rafmacalaba/datause-encoder-data huggingface dataset:
performance in discovery activity, with positive values indicating that the frequency of economic discovery is higher than expected, given a particular population and income level.

We perform t-tests on the means of these standardized residuals, grouped by region. Rejecting the hypothesis that the mean is greater than or equal to zero suggests underperformance, and rejecting the hypothesis that the mean is less than or equal to zero suggests over-performance. Note that this is over- and under-performance relative to conditional world averages, not to an ‘optimal’ level of discovery activity. Determining a theoretical optimal frequency of discovery is outside the scope of this paper. The results of these tests, as well as the relevant significance levels, are reported in Table 5.

**<u>Table 5: Over- and Under-Performance in Discoveries by Region</u>**

||**Latin**<br>**America &**<br>**Caribbean**|**Eastern**<br>**Europe and**<br>**Central Asia**|**Sub-Saharan**<br>**Africa**|**Middle East**<br>**& North**<br>**Africa**|**South Asia**|**East Asia &**<br>**the Pacific**|
|---|---|---|---|---|---|---|
|**HS 4-**<br>**Digit**<br>**1990s**|Over-perform<br>15% level|Over-perform<br>10% level|Under-perform<br>20% level||||
|**HS 6-**<br>**Digit**<br>**1990s**||Over-perform<br>5% level|Under-perform<br>5% level|||Over-perform<br>20% level|
|**SITC**<br>**1990s**||Over-perform<br>15%level|Under-perform<br>5%level||||
|**SITC**<br>**1980s**|Under-perform<br>1%level|Over-perform<br>5%level|Over-perform<br>1%level|Under-perform<br>1%level|Over-perform<br>1%level|Over-perform<br>1%level|
|**SITC**<br>**1970s**|Under-perform<br>1% level|||Under-perform<br>1% level|Over-perform<br>5% level|Over-perform<br>1% level|

Note: Blank indicates no conclusion can be drawn, either because no statistically significant relationship, or because the sample does not include at least three countries from the region. Source: Author’s calculations On the whole, the evidence suggests that based on their populations GDPs, there was under-performance in discovery activity in Africa during the 1990s, and over-performance in Eastern Europe and Central Asia. In addition, contrary to their over-performance during the 25
9 There are two main problems with using the DHS survey data. The countries are only a small, and not necessarily representative, sample of all the countries of the region, accounting for less than 20 percent of the region's birth in 1985. The in-country samples are also small, with the numbers of reported births in the five years before the survey varying from about 3,500 to 7,500, with smaller numbers for earlier five-year periods. The small size of the sample limits the amount of disaggregation that is feasible. Despite these drawbacks, the DHS data provide the only period-specific measures of child mortality covering a substantial proportion of the 1980s.

# CHILD MORTALITY TRENDS IN-SUB-SAHARAN AFRICA IN THE 1980S

Hill and Pebley (1989) have suggested that the pace of decline in child mortality in the early 1980s was roughly similar to that in the 1960s and in the 1970s, showing nc clear pattern of a slowdown during the recession of the 1980s. The DHS data for Sub-Saharan Africa (SSA), not all available to Hill and Pebley, indicate a wide variation in experience among the countries of the region. Figure 1 shows the probabilities of dying by age one (the infant mortality rate) and by age five for the 10 SSA countries covered by the DHS. It also gives comparable measures from the earlier World Fertility Survey (WFS) program around 1980 for countries that had both surveys. Several countries show a rapid decline in child mortality throughout the period, with little difference between the late 1970s and the early 1980s (Mali, Liberia, Senegal). Others show apparently increasing child mortality in the late 1970s, followed by declines in the 1980s (Zimbabwe, Uganda, Burundi). A final group shows an apparent slowing of decline in the 1980s relative to the late 1970s (Botswana, Kenya, Ghana, Togo). The
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WPS3695

# Estimating Household Responses to Trade Reforms

## Net Consumers and Net Producers in Rural Mexico∗

### Guido G. Porto† Development Research Group The World Bank

#### Abstract

This paper explores an empirical methodology to assess the impacts of trade reforms on household behavior in developing countries. I focus on consumption and income responses: when price reforms take place, households modify consumption and production decisions and local labor markets adjust. The paper proposes a joint estimator of demand and wage price elasticities from survey data. The method uses an empirical model of demand to extract price information from unit values, and uses this information to estimate the response of households to price reforms. By correcting unit values for quality effects and measurement error, the method overcomes the problem of the endogeneity of unit values. By endogeneizing household income, the model corrects potential biases in the estimation of own- and cross-price elasticities in consumption. I apply the method to an expenditure and income survey for rural Mexico. It is shown that the corrections suggested in this paper are empirically important. In particular, I show that allowing for consumption and income responses is a key element of an accurate empirical assessment of trade policy.

#### JEL Codes: D12 J43 Q17 I30

#### Keywords: demand elasticities, wage elasticities, agricultural price reform

#### World Bank Policy Research Working Paper 3695, September 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ∗The outstanding research assistance of Jorge Balat is greatly appreciated. This paper was financed by the Knowledge for Change Program (KCP) at the World Bank. I am indebted to A. Deaton, P. Goldberg and G. Grossman for comments and early encouragement. I thank I. Brambilla, S. Galiani, D. Lederman, A. Nicita, M. Olarreaga, W. Sosa Escudero and A. Tarozzi for useful discussion at different stages of this research program, and to seminar participants at Di Tella, La Plata and San Andres University. Special thanks go to A. Nicita, who shared the data on Mexico with us.

†Guido Porto, MailStop MC3-303, World Bank, 1818 H Street, Washington DC 20433. email: gporto@worldbank.org
as the unobservables determining such placement are fixed over time.<sup>3</sup> However, the time-frame for such evaluations may be too short to capture the long-term adaptations to lower transport costs (see also Mu and van de Walle, 2007). Moreover, collecting such data is very time-consuming and expensive, especially given the sample sizes needed to detect the often subtle impacts of road rehabilitation.

This paper takes a different tack. We use a one-shot cross-sectional data set with a novel sampling scheme. Households were surveyed in a small, relatively homogeneous region of rural Madagascar, over which transport costs to the same market vary tremendously. This variation is not due to the particularities of road placement — there are no paved roads and little motorized transport to speak of — but rather to the impenetrable mountains that range up and down the region. At their peak, transport costs amount to a staggering 50% of the final market price of the primary commodity, paddy rice. Arguably, a comparison of household behavior along this steep transport cost gradient approximates the long-run adjustments to an exogenous road improvement.

Nevertheless, to assess the validity of causal inferences about the various impacts of transport costs drawn from such cross-sectional data, we carry out two tests prior to our main estimation. The first test, presented in section 2, looks for systematic differences in land productivity across space. Such differences might explain why roads are not built into certain areas, and thus why these areas remain remote. At the same time, location-specific productivity may well be correlated with household level outcomes, such as land values, farm input use and output. The second test is for the presence of systematic differences in farm productivity across households at a given location; in particular, between migrant and native households. Poorer, less able migrants may be attracted to more remote areas by virtue of cheaper land. This selective migration could make the impacts of inaccessibility appear greater than they really are.

Before turning to these tests, section 2 describes our data and sampling methodology in detail. This section also presents a set of stylized facts that undergird our agricultural household model. Section 3 lays out the model and derives formulae for the willingnessto-pay for a reduction in transport costs. In Section 4, we implement these formulae using a nonparametric estimation procedure. Section 5 recaps the results.

> 3 To the extent that road improvements are planned for areas with high expected growth, this assumption may be invalid. Only a data set that follows the same households over time would be useful for dealing with selective migration to areas with better road access. Khandker et al. (2006) use such data to estimate various impacts of road construction in Bangladesh, but they do not provide a comprehensive measure.

2
do not keep pace with urban growth, and/or no other measures are undertaken for a more controlled use of wastewater. In higher income countries where wastewater irrigation occurs, it is normally planned use with treated wastewater.

# **2.2 Key issues in wastewater use in agriculture**

# _2.2.1 Levels of economic development and wastewater issues_

The assessment of current practice in wastewater use in agriculture reveals important patterns between a country’s economic status (low-income, lower-middle income, upper-middle income, and high-income) and the types of wastewater issues it faces. Inadequate water supply, sanitation, and unplanned, untreated wastewater use in urban areas are associated with a low level of economic development. Higher levels of economic development are associated with the provision of adequate water supply, sanitation, wastewater treatment for the vast majority of the urban population, and the planned use of wastewater. Based on these patterns, this paper uses an innovative approach that distinguishes four country income levels based on 2008 national GNI per capita data from the World Bank’s World Development Report 2010, and relates them to various issues to create a typology for describing and analyzing current issues, trends and, later, priorities in wastewater use in agriculture.<sup>6</sup> Urbanization and the provision of improved urban drinking water supply and sanitation services increases significantly with income growth, and reaches close to 100 percent in high-income countries (Table 2.2).

The table also shows that the disease burdens for diarrheas and Ascariasis decrease rapidly as development occurs—an important observation since both diseases are linked to irrigation of food crops with untreated wastewater.

While Table 2.2 gives the share of urban population with access to improved sanitation rather than just sewer connections, Locussol _et al_ . (2007) have shown that the urban population in developing countries served specifically by sewer connections more than doubled between 1990 to 2004, from 618 million to 1,488 million (an increase of 141%) as urban population grew only 46%.

Countries progress up the treatment ladder from untreated to treated wastewater as per capita incomes increase (Table 2.3). This progression has occurred historically in developed countries and is currently occurring at different paces in emerging countries. It should be noted that there can also be substantial variations among and within the countries that are grouped together in a particular country type. Some countries may be more advanced than others (for example, because of one or more of the drivers discussed in Chapter 2); and within countries, subnational geographic disparities may persist (for example, upper-middle income countries may have pockets with conditions of low-income countries). Nevertheless, the broad typology is useful for assessing key relationships.

> 6 This approach was first developed and applied to assess different urban environmental problems related to levels of economic development (Bartone et al., 1994).

7
**<u>Table 9.</u>** <u>Effects of gender role attitudes and personality traits on profits (</u> _<u>women</u>_ <u>sample)</u>

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Norms|Norms+<br>Controls|Norms+<br>Controls+<br>Personality|_Norms+_<br>_Controls+_<br>_Impulsive_|_Norms+_<br>_Controls+_<br>_Work_<br>_Central_|_Norms+_<br>_Controls+_<br>_Tenacity_|_Norms+_<br>_Controls+_<br>_Polychroni_<br>_city_|_Norms+_<br>_Controls+_<br>_Locus of_<br>_Control_|_Norms+_<br>_Control+_<br>_Achieveme_<br>_nt_|_Norms+_<br>_Control+_<br>_Power_<br>_Motivation_|_Norms+_<br>_Controls+_<br>_Organized_|_Norms+_<br>_Controls_<br>_+_<br>_Optimis_<br>_m_|
|Traditional|-0.015***|-0.003|-0.008|-0.007|0.016|0.013|-0.020|-0.013|0.004|0.012|0.017|-0.013|
||(-3.02)|(-0.55)|(-1.60)|(-0.37)|(0.76)|(0.51)|(-1.36)|(-0.62)|(0.14)|(0.73)|(0.87)|(-0.35)|
|Impulsive|||0.117|-0.013|||||||||
||||(1.07)|(-0.03)|||||||||
|Work centrality|||-0.070||0.257||||||||
||||(-0.74)||(0.99)||||||||
|Tenacity|||0.000|||0.293|||||||
||||(0.00)|||(0.91)|||||||
|Polychronicity|||-0.076||||-0.407||||||
||||(-1.22)||||(-1.64)||||||
|Locus of control|||-0.095|||||-0.285|||||
||||(-1.31)|||||(-0.89)|||||
|Achievement|||0.215||||||0.343||||
||||(1.43)||||||(0.78)||||
|Power motivation|||0.029|||||||0.335|||
||||(0.38)|||||||(1.27)|||
|Organized|||0.188***||||||||0.399*||
||||(2.73)||||||||(1.76)||
|Optimism|||-0.264*|||||||||-0.364|
||||(-1.96)|||||||||(-0.70)|
|Traditional*Impulsive||||0.002|||||||||
|||||(0.27)|||||||||
|Traditional*Work centrality|||||-0.005||||||||
||||||(-0.88)||||||||
|Traditional*Tenacity||||||-0.004|||||||
|||||||(-0.59)|||||||
|Traditional*Polychronicity|||||||0.008||||||
||||||||(1.36)||||||
|Traditional*locus control||||||||0.003|||||

40
- 16 -

<!-- Start of picture text --> FIGURE 2<br>Remittances and Number of Workera Abroad<br>1000<br>R -N<br>g 4000 / -750 N<br>T I , ~~~~~~~~~~~~~~~~~~B N<br>T R<br>A a<br>N 3000 -. ,,-" 500<br>C W<br>in 2000- ' 250<br>Urs$ a<br>A<br>B<br>1000 0 B<br>0<br>A<br>D<br>0 m-m,,r niiir-nrrw -rr-ir-ir i-ir- -rn,ri-ri-i-ri-rn<br>Algeria Morocco Portugal Tunisia Turkey Yugoslavia<br>Remittances (US) - Workers Abroad<br><!-- End of picture text --> Table 1 Remittances, Workers Abroad and Total Population Abroad, 1977-89 Averages (Remittances in US$ Millions, Migrants in Thousands, Per Capita Remittances in US$)

||Algeria|Morocco|Portugal|Tunisia|Turkey|Yugoslavia|
|---|---|---|---|---|---|---|
|Remittances|216|1,007|1,886|338|2,148|1,704|
|Workers Abroad|293|237|496|90|769|550|
|Popul. Abroad|816|696|871|217|1,910|966|
|Remit./Workers|754|4,200|3,810|3,730|2,740|3,090|
|Remit./Popul.|267|1,430|2,180|1,530|1,100|1,760|

Sources: Migration data: OECD. Remittances: Individual Country Sources.
|**Agricultural Products**|**Exports**|**Imports**|**Industrial Products**|**Exports**|**Imports**|
|---|---|---|---|---|---|
|Cereals & preparations|54|57|Iron & steel|126|138|
|Fruits & vegetables|71|79|Chemicals|526|547|
|Sugar & honey|16|17|Automotive products|549|566|
|Coffee, tea & spices|31|32|Office & telecom equipment|769|792|
|Beverages & tobacco|57|57|Textiles & clothing|334|352|
|Alcoholic beverages|30|30|Other manufactures|1182|1966|
|Tobacco|22|21|**Total manufactures**|4186|4361|
|**Total agricultural**|417|441||||
|**products**||||||

SOURCE: United Nations Food and Agriculture Organization (2000), World Trade Organization (2000) Notes: Exports valued FOB and imports valued CIF; totals may not add due to rounding; n/a means not available

# **Table 7 World Trade of Selected Agricultural and Industrial Commodities for 1999 (in billions of current U.S. dollars)**

Unfortunately, tougher interdiction does not seem to raise prices much. Figures for the U.S. in recent years suggest that seizures of cocaine have increased as a share of total shipments in recent years while import prices have fallen. Reuter, Crawford and Cave (1988) built a simulation model in which smugglers used past interception data to make decisions about which routes to pursue. Given the low export price of cocaine and low inputs of both equipment and personnel costs per gram, it turned out to be difficult to raise retail prices substantially with more aggressive interdiction. Crane, Rivolo and Comfort (1997) examined the effects of temporary spikes in seizure rates in source zones and found that they did increase retail prices substantially; there has been considerable controversy about the researchers’ development of a price series<sup>38</sup> and of their approach to modeling the short-run effects of interdiction events to reach this conclusion (see Manski, Pepper and Thomas, 1999).

That leaves open the question as to why cross-border prices are so high yet more enforcement does not have the desired consequence. Consider again the border between Afghanistan and Tajikistan, whose passage increases the price of a kilogram of opium many fold. This border has been porous throughout the period in which the heroin trade between the two countries has developed. As a share of the estimated flow, seizures have been modest; Greenfield 23
Jamasb Tooraj and Cristiano Marantes (2011) “Electricity Distribution Networks: Investment and Regulation, and Uncertain Demand”, EPRG Working Paper Jamasb, T. and Pollitt, M. (2007). Incentive Regulation of Electricity Distribution Networks: Lessons of Experience from Britain, Energy Policy, 35 (12): 6163-6187 Joskow, Paul (2003) “The difficult transition to competitive electricity markets in the U.S.,”Cambridge-MIT Institute Electricity Project Working Paper 28 Joskow, Paul and Jean Tirole (2006) “Reliability and Competitive Electricity Markets”, RAND Journal of Economics, Joskow, P. (2008) “Incentive Regulation and Its Application to Electricity Networks”, Review of Network Economics, 7(4):547-560.

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Levine, P., Stern, J. & Trillas F., (2002), “Independent utility regulators: lessons from monetary policy”, _London Business School Regulation Initiative Working Papers No. 52_ .

Levy, B. & Spiller, P.T., (1994), “The institutional foundations of regulatory commitment: a comparative analysis of telecommunications regulation”, _The Journal of Law, Economics & Organisation,_ Vol. 10 pp.201-246 Stoft, S. (2002), “Power Systems Economics - Designing Markets for Electricity, IEEE Press, John Wiley & Sons.

Vagliasindi, Maria, (2004) "The role of investment and regulatory reforms in the development of infrastructure across transition economies," _Utilities Policy_ , vol. 12(4), 303-314.

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18
incentive effect that arises because strong operations management practices encourage both students and parents to increase their inputs. We also find evidence of these mechanisms in our PISA data for Latin America. Principals in public schools with higher PISA-based operations management scores are less likely to report experiencing teacher shortages and also report higher levels of teacher motivation, teacher effort and household effort, compared to principals in public schools with lower PISA-based operations management scores.

While this is not definitive causal evidence, this combination of theory and descriptive empirical analysis offers a novel insight into why management matters in schools and we therefore move on to consider policy implications. People management practices such as performance pay, while common in the private sector, may not be possible in public schools. But there would seem to be fewer barriers to conducting assessments to judge teacher effectiveness, and letting such appraisals lead to changes in public recognition, opportunities for professional development, likelihood of career advancement, and/or greater responsibilities. That is, these people management practices help to attract, develop and reward good performers, and, our analysis suggests, should improve both teacher selection and incentives.

There is also substantial variation in the strength of operations management practices _within_ the public sector. This suggest a role for government to encourage principals in public schools with weak operations management to follow best practices. Specific areas suggested by our analysis include processes that facilitate: personalization of learning; dialogue among staff, students and parents focused on continuous improvement; and collection and use of student assessment data.

**Related literature.** Our first contribution—a new approach to measure management practices in schools—relates to two bodies of work. The first is the literature that has evolved since the creation of the WMS dataset first described in Bloom and Van Reenen [2007]. The WMS methodology has been adapted to a range of public sector institutions, including schools and universities [Bloom et al., 2015a, McCormack et al., 2014], healthcare facilities [Bloom et al., 2015b, 2019b], social programs [Delfgaauw et al., 2011, McConnell et al., 2009], and the civil service [Rasul and Rogger, 2016], as well as to low-income settings [Lemos and Scur, 2016]. However, it is expensive and timeconsuming to implement at scale; our approach is a feasible alternative. The second is the literature

3