rafmacalaba/datause-encoder-data huggingface dataset:
## **Box 1  What is regional infrastructure?**

In one sense, nearly all infrastructure is national—or indeed local—in that is situated in a single country. Among the exceptions are bridges and tunnels that connect two countries, along with power lines, pipeline, and fiber optic cables that may span several countries. But many national infrastructure projects have a wider regional dimension: they may be planned and coordinated with several countries, connect to existing regional networks, or have spillover effects on neighboring countries.

Regional infrastructure ranges from simple projects that involve two countries, such as building a road link or bridge across a boundary river, to complex ones that involve several countries such as gas pipelines in which many countries cooperate and coordinate to create networks for common benefit. Soft infrastructure also has a regional dimension, since cross-border trade and movement often require, or at least benefit from following common rules, standards, and procedures. For instance, rail connections are smoother if countries use the same rail gauge, and customs procedures are simpler and faster if countries harmonize their rules and standards.

Regional infrastructure projects are defined as:

- projects that involve physical construction works and/or coordinated policies and procedures spanning two or more neighboring countries; and

- national infrastructure projects that have a significant cross-border impact:

- their planning and implementation involve cooperation or coordination with one or more countries;

- they aim to stimulate significant amounts of regional trade and income; and

- they are designed to connect to the network of a neighboring or third country.

_Source_ : ADB (2009).

Connective infrastructure (roads, transportation services, telecommunications, and smooth border arrangements) facilitates regional factor mobility and trade in intermediate inputs. It can effectively reduce the economic distance between locations—the time and cost of trading between them—and thus expand and link sites of production, markets and cities. The distance between people, cities, and production sites is shorter because of high-speed and high capacity modes of transportation, the emergence of pipeline transportation and fiber-optic communications. By enhancing connectivity, regional infrastructure (especially in the form of transport and communication corridors) can facilitate the exploitation of economies of scale and scope, make possible greater specialization in production, and allow for more efficient division of labor.

# _4.3.1 Economies of scale_

In an increasingly globalized world with strong competitive pressures, it is vital for firms to reach minimum scale of efficiency in production. Under increased global contestability, the pressures of international competition reduce considerably the scope for inefficiency in production; i.e., they render any inefficient organization of a national industry increasingly unsustainable. The incumbent national firms must operate in an efficient manner because any unnecessary costs, like any excess 10
**TABLE 9: Investment financial performance and sector.** Sample includes only investments with first cash flow prior to 2010. Sector classification follows the investment classification used in IFC annual reports, except mining, oil, and gas are separated from infrastructure, and fund management companies classified as financial institutions rather than collective investment vehicles. All other collective investment vehicles are called fund investments in this paper.

|||PME vs.|S&P500,|vintage pre-|2010|
|---|---|---|---|---|---|
||Total|Median|Average|Standard<br>error|Standard<br>deviation|
|Direct investments|1.33|0.70|2.41|(0.40)|15.33|
|Financial Institutions|1.45|0.93|2.94|(0.56)|12.21|
|Infrastructure|1.80|0.98|3.12|(0.74)|8.74|
|Manufacturing, Agribusiness, and Services|1.01|0.51|1.09|(0.19)|4.94|
|Mining, Oil, and Gas|2.33|0.57|7.29|(4.07)|44.58|
|Fund investments|1.04|0.69|1.26|(0.30)|4.71|

40
relative wages decrease with the increase of transport costs from the industrial center (Krugman, 1991, and Robertson, 2000).

# **2.3 Poverty**

Despite Mexico’s status as a middle-income country and a member of the OECD, poverty is widespread. According to the Comisión Económica para América Latina y el Caribe (CEPAL),<sup>10</sup> the incidence of poverty remained stable around 40 percent during the 1990s. In 1989 poverty was estimated to be about 39 percent.<sup>11</sup> The economic reform of the late 1980s and early 1990s seems to have had an effect, as poverty was reduced to about 36 percent by 1994. The economic crisis of 1995 and the sharp devaluation of the peso then led to a sharp increase in poverty (43 percent in 1996). Finally, poverty levels were estimated to be around 40 percent in 1998 and around 39 percent in 2000. The incidence of poverty in Mexico varies widely by region. Northern states are on average the richer ones, with a substantially lower incidence of poverty than the states in the central regions (with the exception of the Mexico City area which registers a substantially lower level of poverty). Finally, the southernmost states are the ones that register the highest percentage of poor. Section 5 gives estimates of the effect of trade liberalization on each of the 32 states that compose the Mexican federation.

# **3. Theoretical Framework**

This section presents the framework used to estimate the effect of trade liberalization on social welfare. It starts by describing the farm-household model used to explore the effect of price and factor movement on household welfare. Next, it develops a model to link the trade liberalization to changes in the prices of goods at a regional level. Finally, a third section links the price effect of trade liberalization on labor income.

> 10 CEPAL - Panorama Social de América Latina, various years

> 11 Other studies have estimated a lower poverty incidence with poverty levels at about 10 percentage points below the ones reported by CEPAL (as in World Development Indicators, World Bank and in Lustig and Székely 1997). Since the analysis in this paper is based on real disposable income rather than poverty, the actual level of the poverty line is irrelevant for the purpose of this paper.

7
packages may be higher. Third, <mark>MNEs also tend to report less on national‐level or geographically constrained issues, such as reductions in energy requirements, water usage, and financial assistance by the government. This may support an observation that has been made about multinationals struggling with breaking out their data at the national level. By contrast, issues like anti‐corruption and greenhouse gas emissions may be managed globally and easier to track for MNEs.</mark> <mark>Finally, the fact that MNE status shows a significant correlation with the likelihood of reporting for only 12 of 54 GRI indicators, while company size has a large explanatory power across most indicators, implies that large companies – which are often overlooked due to the policy focus on either MNEs or SMEs – are following the multinationals’ lead in sustainability reporting. While some reporting instruments specifically target MNEs, e.g. the</mark> OECD Guidelines for Multinational Enterprises or a new regulation by the European Parliament from July 2015 to increase the transparency of the finances of multinational corporations including tax havens outside the EU,<sup>15</sup> many instruments target large companies in general – whether they are multinational or not (see Figure 2). This is important as domestic companies are often suppliers to MNEs. Transparency about GVCs should not only be confined to multinationals due to their higher brand exposure.

Much promising ground for additional research remains. Our study suggests two areas for data improvements in particular. First, this paper highlights the role of MNE status in sustainability reporting patterns. Efforts to collect and harmonize the underlying information for various GRI indicators, rather than the status of disclosure only, would enrich the data set tremendously. The possibility to assess a variety of economic, labor and social, environmental and governance indicators – and their relationship with MNE status – would be particularly useful for policy makers. Such data would, for instance, allow to systematically assess the channels through which sustainability reporting affects actual development outcomes.

Second, GVCs have been identified as an entry point into vertically specialized industries for LMICs and means to upgrade. While MNE data give insights into certain aspects of GVC participation, data collection on sustainability reporting should be accompanied by efforts to collect and harmonize GVC integration measures at the company level. GVC participation measures at the company level would allow to measure if companies that are more strongly integrated into GVCs as buyers and/or sellers show a higher economic performance and how GVC integration affects labor practices and decent work, human rights, society, and the environment. Such data would also allow to analyze the relationship between economic and social upgrading through GVC participation and between other types of upgrading at the company level.

> 15 <u>https://ec.europa.eu/info/business‐economy‐euro/company‐reporting‐and‐auditing/company‐reporting/non‐financial‐ reporting_en#companies‐that‐must‐comply</u>

34
# **Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS1491 Equilibrium Incentives for Adopting<br>Cleaner Technology Under Emission<br>Pricing|Peter W. Kennedy<br>s Benoit Laplante|August 1995|E. Schaper<br>33457|
|WPS1492 Trade Policies, Macroeconomic<br>Adjustment, and Manufactured<br>Exports: The Latin American Experie|Sarath Rajapatirana<br>nce|August 1995|J. Troncoso<br>37826|
|WPS1493 Migration and the Skill Composition<br>of the Labor Force: The Impact<br>of Trade Liberalization in Developing<br>Countries|Ramon L6pez<br>Maurice Schiff|August 1995|J. Ngaine<br>37947|
|WPS 1494 Adjustment and Poverty in Mexican<br>Agriculture: How Farmers' Wealth<br>Affects Supply Response|Ramon L6pez<br>John Nash<br>Julie Stanton|August 1995|J. Ngaine<br>37947|
|WPS1495 Raising Household Energy Prices<br>in Poland: Who Gains? Who Loses?|Caroline L. Freund<br>Christine I. Wallich|August 1995|G. Langton<br>38392|
|WPS1496 Reviving Project Appraisal at the<br>World Bank|Shantayanan Devarajan<br>Lyn Squire<br>Sethaput Suthiwart-Naru|<br>August 1995<br>eput|C. Bernardo<br>37699|
|WPS1497 Public Choices between Lifesaving<br>Programs: How Inportant are Lives<br>Saved?|Maureen L. Cropper<br>Uma Subramanian|August 1995|A. Maranon<br>39074|
|WPS 1498 Decentralized Rural Development<br>and Enhanced Community<br>Participation: A Case Study from<br>Northeast Brazil|Johan van Zyl<br>Tulio Barbosa<br>Andrew N. Parker<br>Loretta Sonn|August 1995|M. Williams<br>87297|
|WPS1499 The Dynamics of Poverty: Why Some<br>People Escape from Poverty and<br>Others Don't-An African Case<br>Study|Christiaan Grootaert<br>Ravi Kanbur<br>Gi-Taik Oh|August 1995|A. Sachdeva<br>82717|
|WPS1500 Agricultural Trade Liberalization in<br>the Uruguay Round: One Step Forwar<br>One Step Back?|Merlinda D. Ingco<br>d,|August 1995|J. Ngaine<br>37947|
|WPS1501 Are Partner-Country Statistics Useful<br>for Estimating "Missing" Trade Data?|Alexander J. Yeats|August 1995|J. Ngaine<br>37947|
|WPS1502 Active Labor Market Policies in the<br>OECD and in Selected Transition<br>Economies|Hartmut Lehmann|August 1995|WDR<br>31393|
was -1.4%, well below the average of 0.35% (Figure 4). In contrast, average growth in 19942001 – a growth acceleration – was 1.75%.

<!-- Start of picture text --> Figure 4: Senegal - GDP per capita growth rate and growth acceleration and deceleration<br>15<br>10<br>Growth acceleration<br>5<br>(1994-01)<br>0<br>-5<br>Growth deceleration<br>(1988-94)<br>-10<br>1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005<br><!-- End of picture text --> In the case of South Africa, the average growth rates during the downturns 1982-1987 and 1989-1994 were -1.91% and -1.54%, respectively, compared to the overall mean of 0.12% (Figure 5). During the growth acceleration period, 1999-2005, growth was 1.96%.

Table A1 in the Appendix shows the start years of growth acceleration and deceleration episodes and compares our results with those from two closely related studies. HPR in their seminal work identify growth accelerations worldwide using GDP per capita growth from 1950 to 1999 from the Penn World Tables. They compare seven-year forward and backward moving average growth windows and impose a cutoff that the forward moving average window should exceed the backward moving average window by at least 2%. They further require that average growth must be at least 3.5% during the acceleration episode.

7
# **1. Introduction**

While extensive theoretical and empirical literature has studied the size and composition of government expenditure (see for example Shelton, 2007 for a review of the literature), less is known about the determinants of public employment. Two notable exceptions are Rodrik (2000) and Alesina, et al. (2000) who developed theoretical models to explain the behavior of public employment. The first one shows that public employment could play a welfare-enhancing social insurance role in an economy buffeted by external shocks, and presents evidence of a positive association between the exposure to external risk and the share of public employment across countries. The second one motivates public employment as a redistributive tool to circumvent opposition to explicit tax-transfer schemes, predicting a positive relationship between the size of public employment and inequality or fractionalization, and provide empirical evidence of cities in the United States.

Public employment and its determinants is an important topic because it affects not only the size of expenditure, but it also affects its composition due to the rigidity of the wage bill (Vegh, et al., 2017). Recent empirical literature that uses expenditure-based measures of government size explores the relationship between trade openness and size, but its counterpart with employment measures has received less attention. The robustness in a panel data context over a period that goes beyond the 1990s has not been tested, in part because data on public employment is much more scarse than data on government expenditure. In the same vein, there is not much evidence about how alternative hypotheses to explain the size of the government can help to explain the size of public employment. This paper attempts to fill this gap in the empirical literature.

# **2. Data, Stylized Facts, and Methodology**

## **i. Data**

The paper uses three measures of public employment from the International Labor Organization Statistics (ILO Stats): total public-sector employment, general government employment, and central government employment. The coverage of each employment aggregate is the standard one.<sup>2</sup> Data on private employment and the total labor force come from the same source.

> 2 Total public-sector employment covers all employment of general government sector as defined in the System of National Accounts 1993, plus employment of publicly owned enterprises and companies, resident and operating at central, state (or regional) and local levels of government.  It covers all persons employed directly by those institutions, without regard for the type of employment contract.  The general government sector employment is the total employment of all resident institutional units operating at central, state (or regional) and local levels of

3
Phyto-sanitary standards mean for fresh fruits and vegetables that they must be free of pests and diseases when entering the US. Exporters must meet a complex set of regulations, compiled in the Fresh Fruit and Vegetables Import Requirements (FAVIR) online database. Compliance with these regulations is overseen and administered by the US Department of Agriculture’s Animal and Plant Health Inspection Service (APHIS).<sup>4</sup> In this paper we focus on US sanitary measures. Food products can be the source of numerous foodborne illnesses (due to pathogens, toxins, and chemicals). All food products must be unadulterated (not bear or contain any poisonous or deleterious substances), be fit for consumption, and not contaminated or decaying, in order to be allowed for consumption in the U.S. At the federal level, there are three agencies involved in the oversight of food and food ingredients safety: the US Department of Agriculture’s Food Safety and Inspection Service (FSIS), the Food and Drug Administration (FDA), and the Environmental Protection Agency (EPA). FSIS ensures the safety of imported meats, poultry, and processed egg products. FDA covers all other products. EPA licenses pesticide products and monitors pesticide residues in products.

The FDA enforces the Federal Food, Drug and Cosmetics Act (FD&C) as well as other laws designed to protect consumer health, welfare, and safety. Under Sec. 801 of FD&C, products are subject to inspection when imported. Imported food products are expected to meet the same standards as domestic products, i.e. they must be pure, wholesome, safe to eat, and produced under sanitary conditions. Food imports must also contain informative and truthful labeling in English.<sup>5</sup> Another important requirement is that since 1997, producers must follow FDA's good agricultural practices (GAP) for the control and management of microbial food safety. Likewise, since 1995 fish products imports must meet hazard analysis and critical control point (HACCP) standards, as must

> 4 For an overview of the US fruit and vegetable protection system, see Jouanjean et al. (2011).

> 5 <u>http://www.fda.gov/ForIndustry/ImportProgram/ImportProgramOverview/default.htm</u>

6
Figure 1: **"Open"** LAC Countries as a Percentage of Total, 1950-1995 <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>CL<br>40<br>3 50^ 0<br>20<br>10<br>0<br>O CN -- CD co CD O C co CD Ce 0 03 CD C OND Y Oc CD (N<br>Di z oI nC) f) o oci (o NN cD r Cr-oo 0 o0 c CO o D cD Cm<br>cci C) C) CT) CD CD) CD C) CD CT ) C () C M CD> C ) (m) CDi C M C.> (D CD CD C)<br>Year<br>Source: Authors' calculations.<br><!-- End of picture text -->
# CONTENS

# TABIE OF CONTENTS

# ABSTRACr

- 1 INTRODJCrION

- 2 BACKOMND STATISTICS ON JAMAICA

# 3 MIERAL EALTH PFROBLEDIS AND NEEDS

- 3 .1 ANEMI

- 3.2 FERILITY

- 3.3 INFECTICUS AND CHRONIC DISEASES

- 3.4 STRESS AND SOA PROBLEM

- 4 INFAT AND C{EID MORBIDITY AND MORTAIY

- 5 TERY BIND IDEAL MATERNAL HEALTH SERVICES

- 6 ALTH BEIEFS AND UTILIZATION OF<sup>FORMALSERVICES</sup>

- 7 EXISTING HEALTH SERVICES

- 7.1 FAMILY PLANNING

- 7.2 PRENATAL CARE

- 7.3 DELIVERY AND OBSTETRIC REFERRAL<sup>SERVICES</sup>

# 7.4 GENERAL SERVICE ISSUES

- 8 GAPS THAT CAN BE FILLED

# 9 DISCJSSION

REFERENCES
Table 4: Demand

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|||WTP_≥_Price<br>(BDM)||P|aid full pric<br>(BDM)|e||Paid full pri<br>(TIOLI)|ce|Paid full<br>(Pooled)|
|Price (000)|-0.033<sup>_∗∗∗_</sup><br>(0.003)|||-0.047<sup>_∗∗∗_</sup><br>(0.003)|||-0.055<sup>_∗∗_</sup><br>(0.003)|<sup>_∗_</sup>||-0.047<sup>_∗∗∗_</sup><br>(0.003)|
|Price = 1,000||-0.000<br>(0.000)|0.000<br>(0.005)||-0.318<sup>_∗∗∗_</sup><br>(0.058)|-0.322<sup>_∗∗∗_</sup><br>(0.058)|||||
|Price = 3,000||-0.023<br>(0.023)|-0.015<br>(0.025)||-0.372<sup>_∗∗∗_</sup><br>(0.074)|-0.362<sup>_∗∗∗_</sup><br>(0.074)|||||
|Price = 5,000||-0.058<sup>_∗∗_</sup><br>(0.023)|-0.051<sup>_∗∗_</sup><br>(0.022)||-0.602<sup>_∗∗∗_</sup><br>(0.049)|-0.596<sup>_∗∗∗_</sup><br>(0.049)||-0.290<sup>_∗∗∗_</sup><br>(0.058)|-0.292<sup>_∗∗∗_</sup><br>(0.063)||
|Price = 10,000||-0.220<sup>_∗∗∗_</sup><br><br>(0.038)|-0.214<sup>_∗∗∗_</sup><br>(0.038)||-0.763<sup>_∗∗∗_</sup><br>(0.039)|-0.768<sup>_∗∗∗_</sup><br>(0.040)||-0.667<sup>_∗∗∗_</sup><br>(0.042)|-0.668<sup>_∗∗∗_</sup><br>(0.049)||
|Price = 15,000||-0.520<sup>_∗∗∗_</sup><br><br>(0.101)|-0.514<sup>_∗∗∗_</sup><br>(0.101)||-0.880<sup>_∗∗∗_</sup><br>(0.065)|-0.886<sup>_∗∗∗_</sup><br>(0.065)||-0.813<sup>_∗∗∗_</sup><br>(0.035)|-0.814<sup>_∗∗∗_</sup><br>(0.042)||
|Price = 20,000||-0.679<sup>_∗∗∗_</sup><br><br>(0.089)|-0.668<sup>_∗∗∗_</sup><br>(0.089)||-0.929<sup>_∗∗∗_</sup><br>(0.049)|-0.923<sup>_∗∗∗_</sup><br>(0.053)|||||
|Credit|||0.005<br>(0.036)|||0.015<br>(0.055)|||0.002<br>(0.050)||
|Discount Frame|||-0.053<br>(0.041)|||-0.021<br>(0.055)|||||
|Credit and Discount Frame|||-0.009<br>(0.037)|||-0.118<sup>_∗∗_</sup><br>(0.053)|||||
|TIOLI * Price||||||||||-0.008<sup>_∗∗_</sup><br>(0.004)|
|TIOLI||||||||||0.192<sup>_∗∗∗_</sup><br>(0.039)|
|Observations|457|457|457|457|457|457|374|374|374|831|
|R-squared|0.275|0.296|0.300|0.272|0.344|0.354|0.347|0.360|0.360|0.306|
|P-val. linear Price eff.||0.00|0.00||0.00|0.00||0.04|0.04||
|P-val. effect of 3 treat.|||0.515|||0.046|||||

OLS regressions of demand measures on price (in 1,000s Jamaican dollars). The omitted category for prices is ”Price = 0”. Each coefficient on ”Price” measures in how much demand is reduced when price increases from 0 to that price. In Columns (1)-(3) the dependent variable is a dummy for willingness to pay, elicited with the BDM mechanism, being above offered price regardless of actual payment. In Columns (3)-(10) the dependent variable is a dummy for having paid the full price offered (equal to 1 if offered price is $0). Columns (1)-(6) present results for the BDM sample, and Columns (7)-(9) for the TIOLI sample. Column (10) presents results from a pooled regression including both samples. **Credit** : respondent was allowed to pay in 3 installments, **Discount Frame** : respondent was presented the price as a discount. Standard errors are robust to heteroscedasticity. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01 33