rafmacalaba/datause-encoder-data huggingface dataset:
**Table A.1 Trends in Coverage of Urban Worker Social Insurance Programs**

||Pension<br>|Unemployment<br> <br>Medical<br>|Work Injury<br>|Maternity<br>|
|---|---|---|---|---|
|Year|Insurance|Insurance<br>Insurance|Insurance|Insurance|
|||Contributors (Million)|||
|1993|80.1|79.2<br>2.7|11.0|5.6|
|1994|84.9|79.7<br>3.7|18.2|9.2|
|1995|87.4|82.4<br>7.0|26.1|15.0|
|1996|87.6|83.3<br>7.9|31.0|20.2|
|1997|86.7|79.6<br>15.9|35.1|24.9|
|1998|84.8|79.3<br>15.1|37.8|27.8|
|1999|95.0|98.5<br>15.1|39.1|29.3|
|2000|104.5|104.1<br>28.6|43.5|30.0|
|2001|108.0|103.5<br>54.7|43.5|34.6|
|2002|111.3|101.8<br>69.3|44.1|34.9|
|2003|116.5|103.7<br>79.7|45.7|36.6|
|2004|122.5|105.8<br>90.4|68.5|43.8|
|2005|131.2|106.5<br>100.2|84.8|54.1|
|2006|141.3|111.9<br>115.8|102.7|64.6|
|2007|151.8|116.4<br>134.2|121.7|77.8|
|2008|165.9|124.0<br>149.9|137.9|92.5|
|2009|177.4|127.2<br>164.1|149.0|108.8|
|2010|194.0|133.8<br>177.9|161.6|123.4|
|||Coverage (%)|||
|1993|43.9|43.4<br>1.5|6.0|3.1|
|1994|45.5|42.7<br>2.0|9.8|4.9|
|1995|45.9|43.3<br>3.7|13.7|7.9|
|1996|44.0|41.8<br>4.0|15.6|10.1|
|1997|41.7|38.3<br>7.6|16.9|12.0|
|1998|39.2|36.7<br>7.0|17.5|12.8|
|1999|42.4|44.0<br>6.7|17.5|13.1|
|2000|45.1|45.0<br>12.4|18.8|13.0|
|2001|45.1|43.3<br>22.9|18.2|14.4|
|2002|44.9|41.1<br>27.9|17.8|14.1|
|2003|45.4|40.5<br>31.1|17.8|14.3|
|2004|46.3|40.0<br>34.2|25.9|16.6|
|2005|48.0|39.0<br>36.7|31.0|19.8|
|2006|49.9|39.5<br>40.9|36.3|22.8|
|2007|51.7|39.7<br>45.7|41.5|26.5|
|2008|54.9|41.0<br>49.6|45.6|30.6|
|2009|57.0|40.9<br>52.7|47.9|34.9|
|2010|55.9|38.6<br>51.3|46.6|35.6|

Note: (1) Retirees/beneficiaries are not included; (2) the coverage of social insurance schemes equals contributors divided by urban employment. Source: NBS, China Labor Statistical Yearbook (2010), China Statistical Yearbook (2011), China Statistics Press, Beijing.

41
**Table 7b: Ratio of Per Capita Annual Consumption evaluated at different PPPs to Per Capita Annual Consumption of India evaluated at the corresponding PPP**

|||**PCAgC ev**<br>|**aluated at**<br>|||**PCAgC ev**<br>|**aluated at**<br>|||**PCAgC**<br>|**evaluated at**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Region**|**Country**|**2011 ICP**<br>**consumption**<br>**PPP***|**Counter**<br>**factual**<br>**PPP***|**Region**|**Country**|**2011 ICP**<br>**consumption**<br>**PPP***|**Counter**<br>**factual**<br>**PPP***|**Region**|**Country**|**2011 ICP**<br>**consumptio**<br>**n PPP***|**Counter**<br>**factual**<br>**PPP***|
||Benin|0.487|0.610||South Africa|2.739|1.632||Albania|2.685|<br>7.947|
||Burkina Faso|0.315|0.561||Swaziland|1.926|3.107||Bosnia and H.|2.801|<br>0.387|
||Cameroon|0.760|0.933||Tanzania|0.341|0.601||Bulgaria|3.629|<br>0.591|
||Chad|0.488|0.480||Togo|0.395|0.580||Latvia|4.544|<br>0.903|
||Congo, Rep.|0.148|0.389|**AFRICA**|Uganda|0.460|0.586||Lithuania|5.471|<br>1.250|
||Congo,Dem. Rep.|0.501|0.312||Zambia|0.588|1.026||Macedonia(FYR)|3.137|<br>0.642|
||Cote d'Ivoire|0.655|0.515||Bangladesh|0.707|0.645|**EUROSTAT-**|||<br>|
||Djibouti|0.569|0.337||Bhutan|1.323|0.777|**OECD**|Mexico|3.918|<br>1.911|
||Egypt|2.822|1.282||Cambodia|0.753|0.603||Montenegro|4.074|<br>0.645|
||Ethiopia|0.324|0.287||China|1.433|0.861||Romania|3.700|<br>0.972|
||Gabon|1.977|0.798||Fiji|1.786|1.021||Russia|5.020|<br>0.967|
||Gambia(The)|0.404|0.472||**India**|1.000|1.000||Serbia|3.344|<br>0.671|
||Ghana|0.742|0.732||Indonesia|1.590|1.358||Turkey|4.543|<br>5.576|
||Guinea|0.261|0.290||Lao PDR|0.774|0.631||Bolivia|1.211|<br>0.592|
|**AFRICA**|Kenya|0.641|0.700|**ASIA AND**|Maldives|1.285|0.696||Brazil|3.277|<br>1.509|
||Lesotho|0.835|1.040|**THE**|Mongolia|1.820|1.121||Colombia|2.592|<br>1.223|
||Liberia|0.200|0.004|**PACIFIC**|Nepal|0.611|0.491|**LATIN**<br>|El Salvador|2.410|<br>2.375|
||Madagascar|0.441|1.082||Pakistan|1.299|1.012|**AMERICA**|Guatemala|2.058|<br>0.914|
||Mali|0.346|0.473||Philippines|1.486|1.207||Honduras|1.240|<br>1.141|
||Mauritania|0.691|0.471||Sri Lanka|2.115|0.832||Nicaragua|1.187|<br>2.344|
||Mauritius|3.908|1.632||Thailand|2.805|0.730||Peru|2.365|<br>0.930|
||Morocco|1.425|0.649||Vietnam|0.990|0.832||||<br>|
||Mozambique|0.294|0.560||Armenia|2.416|1.184||Iraq|1.780|<br>1.523|
||Namibia|1.928|2.318|**COMMON**|Azerbaijan|2.768|0.840||||<br>|
||Nigeria|0.686|0.794|**WEALTH**|Belarus|3.830|1.013|**WEST ASIA**|Jordan|2.934|<br>0.662|
||Republic of Cabo|||**OF**|Kazakhstan|3.775|1.073||||<br>|
||<br>Verde|1.571|0.556|**INDEPEND**<br>|Kyrgyz Rep.|1.160|0.649||Yemen|0.914|<br>0.339|
||Senegal|0.636|0.693|**ENT**<br>|<br>Moldova|1.870|0.731|**CARIBBEAN**||||
||Sierra Leone|0395|0308|**STATES**|Tajikistan|1.001|0.492||Jamaica|2.396|<br>1.093|
|||.|.||Ukraine|2.616|0.656|||||

*These columns are derived from Table 6b.

45
aggregate indicators to make comparisons of governance across countries and over time, subject to appropriate consideration of margins of error. In this paper we also gave a brief account of the evolution of the WGI over the past decade or so, including how the margins of error have declined over time, mostly due to the increasing availability of individual sources. Thus, while margins of error remain non-trivial, it is worth noting that for the current 2007 data we see that 65 percent of all cross-country comparisons result in highly-significant differences (at 90 percent confidence levels), and that about onethird of countries have experienced substantial changes in at least one dimension of governance between 1998 and 2007.

We also caution users that the aggregate indicators can in some circumstances be a rather blunt tool for policy advice at the country level. We expect that the provision of the underlying data will help users in identifying -- and acting upon -- more specific aspects of governance that may be problematic in a given country. And we also encourage using these aggregate and individual indicators in conjunction with a wealth of possible more detailed and nuanced sources of country-level data and diagnostics on governance in formulating policy advice.

25
WPS 3233

# **GENDER, TIME USE, AND MODELS OF THE HOUSEHOLD**

October, 2002 Patricia Apps Faculty of Law, University of Sydney

## **ABSTRACT**

The aim of this paper is to explain why time use data are essential for analyzing issues of gender equity and the intra-household allocation of resources, comparing living standards, and estimating the behavioral effects of changes in policy variables. The first step in the exposition is to show that the neglect of these data in much of the literature on household behavior, in both developed and developing economies, can be traced to unrealistic assumptions on domestic production and the mistaken idea that non-market time can be viewed as leisure. It is argued that an approach is required that makes explicit the need for data on the time family members spend on domestic work as well as on labor supply. An approach of this kind is outlined and used to identify the specialized assumptions that are employed when they are missing. The paper also discusses the limitations of available time use survey datasets that are due to deficiencies in survey design. The more serious and common problems are illustrated using as case studies the Statistics South Africa 2000 Time Use Survey and the time use module included in the Nicaraguan 1998 Living Standards Measurements Survey.

<u>Acknowledgements: The author gratefully acknowledges financial support for this study</u> from the Gender and Development Group, Poverty Reduction and Economic Management Network, World Bank, and would like to thank Karen Mason, Director of the Gender and Development Group, for her support for the research. The author would also like to thank Andrew Mason for detailed and very constructive written comments on a series of drafts of the paper. Earlier versions were presented at the World Bank Workshop: _Poverty and Gender: New Perspectives_ , May 7, 2002, at a Development Research Group seminar, World Bank, November 6, 2002, and at an Economics Program seminar, RSSS, Australian National University, December 6, 2002. Thanks are due to the discussants at the Workshop, Kassey Garba and Emmanuel Jimenez, and to participants at the three venues for helpful comments. The paper has also benefited from comments from Glenn Jones, Anders Klevmarken, Ray Rees, Elizabeth Savage, Dianne Steele and Dominique van de Walle. Thanks are also due to Margi Wood for her contribution to the management and analysis of the time use survey data discussed in the paper.

JEL Classification: J22, J82, D13, O17 Keywords: time allocation and labor supply; gender; household production; development
collected well-managed school database before SY 2002/03, which makes it infeasible to examine the parallel trend assumption using the pre-intervention period.<sup>13</sup> We obtain income data on municipalities (or school district) from the 2000 Census. Local income level is an important factor that determines school and family environments. Controlling local income levels is crucial because competition between public and private schools matters in the selection of students in the Philippine context. In high-income municipalities (school districts), students from well-off families and with high test scores are likely to be accepted into private schools. Therefore, we expect differences in the ability distribution in public schools between high- and low-income municipalities. If school quality and student ability are complementary, the effect of TEEP on NAT change is expected to be different between high- and low-income districts.

We assigned an income category to each school district based on the 2000 Census. The census defined income category (ranking from 1, highest, to 6, lowest) for each municipality.<sup>14</sup> Note that some municipalities are split into a few school districts. In cities, we ranked school districts as 1 based on the income threshold used for municipalities.

> 13 We have also detected discontinuity of summary statistics of some key variables between before and after SY 1999/2001.

> 14 The income classification of municipalities (municipality income) used in this paper is based on Republic of the Philippines, Department of Finance (2001), Department Order No. 32-01 (effective November 20, 2001) and Census 2000. The income categories for 1,435 municipalities are defined as follows: 1: Philippine peso (PHP) 35 million (M) or more (number of municipalities: 130); 2: PHP 27M or more but less than PHP 35M (140); 3: PHP 21M or more but less than PHP 27M (204); 4: PHP 13M or more but less than PHP 21M (543); 5: PHP 7M or more but less than PHP 13M (401); 6: less than PHP 7M (17). There are a small number of municipalities in income class 6 in the country, but the Visayas region (our sample) has income classes 1 to 5 only.

9
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used in this study is distinctly different from the residual approach in Singh and Hamid (1992) and Singh (1995). The divergence in estimates for extemal equity for Indian firms reported in this paper and the estimates in Singh and Hamid (1992) and Singh (1995) is on account of these methodological differences. In this context, it is interesting to note that equity estimates for Korea and Turkey by other researchers are lower than the estimates in Singh and Hamid (1992) and Singh (1995).2 Also, given these considerations, the estimates reported in this study are not strictly comparable to the estimates in Singh and Hamid (1992) and Singh (1995).

# **(b) U.S. data**

**Sources and uses of funds:** COMPUSTAT data for the U.S. (table 6) suggests that for the 1972-92 period, on an average internal finance provided about 52 percent of the total funds and external finance provided the remaining 48 percent. While external equity provided 4 percent of total funds, long-term borrowing provided 10 percent. These results for U.S. firms are also consistent with the findings of Samuel (1995a).

# **Comparative analysis of Indian and U.S. firms**

Table 7 summarizes the evidence presented above for Indian and U.S. firms. Based on the analysis of sources and uses of funds, it is clear that Indian firms are far less dependent on internal finance than U.S. firms and more dependent on external finance. Within here, there are some interesting differences between the components of external finance for Indian and U.S. firms (see tables 2, 3, 4, 6). While external debt, debentures, and creditors are more important for Indian firms, other current liabilities are more important for U.S. firms. Interestingly enough, the contribution of external equity as a source of finance is broadly similar for Indian X For Korea, see Cho (1995) and for Turkey, see Sak (1995).

21
Slovak Republic in 2006, compared to 28.4% of GDP in Macedonia, suggesting the imports have also a larger share in Slovak consumption.

**<u>Table 10: Import structure for Macedonia and the Slovak Republic</u>**

||Maced|onia|Slovak R|epublic|
|---|---|---|---|---|
||2005|2006|2005|2006|
|Food and beverages|6.3|6.3|3.6|3.6|
|Industrial supplies nes|22.7|24.6|22.1|23.6|
|Fuels and lubricants|10.3|11.8|10.0|10.8|
|Capital goods|6.5|6.9|17.5|20.9|
|Transport equipment|3.4|4.1|11.5|13.3|
|Consumption goods nes|6.1|6.1|7.5|7.9|
|Goods nes|0.0|0.1|0.5|0.0|
|TOTAL|55.4|59.9|72.6|80.2|
|Included closelyinCPI|26.2|28.4|32.5|35.7|

_Source_ : COMTRADE, Imports according to BEC methodology

# **6. Impulse Response Analysis**

The impact of various shocks on the main variables of interest is commonly analyzed using impulse response functions (IRFs). An IRF gives an answer to a question of what will happen to an endogenous variable of a given economic system when the system is shocked by a certain event, such as aggregate demand shock<sup>21</sup> , while keeping other things constant. The endogenous system is then rolled forward so that in the long run the impulse response is expected to converge back to the equilibrium (the steady state) in response to transitory shocks. We carry out the impulse response analysis using the estimated model for Macedonia and the Slovak Republic and compare the IRFs of the two countries for selected variables. In order to recover IRFs, the linearized rational expectation model is put into state-space form and solved using the QZ solution algorithm of Sims (2002). The solved model has then a VAR structure readily allowing the computation of the IRFs. We focus on the responses of the output gap, inflation, interest rate and the real exchange rate to the aggregate demand, aggregate supply, monetary policy, fiscal and external (exchange rate and foreign) shocks. The estimated IRFs are presented in Figure 3 and

> 21 The aggregate demand shock could be result of e.g. the change in consumers’ preferences.

26
earned in New Zealand and how far away they lived from the immigration office in Tonga. As a result, they would expect this instrument to perform well, and show that it gets within two percent of the income gain predicted by the experimental estimator.

More innovative uses of GPS explicitly use geography as a source of exogenous variation which can be used to identify the impact of the variable of interest. For example, Olken (2006) uses GIS data on community locations and geography to look at the impact of television and radio on social capital in Indonesian villages. To identify this impact, he exploits differences in the over-the-air signal strength in different villages caused by mountains located between some villages and the transmission towers. To mitigate the concern that distance and geography might otherwise be correlated with social capital he controls for district fixed effects, distance and travel time to major cities, and elevation and uses a physical model of radio transmission that predicts how signal strength should theoretically vary with topography.

Geo-referencing is also being used to help create instruments in more aggregate applications. For example, Duflo and Pande (2007) study the impact of dams on agricultural production and poverty in India. They use GIS data to calculate differences in the gradients of rivers across districts and use this gradient as an instrument which predicts whether or not a dam is constructed in that district. A U.S. example is provided by Rosenthal and Strange (2006) who examine whether a greater density of workers in a given area leads to higher wages. Motivated by the Manhattan skyline, they note that the height of buildings, and hence density of employment, is determined in part by the underlying geology. They therefore use GIS information on the type of underlying bedrock, and on seismic and landslide hazards as instruments for the density of employment.

Finally, many unobserved variables, such as climate and soil in agricultural settings, are spatially correlated, leading to spatial autocorrelation in the error term of regression equations.<sup>8</sup> Failure to account for this structure in the error terms will lead to incorrect standard errors being used for inference, possibly lead one to conclude that a policy has a significant effect when it does not, or vice versa. Distances between observations obtained through GPS can be used to account for spatial autocorrelation in the error term of the regression equation. Case (1991) and Conley (1999) provide procedures for doing this.

> 8 See Anselin (2002) for an accessible review of spatial econometrics. Note also that in the agricultural example given here, the omitted climate and soil variables are likely to be correlated with the regressors of interest, and so one will wish to also include detailed spatial variables as controls in the regression.

- 7 -
# **Nonclassical Measurement Error and Farmers’ Response to Information Reveal Behavioral Anomalies**

Kibrom A. Abay<sup>†^</sup> , Christopher B. Barrett<sup>‡</sup> , Talip Kilic<sup>⁑</sup> , Heather Moylan<sup>⁑</sup> , John Ilukor<sup>⁑</sup> and Wilbert Drazi Vundru<sup>⁑</sup>

**JEL Codes:** C83, C93, D83, Q12

**Keywords:** asymmetric learning, inattention, misperception, measurement error, land area, household surveys, Malawi, Sub-Saharan Africa.

> † International Food Policy Research Institute (IFPRI).<sup>^</sup> Corresponding author: <u>k.abay@cgiar.org.</u> ‡ Cornell University, <u>cbb2@cornell.edu.</u><sup>⁑</sup> Living Standards Measurement Study (LSMS), Development Data Group, World Bank, <u>tkilic@worldbank.org; hmoylan@worldbank.org; jilukor@worldbank.org; wvundru@worldbank.org. The authors</u> thank Brian Dillon, Sylvan Herskowitz, Ben Norton and participants at the IPA/Northwestern conference for comments on an earlier draft, and the Malawi National Statistical Office (NSO) management and field staff for their hard work in survey implementation. This paper was produced with the financial support from the World Bank LSMS Program (www.worldbank.org/lsms) and the 50x2030 initiative (www.50x2030.org), a multi-partner program that seeks to bridge the global agricultural data gap by transforming data systems in 50 countries in Africa, Asia, the Middle East and Latin America by 2030. Any remaining errors are the authors' sole responsibility.
manner because the ratio of livestock to crops does not differ significantly between these groups, but the input mix varies considerably, with large farms being relatively more capital intensive and small farms being relatively more labor intensive. Because the social opportunity cost of labor is lower than the actual wage rate (due to massive rural unemployment), and the higher social opportunity cost for capital than the subsidized prices farmers face, the total value of inputs increase more for large farms than for small farms.

- The positive effects of removal of distortions on small farms (or negative effects on large farms) are relatively greater where the distortions have been large. For example, the analysis show that small farms gain more in relative efficiency (compared to the private analysis in table 7) under such situations.

_Non-parametric Efficiency Estimation Using Data Envelope Analysis (DEA)_<sup>_22_</sup> The analysis of efficiency has fallen into two broad categories: parametric and non-parametric. The parametric approach relies on a parametric specification of the production function, cost function or profit function (see, for example, Forsund et al, 1980; Bauer, 1990). Alternatively, production efficiency analysis can rely on non-parametric methods (see, for example, Seiford and Thrall, 1990). Building on the work of Farrell (1957) and Afriat (1972), the non-parametric approach has the advantage of imposing no a priori parametric restrictions on the underlying technology (see, for example, Fare et al, 1985). Also, it can easily handle disaggregated inputs and multiple output technologies. As the non-parametric approach develops, its applications to production analysis have become more refined (Chavas and Aliber, 1993). This provides some new opportunities for empirical analysis of economnic efficiency. This sub-section uses this non-parametric or DEA approach (similar to the analysis of efficiency in the former homelands) to estirnate the farm size-efficiency relationship in the Eastem Orange Free State (1979/80, 1982/83, 1985/86 and 1988/89) and the Vaalharts irrigation area (1990/9 1). In particular, the scale efficiency of each of the farms is determined relative to that for the whole data set.

Non-parametric scale efficiency (SE) measures were developed in response to the earlier work on technical efficiency (TE) and allocative efficiency (AE) (see Baumol, et. al, 1982). While TE and AE take the output level as given, SE is concerned with choosing the output level itself. The key question becomes whether firms are operating under decreasing, increasing, or constant returns to scale. The SE takes on values between 0 and 1, where SE=1 identifies scale efficiency under (local) CRTS. Finding SE<1 means

> 22 The non-parametric analysis of efficiency benefited from discussions with and suggestions by Jean-Paul Chavas from the University of Wisconsin-Madison. The GAMS code for the initial analysis was provided by him, while Paula Despins did some of the initial calculations.

31