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- Gale, D. and L. S. Shapley (1962). College admissions and the stability of marriage. _The American Mathematical Monthly 69_ (1), 9–15.

- Graversen, B. K. and J. C. Van Ours (2008). How to help unemployed find jobs quickly: Experimental evidence from a mandatory activation program. _Journal of Public economics 92_ (10), 2020–2035.

- Groh, M., D. McKenzie, N. Shammout, and T. Vishwanath (2015). Testing the Importance of Search Frictions and Matching Through a Randomized Experiment in Jordan. _IZA Journal of Labor Economics 4_ (1), 1–20.

- Hardy, M. and J. McCasland (2015). Are small firms labor constrained? experimental evidence from ghana.

- Heath, R. (2016). Why do firms hire using referrals? evidence from bangladeshi garment factories. _The Journal of Political Economy (Forthcoming)_ .

- Hoffman, M., L. B. Kahn, and D. Li (2015). Discretion in hiring. _NBER Working Paper No. w21709_ .

- Horton, J. J. (2017). The Effects of Algorithmic Labor Market Recommendations: Evidence from a Field Experiment. _Journal of Labor Economics 35_ (2), 345–385.

- Jensen, R. (2012). Do Labor Market Opportunities Affect Young Women’s Work and Family Decisions? Experimental Evidence from India. _The Quarterly Journal of Economics 127_ (2), 753–792.

- Krueger, A. B. and A. I. Mueller (2016). A contribution to the empirics of reservation wages. _American Economic Journal: Economic Policy 8_ (1), 142–179.

- McKenzie, D. J. (2017). How Effective are Active Labor Market Policies in Developing Countries? A Critical Review of Recent Evidence. _Working Paper_ .

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- Pallais, A. (2014). Inefficient Hiring in Entry-Level Labor Markets. _The American Economic Review 104_ (11), 3565–3599.

24
intermediate input effects, and technological spillovers. Nadvi (1997, 1999) and Meyer-Stamer (1998) recognize that clustering offers unique opportunities for firms to take advantage of a wide array of domestic links between users and producers and between the economy’s knowledge sector and its business sector. Such linkages have the potential for stimulating learning and innovation.

Clusters, however, are not necessarily innovation systems (McCormick and Oyelaran-Oyeyinka 2007), and innovative clusters are not necessarily high-technology clusters. Mytelka (2004) also emphasizes the role of clusters in promoting the kind of interactivity that stimulates innovation but cautions that the geographic proximity of actors does not automatically lead to learning and innovation. However, there is a growing recognition that cluster initiatives could be an effective means for producing an environment conducive to innovation (Andersson et al. 2004). All these arguments can find their roots in different cluster examples.

Although clusters come in several different forms and various scholars have tried different typologies, all clusters share one commonality: each comprises a multitude of firms of different sizes belonging to one branch of industry. Markusen (1996) has classified clusters into four categories: Marshallian, hub and spoke, satellite platform, and state anchored (see table 1.2). Others have described them by development stage, such as agglomeration, emerging, potential, and mature.

# **SEZs and Clusters: “Top-Down” versus“Bottom-Up”?**

While SEZs are normally constructed through a “top-down” approach by government policies, most clusters are formed in an organic way through a “bottom-up” process. Some clusters, however, have emerged from or within industrial parks or exportprocessing zones over time but rarely in developing countries. A study of 11 African clusters across several countries reveals that most of them formed spontaneously, with the exception of the Mauritian textile cluster, which evolved from an export-processing zone (Zeng 2008).

Table 1.2 Markusen’s Typology of Industry Clusters

|Cluster type<br>growth|Characteristics of<br>member firms|Intra-cluster<br>interdependencies|Prospects for<br>employment|
|---|---|---|---|
|Marshallian|Small and medium-size<br>locally owned firms|Substantial inter-firm<br>trade and collaboration;|Dependent on synergies<br>and economies<br>provided by cluster|
|Hub and|One or several large|Cooperation between|Dependent on growth|
|spoke|firms with numerous<br>smaller supplier and|large firms and smaller<br>suppliers on terms of the|prospects of large firms|
||service firms|large firms (hub firms)||
|Satellite|Medium-size and large|Minimum inter-firm trade|Dependent on abilityto|

6
magnitude: 10 _._ 6 percent.

# **India**

The estimates of father-son schooling persistence for India are reported in columns (3) and (4) of the top panel of Table 3. The IGRC estimate for India shows that the downward bias due to coresidency is substantial; the estimate from the full sample is 29 _._ 5 percent higher than the estimate in the coresident sample. The bias in the father-son sample in India is thus significantly larger than the average bias we found earlier for the sample of all children across different measures of parental education (17 _._ 6 percent). In sharp contrast, the IGC estimate suffers from very little coresidency bias: 2 _._ 4 percent only. The estimated bias in the IGC estimate for father-son in India is thus ignorable, while the IGRC estimate suffers from strong downward bias from coresident sample selection.

The bias estimates for mother-daughter schooling persistence in India are reported in columns (3) and (4) of the lower panel of Table 4. The bias in the IGRC estimate for mother-daughter is smaller for India when compared to Bangladesh, but the magnitude of bias is still substantial 21 _._ 8 percent. The corresponding biases in IGC estimates is 9 _._ 7 percent which is less than half of the bias in the IGRC estimate.

# **Gender Differences in Intergenerational Schooling Persistence**

An important policy issue in many developing countries is whether the girls face especially strong barriers to educational mobility. If we rely on the IGRC estimates from coresident samples, the gender gap may seem smaller than it really is, because the truncation bias is usually stronger for the estimates for girls, as coresidency rates are lower (this is true in both Bangladesh and India data). But the IGC estimates from coresident samples provide a reliable measure of the gender gap. For example, consider the estimates of father-son and mother-daughter persistence in Bangladesh (Tables 3 and 5).<sup>21</sup> Averaging over estimates for four age ranges in Tables 3 and 5, the IGRC estimates from coresident

> 21We focus on father-son and mother-daughter links as the persistence runs along gender lines with cross much smaller.

17
are not elsewhere available in the public domain in some form already. Of these six, in many cases we do not average questions anyhow, and so there is no further level of disaggregation to disclose.<sup>23</sup> In fact, for the Control of Corruption indicator, which is by far the most widely-used of six WGI measures, there is not a single case where the data is not freely publicly available at the lowest level of disaggregation, either through our website or elsewhere.

We thus find T's concerns about access to underlying data, and the consequences for scrutiny and peer review, to be vastly exaggerated. With the important partial exception of the CPIA data noted above, all of the data used to construct the WGI is in fact publicly available on our website and can be used for replication and robustness testing. In those cases where users might for some reason want to further disaggregate the data from individual data sources, all such data are available, in most cases freely although sometimes on a commercial basis. We note also that our level of data disclosure exceeds accepted standards in the economics profession (again of course with the unavoidable exception of the confidential CPIA data).<sup>24</sup> We certainly agree with T about the importance of public scrutiny of all types of governance data. Our personal views are that the usefulness and credibility of the CPIAs would be enhanced if they were fully public for all countries and all years. We are also concerned that a number of new initiatives of international organizations to measure public sector accountability, such as the Public Expenditure and Financial Accountability (PEFA) initiative and the OECD-Development Assistance Committee's assessments of public sector procurement practices are proceeding apace with insufficient prior guarantees that the resulting data will be made public.<sup>25</sup>

> 23 This applies to roughly 40 percent of the country-year observations from our main commercially-available data sources.

24 The two most prestigious general-interest journals in economics, the American Economic Review and the Journal of Political Economy, share the same explicit data disclosure policy (see http://www.aeaweb.org/aer/data_availability_policy.html and http://www.journals.uchicago.edu/JPE/datapolicy.html). The general principle is to post data in order to allow replication of reported results, which is what we have done. Beyond this, in the case of commercially-available datasets, the policy requires only that authors provide instructions on how to obtain such data, as long as third parties are in principle able to obtain the same dataset. We have gone well beyond this standard by disclosing the commercial and noncommercial data required to replicate our results. We therefore reject T's claims that we have not made our data sufficiently available for replication and peer review, at least by the accepted standards of our profession.

25 For example, the PEFA indicators have been constructed for roughly 30 countries as of end2006, but in only nine cases are the data publicly available.

28
# **Table 5:  Common Sources in Pairwise Comparisons of Governance**

||**Number of**|**Average**<br>**Number of**|**On Common S**|**Fraction of Com**<br>**ources Numberin**|**parisons Based**<br>**g More Than or**|<br>**Equal to:**|**Number of Com**<br>**as Fraction of:**|**mon Sources**|
|---|---|---|---|---|---|---|---|---|
|**2006**|**Comparisons**|**Common Sources**|**1**|**3**|**5**|**7**|**Minimum**|**Maximum**|
|Voice and Accountability|21736|5.92|1.00|0.84|0.63|0.47|0.91|0.53|
|Political Stability|21736|4.88|0.99|0.73|0.53|0.37|0.95|0.58|
|Government Effectiveness|22366|5.62|0.99|0.74|0.58|0.44|0.86|0.50|
|Regulatory Quality|21115|5.19|0.99|0.70|0.59|0.42|0.85|0.52|
|Rule of Law|22155|7.15|0.98|0.75|0.61|0.55|0.85|0.48|
|Control of Corruption|21321|5.18|0.98|0.67|0.55|0.38|0.80|0.44|
|_Average_|_21738_|_5.66_|_0.99_|_0.74_|_0.58_|_0.44_|_0.87_|_0.51_|
|**2002**|||||||||
|Voice and Accountability|20100|4.38|1.00|0.70|0.52|0.23|0.93|0.55|
|Political Stability|17955|3.90|1.00|0.66|0.38|0.15|0.95|0.54|
|Government Effectiveness|20301|4.49|0.99|0.70|0.45|0.26|0.83|0.49|
|Regulatory Quality|19306|4.32|0.96|0.67|0.47|0.27|0.80|0.49|
|Rule of Law|19306|5.75|0.96|0.71|0.58|0.44|0.80|0.48|
|Control of Corruption|19306|3.77|0.96|0.56|0.38|0.20|0.78|0.44|
|_Average_|_19379_|_4.44_|_0.98_|_0.67_|_0.46_|_0.26_|_0.85_|_0.50_|
|**1996**|||||||||
|Voice and Accountability|18721|2.55|1.00|0.48|0.07|0.00|0.96|0.59|
|Political Stability|16110|2.39|0.99|0.41|0.09|0.00|0.94|0.57|
|Government Effectiveness|16471|2.06|0.89|0.34|0.08|0.01|0.80|0.45|
|Regulatory Quality|16653|2.65|0.93|0.47|0.14|0.01|0.81|0.50|
|Rule of Law|14535|3.87|0.98|0.65|0.40|0.12|0.91|0.53|
|Control of Corruption|11781|2.71|0.94|0.51|0.14|0.08|0.92|0.50|
|_Average_|_15712_|_2.70_|_0.96_|_0.48_|_0.15_|_0.04_|_0.89_|_0.52_|

31
Resources Assessment data of the FAO.<sup>7</sup> For example, a sq km of forest in India stores 4,100 tons of CO2. In Brazil, that figure is 12,100 tons; in Indonesia it is 13,800 tons.

The CO2 equivalent of excess forest can be priced at the GCI to get an excess forestry benefit for a country. Of course, the specific model used to determine the predicted area of forests needs to be worked out. A potential first pass is in Annex 1 which computes the CO2 stored by the ‘excess’ forest in 2019 controlling for a country’s income level, population density, temperature and precipitation, land area, agricultural land, and forest endowment in 1990. Table 3 highlights the top 10 countries that could be credited for forestry based on our first approximation estimates.<sup>8</sup> China leads the way. Its initial forest coverage was around 1.6 million sq km in 1990. Given its zChina to have around 1.8 million sq km of forest in 2019. Its actual forest cover in 2019, however, is around 2.2 million sq km. Hence it overperformed compared to the global average, making it eligible for USD 0.36 billion as credit using a GCI of USD 0.3 per ton.

The key finding is that measures that protect forest cover, over and beyond a country’s development, natural endowment and demography, have only a limited impact in offsetting carbon emissions. The change in the stock of emissions since 1990 is mostly the result of rapidly increasing production and consumption of carbon.

> 7 http://countrystat.org/home.aspx?c=FOR.

> 8 Countries could also be credited at the GIC rate on a flow basis for the carbon absorbed annually by their “excess” forests. This would create an additional incentive to maintain, or even expand, forest cover. These calculations can be done using the methods already outlined.

12
# **Conclude Doha: It Matters!**

Bernard Hoekman<sup>1</sup> Will Martin Aaditya Mattoo Keywords: trade, liberalization, WTO, Doha Agenda JEL codes: F1, F2, F5

> 1 World Bank. E-mails: <u>Bhoekman@worldbank.org; Amattoo@worldbank.org; Wmartin1@worldbank.org.</u> The views expressed in this paper are personal and should not be attributed to the World Bank. We are grateful to Olivier Cattaneo, Jean-Pierre Chauffour, Stuart Harbinson, Alejandro Jara, Richard Newfarmer, Bonapas Onguglo, Roy Santana and Alan Winters for comments on an earlier draft, and to Marcelo Olarreaga, Francis Ng and Chad Bown for data and analytical inputs. This paper was prepared under the aegis of the DFID-supported Global Trade and Financial Architecture project.
4

**Table 1: Evolution of GNP per Capita (in US$) for a Selected Group of CEE Countries.**

||**GNP Data**<br>**1990**|**Based on W**|**B Atlas M**<br>**1992**|**ethodology**|**1994**||**GNP-PPP**<br>**1994**|**Estimate**|
|---|---|---|---|---|---|---|---|---|
||Level|Share<br>of EU<br>Average|Level|Share<br>ofEU<br>Average|Level|Share<br>of EU<br>Average|Level|Share<br>ofEU<br>Average|
|Czech|3490|22|2570|14|3200|17|8900|53|
|Hungary|2910|18|3120|17|3840|20|6080|36|
|Poland|1700|11|1950|11|2410|13|5480|33|
|Slovakia|3330|21|2250|12|2470|13|6450|38|
|Slovenia|9500|59|6770|37|7020|37|6230|37|
|EU Average|16185||18532||19005||16757||

Source: The GNP data for 1994 in PPP terms is from the 1996 World Development Report.

Considerable thinking has gone into the elaboration of policy agendas that would allow sustained medium-term growth. Not surprisingly, given the relative similarity of initial structural conditions, the prescriptions for most CEE countries have been fairly similar. They include (i) strengthening property rights and the administration of government services, including the adequacy of general legal provisions to functioning of market mechanisms (from bankruptcy laws to collateral legislation); (ii) completing the privatization agenda; (iii) strengthening banking regulation and supervision to support the development of capital markets, crucial for the continued development of private sector activities; (iv) controlling the size of the state (particularly on the social spending front) and eliminating disincentives against labor in the tax system; and (v) opening these economies to the flow (both inward as well as outward) of goods and services to enable a more efficient allocation of resources. Although these policy agendas (both those implemented as well as those that are still pending) are widely accepted as conducive to high economic growth rates, little has been done to examine their growth implications. The aim of this paper, therefore, is to derive lessons from the economic literature that may help to link these structural reforms with the growth prospects of CEE countries.

We develop a model to examine the issue of bridging of income gaps and derive some lessons for the EU accession of Central and Eastern European countries. This paper is organized as follows. We begin by presenting a background discussion on alternative concepts of convergence
# **1 Introduction**

Parents play an important role in their children’s educational experiences and outcomes (Cunha, Heckman, Lochner, & Masterov, 2006; Houtenville & Conway, 2008; Todd & Wolpin, 2007). However, parents often face challenges when supporting their children through school. For example, parents can hold inaccurate beliefs about the returns to education (Attanasio & Kaufmann, 2014; Jensen, 2010; Nguyen, 2008) and about their own children’s academic performance and behaviors (Banerjee, Banerji, Duflo, Glennerster, & Khemani, 2010; Dizon-Ross, 2019). These biased beliefs can lead to misallocation of educational investments. Parents may also have limited cognitive bandwidth to respond to the various tasks associated with supporting their children’s education (Mani, Mullainathan, Shafir, & Zhao, 2013; Mullainathan & Shafir, 2013). Challenges may also derive from organizational structures. Schools often assume that parents are familiar with how to engage with teachers. This assumption can lead to systematic exclusion of low-income, culturally, and linguistically diverse parents from advocating for their children’s needs and accessing school resources (J. S. Lee & Bowen, 2006).

To overcome the range of challenges that parents face, parental involvement programs (also known as family engagement programs) increase school-and-parent communication to support children’s overall learning environment. In this paper, we analyze data from two field experiments to examine the impacts of parental involvement interventions on parent and teacher behavior, and educational outcomes. The experiments were conducted across 430 public schools in four states in Mexico. These states have a large indigenous population that has faced a long history of discrimination. In this setting, parental involvement programs hold particularly great promise for improving school-and-parent communication and supporting the education of indigenous children.

The first experiment focuses on financial grants to parent associations. Schools assigned to the treatment condition received double the typical grant amount allocated to parent associations. This additional grant money was modest, as it covered approximately 83% of the out-of-pocket educational costs spent by parents in our study setting. Schools assigned to the control group received the standard grant amount that is allocated to parent associations in Mexican public schools.

The second experiment focuses on information provision to parents. Parents in treatment schools 1
<!-- Start of picture text --> mEmanagementsore (median) ©——-managementsore (SD)<br><!-- End of picture text -->

||Mean|Std.Dev.|p(10)|p(25)|p(50)|p(75)|p(90)|
|---|---|---|---|---|---|---|---|
|||<br>Manuf<br>|acturin<br>|g<br>|||<br>|
|Exporter|0.567|0.125|0.403|0.485|0.576|0.673|0.712|
|Non-Exporter|| 0.522|0.159|0.313|0.413|0.521|0.646|0.718|
|||Ser|vices|||||
|Exporter|0.534|0.158|0.318|0.431|0.550|0.647|0.717|
|Non-Exporter|| 0.525|0.146|0.326|0.450|0.536|0.638|0.694|
|Notes:‘Managem|entScore’|iscalculated|followi|ngMOPS|(SeeBl|oometal|.,2018).|
54

# **Figure lb: Macroeconomic instability in Brazil: Per capita GDP**

<!-- Start of picture text --> 10<br>8 7<br>8_<br>|~~~~~~~~~~~~<br>6 4 1=&||3 3-<br>2IiIIII<br>g~~~~ L,1:11<br>i (2) _ -1--<br>u ~~~~~~~~~~~~~~~~-2 -2<br>A4 (4) __ _ _<br>(6) -5 _<br>-6 -6<br>(8) - - _ _<br>1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996<br><!-- End of picture text -->

<!-- Start of picture text --> Years<br><!-- End of picture text -->

## Source: IBGE (1999).
(9 years of schooling), and for comparability across generations, we set primary schooling as the cut-off. However, in children’s generation, at least one of the parents has more than primary schooling in 60 percent households. The evidence also indicates that having more than middle schooling makes a significant difference in non-farm occupations (de Baruw and Rozelle (2008)). This implies that middle school may be a more discriminatory cutoff for the parents in the children’s generation. In this appendix, we report estimates of the effects of family background for the children’s generation using middle school as the relevant threshold for parental education.

Using middle school (9 years schooling) as the relevant cut-off, however means that the sample of low educated (less than or equal to 9 years of schooling) farmers now includes the subset of farming parents with 7-9 years of schooling. If we use this subsample as the comparison group, then the estimates are not comparable to the estimates reported earlier in Tables 2 and 3 in the text where the comparison group is composed if farmers with primary or less schooling. For the sake of consistency, we thus use the subsample of farmers with primary or less schooling as the comparison group, and estimate the effects of having at least one parent more than middle schooling for the farmer and non-farmer parents. The results are reported in Tables A1, A2, and A3 in this appendix.<sup>38</sup> Table A1 reports the estimates from OLS, matching, and NIPW estimators, similar to Tables 2 and 3 in the text. Table A2 provides the estimates from sensitivity analysis and Table A3 reports the causal estimates from MB-NIPW and Lewbel estimators.

The estimates in Table A1 are consistent with a priori expectations: the effects of having at least one parent with more than middle school education on the schooling of children are larger compared to the effects in Table 2 where the cut-off is primary schooling.

> 38We also estimated the effects using the subsample of farmers with 9 years or less schooling as the comparison group. The results are available from the authors.

49