rafmacalaba/datause-encoder-data huggingface dataset:
**An analysis of export destinations shows that Argentina has been able to diversify export destinations over the 1997-2007 period.** The five top export destinations have not changed over the last decade, despite a change in their ranking (Table 4). In fact the US and Chile have slightly lost importance over the last decade—even though their share in the Argentinean market has not changed—due to a rise of exports (largely agriculture-related)  to China, whose share in Argentina’s exports has tripled from 3 to 9 percent from 1997 to 2007. However, the following five destinations have radically changed, due to the emergence of countries like Mexico, South Africa, Peru and India as new important destinations. Argentina has signed trade agreements with Mexico, India and members of the Southern African Customs Union. Moreover, as a member of Mercosur, it has signed preferential trade agreements with Bolivia, Chile and Peru.

**Table 4. Argentina’s Top Destinations** **<u>and Share in World Exports in 2007 and 1997 SITC3</u>**

|**Partner Name**|**Flow Name**|**2007**|**Share**|**Partner Name**|**Flow Name**|**1997**|**Share**|
|---|---|---|---|---|---|---|---|
|Mercosur|Gross Exports|12,469,594|22|Mercosur|Gross Exports|9,600,503|36|
|EU25|Gross Exports|9,741,452|17|EU25|Gross Exports|4,136,773|16|
|China|Gross Exports|5,166,609|9|United States|Gross Exports|2,204,026|8|
|United States|Gross Exports|4,344,407|8|Chile|Gross Exports|1,931,584|7|
|Chile|Gross Exports|4,176,201|7|China|Gross Exports|871,003|3|
|Mexico|Gross Exports|1,433,739|3|Iran|Gross Exports|659,103|2|
|Venezuela|Gross Exports|1,176,183|2|Japan|Gross Exports|554,427|2|
|South Africa|Gross Exports|999,362|2|Bolivia|Gross Exports|464,214|2|
|Peru|Gross Exports|957,848|2|Egypt|Gross Exports|459,097|2|
|India|Gross Exports|858,817|2|Venezuela|Gross Exports|315,320|1|

_Source:_ COMTRADE and own calculations

**Argentina has been successful in boosting trade relationships with its regional partners, but it should improve its trade relationship with higher GDP export destinations such as the EU and the US** . The increase in trade intensity between Argentina and Chile over the 1998-2003 period is noticeable, but it was followed by a decrease over the last years (Figure 12). The trade relationships with Mercosur—and more specifically Brazil—have been stable over the last decade, with a shock registered during the crisis. The low band in the graph includes the countries with which Argentina has smaller bilateral trade flows, including important markets like the EU, the US, India and China.

**Figure 12. Trade Intensity Index of Argentina with Main Export Partners**

<!-- Start of picture text --> Chile<br>50<br>China<br>40 EU25<br>30 India<br>Mercosur<br>20<br>Mexico<br>10<br>Peru<br>0 USA<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Brazil<br><!-- End of picture text --> _Source:_ COMTRADE and own calculations.

11
for the variables with annual data and in Table 3 for the variables with quarterly data.

Using these variables, the following equations are estimated.

# **Annual ZEuationh for the reed Grain Supply Block**

The feed grain supply block consists of a logistic equation that explains program participation, an equation that explains nonparticipating feed grain acreage and variation from program acreage (base acreage less minimum diversion requirements) on participating farms, an equation that represents feed grain yield, and an equation that explains how per acre costs of feed grain production respond to feed grain prices. The participation equation follows (6) with a dummy variable added to represent years when diversion was not required to receive program benefits. The acreage equation follows (5) with soybeans as the competing crop. The yield equation is a simple time trend modified to represent response of yields to diversion which presiumably removes poorer acreage from production first. The cost equation specifies cost of production as a function of output price following the arguments of Gardner whereby the prices of inputs are bid up to exhaust rents. Estimated Equation for LCOMPFGA LCOMPFGA = .6884 + .02161 PROFFGC - .02386 PROFFGN - 7.364 NOPROG (.5078) (.008614) (.008234) (.4601) R<sup>2</sup> -. 956, R2 .949, DW = 1.23, _ry_ - .8572, Data - 1962:1 - 1987:1 Estimated Equation for ACGSN ACGSN - 91.46 NON + .01898 NONPROFC - .04626 NONPROFS + .1281 NONLAGA (30.37) (.05186) (.06921) (.2842) -.003978 BVDPC (.0008560) R<sup>2</sup> _ .978, R2 - .973, DW - 1.47, **ay** - 6.104, Data - 19,62:1 - 1987:1 Estimated Equation for YLDCGS YLDCGS - -68.03 + 1.856 YEAR + .2320 DIV 16
Figure 3: Unemployment Risks in Brazil <!-- Start of picture text --> 7<br>6 Informal W (3.8%)<br>5<br>Informal SE (2.85%)<br>4<br>3<br>Informal E (1.05%)<br>2<br>1<br>Formal W (1.11%)<br>0<br>Apr-02Aug-02Dec-02Apr-03Aug-03Dec-03Apr-04Aug-04Dec-04Apr-05Aug-05Dec-05Apr-06Aug-06Dec-06Apr-07<br>Probability of Unemployment (%).<br><!-- End of picture text --> _Source:_ Authors’ calculations based on PME.

# 3  The dynamic stochastic behavioral model

We are interested in formalizing the effect of the social insurance system on three economic decisions: (i) the level of savings; (ii) efforts to preserve/find jobs in the “formal sector” (defined by access to social security); and (iii) retirement decisions. We use as our starting point the standard life-cycle utility maximization framework and introduce uncertainty in employment status and life expectancy. Clearly, the assumptions of this framework are controversial; individuals are usually not fully rational and do not have perfect foresight. If they were, there would not be a need for a social security system in the first place. The model/framework is nonetheless useful as a benchmark to understand the _direction of change_ in certain behaviors as a response to change in the rules of the social security system. Moreover, in our application, we explore a large range of possible behavioral responses to policy changes. Behaviors that are more like to have generated actual observations receive a higher weight. In essence, we use the model as a data generation mechanism and from this point of view it is not very different from linear single-equation econometric models.

The dynamic stochastic problem that representative individuals are assumed to solve is formally given by: 9
**Table 3:** Baseline Characteristics of PSI-PMI Pilot Senior Secondary Schools and Other Senior Secondary Schools after Matching, 2014

|Variable|PS<br>s|I-PMI pil<br>econdary|ot senior<br>schools|M<br>seco|atched se<br>ndary sc|nior<br>hools|Diff|P-value|
|---|---|---|---|---|---|---|---|---|
||_N_|_Mean_|_Std. dev._|_N_|_Mean_|_Std. dev._|||
|**_School hardware characteristics_**|||||||||
|Good desks (%)|16|0.92|0.34|16|0.93|0.55|-0.85|0.90|
|Has library (%)|16|1.00|0.00|16|0.94|0.06|0.06|0.33|
|Has computer lab (%)|16|1.00|0.00|16|0.94|0.06|0.06|0.33|
|Has water tap (%)|16|0.88|0.09|16|0.94|0.06|-0.06|0.56|
|Has electricity (%)|16|0.88|0.09|16|1.00|0.00|-0.12|0.15|
|**_School software characteristics_**|||||||||
|Total enrollment|16|868.38|168.06|16|677.69|98.69|190.69|0.34|
|Student-teacher ratio|16|34.53|2.87|16|32.18|3.85|2.35|0.63|
|Qualified teachers (%)|16|0.97|0.55|16|0.97|0.14|0.39|0.84|
|Female teachers (%)|16|0.92|0.19|16|0.12|0.31|-2.52|0.50|
|**_Students’ performance_**|||||||||
|_Mathematics_|||||||||
|Student passing the test (score 1-8) (%)|16|0.16|0.05|16|0.15|0.01|0.01|0.89|
|Students with excellent or good scores (1-3) (%)|16|0.01|0.01|16|0.01|0.00|0.01|0.26|
|Students earning credit (score 4-6) (%)|16|0.05|0.02|16|0.04|0.01|0.01|0.56|
|Students with passing scores (7-8) (%)|16|0.09|0.03|16|0.10|0.04|-0.01|0.74|
|_English_|||||||||
|Students passing the test (score 1-8) (%)|16|0.37|0.06|16|0.40|0.05|-0.03|0.70|
|Students with excellent or good scores (1-3) (%)|16|0.01|0.003|16|0.00|0.00|0.003|0.39|
|Students earning credit (score 4-6) (%)|16|0.11|0.03|16|0.10|0.03|0.01|0.96|
|Students with passing scores (7-8) (%)|16|0.26|0.04|16|0.30|0.03|-0.04|0.45|
|Students taking science subjects in the WASSCE<br>2014(%)|16|0.34|0.08|16|0.40|0.10|-0.06|0.64|

_Note:_ The WASSCE is graded on a score of 1 to 9, with lower values being better. The P-value is based on using the t-test to examine whether these two groups (program versus non-program schools) have equal means for the WASSCE scores, by clustering at the school level. Robust standard errors are presented. PSI-PMI = Progressive Science Initiative and Progressive Mathematics Initiative; WASSCE = West African Secondary School Certificate Examination.

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2) was actively looking for work. Early studies of developing countries by Turnham (1971) and Squire (1981) reported unemployment rates on the order of 5-10% in most of them. Even today, the ILO (2007) reports that just 15% of low earners in the world are unemployed; the other 85% are employed but fall below the low-earnings line.

A generally-accepted explanation for why the unemployment rate is so low in most developing countries is the so-called “luxury unemployment hypothesis.” On this view, only supplementary earners in well-to-do families can afford to be doing no work whatsoever for very long. On the other hand, potential workers in poor families are compelled by economic necessity to take up whatever form of wage employment or selfemployment they can get.

The preceding explanation is so widely accepted that when high unemployment is found in a developing country, researchers are sent scrambling. Such is the case in South Africa. Using the ILO’s definition, South Africa’s unemployment rate is 26%. The ILO definition, however, excludes those people who were not working, were not actively looking for work, but say they would take a job if one were offered. Using this so-called “broad definition of unemployment,” South African unemployment reaches 41%, clearly a crisis level for the society and for the government.

What is going on? Either the data are wrong or the theoretical models are wrong. The data could be wrong, because South Africans are working by the ILO definition but not according to their own self-conception.<sup>4</sup> Alternatively, the data could be right and the models wrong. A leading South African labor economist, Haroon Bhorat, calls this “the international visitor disbelief” position. Kingdon and Knight (2004) and Cichello et al. (2006), among others, have analyzed the barriers to self-employment in South Africa and found significant costs to entering the informal sector. Although costly entry exists in

> 4 On a recent visit to South Africa, I talked to many people about their employment and earnings statuses and heard similar stories from many of them. One was from a middle-aged man who said both he and his wife were unemployed. Given that they had no children or elderly in their family, they received no transfer payments from the government. If they weren’t working and not receiving government transfers, how could they be living? Bit by bit, we learned that he received money for leading the local church and doing traditional healing and that his wife received money for selling handicrafts at the city market. Yet, he said, they were unemployed. What would it take for you to be employed, we asked? His answer: You are employed when you get paid every Friday by the same employer.

44
Natural Disasters, Self-Insurance and Human Capital Investment: Evidence from Bangladesh, Ethiopia and Malawi<sup>1</sup> Futoshi Yamauchi, Yisehac Yohannes and Agnes Quisumbing<sup>2</sup>

> 1 This paper was prepared as a background paper to the joint World Bank - UN Assessment of the Economics of Disaster Risk Reduction. Funding from the Global Facility for Disaster Reduction and Recovery is gratefully acknowledged. We would like to thank Harold Alderman, Apurva Sanghi, Alejandro De la Fuente and seminar participants at the World Bank for their useful comments. Special thanks to Wahid Quabili¥ for assistance on Bangladesh data. We are responsible for any remaining errors.

> 2 Yamauchi (corresponding author), Yohannes, Quisumbing: International Food Policy Research Institute, 2033 K Street, NW, Washington DC 20006. Email: f.yamauchi@cgiar.org, Phone: 202-862-5665.
enterprise census compiled by the national statistical agency ( _Instituto Nacional de Estatistica_ ), stratifying by region (Cabo Delgado, Nampula, Zambézia, Tete, Manica, Sofala and Greater Maputo), firm size (5-19, 20-99, 100+) and industry (mining and quarrying, food and beverages, metals/machinery/computers/electronics, other manufacturing, tourism, retail and other services).<sup>5</sup>

# _Measuring management and organizational practices_

In addition to the regular Enterprise Survey questions (see Appendix 1 for full set of modules used in the Enterprise Surveys), entrepreneurs were asked several questions on management practices, based on the _Management and Organizational Practices Survey (MOPS)_ . The MOPS, which was designed by the US Census Bureau to be used as part of the Annual Survey of Manufacturers (ASM), has been used in a number of developing countries to measure business practices, including outside of manufacturing.

The MOPS is a closed question questionnaire consisting of 16 questions (Bloom et al., forthcoming). The survey scores firms across four broad areas: operations (e.g. improvement of production process), monitoring (e.g. measurement and tracking within the production process), target setting and people management (e.g. the use of incentives, promotion and reward strategies). Examples of the closed questions are “how many key performance indicators were monitored at this establishment?” (with answers varying between “1-2 key indicators” and “10 or more indicators”) and “who was aware of the production targets at this establishment?” (with answers varying between “only senior managers” and “all managers and most production workers”). For the Mozambique survey, a subset of 11 MOPS-based questions were included (see Table 1).

The management scores of the MOPS have been shown to be strongly correlated with scores given by the _World Management Survey (WMS)_ , developed by Bloom & Van Reenen (2007), which has been the largest effort around the world to collect data on management practices. It relies on double-blind phone interviews conducted by a trained interviewer who scores a firm based on a set of open questions (e.g., “tell us how you monitor your production process”). The advantage of the double-blind approach is that firm managers are unaware that their management practices are scored, potentially reducing bias due to social desirability. The disadvantage of the WMS is that it is more difficult to incorporate in existing data collection efforts.

> 5 See enterprisesurveys.org for a detailed description of the sampling methodology.

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To investigate the dynamics of migrants’ wages, the study of reference is Borjas, Bronars and Trejo (1992) who focus on interstate movements in the United States. After constructing a migrant dummy for individuals who changed states during the period considered, the authors regress the logarithm of workers’ wages as follows: where _wi_ is the wage, _Xi_ is a vector of personal characteristics, _Mi_ is a dummy variable for migrants, _Ei_ is the duration of labor market experience, _Ti_ is years since migration and ε _i_ is a random error term. In a regression such as (3.6), one should expect to find _δ0_ < 0, measuring the initial disadvantage of migrants (since migrants can only partially transfer their region or firm-specific knowledge when migrating), and _δ1_ > 0, capturing the pace at which migrants catch-up with non-migrants. _δ2_ < 0 expresses a slowing-down in the convergence.<sup>25</sup> The authors explicitly do not include any job characteristics in (3.6) because they argue that the process of assimilation precisely involves job mobility: they want to compare the earnings of migrants and non-migrants for the same type of jobs, but to account for the better job matches of migrants over time.<sup>26</sup> As hypothesized, Borjas _et al._ show that internal migrants initially earn less than natives but experience a higher growth rate so that the wage differential disappears after a few years. The initial disadvantage of migrants increases with the distance migrated, reflecting an increasing mismatch in job-specific experience.

Other studies that try to open up the black box of post-migration labor-market assimilation and to identify the key factors of success are scarce. Friedberg (2000) replicates Borjas _et al._ ’s methodology to study the assimilation of migrants to Israel. She shows that human capital acquired abroad is only partially transferable, and that more educated migrants have a faster assimilation rate, which suggests that immigrants could

> 25 As otherwise standard in wage equations, one should also expect to observe γ1>0 and γ2<0 reflecting a positive but concave effect of experience on wages. The vector β measures how the market values personal characteristics.

> 26 In a situation where it is believed that the catching-up process occurs through pay raises, it would be necessary to control for job characteristics. In developing countries, given the mixed evidence on labormarket segmentation, it is not clear what pattern of labor-market assimilation prevails.

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**Table 4.1.2 : Variables in the AD-OTH-Master Spreadsheets**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**A.**|**AD_CTY_NAME**|Country name of the user initiating the antidumping investigation|
|**B.**|**CASE_ID**|Case identifier used to link observations across different elements of the database<br>[generated by us]|
|**C.**|**CASE_REPCODE**|Related antidumping investigations of multiple countries at the same time over<br>the same product|
|**D.**|**INV_CTY_NAME**|Country name of the foreign country under investigation|
|**E.**|**INV_CTY_CODE**|3-letter UN country code of the foreign country under investigation (see table 2.2)|
|**F.**|**PRODUCT**|Description of the product under investigation|
|**G.**|**INIT_DATE**|Date of initiation of the investigation (MM/DD/YEAR)|

# **5 Countervailing Duty-Using Countries in the Database**

The data for each CVD user country is contained in a Microsoft Excel 2003 Workbook file (posted at ~ <u>http://www.brandeis.edu/ cbown/global_ad/</u> ), within which are typically two spreadsheets of data: 1) a master spreadsheet with basic antidumping case investigation information; and 2) a spreadsheet containing information on the Harmonized System (HS) products under investigation. There are only three countries with detailed countervailing duty data in the database thus far:

**Table 5.1 : Antidumping User Countries with Detailed Data in the Database**

|**Country Code**<br>**(3-letter CTY)**|**Country**<br>**Name**|**Excel Workbook File**<br>**Name**|**Spreadsheet Names within**<br>**Country Workbook**|**Available**<br>**Years of**<br>**Data**|**Current**<br>**Version**<br>**(last updated)**|
|---|---|---|---|---|---|
|CAN|Canada|CVD-CAN-v1.0.xls|CVD-CAN-Master ,<br>CVD-CAN-Products|1985-2005|v1.0<br>(8/2005)|
|MEX|Mexico|CVD-MEX-v1.0.xls|CVD-MEX-Master ,<br>CVD-MEX-Products|1990-2003|v1.0<br>(8/2005)|
|USA|United<br>States|CVD-USA-v1.0.xls|CVD-USA-Master ,<br>CVD-USA-Products|1980-2005|v1.0<br>(8/2005)|

38
24 The EC's own analysis of the effects of EC-92<sup>(EEC, 1988b) emphasises the</sup> increase in total output, resulting from traditional<sup>trade creation and</sup> consequent efficiency and income effects within the<sup>EC itself. Later, when the</sup> effects on the rest of the world became an<sup>issue, this vas translated into</sup> increased imports. But of course there are also going<sup>to be lower prices within</sup> the EC, leading to the trade creation vs. trade diversion<sup>trade-off. If the</sup> relative costs between EC and other suppliers<sup>change, then applying a</sup> conventional income elasticity, without allowing<sup>for an unfavorable relative</sup> price effect on outside suppliers, is not correct.<sup>Further, a higher estimate</sup> for the income effect (or finding a dynamic effect<sup>on the growth rate, not just</sup> the level effect) causing greater external trade creation,<sup>can occur only if the</sup> initial trade effects are also positive.

# 4 1 Trade Creation Outside the EC

If the EC-92 program has any impact, it will be to raise<sup>the level of EC</sup> income. This was calculated by the EC at about 5% over<sup>5 years.6 That implies</sup> a small but plausible figure of roughly 1% on GDP each<sup>year. Much the same</sup> estimates have been put forward for the gains to be expected<sup>from explicit policy</sup> coordination in Europe. [In that literature such gains were, by<sup>common consent,</sup> regarded as worthwhile but small]. Any conventional<sup>import elasticity can be</sup> applied to this. If developing countries are assumed to<sup>be balance-of-payments</sup> or demand constrained, a reasonable assumption at the moment, an upper limit<sup>for</sup> their gains can be obtained. The results tend to be<sup>small because the EC as a</sup> block is a small part of their markets, and tends<sup>to import relatively low</sup> income-elasticity goods. Income effects are higher for the more<sup>advanced</sup> exporters of manufacturers. Given the low shares<sup>and import elasticities in the</sup> EC and the rather high ones of some developing countries<sup>the relationship is</sup> likely to be less than half a point in income growth for every<sup>extra point on EC</sup> growth, and substantially below that for the poorest.
Using panel data allows considering various complexities in assessing the impact on economic growth, chief among them controlling for the effect of other growth determinants. In the second step, the paper evaluates how expenditures in infrastructure translate into improvements in the level of infrastructure. This is a limited and direct exercise for which only Egypt-specific data are used. Focusing on Egypt is both a necessity (given that comparable data do not exist for a sufficiently large group of countries) and an advantage (given that the expenditure-improvement connection may vary significantly across countries). The paper takes great care to make the two chains in the estimation process consistent with each other. For instance, the choice of infrastructure measures used in the growth analysis is driven by the existing data on infrastructure expenditures in Egypt. Since the latter are historically presented for only two categories, power generation and transport/telecommunication, the paper constructs indices of the level of infrastructure aggregated at exactly those categories.

Then, using the estimates just described, the paper generates some projections for the likely impact of further increases in infrastructure expenditures on the rate of economic growth of Egypt. It considers a couple of scenarios, including a moderate and a strong increase in expenditures. In assessing the growth impact of higher infrastructure investment, the paper considers the importance of evaluating the fiscal burden that these expenditure increases may entail.

The rest of the paper is structured as follows. Section II provides a review of the literature on, first, the connection between infrastructure and economic growth across the world, and, second, related issues that are especially important to Egypt. Section III analyzes the situation of infrastructure in Egypt, introducing indicators that quantify and place the Egyptian situation in an international context. Section IV presents some new results on the relationship between infrastructure measures and economic growth. Section V reviews the trends in infrastructure expenditures in the country, analyzes how they are related to improvements in infrastructure measures, and finally estimates the economic growth impact of further infrastructure improvements in Egypt **.** Section VI presents a summary of the paper and offers some concluding remarks.<sup>1</sup>

> 1 The appendices provide information on sources of data used in the paper, present additional cross-country comparisons, describe details on econometric methodologies, and present additional regression analysis.

3