rafmacalaba/datause-encoder-data huggingface dataset:
even bigger increases in worker hires. Nevertheless, one may interpret these findings as saying that more heavily regulated labor markets have more people who would be hurt by the adoption of pro-market reforms.

A concrete example may make the above point clearer. Using an index of labor market rigidity that comes from the Economic Freedom of the World report issued by the Fraser Institute, Chile has the most flexible labor market among the 14 countries studied while Argentina has the least flexible labor market. Firms in Chile report that, if labor regulations were made more flexible, the net gain to employment would be 0.76% while Argentinian firms report a net gain of 2.82% of total employment. This finding should not be surprising given the extensive literature that shows that making labor regulations more flexible generates gains to employment.

The focus of the current paper, however, is how these gains in net employment are accomplished. Chilean firms report that the additional dismissals that would result from making labor regulations more flexible would be 0.94% of total employment, while Argentinian firms report an additional dismissal figure of 2.34%. We therefore see that, despite the fact that Argentina would gain more in terms of aggregate employment by reforming its more rigid labor regulations, Argentina would also see a higher percentage of its workers losing their jobs. The larger net employment gains in Argentina arise because their higher dismissal figures are more than compensated for by even higher figures for additional hires.<sup>2</sup> The outline of the rest of the paper is as follows. Section 2 briefly reviews the literature on worker and job reallocations and places the current paper into this broader literature. Section 3 describes the essential features of the data set used in the paper and documents the manner in which all variables are calculated. Section 4 presents the main empirical results. Section 5 presents evidence that some of the results from section 4 can be attributed to changing the status of temporary workers to permanent ones, but that the main conclusions of the paper hold even when firms that employ temporary workers are excluded from the analysis. Section 6 concludes.

> 2 Specifically, Chilean firms report that making labor regulations more flexible would result in additional hires as a percentage of employment of 1.70% while Argentinian firms report a figure of 5.15%. Details on these calculations are presented in the data and methodology section.

3
- 12 -

The general picture shown in the other studies is similar, with some exceptions.

Cigarettes: According to virtually all of the studies, taxes on cigarettes are regressive. One of the most recent studies, in Korea, shows a high degree of regressivity<sup>7</sup> , as do studies in industrialized countries. There appear to be a few exceptions, in Lebanon and the Argentine, and possibly in Indonesia. The reason in these countries is that there are two types of cigarettes, one much cheaper and subject to a lower tax than the other, bought primarily in the lower income groups.

Beer: Generally shown to be regressive, but less so than cigarettes. Distilled spirits: Less regressive, but some at higher levels.

Alcoholic beverages: Overall, possibly progressive, as higher income groups concentrate on distilled spirits, with a high tax, lower income groups concentrate on beer.

Motor fuel: The Hughes study in Thailand and others show the tax to be regressive, though not universally so, in developing countries. In the United States, however, the tax appears to be highly regressive.8 On other comunodities, studies are limited. Entertainmen **t** axes appear to be progressive (India, Philippines); taxes on public utilities to be regressive; salt, very regressive (Lebanon); radios, TV and refrigerators, very progressive to middle incomes, then regressive (Panama); clothing, proportional to middle incomes, then regressive (Panama); sugar, progressive to mniddle incomes, then regressive (Greece).

7Peter S. Heller, The Incidence of Taxation in Korea, IMF Staff Paper, DM/81/14, Feb. 15, 1981.

8U.S. Congressional Budget Office, Federal Taxation of Tobacco, Alcoholic Beveracres and Motor Fuel (Washington: Congressional Budget Office, 1990) p. 78. This conclusion is challenged by James W. Poterba, "Is the Gasoline Tax T.egressive?," NBER Working Paper 3578, (Cambridge, Mass., National Bureau of Economic Research) January 1990, who argues that in this instance expenditures are a better base for measuring regressivity as so many persons in the lowest income group are there only temporarily.
<!-- Start of picture text --> Figure B1: Returns to Education and Access to Credit<br>Education Credit<br>.07 .6<br>.06 .5 2001<br>.4<br>.05<br>2001 .3<br>.04<br>.2<br>.03 2007 .1 2007<br>.02 .0<br>.01 -.1<br>.00 -.2<br>-.01 -.3<br>10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90<br>Quantile Quantile<br>Figure B2: Returns to Land and Migration<br>Land Migration<br>.006 .20<br>2007<br>.005 .15<br>.004<br>.10<br>.003<br>.05<br>.002 2001<br>2007 .00<br>.001<br>2001 -.05<br>.000<br>-.10<br>-.001<br>-.002 -.15<br>-.003 -.20<br>10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90<br>Quantile Quantile<br>Coefficient Coefficient<br>C o efficien t C o e ffic ie n t<br><!-- End of picture text -->

# <u>Figure B3: Returns to Formal Sector Employment</u>

<!-- Start of picture text --> Private Sector Public Sector<br>.6 .6<br>2001<br>.4<br>.4 2001<br>.2<br>.2<br>.0<br>2007<br>.0<br>-.2 2007<br>-.2<br>-.4<br>-.4 -.6<br>10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90<br>Quantile Quantile<br>Coefficient Coefficient<br><!-- End of picture text --> 44
- 39 -

# **Vil Appendix 2: Data**

Data on financial assets of pension funds, life, and non-life insurance companies for OECD countries come from OECD 1997 and 1998 Institutional Investors Statistical Yearbooks. For non-OECD countries the sources are the following:

- a) data for Chile, were specially assembled by Central Bank of Chile at our request.

- b) data for Thailand was obtained from the Association of Provident Funds and the Annual Report of the Department of Insurance in the Ministry of Commerce.

- c) data for South Africa is published in the Federal Reserve Bank quarterly bulletin.

- d) data for Malaysia is published in the insurance annual report and the EPF annual report by Bank Negara.

- e) data for Singapore is published in the yearbook of statistics by the Departrnent of Statistics.

All other variables come from the World Development Indicators database.

**Stock Market Value Traded** Stocks traded refers to the total value of shares traded during the period. Data are in current local currency.

**Stock Market Capitalization:** Market capitalization (also known as market value) is the share price times the number of shares outstanding. Listed domestic companies refer to the number of domestically incorporated companies listed on the country's stock exchanges at the end of the year. Data are in current local currency.

**Table 12: List of countries**

|AUS|Australia|ISL|Iceland|
|---|---|---|---|
|AUT|Austria|ITA|Italy|
|BEL|Belgium|JPN|Japan|
|CAN|Canada|KOR|Korea, Rep.|
|CHE|Switzerland|NLD|Netherlands|
|CHL|Chile|NOR|Norway|
|DEU|Germany|NZL|New Zealand|
|DNK|Denmark|PRT|Portugal|
|ESP|Spain|SGP|Singapore|
|FIN|Finland|SWE|Sweden|
|GBR|United Kingdom|THA|Thailand|
|GRC|Greece|**USA**|United States|
|HUN|Hungary|ZAF|South Africa|
HCES and WMS data are used rather than the Demographic and Health Survey (DHS) because they allow dimensions of wellbeing to be compared to the monetary poverty data.<sup>3</sup> However the trends in wellbeing that were documented in Carranza and Gallegos (2013) using the DHS are reported where relevant. While the HCES and WMS surveys conducted in different years are in general similar in their coverage and representativeness, some content differences exist and Appendix-A (Tables A1 and A2) provides more details and compares the indicators used in this study to those indicators selected for the MDGs and the MPI

# **3. Progress in various dimensions of wellbeing**

This section assesses progress in wellbeing taking one indicator at a time. The performance of each deprivation indicator is analyzed over the period 2000-2011 and the change is tested for significance.<sup>4</sup> Table 2 presents how the deprivation incidence has changed over time for all indicators.

Table 2 shows significant improvements in the dimensions of education, health, water and sanitation. The proportion of households with a child between the ages of 7 and 15 that had a child out of school fell from 83 percent to 58 percent in rural areas, and 26 percent to 16 percent in urban areas. Progress would have been even more dramatic had the age range been restricted to younger children. The WIDE-3<sup>5</sup> study found that nearly all 7 year olds were enrolled in school in the six study sites visited in 2013 (Bevan, Dom and Pankhurst, 2014). Carranza and Gallegos (2013) also document considerable progress in education enrollment and outcomes using the DHS data. The net attendance rate for primary education increased from 30 percent in 2000 to 62

> 3 Notably, the 2005 and 2011 WMS surveys do not collect anthropometric measurements of children or immunization coverage. Children’s physical health is not focused upon in this study. Other recent studies investigated multidimensional child wellbeing in Ethiopia using DHS data (Plavgo et al., 2013).

> 4 The sole indicator that captures households’ cultural practices is only available in 2011 and thus no trends can be confirmed.

> 5 Wellbeing and Illbeing Dynamics in Ethiopia (WIDE)

6
of market value. Table 12 shows the results. The tax take is assumed fixed, since what varies is the definition of normal profit, not actual profit.

Table 12. Appropriated Natural Gas Rent - Sensitivity Analysis (1999)

|Normal<br>profitrate*||US$/<br>thousandm3|
|---|---|---|
|20%|Normal profit|2.7|
||Taxtake|8.6|
||Appropriated rent|7.1|
|25%|Normal profit|3.4|
||Taxtake|8.6|
||Appropriated rent|6.4|

* Normal profit = % of market capitalization.

Table 13. Total Current Rent on Natural Gas in 1999

|(US$/thousandrn<sup>3</sup>)||
|---|---|
|Unrealizedrent (implicitsubsidy)|36.4|
|<br>Entrepreneurialrent(normalprofit)|2.0|
|Captured rent (notionaltaxtake)|8.6|
|<br>Appropriated rent (excessprofit)|7.8|
|Totalrent|54.8|

2.38 The largest portion of the rent was the unrealized rent, which amounted to US$ 36.4 per thousand cubic meters, or 66% of the total; normal profit equaled US$ 2 or 4%, the tax take or captured rent amounted to US$ 8.6 (or 16%), and the appropriated rent to US$ 7.8 (or 14%).

# **Timber**

2.39 Russia has the largest forest resources of any country, comprising 764 million hectares or 22% of the world's forest cover. Russian forests also account for 21% of the world's standing timber volume (82 billion cubic meters) and provide the largest land-based carbon storage in the world (World Bank 2000a).

# **The Russian Forest Revenue System**

2.40 Like other natural resources, timber is under-priced in Russia. Stumpage fees were first established under Czar Paul 1. Over time, they were refined to reflect market prices for wood products and the rent-generating factors specific to different forest areas. With the advent of communism, however, market relations were obliterated. In the forest sector this implied a complete overhaul of the stumpage fee system. In keeping with the Marxist labor theory of value, Soviet economists in 1949 made stumpage fees a function of the labor costs 21
implementation. China’s cities vary in ways that could well be relevant to the outcomes of _DB_ ; for example, across the 35 largest urban areas studied in the paper, the highest mean household income per person (the city of Shenzhen) is over four times that of the lowest (Chongqing). The proportion living below the DB poverty line varies from 2% (in Fuzhou) to 19% (Haikou).

The analysis uses China’s Urban Household Short Survey (UHSS) for 2003/04, as discussed in Chen et al. (2006). The UHSS was done by the Urban Household Survey Division of the National Bureau of Statistics (NBS). I use the UHSS sample for the 35 largest cities, giving a total sample of 76,000, varying from 450 (in Shenzhen) to 12,000 (in Beijing). For these 35 cities, the definitions of geographic areas in the UHSS coincide with those for the DB lines and the entire data set has been cleaned by NBS staff and made available for this research.

While the UHSS is a relatively short survey, it allows us to measure a fairly wide range of household characteristics. The survey also included a question on household income and questions were added on DB participation and income received from DB.

As noted in the last section, in measuring targeting and poverty impacts I assume that income in the absence of the program is observed income less payments received under DB. While this is a common assumption, it is clearly questionable. Testing the assumption is difficult without panel data (and even then there can be severe identification problems). With only a single cross-sectional survey it is hard to be confident in the results, given the likelihood of omitted variables correlated with both program placement and the behaviors of interest.

However, I can offer some observations that are at least consistent with this assumption. The design of DB intends that the benefits received will decrease as income rises, implying that participants face a positive marginal tax rate. Indeed, if the program works the way it is supposed to then it exactly fills the gap between current non-DB income and the DB line. Then participants will have no incentive to work (under the usual assumptions that leisure is a normal good and work yields no direct utility). Earned income net of DB will fall to zero. The program will have created a poverty trap, whereby participants do not face an incentive to raise their own incomes, because of the loss of benefits under DB.

The extent to which this is a real problem in practice is unclear. Benefits are unlikely to be withdrawn quickly. There are reports that local authorities allow DB benefits to continue for some period after the participant finds a job (O’Keefe, 2004). Observations from field work also indicate that a notion of “imputed income” was used in a number of provinces. This was a 10
devices (Nabi, 1986, provides similar evidence in a smaller scale survey).<sup>18</sup> We certainly do not want to treat supervision intensity as linear in the number of meetings, since there must be diminishing returns beyond a point, and possibly increasing returns at very low numbers of meetings as well. The simplest empirical approach, and the one we adopt here, is to assume a threshold number of annual meetings above which a tenant can be considered ‘supervised’. But, what should this threshold be? This is a question on which we will let the data speak.

To this end, we estimate a version of Hansen’s (1999) threshold regression model for panel data. Let mci be the number of meetings that cultivator c on plot i had with his landlord (de fi ned only for sharecropped plots). Our modi fi ed yield regression is then where I(·) is the indicator function and k is the threshold, which is treated as a parameter to be estimated. For ease of interpretation, we demean the supervision indicator using E [I(mci > k)|sci = 1], so that γ continues to estimate the mean difference in yields between sharecropped and owner-cultivated/rented plots.

Selection bias could be a problem here as well. Or, to put it another way, supervision could be endogenous. Landlords may choose which tenants to supervise based on ability or other unobserved productive attributes. If, for example, low productivity cultivators are monitored more intensively, then we would fi nd a spurious negative relationship between yields and supervision. Estimation of equation 4 with household fi xed effects, as in section 2, can correct for this problem to the extent that the cultivator-speci fi c unobservable is constant across tenanted and owned plots. But, this estimator it not robust to selection on tenant type. In particular, landlords may decide which tenants to supervise based on tenant characteristics that do not in fl uence productivity on the tenant’s own land. This would essentially give rise to an additional error component of the form sciμc. To purge this component, we use a sample of tenant households with multiple sharecropped plots (sci = 1 ∀i) to estimate the following regression by household fi xed effects: where μc + νc is the fi xed effect (and γ is absorbed in the constant term).

> 18 Supervision is most intense in Sindh province (with a median of 48 annual meetings versus 8 for the remaining three provinces taken together), where landlords typically have larger holdings and are more likely to employ kamdars.

14
<!-- Start of picture text --> "(0) = Sew “COlW) (Ww) aw<br>0 0) = Sloan “COlW) ‘(wy aw<br>ww (0) = Seay *COlW) (Ww) aw<br><!-- End of picture text --> k <!-- Start of picture text --> 4 4<br>k<br><!-- End of picture text -->
**- 38**

# **UNITED NATIONS POPULATION** FUND **(UNFPA)**

Expenditure levels of the Fund, based on July 1988 allocations, reached an all-time high of S181.S million, reversing the S26.9 million decline in 1986 from 1985's S128.2 million. The outlook for the future projects further increases.

The Fund has not specifically identified SM as a program initiative, although it co-sponsored the Nairobi Conference and supports the effort to reduce maternal mortality and morbidity. No efforts have been made to define the SM components of the hudget. Family planning programs constitute about half of annual expenditures. In the wider definition of SM, one can rationalize the inclusion of all budgetary components -- data collection, population dynamics, policy, communication, education, and multisectoral act:vities. Within the Family Planning Program (health-related delivery systems, community-based delivery systems, fertility regulation, and program management), financing has increased since 1985 from S64.8 million to S102.5 million (1988).

Global financing for population assistance (all sources) was assessed in the Global Population Assistance Report, 1982-85. The document estimated a 1985 level of 5560.3 million. UNFPA has contracted with the University of Michigan to update this document for 1986 to 1988. The report is to bc; released in October 1989.

The Center for Population and Family Health, Columbia University, has published financial trends for some 45 developing countries in 'Family Planning and Child Survival." But as only 10 of the 45 data sets have 1987 information on population programs, and only eight have 1987 health expenditures, the global contributions of developing countries are difficult to assess.

# Estimated Financial Flows to Developing Countries S millions

||1985|1986|1987|1988|
|---|---|---|---|---|
|Total UNFPA Project<br>and Program|||||
|<br>Expenditures|128.2|101.3|106.9|181.5|
|Safe Motherhood<br>activities /I|81.8|68.3|70.8|119.8|

- _/I_ Safe Motherhood components are derived from two statistical categories: family planning programs, and communication and education.

United Nations Population Fund 220 East 42nd St. New York, N.Y. 10017
# **4.2. Estimation results**

_Impacts on school enrollment_ The regressions of children‟s school enrollment on parental disability are reported in Table A.3. Following studies on child education, the control variables (variables X) are education of parents, household income, age and sex of children, and geographic variables (e.g., Black et al., 2005; Lee, 2008). It should be noted that parents are labeled as household head and head‟s spouse to indicate the role of household head. The summary statistics of control variables are reported in Table A.1 in the Appendix.

To examine the sensitivity of the estimates to model specifications, we compare three different models, which mostly vary in the number of explanatory variables. Model 1 includes only exogenous variables, and Model 2 adds household heads‟ education. Model 3 also includes per capita income. It should be noted that parents‟ education and household income can be affected by disability, thus they should not be included in the model (Heckman et al., 1999). However, since these variables are important factors for education of children, we examine whether the estimates of the impact of parental disability are sensitive to these variables. For each model, we estimate using both probit and probit with instrumental variables regressions.

Table A.2 presents the first-stage regression of parental disability. It shows that both age of father and proportion of disability strongly increase the probability of parental disability. We perform a Cragg-Donald weak identification test of the instruments, and the statistic is extremely high, indicating that the instruments are strong.<sup>5</sup> Very strong instruments are expected to reduce the bias caused by invalid instruments. We can also perform the test of valid instruments by an overidentification test which is reported in Table A.3. It shows that for the three models, we are not able to reject the null hypothesis of valid instruments at the 5% significant level. However, the test on endogeneity of

> 5 As a rule of thumb, if a test is under 10, the instruments might be weak (Staiger and Stock 1997).

12
|||**Table 2: M**<br>|**alaysian Fore**<br>|**ign**<sup>**Labor Ma**</sup><br>**'**|<sup>**nagement Syste**</sup><br>**Ntiliti**|<sup>**m of Passes**</sup><br>**Sit**|**Comments**|
|---|---|---|---|---|---|---|---|
|**Type of**<br>**permit**|**Type of**<br>**worker**|**Duration**|**Sector**<br>|**Levy**<br>**(annual)**<br>|**aonaes**<br>**Allowed**<br>|**ecury**<br>**Deposit**||
|Visit Pass -<br>Temporary<br>Employment|Unskilled and<br>semi-skilled<br>workers, age<br>18-45 years.|up to I year,<br>renewable<br>up to 5 yrs.<br>in Plantation<br>sector,<br>3 yrs. in<br>Manf.,<br>Services|Manu-<br>facturing|Unskilled:<br>RM840;<br>Semi-skilled:<br>RM1,200;<br>Skilled:<br>RMI,800|Bangladesh,<br>Philippines,<br>Indonesia,<br>Pakistan,<br>Thailand.|yes|Pass is employer- and job-<br>specific;<br>no resettlement of families into<br>Malaysia.|
||||Construction<br>|yes||||
||||Plantations|yes||||
||||Services|yes||||
||||Domestic<br>Worker|yes|Philippines,<br>Indonesia,<br>Thailand.|._______ .__|Minimum income requirements<br>for employers of domestic help<br>(RM2,000/month to employ<br>Indonesian or Thai maids,<br>RM4,000/month to employ<br>Filipino maids); levy payments<br>are tax deductible.<br>|
|Visit Pass -<br>Professional<br>Employment|Professional<br>workers on<br>short-term<br>contracts with<br>any agency.|up toIyear<br>____|na<br>___|____<br>|||Pass is employer- and job-<br>specific;<br>no resettlement of families into<br>Malaysia.<br>|
|Employment<br>Pass|Skilled<br>workers with<br>a minimum<br>salary of<br>RM1,200 per<br>month.|at least 2<br>years,<br>renewable<br>for 5 years.|Manu-<br>facturing<br>Other|Technical:<br>RM2,400;<br>Professional<br>& middle<br>management:<br>RM3,600;<br>Upper<br>management:<br>RM4,800<br>yes||.<br>..|Can obtain visas for<br>dependents (i.e. dependent<br>pass); number of key posts<br>allowed depends on foreign<br>paid-up capital.|

1/ Exchange rate averaged over January-September,<sup>1998: 3.97RM/US$.</sup>
<!-- Start of picture text --> GDP per capita growth, period averages<br>vt<br>o<br>Ry<br>o<br>RR Non-RR: Non SSA, RR countries<br><!-- End of picture text --> <!-- Start of picture text --> Nonresource GDP per capita growth, period averages<br>Tv<br>oO<br>Rey<br>o<br>RR Non-RR Non SSA, RR countries<br><!-- End of picture text -->