators, such as child malnutrition, maternal mortality, and educational attainment remain discouraging. The Household Budget Survey of 2005/06 indicates that about 35 percent of the population lives below the national poverty line, with poverty more widespread and persistent in rural areas. However, given persistent increases in consumer prices, notably food prices, the rate of poverty had increased to 42.4 percent in 2010 (MOPIC, 2011).<sup>1</sup> There are large gender disparities, with significant gaps in women’s access to economic, social and political opportunities. As indicated by its first and second MDG Reports, Yemen was off track with respect to meeting the MDG

> 1 The
considers the household’s prospects due to the adverse effects from shocks such as natural disasters, their exposure, and the capacity to mitigate or recover from them. One of the best ways to understand the capacity to recover, bearing in mind the household's current characteristics and future shocks, is to involve the well-being dynamics perspective in the vulnerability to poverty assessment through long-term panel data (Chaudhuri, 2003). However, even though the quality and the number of panel data have increased in Latin America and the Caribbean, long-term panel databases still need to be available in the region. Nonetheless, the literature has built measures of vulnerability to poverty in the context of cross-sectional data (Chaudhuri et al., 2002; Chaudhuri, 2003; Dercon, 2006; Bérgolo et al., 2010; Hill & Porter, 2017; Skoufias, Vinha, and Beyene, 2021, among others).

In this study, we develop vulnerability to poverty measures under a climate risk framework using cross-sectional data. To do this, we model vulnerability to poverty as a function of variables that can be related to hazards, exposure, sensitivity, adaptative capacity, and household demographic characteristics. Based on a micro-framework, we model the probability of being poor, including current households' coping mechanisms and observable aggregate measures that are proxies of exposure and natural shocks. Our analysis aimed to cover as many countries as possible, focusing on those where administrative-level data on hazards and exposure could be aggregated due to the survey design at the district level. Ultimately, we included seven countries in our analysis: Brazil, the Dominican Republic, Ecuador, El Salvador, Honduras, Mexico, and Peru.

Our approach presents differences from others. Typically, natural disaster literature measures disasters from risk metrics and maps, implicitly including household adaptation capacity (Ward et al., 2020; Cremen et al., 2022, among others). In contrast, Chaudhuri's (2003) and earlier works measure vulnerability to poverty by estimating the consumption process and drawing