rafmacalaba/datause-encoder-data huggingface dataset:
WPS4276

**Human Capital, Trade Liberalization and Income Risk**<sup>1</sup>

# **Tom Krebs Mannheim University**

# **Pravin Krishna Johns Hopkins University and NBER**

# **William Maloney World Bank**

# **Abstract**

Using data from Mexico, this paper studies empirically the link between trade policy and individual income risk and the extent to which this varies across workers of different human capital (education) levels. Longitudinal income data on workers are used to estimate time-varying individual income risk parameters in different manufacturing sectors in Mexico between 1987 and 1998, a period in which the Mexican economy experienced substantial changes in trade policy. In a second step, the variations in trade policy − across different sectors and over time − are used to estimate the linkage between trade policy and income risk for workers of varying education levels. Our findings are as follows. The level of openness of an economy is not found to be related to income risk for workers of any type. Furthermore, changes in trade policy (i.e., trade policy reforms) are not found to have any effect on the risk to income faced by workers with either low or high levels of human capital. However, workers with intermediate levels of human capital are found to experience a statistically and economically significant increase in income risk immediately following liberalization of trade. Our findings thus point to an interesting nonmonotonicity in the interaction between human capital, income risk and trade policy changes.

_JEL Classification_ : F13, F16, D52, E21 _Keywords_ : Trade Policy, Income Risk, Human Capital, Incomplete Markets World Bank Policy Research Working Paper 4276, July 2007 _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._

> 1 Paper prepared for presentation at the Festschrift conference organized in the honor of Jagdish Bhagwati, _Globalization: Prospects and Problems_ , University of Florida, Gainesville, January 28-30, 2005. We wish to thank our discussant, J. David Richardson, for many valuable comments on an earlier draft of the paper.
they include more restrictions that the Core NTBs and agricultural domestic support we estimated because they include many other policies (e.g., exchange rate controls). Also differences in tastes and quality across countries can partly explain differences in prices. For these reasons one should expect them to be a bit higher (and they generally are). Our estimates compare to Bradford (2003, Table 2) as follows: 8 percent for Australia (compared to 15 percent), 5 percent for Canada (compared to 8 percent), 10 percent for Japan (compared with 58 percent), 8 percent for the United States (compared with 9 percent), 13 percent in Belgium (compared to 32 percent), 10 percent in Germany (compared to 18 percent), 10 percent in Italy (compared to 12 percent), 11 percent in the Netherlands compared to 31 percent, and 10 percent in the United Kingdom (compared to 38 percent). There are obvious reasons why these numbers may differ, but the order of magnitudes seem more or less on line, except perhaps for Belgium, Netherlands, United Kingdom and Japan, where Bradford’s estimates are much larger.<sup>17</sup> Andriamananjara et al. (2004) also provide estimates of AVEs of NTBs for 12 groups of product (that correspond to some aggregate of the GTAP product classi fi cation). They use price data from the Economist Intelligence Unit for 18 regions/countries and estimate the impact NTBs on retail prices controlling for several variables capturing distribution costs (GDP per capita, distance, wages in the non-traded sector, etc..). The most complete exercise is undertaken for Apparel. Andriamananjara et al. (2004) estimate a simple average AVEs of NTBs in apparel across countries of 73 percent (it varies between 16 and 190 percent). Our simple average for apparel is 20 percent (it varies between 0 and 95 percent). Thus, the order of magnitude seems a bit higher in Andriamananjara et al. 2004. This difference could be as before due to the assumption of perfect substitution between domestically-produced and imported goods or the fact that the averages are only reported for products for which their results were theoretically consistent ignoring other products. Our non-linear estimation avoids this problem and includes those products in which NTBs may have a very small impact on imports (or domestic prices). One could therefore expect lower AVEs than in Andriamananjara et al. (2004).

> 17 One reason for this could be that the price comparisons in Bradford (2003) assume that domestically produced goods and import goods are perfect substitutes, and ignores product differentiation, which could be quite signi fi cant in Japan. So large differences in taste can lead to large NTB estimates. The precision with which distribution margins are calculated can also be questioned in these type of exercise. For example, all fresh, frozen or deep frozen fi sh is lumped together in one product category with a single distribution markup. Given that distribution and transport cost can vary signi fi cantly between fresh and deep frozen fi sh, the composition of this aggregate product matters when determining the markup cost.

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single factor that explains the scarce amount of research on refugees as documented at the outset of this paper.

In addition to the UNHCR registry, data on refugees are collected via home visits and sample surveys by a variety of organizations including the UNHCR, the World Food Program (WFP), other UN organizations and many NGOs working independently or on behalf of UN organizations. However, it is important to stress that any home visit or sample survey will have to rely on the UNHCR registry. The UNHCR registry system functions _de facto_ as the only existing population survey for refugees. By definition, refugees are not included in national censuses unless governments make a special effort to do so. Ministries of interior would generally keep records of people coming into and out of the country, including refugees, but these data are seldom shared, records are not always accurate and these records only include a handful of information strictly necessary to border police to grant entry and exit. Therefore, in many countries, the UNHCR registry system is the only system that contains population information on refugees and if this system does not exist or is managed by the host government information is scarce or not available. In a sense, the UNHCR is the statistical agency of refugees and the UNHCR registry system is the population census of refugees, which means that any other survey on refugees will have to rely on this registry, unless proper government registry systems are in place.

In addition to being scarcely available, the UNHCR registry also suffers from a number of deficiencies that span from records that are not filled or incomplete to data input and data cleaning issues to data management and data use. The quality of the UNHCR data registry varies significantly across countries with better quality usually available in middle‐income countries and lower quality data available in low‐ income countries. Moreover, where data are generally perceived to be of good quality, many records may be missing and variables not usable. In other cases, data entry errors pollute the data while in some cases existing good data are not centralized and available for analysis.

These are problems that affect any data collection exercise but they tend to be exacerbated in a refugee context because of the urgency of the operations and because field staff are not necessarily data experts or users. For example, the UNHCR registry system is largely managed by IT specialists in collaboration with field registry staff and it is rare to have economists or social scientists involved. That is because the UNHCR had historically low demand for research on refugees and the focus was on protection rather than research on programs. Because of the shift in paradigm from the humanitarian to the humanitarian‐ development approach, this is now changing. The UNHCR is increasingly searching for social scientists to improve on their data collection system and data use for analytics and development organizations such 16
**Table 3: Descriptive statistics**

|||A.<br> <br>|By trea<br>Men<br>|tment<br>||W<br>|omen<br>||
|---|---|---|---|---|---|---|---|---|
||Detailed|Short|Self|Proxy|Detailed|Short|Self|Proxy|
|Labor force participation|85%|90%|91%|82%|79%|89%|85%|82%|
|Wage workers (as % of lfp)|19%|13%|17%|13%|11%|5%|10%|6%|
|Daily earnings|3987|4781|3634|6255|3912|4388|3552|5367|
||(5724)|(4675)|(3492)|(8275)|(6428)|(8422)|(6540)|(8534)|
|Number of observations|687|723|909|501|785|750|970|565|

Standard deviations in parentheses.

# <u>B. Wage workers only</u>

||men|women|
|---|---|---|
|Daily earnings|4321|4066|
|Years of schooling|6.60|4.89|
|Zero years of schooling|15%|37%|
|1‐7 years of schooling|59%|44%|
|8‐11 years of schooling|17%|15%|
|12‐17 years of schooling|8%|4%|
|Age|33.82|34.09|
|Married|64%|57%|
|Number of children below age 6|0.88|1.08|
|Number of children age 6‐15|1.16|1.5|
|Number of old hh members (65+)|0.10|0.21|
|Received Detailed questionnaire|57%|68%|
|Received Short questionnaire|43%|32%|
|Interviewed self|72%|.72%|
|Interviewed Proxy|28%|28%|

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Table A.2: Does dowry follow a random walk?

|Lags|Test Statistic|1% Critical Value|5% Critical Value|10% Critical Value|
|---|---|---|---|---|
|9|-0.693|-3.770|-2.853|-2.405|
|8|-0.734|-3.770|-2.808|-2.407|
|7|-1.260|-3.770|-2.820|-2.453|
|6|-0.917|-3.770|-2.877|-2.531|
|5|-0.763|-3.770|-2.967|-2.632|
|4|-0.964|-3.770|-3.078|-2.747|
|3|-0.910|-3.770|-3.197|-2.864|
|2|-1.061|-3.770|-3.313|-2.974|
|1|-1.644|-3.770|-3.414|-3.067|
|N|23||||

NOTES: This table reports the results from the modified Dickey-Fuller _t_ test (known as the DF-GLS test) proposed by Elliott, Rothenberg, and Stock (1996) using STATA’s _dfgls_ command. _dfgls_ performs the DFGLS test for the series of models that include 1 to _k_ lags of the first differenced, de trended variable, which is the average real net value of dowry paid during marriages in a given year. We adopt STATA’s default approach to picking the optimal _k_ . In column (1), we report DF-GLS tau statistic (and its critical values) for the null hypothesis that dowry is a random walk, possibly with drift and the alternate hypothesis that dowry is stationary about a linear time trend.

Table A.3: Does our expected dowry variable predict actual dowry?

|**Dependent variable:**||Actual do|wry paid||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Expected dowry|0.537***|0.530***|0.187**|0.144**|
||[0.099]|[0.090]|[0.095]|[0.056]|
|N|19,664|19,664|19,664|19,664|
|_Xi_||x|x|x|
|Caste FE|||x|x|
|State FE|||x|x|
|YOB FE|||x|x|
|State FE*YOB FE||||x|
|Caste FE*YOB FE||||x|
|Caste FE*State FE||||x|

NOTES: In this table, we regress the net dowry paid or received by an individual on our expected dowry variable. **X** _i_ controls for indicators of household religion. Standard errors in brackets are clustered by state. *** 1%, ** 5%, * 10%.

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name, we report a few useful characteristics: combined GDP (in 1985 dollars); total population and a measure of income disparity among members.<sup>4</sup> The column "comments" gives broad targets, instruments, achievements, and difficulties.<sup>5</sup> Table I highlights a sharp contrast ill implementation record between Nonth-North RI schenes on the one hand, and South-South schemes, on the other. Implementation was by and large successful for developed-country RI arrangements (although it could be argued that the goal of a common market reflected in the 1957 Treaty of Rome is yet to be reached). By contrast, by and large, implementation was unsuccessful for developing-country RI schedules. Several implementation difficulties are noted in the "comments" column. First, reductions of trade barriers among members was often postponed or delayed because the .rocess lacked automaticity ("positive" lists, restrictive rules of origin for the coverage of products eligible for p-eferential tariffs, etc.). And, in the cases where implementation was fairly successful (e.g. the CACM), macroeconomic difficulties in the eighties led to the imposition of QRs. Second, when the establishment of a CU was the target, implementation of a Common External Tariff (CET) was often difficult to achieve in part because I Whenever members leave/join, latest membership is indicated [e.g. EC (12), EFTA (7)]. All figures in this table, and in the rest of the paper, are from Heston and Summers (1991).

_5_ "Natural trading partners", an indication that members are likely to have benefitted (compared with the ex-ante status quo) is only mentioned when members have highly integrated transport networks and similar production structures thereby giving scope for the expansion (contraction) of relatively efficient (inefficient) industries. On why SSA countries are not "natural trading partners" see Stolper and Deardorff (1991). They argue that much smuggling and unrecorded trade takes place among SSA countries because countries and borders are organized along North-South routes while natural routes to minimize transport costs are organized along East-West lines. Under such circumstances, they show that unrecorded trade is likely to be welfare increasing. Also, there is no mention is made of "trade-creation" or "trade-diversion" effects unless these are widely recognized to be significant, and there is general agreement as to their direction. For example, there is no entry for the CACM or for the Canada-U.S. FTA in spite of numerous studies. This is because of indecisive (and/or controversial) findings. [See Whalley (forthcoming) table for a comparison of the results of the welfare estimates of the Canada- U.S. FTA, and Langhammer and Hiemmenz (1991) tables 4 and 5 for a survey of the results of estimates of trade creation and trade diversion and of the training ground argument for developing countries.] _5_
_39_ _WATER WHEN IT COUNTS_ Experimentation and Communication.” _Review of Economic Studies_ .

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**FAO.** 2016. “Aquastat: Mozambique.”

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percent-come from the use of gasoline vehicles and associated gasoline distribution systems. As table 3.1 shows, emissions standards for new gasoline vehicles have helped to reduce emissions. The share of modem cars with three-way catalysts (electronic systems) has risen to over 10 percent of the total Mexican fleet of gasoline cars (about 70 percent of all cars). In the ZMVM all new gasoline cars are equipped with catalytic systems. According to SMA (1999), however, 32 percent of the cars in the ZMVM are pre-1980 models. And we know that emissions from pre-1986 cars are ten times greater than 1999 cars. Hence, total future emissions depend on the rate at which old cars are replaced with newer models.

**Table 3.1 Environmental Characteristics of Cars Sold in Mexico around 1998**

|_Year/model_|_Features_|_Percentofvehiclefleet_|
|---|---|---|
|Pre-1986|Withcarburator|37.2|
|1986-1992|Fuelinjection|23.8|
|1992-1993|Catalyticconverter|28.6|
|1994andafter|Fullelectronicsystems|10.4|
|Total||100.0|

_Source:_ Mexican Association of Vehicle Distributors (April 2000) as cited at www.tradeport. orQ/ts/ countries/mexico/isa/isar0013.html.

As the composition of the future vehicle fleet in Mexico City is unknown, it is assumed here-in the absence of an integrated model on emission projections for 2010 for mobile sources-that emissions in 2010 will equal those of 1998. This could be the case, for instance, if all improvements in vehicle emissions were exactly offset by the growing importance of road transport in the ZMVM. This would also hold for VOC emissions. Hence, in the absence of more detailed information on emission patterns, we assume that NOx and VOC emissions will be the same as at the end of the 1990s. Once again, we note the enormous uncertainty of baseline predictions.<sup>8</sup>

**Table 3.2 Main Assumptions on Reference Scenario**

|_Pollutant_|_Mainobservations_|_Result_|
|---|---|---|
|NO,and<br>VOCs<br>|Therearetwoopposingtrends:(i)increasein<br>cars,busesandotherpollutionsources;(ii)the<br>emissionsperunitisdecreasing overtime.The<br>resultantofthesetwo trendsisinconclusive.<br>|Duetolackofinformation it isas-<br>sumedthatthebaselinesituationin<br>2010isequaltocurrent conditions.<br>|
|PM,0|OriginsofPM10areuncertain.Trendsinmeas-<br>uredairquality(since1995)areinconclusive.|Duetolackofinformationit isas-<br>sumedthatthebaselinesituationin<br>2010isequal to current conditions.|

> 8As mentioned in the previous section, meteorology is an important explanatory variable for actual air quality, but is unlikely to change during the time horizon of this analysis. We, therefore, assume meteorological conditions in 2010 to be similar to the meteorological conditions of the 1995-99 reference air pollution year. Economic variables are equally uncertain and different growth patterns will greatly influence the actual future emissions.

_I0_
to deduct (or impute) the part of the total consumption expenditure falling into indirect taxes (or direct effects). Moreover, the input-output matrix is used to determine the indirect effects of each tax, taking into account that the nonexempt sectors of VAT only have direct effects.

# _Indirect Subsidies_

Subsidies on agriculture, consisting of agricultural extension services, are provided by the government to farmers to help train them in farming techniques, mechanization, and so on. The surveys have one section on agricultural extension services that allows researchers to directly identify the beneficiaries of the program. Then the amount of the program total expenditure is divided by the total number of beneficiaries to determine and impute to each beneficiary the per capita program cost.

# _Monetized Value of Public Expenditure in Health and Education_

The surveys provide information about the level of education, and the type of school each individual is attending. Education spending is organized by school levels (primary, secondary, and university). The average cost of public spending, depending on the number of students and the school level (primary, secondary or university), is imputed to each individual.

The surveys make it possible to determine which individuals are benefitting from free health services, and those who are using other public health services. For free health services, the average cost of public spending in those services is imputed to each beneficiary. For other public health services, the average cost of central health expenditure is imputed to each beneficiary, while the average cost of regional health expenditure is imputed to beneficiaries in the corresponding regions.

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