rafmacalaba/datause-encoder-data huggingface dataset:
- 24 -

Tdble _2:_ Impdct on Worild t'rice vi1li :vexAle Production (1984 USf/Iu)

|Yedr|BaselIne|Naive<br>Fxpectat<br>cii,|Mo<br>Con<br>Expe|del<br>sistent<br>ctations|Pertect<br>Foresight|Model<br>Consistent<br>7.5-25<br>USf/Ib<br>2.5%Limit|odel<br>Consistent<br>7.5-25<br>USt/lb<br>5.0% Limit|
|---|---|---|---|---|---|---|---|
|1985|7.3|7,3<br>(0.0)|7.2|(-0.09)|7.3<br>(-0.03)|7.2<br>(-0.13)|7.1<br>(-0.23)|
|1986|6.1|6.1<br>(-0.02)|6.0|(-0.04)|6.1<br>(0.0)|6.0<br>(-0.09)|5.9**(-0.16)**|
|1987|8.3|8.3<br>_(-0.02)_|8.1|(-0.15)|8.2<br>(-0.08)|8.0<br>(-0.25)|7.8<br>(-0,44)|
|1988|9.0|8.9<br>(-0.09)|8.7|(-0.21)|8.8<br>(-0.1)|8.6<br>(-0.36)|8.3<br>(-0,62)|
|1989|14.5|14.3 (-0.18)|13.4|(-1.09)|i3.5<br>(-1.01)|13.2 (-1.31)|12.3 (-2,17)|
|1990|14.1|13,.(-1.13)|13.0|(-1.13)|13.1 (-1.0)|12.8 (-1,34)|11.9_(-2,22)_|
|1991|11.8|11.0(-0.77)|11.5|(-0.36)|11.5 (-0.28)|11.3 (-0.54)|10.8(-1.01)|
|1992|15.2|14.6(-0.55)|14.6|(-0.59)|14.6 (-0.61)|14.4 (-0.76)|13.7 (-1.49)|
|199w|26.6|24.8 (-1.72)|23.1|(-3.5)|22.8 (-3.79)|23.0<br>(-3.53)|20.1<br>(-6.5)|
|1994|22.4|18.6 (-3.72)|20.2|(-2.11)|20.2<br>(-2.2)|20.2<br>(-2.15)|18,2 (-4.12)|
|1995|13.7|12.4 (-1.33)|14.6|(0.94)|14.9<br>(1.22)|14.4 (0.74)|14.4(0,73)|
|1996|11.9|12.3 (0.39)|13.5|(1.58)|13.6 (1.69)|13.3 (1.34)|13.7 (1.78)|
|1997|13.0|13.8 (0.75)|14.3|(1.27)|14.4 (1.33)|14.0 (0.98)|14,1(1.06)|
|1998|14.0|14.1 (0.08)|14.5|(0.49)|14,7 (0.66)|14.3 (0.24)|13,9 (-0.15)|
|1999|13.6|13.0 (-0.06)|13,8|(0.21)|14,1 (0.5)|13.5 (-0.03)|13,1 (-0,49)|
|2000|12.9|12.3 (-0.54)|13.2|(0.36)|13.5 (0.64)|13.0 (0.14)|12.7 (-0.18)|
|2001|18.6|18.3 (-0.34)|18.4|(-0.22)|18.5 (-o.1)|18.2 (-0.44)|17.0 (-1.56)|
|2002|20.6|18.8 (-1.86)|19.7|(-0.96)|19.6 (-1.07)|19.5 (-1,13)|18.0 (-2.67)|
|2003|17.3|15.2 (-2.06)|17.0|(-0.29)|17.2_(-0.0?)_|16.8 (-0.44)|15.9 (-1.33)|
|2004|15.4|14.6 (-0.85)|15.8|(0.38)|16.3 (0.86)|15.6 (0.16)|15.2<br>(-0.27)|
|Average|14.3|13.6 (-0.7)|14.0|(-0.3)|14.1(-0.2)|13.9 (-0.4)|13.2<br>(-1.1)|
|Coeff. Va|r0.466|0,429|0|.392|0.382|0.397|0.357|
|Average**I**<br>Increade|impactPer1<br>in txports|-0.30|-|0.11|-0,07|-0.16|-0.21|
|Average%<br>Exports|Increase in|14.1||17.1|16.5|19.9|36.2|
|Average<br>E<br>xport<br>D|lasticity<br>ot<br>cmand|_-2.77_|-|8.82|-14.15|-6.35|-4.72|

Note. ( i **iS** ch3'qe _5¢'11 (1984 - **uii** ars, comnodred to the baseline.

Source: ori d **L.** i **i** ., ! ; . Octctier 10, 1990
# PRIVATE INVESTNINT IN MEXICO: AN EDIRICAL ANALYSIS

# Alberto R. Musalem

# Introduction

Mexico's growth rate averaged 6.6Z p.a. between 1950 and 1974. Between 1978 and 1982, the economy grew at no less than an average of 8.72 p.a.; while between 1982 and 1987, Mexico did not grow at all. However, during the 1978-1982 period, the external debt tripled from US$29 to 86 billion. The ratio of external debt to GDP jumped from 28 to 521. This period was brought to a crashing halt during the mid-1982 debt ctisis. Lower investment on the one hand, and restrictive demand management on the other, induced no real growth between 1982 and 1988 and hence a severe decline in per capita income.

The analysis of Mexico s private investment explains some of the country economic performance and its determinants are examined in this paper. The findings suggest that investment responded extremely slowly to changes in the incentive system in Mexico. This result suggests that the adjustmient costs of changing capital to the desired stock were relatively high --possibly due to high uncertainties and risks, and credibility problems induced by macroeconomic instability-- and also, that distortions in the factor and goods markets impaired the mobility of resources. The policy implications of this atudy indicate that to iicrease investment and, therefore resume growth, Mexico would be better off with policies aimed at reducing the cost of adjustment on attaining the desired capital stock and
# **1. Introduction**

Ethiopia has one of the highest child malnutrition rates in the world. A considerable effort to monitor child malnutrition rates over the last two decades shows that, despite some improvements, approximately half of the children under five are still malnourished (Alderman and Christiaensen, 2001). Much of the burden of deaths resulting from malnutrition, estimated to be over half of childhood deaths in developing countries, can be attributed to just mild and moderate undernutrition.<sup>1</sup> Underweight children are particularly vulnerable to increased risk of death from infectious illnesses such as diarrhea and pneumonia (WHO, 2002). For those children that do survive, the impact of chronic malnutrition in the first few years of life are long lasting and can lead to cognitive and physical developmental deficits, higher levels of chronic illness and disability in adult life (resulting in reduced work capacity), as well as adverse pregnancy outcomes (low birth weight).

Several biological and social economic factors contribute to malnutrition. A conceptual framework developed by UNICEF, and extended by Engle, Menon, and Haddad (1997), recognizes three levels of determinants of children’s nutritional status. The immediate determinants of children’s nutritional status are dietary intake and health status. These are in turn influenced by underlying determinants: food security, adequate care for mothers and children, and a proper healthy environment, which includes the availability of safe water, sanitation, health care, and environmental safety. The ability of households to translate resources to achieve food security, care, and a healthy environment are limited by political, economic, cultural, and social factors at the community and national level, which are the basic determinants of children’s nutritional status (Smith and Haddad, 1999).

Previous studies have concentrated on the impacts of several of the underlying determinants, particularly maternal education and access to health care, on malnutrition rates. Maternal education, which is often used as a proxy for the quality of care received by children, is identified as a key determinant of nutritional outcomes. Maternal education can

> 1 Pelletier (1994) is the source of this much quoted statistic. The analysis in the current World Health Report estimates the deaths of 3.7 million young children in 2002 were related to malnutrition. The World Health Organization benchmark considers the proportion of children whose weight for age score falls one standard deviation below the standardized population mean and a woman’s body mass index below 20 to be mildly malnourished.

2
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**Table 8. Piecemeal Sensitivity Analysis–Welfare effects**

|||Parameter value||Hicksi<br>with c|an equivalent var<br>orresponding par|iation<sup>b</sup><br>ameter|
|---|---|---|---|---|---|---|
|Parameter<sup>a</sup>|Lower|Intermediate|Upper|Lower|Intermediate|Upper|
|esubs|0.5|1.25|2.0|4.5|6.7|10.7|
|esub|2.0|3.0|4.0|11.5|6.7|5.7|
|sigmadm|2.0|3.0|4.0|6.5|6.7|6.9|
|esubva|0.70|1.00|1.30|6.6|6.7|6.8|
|esubt|0.0|0.0|0.25|6.7|6.7|7.0|
|esubc|0.5|1.0|1.5|6.2|6.7|7.3|
|etadx|3.0|5.0|7.0|6.5|6.7|6.9|
|etad|5.0|7.5|10.0|6.8|6.7|6.8|
|etaf|10.0|15.0|20.0|5.3|6.7|7.7|
|theta_m(i)||see table below||6.7|6.7|6.8|
|theta_fdi(i)||see table below||4.2|6.7|8.2|

- a   The piecemeal sensitivity analysis employs central values for all parameters (see below) other than the tested parameter and lump sum tax replacement.

b Hicksian equivalent variation as a percent of the value of consumption in the benchmark equilibrium.

**Key: Parameter Value Definitions of the parameter** esubs 1.25 Elasticity of substitution between value-added and business services esub 3.0 Elasticity of substitution between firm varieties in imperfectly competitive sectors sigmadm 3.0 "Armington" elasticity of substitution between imports and domestic goods in CRTS sectors esubva 0.0 Elasticity of substitution between primary factors of production in value added esubt 0.0 Elasticity of substitution in intermediate production between composite Armington aggregate goods esubc 1.0 Elasticity of substitution in consumer demand etadx 5.0 Elasticity of transformation (domestic output versus exports) etad 7.5 Elasticity of Russian service firm supply with respect to price of output etaf 15.0 Elasticity of multinational service firm supply with respect to price of output theta_m(i) varies Share of specialized imports V as a share of value added in multinational firms in sector _i_ in the benchmark equilibrium theta_fdi(i) varies Share of output of service sector _i_ captured by multinationals firms in the benchmark equilibrium

|**Parameter values for:**|low|theta_fdi(i)<br>central|high|theta_m(i)<br>low<br>central|high|
|---|---|---|---|---|---|
|Crude oil & gas|0.40|0.60|0.80|0.07<br>0.10|0.13|
|Communication|0.35|0.55|0.75|0.07<br>0.10|0.13|
|Financial intermediation|0.10|0.20|0.30|0.01<br>0.03|0.05|
|Insurance|0.03|0.06|0.09|0.01<br>0.03|0.05|
|IT services|0.01|0.05|0.10|0.07<br>0.10|0.13|
|Land and pipeline transport|0.05|0.15|0.25|0.03<br>0.05|0.07|
|Water transport|0.10|0.15|0.20|0.01<br>0.03|0.05|
|Airtransport|0.10|0.25|0.50|0.10<br>0.125|0.15|

27
Moreover, only 11 percent of the youth had their workbook signed by the employer, that is, contributed to pension and other social benefits and received at least a minimum salary in 2003.<sup>15</sup> Unemployment rates are high among the low and unskilled segment of Brazilians. The youth are particularly hampered by the lack of jobs in poor neighborhoods. Even skilled young people have very few job opportunities. In Fortaleza, as elsewhere in Brazil, youth unemployment rate is much higher than adult unemployment. In the poor neighborhoods, 39 percent of the youth declared themselves unemployed compared to 28 percent of the adult population. Moreover, in many households, not a single adult has a job. Hence, there is no income arriving on a regular basis to sustain the household.

Given that very few young people have a decent level of formal education, on-the-job or other forms of training may enhance their opportunities. Data reveal that only 38 percent of the youth have obtained some kind of training, although 87 percent indicate that training would enhance their opportunities obtaining a job.

Finally, anecdotal evidence indicates that the more skilled youth group, for example the group that has completed secondary education, does not search for work and look for opportunities outside their neighborhood and nearby area because they lack money for transportation. Some of the young dwellers mentioned that a simple bus ticket could solve some of the youth unemployment problems they face.

# **4. Conclusion**

In low-income areas in Brazil, similar to the rest of the developing countries, youth from poor households face a considerable number of risks that their peers from middle class or rich households do to a far less extent. This paper presents a survey instrument that sheds light on some of the risk factors that poor youth face and the intergenerational transfer of these risks, e.g.

> 15 In 2003, one minimum salary was R$240.00 in Fortaleza (Source: Diario O Povo).

21
**Table 19. Contract Enforcement and Commercial Disputes in Benin and Malawi**

|||Benin||M|alawi||
|---|---|---|---|---|---|---|
||Mean|S.d.|N|Mean|S.d.|N|
|**Problems with suppliers (% of traders):**|||||||
|Bad quality|3%||663|41%||736|
|Disagreement over measuring|7%||663|35%||733|
|Renegotiate price|12%||662|25%||731|
|Cases of bad quality per year|0.3|2.8|663|63.9|340.9|736|
|Cases of measuring dispute per year|2.3|12.4|662|99.5|410.9|733|
|<br>Cases of price renegotiation per year|1.6|6.0|657|45.7|217.5|731|
|**Orders with Suppliers**|||||||
|<br>% Traders who place orders|6%||658|32%||735|
|% Total purchases on order|1.2|6.4|655|6.3|12.7|733|
|Number suppliers from whom order|0.0|0.4|656|0.7|3.9|732|
|% Traders with late delivery problems|18%||50|41%||296|
|% Traders with problerns of partial delivery|20%||50|31%||296|
|% Traders with problems of no delivery|16%||50|27%||295|
|Cases of late delivery per year|_5.0_|20.8|50|37.5|197.5|296|
|Cases of partial delivery per year|3.1|9.3|50|19.0|57.7|296|
|<br>Cases of no delivery per year|0.3|0.8|50|31.3|148.0|294|
|**Problems with clients (% of traders):**|||||||
|Late payment|24%||660|42%||734|
|Partial payment|21%||660|34%||733|
|No payment|20%||660|25%||733|
|Renegotiate price|_5%_||658|20%||731|
|Cases of late paymentper year|10.8|34.1|656|15.2|36.5|734|
|Cases of partial payment per year|9.8|62.2|656|14.9|71.8|733|
|Cases of no payment per year|0.9|3.4|659|7.1|62.4|732|
|Cases of price renegotiation per year|0.4|2.1|655|116.0|506.7|730|
|Number clients who order|0.1|0.6|662|_0.5_|1.6|722|
|Number of sales|3102|4433|198|7898|9140|734|
|% Traders for whom others will find out if|||||||
|they encounter non payment problems|_53%_||619|70%||734|
|Number of employees dealing with debt|||||||
|<br>collection|1.1|1.0|641|0.7|0.6|682|

35
- (b) **Geolocate reports.** We extract all landmarks and roads that have an exact match between the gazetteer and the tweet. In Figure 1, “kianda” and “abc way” match several entries in the gazetteer. We extract misspelled matches based on Levenshtein distance varied by length of the n-gram, matches based on the word following a preposition, and matches based on intersections between multiple roads.

- Existing geoparsers extract all possible location references without identifying the unique location that makes the data useful. We resolve two technical challenges to find the location of the crash:

- i. When multiple locations are mentioned in the tweets, we use prepositions to sort locations into tiers, based on the probability of a location being correct after a particular preposition. For example, in Figure 1, “next to” is ranked as tier 1 while “toward” is ranked as tier 6, resulting in the correct geolocation for the crash at “kianda” and not “abc place”.

- ii. When a name refers to multiple landmarks, we adopt a toponym resolution approach. In Figure 1, more than 6 landmarks across Nairobi have “kianda” in the name. We resolve the toponym in three steps: (1) we search for landmarks that are within 500 m of a road if it is mentioned, (2) we use the centroid of the clustered location if 90% or more of the landmarks are in a 500 m radius, or (3) we rank the landmarks by the probability of being correct using the landmark type in the truth data (see SI for statistics on location type). In the example, we use “Waiyaki Way” to narrow down the landmarks “kianda” in a 500 m radius (from 6 to 3) and then use the centroid as the crash location.

We define a correct geoparse as one located within 500 m of the coordinates in the truth dataset. As a benchmark, we compare our algorithm to the Location Name Extraction tool (LNEx), which was shown to have better accuracy than other geoparsers <mark>[40]</mark> . As LNEx and other geoparsers are not designed to extract one unique location from text [26,40,47], we first judge performance by examining whether any location references are near the true coordinates. Next, we define the crash location as determined by LNEx to be the centroid of all locations it finds in the tweet and compare this with the unique location identified by our algorithm.

- (c) **Identify unique reports.** To avoid over-counting, we develop a clustering algorithm that uses time and location to identify which tweets refer to the same crash. In Figure 1, five tweets report a crash within two hours of each other, referencing different landmarks that are all close together. To develop reasonable parameters for clustering, we manually identify tweets that report the same crash in the truth dataset based on the time, location and crash characteristics. The 4,192 crash reports are clustered into 2,648 unique crashes. For unique crash clusters, 97% of tweets reported

6
_Why Infrastructure Financing Facilities Often Fall Short of Their Objectives_ _3_

|Country<br>(Status)|Sector|Ownership Structurel<br>Legal Structure|Management<br>Structure|Instrument|Funding Base|
|---|---|---|---|---|---|
|Sri Lanka<br>(recently canceled)|*<br>Multi Sectoral|*<br>100% public<br>*<br>Set up as a separate<br>entity|*<br>Public|*<br>Mezzanine<br>financing|*<br>Proceeds from WB<br>US$70 million<br>loan and KfW loan<br>of US$14 million|
|Thailand<br>(operational since 1994)|*<br>Cross-Sectional|*<br>Public and private|*<br>Private|*<br>Loan<br>guarantee;<br>bond<br>insurance|*<br>US$150 million<br>from government;<br>USAID, domestic<br>and foreign<br>financial<br>institutions|

_Source:_ World Bank reports.

**Table 2: Overview of Other Government Sponsored Financing Facilities**

|Country<br>(Status)|Sector|**Ownership Structure/**<br>**LeIgal**<br>**Structure**|**Management**<br>**Structure**|Instrument|**Funding Base**|
|---|---|---|---|---|---|
|Argentina<br>(canceled)|*<br>Financial Sector|*<br>100% public.<br>*<br>Set up as a shell<br>company|*<br>Private|*<br>Refinancing<br>commitments;<br>.|*<br>Proceeds of WB<br>US$500 million<br>loan;|
|Bosnia<br>(operational since<br>December 1996)|*<br>Pre-export|*<br>100% public;<br>*<br>Set up as a separate<br>corporate entity|*<br>Public|*<br>Pre-export<br>partial<br>risk<br>guarantee;|*<br>Proceeds of WB<br>US$10 million<br>loan; plus US$5<br>million from<br>Switzerland.|
|Moldova<br>(canceled)|*<br>Pre-Export|*<br>100% public.<br>*<br>Set up as a shel<br>company|*<br>Public|*<br>Pre-export<br>political risk<br>guarantee.|*<br>WB contingent<br>line of credit of<br>US$30 million.|

_Source:_ World Bank reports.

4. This paper attempts to take stock of some of the cross country experiences with IFFs via desktop review, evaluates their preliminary success and attempts to draw some initial lessons. It provides examples of infrastructure funds/facilities with varying institutional arrangements, sources of funding, sector(s) targeted, instruments offered and policy orientations. The success of the facility will be determined by assessing whether the facility has (i) minimized and leveraged government's involvement in the financing of infrastructure projects; (ii) increased the efficiency of sector development through the provision of a transparent and systematic review of eligible projects; (iii) allocated risks to those parties best able to manage them (i. e. distance the government from commercial risks); and (iv) provided a transitional mechanism to facilitate the closing of a number of benchmark transactions.
Figure B2: Individual Clinic Treatment Effects for 1<sup>st</sup> Prenatal Visit before Week 13 of Pregnancy Notes: This figure plots individual clinic treatment effects for the outcome of first prenatal visit before week 13. We run OLS regression of the outcome comparing each clinic assigned to the treatment group to all clinics assigned to the control group pooling the intervention period and post intervention period I (hence May 2010‐ March 2012). One treatment clinic is not included because of its insufficient sample size. This clinic corresponds to one of the two that did not take up treatment. The triangle symbol refers to the clinic that was assigned to treatment but did not take up the treatment. The x‐axis is sorted from the lowest to the highest clinic‐specific impact. The dashed blue line is the intent‐to‐treat effect calculated by pooling the intervention and the first post intervention period. The vertical lines are 95% confidence intervals constructed using standard errors obtained from the Wild bootstrap procedure.

47
loser in the aftermath of the reform may add to distributional conflicts in determining lags of implementation. Not only outward-oriented reforms favour workers and entrepreneurs already active in exportable sectors, but also attract some of them previously employed in protected sectors, whose firms were producing for the domestic markets before the opening up. Besides that. the new relative price structure will enable completely new exportable sectors to grow and develop. Trade liberalization brought about drastic changes in the volume and compasition of trade in South Korea (mid-1960s and early 1980s), Chile (second half of Seventies) and Turkey (early 1980s). World Bank (1989) also quotes the relatively unknown, but nonetheless relevant, Colombia's experience with exports of cut flowers, which rapidly grew from nothing, in the late Sixties, to more than $200 million ten years later.

All in all, it seems unreasonable to expect such dramatic changes whose beneficiaries are unidentified in advance to be both perfectly anticipated and supported by all individuals. The most vigorous opposition to reform proposals sometimes arises from inside the Government bodies: e.g. Taiwanese Government officials distrusted the outward-oriented reforms proposed by local economists<sup>2</sup> , while a proposal to reduce protection for an automobile assembly firm was successfully resisted by the Government agency holding shares in the firm and by the head of a trade regulatory agency tied to the firm<sup>3</sup> .

Finally, implementation lags may be attributed to the short time horizon of the electorate or the Government, which causes short-term adjustment costs to be valued more than the forthcoming benefits of reforms.

Once started, reforms do not always carry through the end. The Philippines

- 2 Fernandez and Rodrik (1990).

- 3 World Bank (1990, p.49).

2