rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4: Cross-Tabulations of Clusters Based on All Variables vs. Only Variables on Rights**

||**E**<br>Cluster w/|**thiopia**<br>All Variabl|es|**T**<br>Cluster w/|**anzania**<br>All Variab|les|||Cluster w/ A|**Malawi**<br>ll Variabl|es||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Cluster w/|1|2|3|Cluster w/<br>1|2|3||Cluster w/|1|2|3||
|Right Variables||||Right Variables||||Right Variables|||||
|1|33.95|0.26|0.13|1<br>46.84|0.00|1.20||1|44.64|0.00|0.15||
|2|0.32|19.34|0.16|2<br>0.24|17.69|0.00||2|0.00|14.28|0.00||
|3|1.26|0.63|43.95|3<br>0.08|0.08|33.87||3|1.24|0.21|39.48||
||S|um of Diag|onal|97.24<br>S|um of Dia|gonal|98.40||S|um of Dia|gonal|98.40|

<u>Notes: The table reports the cluster in which the landowner is assigned to. Those assigned in the diagonal are the proportion of</u> landowners that were grouped to the same people in both clustering algorithms.

As an additional check, we further analyze different combinations of rights variables in creating clusters. Given that the MCA analysis shows that the right to make improvements on land does not exhibit similar patterns as the other rights, one could potentially take out the right to improve from the clustering algorithm without changing the resulting cluster categorization significantly. When comparing clustering results with all variables and transfer rights (right to use as collateral, bequeath, rent, and sell), and dropping improvement rights, we found that 94 percent of landowners in Malawi), 97 percent in Ethiopia, and 99 percent in Tanzania were groups in the same three clusters.<sup>21</sup> For the rest of the paper, our preferred specification for hierarchical clustering is based on the four transfer rights (without the right to improve).

In Table 5, we experiment with taking out each transfer right one by one, to see how the results change. Again, the clusters created by different combinations of rights are quite robust, with more than 90 percent of individuals generally being grouped the same way. One exception is Ethiopia, where clustering across two rights were effectively compared after dropping one (the right to sell was not available, given how land markets operate in the country, and improvement was automatically taken out in this exercise) and the algorithm created more than 3 clusters. Fortunately, there were only 3 clusters with the most observations, which is what was counted in Table 5, but this resulted in a lower percentage of matches in Ethiopia. For Ethiopia and Tanzania, taking out the right to use as collateral from the algorithm created the least percentage of matches but still fairly large. For Malawi, taking out the rights to bequest, sell or collateral provides a similar percentage of matches.

> 21 Interestingly, neither documented nor economic ownership in Ethiopia followed similar patterns as the right to transfer variables in the MCA. These two variables have to do with land transfers; however, the results indicate that they are separate from the rights to transfer in the country.

19
WPS3728

# Local Economic Structure and Growth

## Rita Almeida∗ World Bank Research Department

#### Abstract

This paper tests how the local economic structure - measured by local sector specialization, competition and diversity - affects the technological growth of manufacturing sectors. Most of the empirical literature on this topic assumed that in the long run more productive regions will attract more workers and use employment growth as a measure of local productivity growth. However, this approach is based on strong assumptions as that of national labor markets. In this paper I show that if we relax this assumption, regional adjusted wage growth is a better measure of regional productivity growth than employment growth. I compare the two measures using data for Portugal between 1985 and 1994. With the regional adjusted wage growth I find evidence of MAR externalities in some sectors and no evidence of Jacobs or Porter externalities in most of the manufacturing sectors. These results are at odds with my findings for employment based regressions which show that concentration and region size has a negative and significant effect in most of the manufacturing sectors. These employment based results are in line with most of the existing literature, which suggests that using employment growth to proxy for productivity growth leads to misleading results.

Keywords: Local Growth, Knowledge spillovers and Productivity. JEL Classification codes: R11, R12, O47.

### World Bank Policy Research Working Paper 3728, October 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._

> ∗I am very grateful to Antonio Ciccone for his guidance and to Pedro Carneiro for several comments. I thank the Economics Department at Universidade do Minho, Braga, and the Ministry of Employment for access to the data and to Ignácio Garcia, Jaume Garcia, Adriana Kugler and Rosella Nicolini for useful comments. I acknowledge the financial support of Fundação para a Ciência e Tecnologia and of the CEPR Research Training Network “ Specialization versus Diversification”. Address: 1818 H Street, NW, Washington DC 20433 USA. Email: ralmeida@worldbank.org.
# **Childcare and Mothers’ Labor Market Outcomes in Lower- and Middle-Income Countries**<sup>1</sup>

Daniel Halim Elizaveta Perova Sarah Reynolds World Bank World Bank University of California, Berkeley

**Keywords:** female labor market outcomes; childcare.

**JEL codes:** J13, J16, J22.

> 1 We thank Frances Mary Beaton-Day, Kathleen Beegle, Helle Bucchave, Amanda Devercelli, Erik Caldwell Johnson, Sherrilee Ann Le Mottee, Rudaba Zahra Nasir, Norbert Schady, Achim Daniel Schmillen, Andrew Mason and members of the Fernald Research Group for helpful discussions and suggestions. All errors remain our own. Funding acknowledgement: This work was partially supported by the Umbrella Facility for Gender Equality (UFGE) in partnership with the Australian Department of Foreign Affairs and Trade. The UFGE has received generous contributions from Australia, Canada, Denmark, Finland, Germany, Iceland, the Netherlands, Norway, Spain, Sweden, Switzerland, the United Kingdom, the United States, and the Bill and Melinda Gates Foundation. The findings, interpretations, and conclusions expressed in this report do not necessarily reflect the views of the Board of Executive Directors of the World Bank or the governments they represent.
- O’Mahony, M. and Timmer, M. P.: 2009, Output, Input and Productivity Measures at the Industry Level: The EU KLEMS Database, _The Economic Journal_ **119** (538), F374–F403.

- Peltzman, S.: 2000, Prices Rise Faster than They Fall, _Journal of Political Economy_ **108** (3), 466–502.

- P ́erez-Barahona, A. and Zou, B.: 2006, A Comparative Study of Energy Saving Technical Progress in a Vintage Capital Model, _Resource and Energy Economics_ **28** , 181–191.

- Pindyck, R.: 1999, The Long-run Evolution of Energy Prices, _The Energy Journal_ **20** (2), 1–27.

- Pindyck, R. and Rotemberg, J.: 1983, Dynamic Factor Demands and the Effects of Energy Price Shocks, _American Economic Review_ **73** , 1066–1079.

- Popp, D.: 2001, The Effect of New Technology on Energy Consumption, _Resource and Energy Economics_ **23** (3), 215–240.

- Popp, D.: 2002, Induced Innovation and Energy Prices, _American Economic Review_ **92** (1), 160–180.

- Sheshinski, E.: 1967, Balanaced Growth and Stability in the Johansen Vintage Model, _Review of Economic Studies_ **34** (2), 239–248.

- Solow, R. M., Tobin, J., von Weizs ̈acker, C. C. and Yaari, M.: 1966, Neoclassical Growth with Fixed Factor Proportions, _The Review of Economic Studies_ **33** (2), 79–115.

- Steinbuks, J.: 2012, Interfuel Substitution and Energy Use in the UK Manufacturing Sector, _The Energy Journal_ **33** (1), 1–30.

- Sue Wing, I.: 2008, Explaining the Declining Energy Intensity of the US Economy, _Resource and Energy Economics_ **30** (1), 21–49.

- Thompson, P. and Taylor, T.: 1995, The Capital-Energy Substitutability Debate: A New Look., _The Review of Economics and Statistics_ **77** (3), 565–69.

- Timmer, M., O Mahony, M. and van Ark, B.: 2007, _EU KLEMS Growth and Productivity Accounts: An Overview_ , Working Paper, University of Groningen and University of Birmingham.

35
We compute the continuous time matrices for every quarter (month in Brazil). All continuous matrices obtained in the paper show high probabilities of embeddability, ranging between 0.98 and 1. Therefore, compatibility of discrete and continuous time estimates does not seem to be an issue in theses particular datasets.

Having obtained the transition rates from the discrete matrices, we then quarterly average the data for Brazil and smooth both Mexican and Brazilian series using a 4 quarter backward moving average as in Fujita and Ramey (2006) to remove high frequency fluctuations in the data. We study the cyclical components of the data by logging and de-trending the series using a HP filter with lambda 1600.

# **III. Cyclical Patterns of Employment Shares**

To set the overall context, we first focus at the evolution of each sector's share of the labor force across 1987-2004 in Mexico and 1983-2001 in Brazil. As a first approximation to the data, Figure 1 shows the unemployment rate and the share of formal employment for both countries. The upper panels show the levels of the series while the lower panels show the HP de-trended cyclical components. The regions corresponding to a recessionary period are shaded to facilitate the visual analysis.<sup>15</sup> Initial inspection suggests two features of the series common to most developing countries. First, for both countries, the behavior of the unemployment rate denotes that the period of analysis includes a number of significant recessions and rapid periods of growth. Output fluctuations for Mexico (not shown), ranged from 8% below trend in the depths of the 1995 recession to 5% above trend in the posterior recovery. Output in Brazil was slightly less variable oscillating between 4% above and below trend.<sup>16</sup> Second, recessions are generally characterized by increasing unemployment rates and shifts in the composition of employment towards informal activities.<sup>17</sup>

> 15 Neither Mexico or Brazil has an institution such as the NBER determining the starting and ending point of a recession, we have shaded those quarters where output is below trend.

> 16 Quarterly output data used in the paper is provided by the national statistical institutes, INEGI for Mexico and IBGE for Brazil.

> 17 Loayza and Rigolini (2007) show that these countercyclical movements in informality are the average for a broad cross section of developing countries.  However,  Fiess, Fugazza and  Maloney  (2007) also show that in numerous Latin American countries, including Mexico,  informality behaves procyclically

12
# Table **B8.** Distribution of Manufacturing Establishments Radio Equipment by Employment Size, Nigeria

_ADIO_ (0WN RADIO-TRANSMITTING EQUIP) FIRMSIZE CEMPLOYEES per FIRM) <!-- Start of picture text --> Frequmncy<br>Percent<br>Roa Pet<br>Cot Pct 11-19 120-49 150-9 1100-1"9 1200-499 1500-999 11000 I<br>I 11 I I grmlover Totcl<br>.......... ................ ........ ........................... ''''+'''''v''''''<br>YES 01 2 10' 119 15 7.14 33 6 36<br>0.00 i .2 559 0 061 8.38 .2 8 3.5 1 36.87<br>0.00 3 03 15.15 i 28.79 22.73 21.21 9.09<br>. 000 571 22.73 54.29 57.69 |9333 75.00<br>.......... ........ ...... ........- ............ - ............ .+<br>NO 1 161 331 34' 16 11 1 21 113<br>8 94 s 18.44 18 8W ' 894 1 0.56 1.12 63.13<br>14.:16 1 29:201 30:09 I 14.16 9.731 0.88 1.77<br>100.00 1 94.29 1 77.27 1 45.71 42.31 1 6.67 25.00<br>,,,,,. . t-. . 4 ---------- . .------- 4.. '.''.''.' . 4..<br>TotaL 16 35 44 35 26 15 a 179<br>8.94 19.55 24.58 19.55 14.53 8.38 4.47 100.00<br><!-- End of picture text -->
# **1. Introduction**

There is a well-established theoretical literature examining the nexus between preferential trading arrangements and multilateral liberalization. Depending on the underlying model, however, theory predicts that preferential liberalization could act as either a stumbling block or a building block to further multilateral liberalization.<sup>1</sup> Beginning in the mid-2000s, empiricists began to provide empirical evidence of the relationships for whether preferential tariff cuts are building blocks or stumbling blocks to subsequent MFN liberalization; nevertheless, even this evidence has left us with a number of puzzles.

The first puzzle is that some environments have identified clear evidence of stumbling block relationships arising in the data, whereas others found evidence of building block effects. The major evidence of stumbling block effects includes Limão (2006) and Karacaovali and Limão (2008) for the effect on Uruguay Round negotiated tariffs for the United States and European Union, respectively, whereas Estevadeordal, Freund, and Ornelas (2008) find evidence of a building block effect for the free trade areas arising for 10 Latin American economies covering 1990-2001.<sup>2</sup> The empirical answer as to whether preferential liberalization leads to multilateral liberalization is thus still substantially unsettled.<sup>3</sup> The natural question that arises is, what is the explanation for the differences in the results?

A secondary puzzle that arises from the rich, cross-country setting for Latin America, is that Estevadeordal et al. (2008) are also able to empirically capture variation arising across different types of preferential trading arrangements. In particular, their building block result is robust across the Latin American countries involved in free trade areas, but there is no evidence of a building block result for the Latin American countries that go “beyond” the free trade area to adopt a common external tariff and ultimately form a customs union.

This latter result is arguably important for a number of other reasons, many of which are tied to the fact that Argentina and Brazil are two of the main countries that are likely driving this particular result, through their ultimate formation of the MERCOSUR customs union. First, these are two of the largest economies in Latin America. Second, unlike a number of the other countries in Latin America whose trade

> 1 Theoretical surveys include Panagariya (2000) and Baldwin and Venables (1995).

> 2 See also Tovar (2012) for Central American Free Trade Agreement (CAFTA) countries; Calvo Pardo, Freund and Ornelas (2010) for ASEAN; Ketterer, Bernhofen and Milner (2015) for Japan; and Ketterer, Bernhofen and Milner (2014) and Mai and Stoyanov (2014) for Canada.

> 3 Recent surveys include Freund and Ornelas (2010) and Limão (forthcoming).

1
|**Table 1**|**0:** **IPAs with 3 or**<br>|**more Core P**<br>|**atents and T**<br>|**est Data Provi**<br>|**sions(Bilatera**<br>|**l Trade, 31 I**<br>|**PAs)**<br>||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Low-IP|(2)<br>AI|(3)<br>BIO|(4)<br>CHEM|(5)<br>ICT|(6)<br>MED|(7)<br>PT|(8)<br>Other|
|**Exports**<br>log(GDPi)|0.303***<br>(0.072)||||||||
|Sector × log(GDPi)||0.478***|0.031|0.239***|0.282***|0.393***|0.378***|0.284***|
|||(0.087)|(0.115)|(0.082)|(0.093)|(0.093)|(0.105)|(0.071)|
|Sector × LI × IPAi|–0.429|0.717|0.709|–0.014|–1.051|0.768|1.355|1.151***|
||(0.315)|(0.441)|(1.043)|(0.699)|(0.863)|(0.545)|(0.852)|(0.399)|
|Sector × LMI × IPAi|–0.559***|0.845*|0.392|0.493|0.844|0.952**|1.033***|0.716***|
||(0.208)|(0.453)|(0.458)|(0.314)|(0.549)|(0.420)|(0.291)|(0.204)|
|Sector × UMI × IPAi|–0.295|0.631|0.248|–0.013|0.945*|0.108|0.520|0.141|
||(0.316)|(0.423)|(0.520)|(0.250)|(0.503)|(0.509)|(0.358)|(0.278)|
|Sector × HI × IPAi|0.135|–0.156|0.271|–0.157|–0.130|0.221|–0.162|–0.072|
||(0.194)|(0.231)|(0.304)|(0.193)|(0.309)|(0.141)|(0.218)|(0.102)|
|**Imports**|||||||||
|log(GDPj)|0.523***<br>(0.048)||||||||
|Sector × log(GDPj)||0.146***|0.014|0.007|0.040|0.114***|0.061***|0.040|
|||(0.020)|(0.056)|(0.029)|(0.046)|(0.017)|(0.023)|(0.025)|
|Sector × LI × IPAj|0.027|0.016|0.770|–0.151|–0.268|0.892***|0.316|0.495***|
||(0.111)|(0.294)|(0.477)|(0.399)|(0.361)|(0.211)|(0.345)|(0.155)|
|Sector × LMI × IPAj|–0.083|0.253|–0.196|–0.082|0.184|–0.017|–0.081|–0.005|
||(0.125)|(0.165)|(0.246)|(0.146)|(0.152)|(0.164)|(0.116)|(0.115)|
|Sector × UMI × IPAj|–0.048|0.194|–0.376|–0.355**|0.141|–0.006|–0.001|–0.136|
||(0.132)|(0.170)|(0.372)|(0.173)|(0.289)|(0.277)|(0.156)|(0.119)|
|Sector × HI × IPAj|<br>0.203**|<br>–0.027|<br>0.282*|<br>–0.028|<br>–0.501**|<br>0.205**|<br>0.077|<br>–0.007|
||(0.081)|(0.110)|(0.169)|(0.172)|(0.234)|(0.083)|(0.104)|(0.062)|
|Observations||||||||1,031,720|
|Number of country pairs||||||||27,089|
|Pseudo_R_<sup>2</sup>||||||||0.950|

_Notes_ : Robust standard errors two-way clustered by exporter and importer in parentheses. Includes exporter- and importer-specific time trends, exporter- and importer group-sector year fixed effects, and bilateral pair fixed effects. TRIPS compliance by country included not reported. Observations at 5-year intervals: 1995, 2000, ... 2015. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1.

30
results square with descriptive statistics indicating that few small firms ever grow up to become large (Biggs, Ramachandran and Shah, 1999, cited in Biggs and Shah, 2003).

**Figure 1: Firm Growth, Size and Age in Côte d’Ivoire**

<!-- Start of picture text --> 220<br>200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30<br>Firm Age (years)<br>Initial Size: 50 Initial Size: 10 Initial Size: 150<br>Employment<br><!-- End of picture text --> _Note_ : The predictions were generated based on the estimates reported in Sleuwaegen and Goedhuys (2002), Table 3, column 1. Specifically, we calculate total growth after _AGE_ years using the formula ( exp ( _g_ ˆ ))<sup>_AGE_</sup> , where _g_ ˆ = α − .0157ln _AGE_ − .0199ln _SIZE_ + .0006 ( ln _SIZE_ ) 2 + .0041 ( ln _AGE_ ln _SIZE_ ) , and α is a constant set to a value that implies that the firm with initial size 100 will have the same size after 30 years. We then multiply initial size by ( exp ( _g_ ˆ ))<sup>_AGE_</sup> to give predicted size after _AGE_ years.

22
Policy Research Working Paper 10617

# **Abstract**

The environment has long been the foundation of human flourishing, but its continued degradation is threatening to reverse recent development gains, especially in human health. This paper analyzes the possible complementarity between natural and human capital by linking high-resolution deforestation data with health outcomes for 0.7 million children across 46 countries. Forest loss is often a consequence of economic activities that may confer market and other benefits. At the same time, it can adversely affect the provision of forest ecosystem services and reduce the associated socioeconomic and environmental benefits for rural communities. The net effect is thus ambiguous.

The paper focuses on the hydrological services provided by forests and exploits quasi-random variation in deforestation upstream to assess the impacts on waterborne disease outcomes for rural households downstream. The results not only indicate increases in diarrheal disease incidence among children under 5 years old, but also offer new evidence of early-life exposure to deforestation on childhood stunting, a well-known indicator of later-life productivity. A case study for Peru shows similar results for diarrheal disease, but a weaker effect of forest loss on stunting. The paper concludes that maintaining natural capital has the potential to generate meaningful improvements in long-run human capital.

This paper is a product of the Environment, Natural Resources and Blue Economy Global Practice and the Office of the Chief Economist, Sustainable Development Practice 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://www.worldbank.org/prwp. The authors may be contacted at rdamania@worldbank.org, lherreragarcia@worldbank.org, hkim32@worldbank.org, leonardoviotti@gmail.com, ezaveri@worldbank.org, sonder@worldbank.org, and chrissiepantoja@gmail.com.

_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
## **4.6 Project Management Measures**

A few key project attributes, including the loan type and size, the year of approval, project duration, and time elapses between different project milestones are directly available in the World Bank’s management information system and included in the estimations.

# **5 IDENTIFICATION STRATEGY**

This review employs three distinct sets of estimation models.

## **5.1 Ordered Probit Regression Models**

The primary model for estimating which country context, reform content, process, and project management characteristics predict the performance of PSM projects (research question 1) is an ordered probit model. This choice seeks to do justice to the ordered nature of the IEG outcome ratings (see section 4.3). A probit model using the simplified binary dependent variable yields more intuitive results and serves as a robustness check. (The estimation results for these models are reported in annex tables A.7 to A.10.)

## **5.2 Heterogeneous Marginal Effect Estimations for PSM and Non-PSM Projects**

The second set of regression estimators seeks to identify whether PSM projects are distinctly sensitive to certain country context factors, relative to non-PSM projects (research question 3). The estimated ordered probit regression models are the same as above, but include interaction terms between the PSM project marker variable and selected country context factors, to estimate differences in the marginal effects of country context characteristics on PSM and non-PSM projects (see annex figures A.2 to A.4).

## **5.3 Nearest Neighbor Matching**

The third set of estimators seeks to identify whether PSM projects are distinctly sensitive to country context factors, by matching PSM projects with similar non-PSM projects in the same country. The review employs nonparametric matching estimators (nearest neighbor matching and matching based on subclassification) to assess whether PSM projects perform distinctively better or worse than projects in other policy fields/sectors in the same country (research question 2). Matching PSM projects with nonPSM projects within the same country serves to reduce bias in the estimated difference due to timeinvariant, unobservable country characteristics.<sup>39</sup> Matching estimations also serve to corroborate the

> 39 To the extent that it can be assured that both PSM projects and non-PSM comparators are approved and implemented around the same time, this matching also allows for reducing bias due to time-variant country characteristics.

22