rafmacalaba/datause-encoder-data huggingface dataset:
As we show in the Data Appendix, our estimates of aggregate infant mortality are internally consistent and are highly correlated with other sources of data that have been used to assess the relationship between per capita GDP and infant mortality, in particular data from the World Development Indicators (WDI) data base (World Bank 2007). However, the estimates of infant mortality we calculate are likely to be more useful to estimate the relationship between fluctuations in per capita GDP and infant mortality for a variety of reasons. First, we have constructed annual series of infant mortality to look at higher-frequency changes than what can be observed in the bi-decennial WDI series. Data like those in the WDI series will have smoothed some of the year-on-year variation in infant mortality in the DHS. A share of the variation that is smoothed is likely to be measurement error, but the remainder likely reflects genuine annual fluctuations in infant mortality. Second, the data in the WDI series would not allow us to adjust for the changing composition of women giving birth during economic expansions or contractions, or to estimate the heterogeneity of responses to economic fluctuations by characteristics of the mother and child.

# **3. Econometric specification and results**

To estimate the association between infant mortality and per capita GDP in our data we proceed in two ways. Our first approach involves collapsing the unit data to the level of the country-year observation. As we show, the results we obtain with the country-year data are similar regardless of the approach taken to control for country-specific deterministic trends or non-stationary processes in the observed GDP and infant mortality series. We therefore next turn to the unit data in order to control for mother- or child- specific covariates, as well as countryspecific deterministic trends, and to inspect possible heterogeneity in the relation between GDP and infant survival.

7
I PoIIcy RESEAR H WORKING PAPER 2779

# Abstract

> L6pez-Acevedo investigates the effects of technology on

> the employment and wages of differently skilled Mexican manufacturing workers using firm panel data from 1992-99. She analyzes the relationship between technology and skill demand. Findings support the skill-

> biased technical change hypothesis. She then examines the temporal relationship of technology adoption to firm

productivity and worker wages. The author finds that skilled labor increases after technology adoption. And wages of both skilled and semi-skilled workers exhibit markedly increased growth rates compared with the growth rate of low-skilled workers. The results show that investment in human capital improves technology-driven productivity gains.

This paper-a product of the Latin America and the Caribbean Region, Poverty Reduction and Economic Management Sector Unit-is part of a larger effort in the region to reduce poverty and inequality through human capital investment. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Michael Geller, room 14-046, telephone 202-458-5155, fax 202-522-2 I1, email address mgellerQu worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gacevedo(a worldbank.org. February 2002. (22 pages)

> _The Policy Research Working Paper Series dissemiinates the findings of work in progress to encourage the exchange of ideas abNoit development issues. An objective of the series is to get the findings out quickly, even if the presentations are less thanT ftully polished. The papers carrv the niames of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are enttirely those of the authors. They do not niecessarily represent the view of the World Bank, its Executive Directors, or the countries thew' representt._

Produced by the Research Advisory Staff
Table 9: Coverage across countries and methods

|||In sa|mple|||Out of|sample||
|---|---|---|---|---|---|---|---|---|
||EBP|Cubist|XGB|BRF|EBP|Cubist|XGB|BRF|
|**Panel A: Assets**|||||||||
|Burkina Faso|0.903|0.768|0.938|0.916|0.968|0.670|0.884|0.872|
|Madagascar|0.673|0.959|0.953|0.976|0.702|0.949|0.944|0.972|
|Malawi|0.777|0.766|0.775|0.833|0.917|0.676|0.702|0.707|
|Mozambique|0.577|0.937|0.901|0.938|0.721|0.952|0.930|0.960|
|Sri Lanka|0.919|0.920|0.892|0.928|0.876|0.806|0.762|0.839|
|Tanzania|0.343|0.723|0.754|0.728|0.519|0.637|0.685|0.662|
|Vietnam|0.974|0.983|0.976|0.983|0.920|0.955|0.926|0.951|
|Average|0.738|0.865|0.884|0.900|0.803|0.806|0.833|0.852|
|**Panel B: Poverty**|||||||||
|Malawi|0.757|0.857|0.899|0.952|0.734|0.727|0.839|0.794|
|Tanzania|0.878|0.910|0.883|0.933|0.865|0.753|0.715|0.886|
|Average|0.818|0.884|0.891|0.942|0.799|0.740|0.777|0.840|

Note: The first four columns present the average coverage rate (using 95-percent confidence intervals) for in-sample areas across simulations for each country. The last four columns present the the average coverage rate for out-of-sample areas across simulations for each country. An area is defined as in-sample if at least one of that area’s enumeration areas is in the sample on a given simulation.
For the percentage of the population over 60 receiving some public sector pension (including means-tested and universal flat), the known pensioner population was divided by the population over 60 in a subset of countries where data were available. This ratio was regressed on labor force coverage where data for both concepts were available. This ratio tended to be slightly lower than the labor force coverage rate due to the immaturity of many schemes, although some countries exhibited ratios greater than one due to early retirement or below 60 retirement ages. The resulting coefficients were applied to the predicted coverage rates for each country to produce the predicted percentage of persons over 60 receiving pensions. These figures were multiplied by the number of persons over 60 in each country in 1990, summed and divided by the total world population over 60 in the same year. The estimates have the advantage of including known coverage data for approximately three-fourths of the world's population, including China, India, Pakistan, Bangladesh, Indonesia, the Former Soviet Union, the United States and most of Europe and Latin America. Nevertheless, there is no attempt in the book to draw more than impressionistic conclusions based on these data and the global figures mentioned should be considered rough estimates. It is also important to note that coverage rates vary year to year and are currently falling in many of the transition economies of Eastern Europe and the FSU and other countries witnessing an increase in the informal sector and a reduction of the state's role as employer.

Sources of Income. Table 2.3 on page 63, refers to the sources of income of old people in low, middle and high income countries. The income divisions used here conform to the categories applied in the World Development Report 1992. Most of the data were derived from surveys including the Luxembourg Income Study which was the source of information for the industrial countries. The citation given in the book refers to a mimeograph paper by Dr. Deborah Mitchell who produced a series of tables related to the income of household heads aged 65 or more based on LIS data from the middle and late 1980s. This citation also applies to Figure 7.1 on page 250 which omits any mention of the group whose non-wage income shares are being described. The most recent LIS data is sometimes referred to as "Wave 2" data as opposed to an earlier survey in the late 1970s-early 1980s. Appendix III presents some of Mitchell's results. Mitchell's summary of income sources for the elderly by country are presented in Appendix III of this Annex.

51
(Maddala 1971), and it is therefore inconsistent. The source of the bias is the jointness effect.

6) Measurement error: The within estimator is more sensitive to measurement errors.<sup>37</sup> This statement assumes implicitly that the measurement error is unaffected by the transformation, so that its relative contribution to the within SS is larger than to the between SS. This possibility is not ruled out, but it should be noted that there is good reason to believe that part of the measurement error is country (or firm) specific, and by the same token it is time specific, and is thus eliminated by the within transformation.<sup>38</sup> It is impossible to generalize on the relative importance of the measurement error in the universe of all panels. What we learn from this study is that the most sensible results come from the within transformation, and this transformation is consistent with the theory formulated above.

7) Diversity of results: Concern has been expressed from the fact that there is a great deal of diversity in the results obtained in production function estimates from panel data depending on how the data are pooled (Griliches and Mairesse 1998; Mairesse 1990). The diversity is a problem when the working hypothesis is that the estimates should be invariant to way the data are pooled. The general model presented here indicates that one should expect diversity, and in fact the diversity serves as a starting point for the construction of more meaningful models.

8) Instrumental variables: The use of instrumental variables was suggested as a way to overcome the bias in the estimates of panel data (Hausman and Taylor 1981). In the present framework the scope for the use of instrumental variables is rather limited because variables which are associated with the choice of inputs are assumed also to affect the choice of the function itself. In other words, the instrumental variables fall in the category of state variables in the present framework. The same argument also rules out the GMM estimator.

> 37  See Griliches and Mairesse (1998).

> 38  For a fuller discussion, see Mundlak (2001).

29
> <sup>35</sup>

First, the results are the first direct evidence of the significant policy effects of voter attachment to parties and, more generally, of strong parties; these effects merit more investigation, however. For example, while our data have allowed us to show what legislators do _not_ do in constituencies that exhibit voter attachment, we have little evidence on whether they reallocate their effort to non-legislative endeavors (shirking), to effort on the provision of public goods, or to party-building. Similarly, our mixed evidence on incumbent experience and constituency service argues for greater empirical and theoretical examination of the role of legislator experience in constituency service.

Second, the policy importance of voter attachment underlines the need to understand it better. How and under what conditions, for example, do voters develop an ideological or social affinity for parties and candidates, independent of their policies? What is the contribution that candidate selection makes to attachment?

Third, a growing body of influential research points to the large impact that citizen information has on government policy performance. The qualitative evidence that we present on the evolution of MPLADS reinforces the catalytic role of information: MP disbursements of their MPLADS allocations surged with a dramatic increase in media focus on those disbursements. However, the evidence suggests that a complementary factor was critical for media influence to have this impact: the production of a report by a non-media organization (the Auditor General). This suggests that the information- _gathering_ and information- _dissemination_ roles of media need to be separately analyzed.

Fourth, research on the effect of electoral rules that privilege particular social groups to compensate for generations or centuries of discrimination and oppression has yielded mixed evidence of their efficacy. Our analysis of seat reservations in India suggests why this might be the case: the legislator incentives generated by these rules are significantly affected
# **Table 10**

Dependent Variable: Female Employment

|VARIABLES|(1)<br>OLS-FE|(2)<br>OLS-FE|(3)<br>IV-FE|(4)<br>IV-FE|(5)<br>IV-FE|(6)<br>IV-FE|(7)<br>IV-FE|(8)<br>IV-FE|(9)<br>IV-FE|(10)<br>IV-FE|
|---|---|---|---|---|---|---|---|---|---|---|
|POST|-0.33*|0.06|-441.90|2.15|-5.59|0.67|-70.28|1.02|-41.74|3.47|
||(0.17)|(0.11)|(3,142.36)|(10.42)|(19.65)|(8.83)|(115.76)|(12.18)|(48.40)|(11.82)|
|POST_Dictator|-0.02|-0.29**|-1.66|-0.43|-0.97|-0.37|-1.75|-0.40|-1.68|-0.44|
||(0.14)|(0.11)|(18.43)|(0.57)|(1.56)|(0.50)|(2.99)|(0.33)|(2.06)|(0.48)|
|POST_Labor|0.33***|0.18|403.52|-1.30|6.18|-0.01|69.53|-0.09|44.16|-2.90|
||(0.12)|(0.12)|(2,861.52)|(10.38)|(20.56)|(9.08)|(106.69)|(13.09)|(49.88)|(12.12)|
|Time|0.11**|-0.02|4.80|-0.10|0.13|-0.07*|0.36|-0.11|-0.01|-0.07*|
||(0.05)|(0.02)|(33.77)|(0.09)|(0.08)|(0.04)|(1.46)|(0.13)|(0.14)|(0.04)|
|Timesq|-0.00|0.00*|-0.20|0.01|-0.00|0.01**|-0.01|0.01|0.01|0.01**|
||(0.00)|(0.00)|(1.38)|(0.01)|(0.01)|(0.00)|(0.07)|(0.01)|(0.01)|(0.00)|
|WorkingAgePop||0.00***||0.00**||0.00***||0.00**||0.00**|
|||(0.00)||(0.00)||(0.00)||(0.00)||(0.00)|
|rGDP||0.00||-0.00||0.00||0.00||-0.00|
|||(0.00)||(0.00)||(0.00)||(0.00)||(0.00)|
|LFPRate||0.04***||0.07*||0.07*||0.08**||0.07**|
|||(0.01)||(0.04)||(0.04)||(0.04)||(0.03)|
|Observations|785|770|564|564|564|564|564|564|564|564|
|Number of Countries|69|69|59|59|59|59|59|59|59|59|
|R-squared|0.29|0.83|||||||||
|Partial R-squared|||0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Hansen J p-value|||.|.|0.23|0.72|.|.|0.74|0.82|
|Kleibergen-PaapF statistic|||0.01|0.19|0.36|0.13|0.11|0.12|0.33|0.12|

Note: Robust and clustered standard errors at the country level are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. POST is instrumented with the debt-to-GDP ratio, terms of trade, and five-year change in democracy score in columns (3) and (4). In columns (5) and (6) the instruments also include lagged POST. POST is instrumented with external debt-to-GDP ratio interacted with the change in world interest rates, terms of trade, and five-year change in democracy score in columns (7) and (8). In columns (9) and (10) the instruments also include lagged POST.

36
**Table S4.13.** ITT effects on number of adopted ISFM technologies and integrated adoption of the full ISFM package, excluding model farmers.

|||Number|of ISFM te|chnologies|adopted||Integrated a<br>|doption of full<br>|
|---|---|---|---|---|---|---|---|---|
||OL|S|Pois|son|Opr|obit|ISFM|package|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|T1 (extension)|0.582***|0.390***|0.582***|0.404***|0.460***|0.463***|0.077*|0.066***|
||(0.183)|(0.076)|(0.176)|(0.085)|(0.136)|(0.082)|(0.042)|(0.024)|
|p-value|0.002|0.000|0.001|0.000|0.001|0.000|0.068|0.005|
|T2 (extension + video)|0.737***|0.526***|0.717***|0.508***|0.597***|0.629***|0.106**|0.091***|
||(0.178)|(0.081)|(0.166)|(0.086)|(0.138)|(0.090)|(0.044)|(0.025)|
|p-value|0.000|0.000|0.000|0.000|0.000|0.000|0.015|0.000|
|Test T1 >= T2 (p-value)|0.221|0.064|0.220|0.117|0.196|0.051|0.276|0.189|
|Endline control mean|||2.2|22|||No|Yes|
|Additional controls|No|Yes|No|Yes|No|Yes|0.|152|
|(Pseudo)R-squared|0.054|0.525|0.012|0.121|0.019|0.221|0.015|0.271|
|Observations|2,300|2,300|2,300|2,300|2,300|2,300|2,078|2,078|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Poisson and probit models (columns (3), (4), (7) and (8)) show average marginal effects (AME). Number of ISFM technologies adopted ranges from 0 to 5. Integrated adoption of full ISFM package is a dummy variable. 82 model farmers from treatment groups excluded. Additional control variables identical to those listed in notes of Table 2. One-sided equality tests of T1 and T2 are F-tests or Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

20
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