rafmacalaba/datause-encoder-data huggingface dataset:
data.<sup>1</sup> Thus, unsurprisingly, the econometrics of pseudo-panel data is a rapidly growing field of research.<sup>2</sup> Perhaps because of their emphasis on cohorts rather than the household or individual, pseudo panel methods have not been widely applied to the study of poverty dynamics. Two notable exceptions are Bourguignon, Goh and Kim (2004) and Güell and Hu (2006) who construct synthetic panels at the household level. However, these two approaches require certain assumptions that may not always be easily satisfied in available cross sections: the former requires at least three rounds of cross section data and assumes a first-order autoregression (AR (1)) process through which past household or individual incomes (earnings) can affect present outcomes; the latter is exclusively restricted to duration analysis.

Against this background, a recent paper by Dang, Lanjouw, Luoto, and McKenzie (2014) proposes both parametric and non-parametric approaches to construct synthetic panels at the household level from two rounds of cross sections with rather parsimonious assumptions.<sup>3</sup> These synthetic panels are then used to predict lower-bound and upper-bound estimates of household transitions into and out of poverty in Vietnam and Indonesia. Drawing on both cross sectional data and genuine panel data, the authors compare mobility estimates based on synthetic panels to those that would be obtained from actual panel data and find that the “true” estimate of mobility (as revealed by the actual panel data) is generally sandwiched between

> 1 See, for example, Glewwe and Jacoby (2000) and Kalton (2009) for recent overviews of the advantages and disadvantages of cross sections and panel data in both developing and richer country contexts.

> 2 See, for example, Inoue (2008) for a recent development and a brief review of this literature.

> 3 This method focuses on temporal imputation or survey-to-survey imputation over time. For related studies that investigate spatial imputation or survey-to-census imputation, see, e.g., Elbers, Lanjouw, and Lanjouw (2003), some recent economic applications of which include Agostini and Brown (2010), Elbers et al. (2007), and Demombynes and Ozler (2005). More broadly, this method is related to the literature on identifying the bounds on the joint distribution for outcomes in different samples (see, e.g., Cross and Manski, 2002) and the statistical literature on imputing missing data (see, e.g., Little and Rubin, 2002). See also Ridder and Moffitt (2007) for a recent review on the econometrics of data combination.

3
# TABLE C.4: Estimation of rent, ADDIS ABABA

||Simple||Job densit|y|Interactio|n|Tenure||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Directly affected by food|-1.054**||-0.863**||||-0.822**||
||(0.391)||(0.361)||||(0.335)||
|Flood risk perception||-0.249||-0.269||||-0.197|
|||(0.257)||(0.227)||||(0.226)|
|Job density|||-0.744***|-0.773***|-0.687**|-0.706**|-0.464**|-0.492**|
||||(0.250)|(0.255)|(0.284)|(0.304)|(0.192)|(0.196)|
|Number of rooms|1.044***|1.046***|0.919***|0.916***|0.919***|0.923***|0.813***|0.812***|
||(0.205)|(0.214)|(0.140)|(0.146)|(0.138)|(0.145)|(0.142)|(0.140)|
|Number of rooms squared|-0.081***|-0.082***|-0.070***|-0.070***|-0.070***|-0.071***|-0.060***|-0.060***|
||(0.018)|(0.019)|(0.013)|(0.013)|(0.013)|(0.013)|(0.013)|(0.013)|
|Unimproved house|-1.102***|-1.195***|-0.712**|-0.776***|-0.696**|-0.771***|-0.600**|-0.668**|
||(0.326)|(0.350)|(0.263)|(0.273)|(0.260)|(0.267)|(0.270)|(0.280)|
|Unimproved roof material|0.002|0.092|0.030|0.120|0.081|0.135|0.066|0.141|
||(0.236)|(0.253)|(0.231)|(0.260)|(0.249)|(0.264)|(0.200)|(0.229)|
|Unimproved foor material|-0.548*|-0.608**|-0.619**|-0.664**|-0.606**|-0.661**|-0.529*|-0.573*|
||(0.268)|(0.283)|(0.267)|(0.277)|(0.267)|(0.276)|(0.282)|(0.294)|
|Unimproved drinking water source|0.938**|0.577|0.592|0.294|0.347|0.233|0.554|0.271|
||(0.381)|(0.370)|(0.409)|(0.505)|(0.493)|(0.525)|(0.336)|(0.402)|
|Unimproved toilet facility|-0.555***|-0.618***|-0.511**|-0.558***|-0.527***|-0.564***|-0.409***|-0.463***|
||(0.161)|(0.162)|(0.186)|(0.187)|(0.177)|(0.185)|(0.121)|(0.124)|
|Not collected solid waste|-0.361|-0.367|-0.287|-0.300|-0.305|-0.318|-0.493|-0.482|
||(0.409)|(0.437)|(0.379)|(0.389)|(0.377)|(0.385)|(0.446)|(0.456)|
|Electric lighting|-1.153|-1.347|-0.497|-0.614|-0.567|-0.701|0.154|0.018|
||(0.896)|(0.991)|(1.002)|(1.088)|(0.978)|(1.119)|(1.039)|(1.107)|

_Continued on next page_
Bloch, F., and V. Rao. 2002 _._ Terror as a Bargaining Instrument: A Case Study of Dowry Violence in Rural India. _American Economic Review_ 92 (4): 1029-1043.

Bloom, S., A.O. Tsui, M. Plotkin, and S. Bassett. 2000. “What Husbands in Northern India Know about Reproductive Health: Correlates of Knowledge about Pregnancy and Maternal and Sexual Health.” _Journal of Biosociology Science_ 32: 237-251.

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Brines, J. 1994. “Economic Dependency, Gender, and the Division of Labor at Home _.” American Journal of Sociology_ 100 (3): 652-688.

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Daly M., and M.I. Wilson. 1998. _The evolutionary social psychology of family violence_ . Pp. 431-456 in C Crawford & D Krebs, eds _., Handbook of evolutionary psychology: ideas, issues and applications._ Mahwah, NJ: Erlbaum.

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31
lifetime of individuals, per capita household wealth rises as a population ages. Moreover, gains in life expectancy have led to an extended period of retirement, providing a powerful incentive to accumulate assets in countries where the elderly rely on funded pensions and other assets to support at least part of their old-age needs. Table 1 summarizes the first and the second demographic dividends by explaining the transmission mechanisms.

**Table 1 Demographic dividends in a nutshell**

|**Channel**|**Transmission mechanisms**|**Demographic**<br>**dividend**|
|---|---|---|
|Labor force|Increase in the support ratio (ratio of effective labor to effective<br>consumers) holding other factors, including saving and income<br>per effective worker, constant.|First|
|Savings|Changes in saving and capital per effective worker influence<br>income, from labor and assets, per effective worker.|Second|
|Human capital|Lower fertility and the quantity-quality trade-off lead to greater<br>spending on health and education for children.|Second|

_Source:_ Authors, based on Lee and Mason (2006) _Note:_ For both the first and second demographic dividends, changes in the factor given in the first column of the table, via the transmission mechanism described in the second column, results in a boost to growth.

Figure 1a describes the association between demographic transition and demographic dividends.<sup>9</sup> In countries with low levels of income and education, birth rates and mortality rates are relatively high, contributing to low life expectancy. In a first stage of the demographic transition, the increase in the number of children is proportionally larger than in the working age population, or the elderly people, leading to a decrease of the share of working age population driven by a rise in the share of children. As income and education improves, fertility and mortality rates decline, leading to an increase of the share of working age population, concomitantly to a reduction in the total dependency ratio. This is the stage of the demographic transition that provides the condition for the first demographic dividend. The third stage of demographic transition happens when the fertility rate is very low, usually below the replacement level, and the mortality rate is also low, which leads to high life expectancy. At this stage the growth of the elderly population more than compensates the reduction in the share of children, leading to an increase in the total dependency ratio, driven by a larger number of elderly people.

> 9 This association can be linked to the Demographic Transition Model (DTM). The DTM describes the transition of populations from high to low fertility and mortality rates. This transition generally parallels the economic development of a country (Szreter 1993). The model consists of at least four distinct phases, with countries effectively moving from high fertility and low life expectancy to low fertility and high life expectancy as they move through the demographic transition. At the same time, they go from high proportions of children and few elderly to low proportions of children and many elderly.

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# <u>Table 2:  Summary Statistics</u>

|<br>1 for variable definitions.<br>**Variable Name**|<br>**Obs.**|<br>**Mean**|<br>**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|AR_d|2,157|0.39|0.49|0.00|1.00|
|AR_per|2,184|14.02|27.97|0.00|100.00|
|AR_days|818|60.15|47.41|1|180|
|AR_discount|823|0.20|0.40|0.00|1.00|
|AR_gap|809|0.08|0.79|-1.00|1.00|
|AP_d|2,100|0.45|0.50|0.00|1.00|
|AP_per|2,069|9.58|21.41|0.00|100.00|
|AP_days|656|31.67|25.77|1|90|
|AP_discount|829|0.07|0.26|0.00|1.00|
|AP_gap|656|0.09|0.70|-1.00|1.00|
|L_Age|2,243|2.57|0.74|1.39|3.99|
|L_Emp|2,239|4.94|1.48|0.00|11.16|
|D_Foreign|2,242|0.07|0.26|0.00|1.00|
|D_State|2,242|0.23|0.42|0.00|1.00|
|D_Export|2,265|0.09|0.28|0.00|1.00|
|Saleslargest_5|1,762|0.57|0.49|0.00|1.00|
|Compmktshare_1|2,180|0.28|0.45|0.00|1.00|
|Lower_price|2,222|0.48|0.50|0.00|1.00|
|New_product|2,223|0.42|0.49|0.00|1.00|
|Suplcust_5|1,646|0.44|0.50|0.00|1.00|
|Bi_mktpower|1,205|0.29|0.45|0.00|1.00|
|LC_unused|2,152|0.07|0.21|0.00|1.00|
|D_Bank|1,549|0.49|0.50|0.00|1.00|
|D_RE|1,457|0.36|0.48|0.00|1.00|
|D_Fam_Informal|1,421|0.20|0.40|0.00|1.00|
|Property_rights|1,935|64.42|38.52|0.00|100.00|
|Uniqueness|2,047|37.53|42.05|0.00|100.00|
|Contract|2,216|0.88|0.33|0.00|1.00|

See Table 1 for variable definitions.

38
Policy Research Working Paper 6504

# **Abstract**

Panel data conventionally underpin the analysis of poverty mobility over time. However, such data are not readily available for most developing countries. Far more common are the “snap-shots” of welfare captured by cross-section surveys. This paper proposes a method to construct synthetic panel data from cross sections which can provide point estimates of poverty mobility. In contrast to traditional pseudo-panel methods that require multiple rounds of cross-sectional data to study poverty at the cohort level, the proposed method can be applied to settings with as few as two survey rounds and also permits investigation at the more disaggregated household level. The procedure is implemented using cross-section survey data from several countries, spanning different income levels and geographical regions. Estimates fall within the 95 percent confidence interval— or even one standard error in many cases—of those based on actual panel data. The method is not only restricted to studying poverty mobility but can also accommodate investigation of other welfare outcome dynamics.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hdang@worldbank.org and p.f.lanjouw@vu.nl.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- 92 -

respect will be increased capacity to monitor the labor market outcomes of training through tracer studies, and enterprise and labor market surveys.

6.46 Alternative Financing: The Bank's exper'.ence is, of course, restricted to investment in training systeris in which the government is a financial partner. With the exception of the subsidized loan program for private vocational schools in Korea (paragraph 3.85), all the institutions financed were, to some degree or another, directly subsidized by governments. Thus the possibilities inherent in privately owned non-profit or profit-making training institutions fall outside the scope of this review.

6.47 In fact, government financing in the establishment of vocational education end training institutions has been the norm in developing countries. The rationale for public financing, when explicitly addressed, has generally been couched in terms similar to those of investments in infant industries. Skilled manpower is necessary for economic growth; public investment in establishing VET capacity is therefore justifiable on the grounds of externalities beyond productivity and earnings gains. Much VET, of course, has been directed towards public sector employment. And in any case, neither fledgling enterprises nor poor clients have had the means to finance training.

6.48 Some vocational and diversified secondary school investments, though justified primarily on anticipated employment outcomes, may in fact have been rather more intended to have broader social benefits (i.e., broader awareness of technology in society), and thus could be thought to hpve merited public financing on the same basis of general education.

6.49 While direct public subsidy has been justifiable in early stages of systems development, even :ln middle income countries, increasing attention has been given to alternative financing arrangements. The impetus for such arrangements comes from two sources. The first is economic. To the extent possible, socially efficient investment requires that those who benefit pay for services received. Vocational training and education ostensibly intended to increase employability and productivity benefit the worker and the firm most directly. [igh private rates of return to training substantiate the theoretical basis for this principle.

6.50 The second is institutional. The sustained effort required to develop complex VET systems depends heavily on stable financial resources. Uncertain government budgets constrain longer-range institutional development, and can have devastating effect on recurrent cost budgets -- and thus institutional effectiveness.

6.51 The principal alternative to direct public financing of training has been the fixed payroll levy to finance nonformal training. Jbiquitous in Latin America, this model of financing has spread to MENA, and to a lesser extent Africa and Asia.
To get around this issue, Juhn, Murphy and Pierce (1991) assume that the returns to observable characteristics are the same for both groups and apply OLS to only one group, and construct an auxiliary equation for the other group. In the context of gender wage gap studies, OLS is applied to the equation for males only. The equation for female workers is constructed as follows: . The implied decomposition is: where . The above expression is computed on the basis of the sample analogs of the parameters of interest. The first term in the twofold decomposition presented in (34) represents the predicted gap while the second stands for the residual gap. As Yun (2009) points out, the residual gap is equal to the structural effect in the standard Oaxaca-Blinder decomposition. Yet, this structural effect represents returns to observable characteristics. It is therefore hard to see how the Juhn, Murphy and Pierce (1991) procedure helps identify the contribution of unobservables. Yun (2009) proposes instead the decomposition defined by equation (33), under the assumption that the expected value of unobservable terms is not equal to zero. However, that author does not provide an implementation procedure corresponding to this situation.

# **4.  Behavioral Responses to Changes in the Socioeconomic Environment**

A key limitation of both the basic Oaxaca-Blinder decomposition and its generalization along the lines discussed in sections 2 and 3 is that they do not account for changes in the behavior of agents in response to changes in their socioeconomic environment which may be due to shocks or policy reform. Given the maintained hypothesis that the living standard of an individual in a given society depends crucially on what he decides to do with his assets (innate and external) subject to the opportunities offered by society, this section focuses on ways of modeling agents’ behavior to account for their reaction to changes in their socioeconomic environment. Standard economic theory explains behavior in terms of the principles of _optimization_ and _market interaction_ . Modeling behavior entails the specification of the following elements (Varian 1984): (i) actions that a socioeconomic agent can undertake, (ii) the constraints she faces, and (iii) the objective function used to evaluate feasible actions. The assumption that the agent seeks to maximize the objective function subject to 31
We conclude this section by noting that there is a risk for regional initiatives to create their own negative externalities. This, for instance, can occur when countries take part in RTAs with policies going against trade facilitation objectives. The obvious example is the need for rules of origin in the absence of a common external trade regime, which represent specific difficulties in for customs enforcement (World Bank, 2005: 68). Another problem can be created by the incentives to harmonize RTA members’ practices, ending up in adopting higher standards than strictly necessary.<sup>32</sup> Thirdly, there is also the risk of incoherence with trade facilitation objectives of the policies agreed when signing the RTA: when adopting the East African Community common external tariff, Uganda had to raise its tariff on vehicles from 9% to 25% (customs duty and import commission), which increases the cost of transport in Uganda (Uganda DTIS, 2006). Finally, regional trade facilitation should not result in trade diversion effects. There are two ways in which this can happen. The most serious one is when trade facilitation measures have their own discriminatory effect. For most trade facilitation measures this is not the case, but it is nevertheless a possibility for several important policies, such as mutual recognition and transport infrastructure policies, in the latter case if the improvement of one mode of transport (road for instance) is privileged over others. More benignly, in a second best world, trade facilitation may simply augment existing distortions if these are left untouched.

# <u>WHICH PLATFORM TO DELIVER REGIONAL TRADE FACILITATION?</u>

_A public good perspective_ The benefits from regional trade facilitation display public or quasi-public goods properties. Public goods indeed vary in their degree of “publicness” (Sandler, 2006) and thus display different properties. One reason why it is important to understand these properties is because this has a bearing on understanding better how the benefits from regional trade facilitation reform ought to be delivered.<sup>33</sup> A public good is a good giving rise to two distinct market failures. The first failure arises because of _non excludability_ : providers of the good cannot prevent others from free riding by consuming it at no cost. The second failure is caused by _non rivalry_ : the consumption of one

> 32 This is a question that is difficult to assess in practice, but if one takes the example of regional trade agreements signed by the European Union, they make clear reference to harmonization to European standards (see Maur, 2005). Otsuki, Wilson and Sewadeh (2001) give an example in how European SPS measures can be particularly burdensome.

> 33 The growing literature on regional public goods offers a general discussion of this. See: Sandler (1998; 2006); Estevadeordal, Franz and Nguyen (2004); Cook and Sachs (1999); Ferroni (2002); Arce and Sandler (2002); Kaul et al. (2003), and Kanbur et al. (1999). Stålgren (2000) offers a review of the literature. See also Rufin (2004) on transport and communication infrastructure, and Holmes et al. (2006) on standards.

14