rafmacalaba/datause-encoder-data huggingface dataset:
of schooling increased earnings by 17 percent. In 1992, on average, the estimates of rates of return to schooling were relatively low, at about 5 percent. Private rates of return to primary and university education averaged 13 and 11 percent, but only 4 to 5 percent at the secondary and vocational levels. Returns to higher education were higher for females (12 percent) than for males (10 percent) (Moock, Patrinos and Venkataraman 20032003). In 1993, the returns to schooling were 4.3 percent (Nguyen 2004). In 1997 they were 4.7 percent, and slightly higher for women (Liu 2006). In 1998 they were 4.8 percent (Nguyen 2004). Gallup (2002) found that returns to education in Vietnam increased from 1993 to 1998. In 1997, the returns to schooling were 2.6 percent, but when using IV, as high as 7.0 percent (Arcand, d'Hombres and Gyselinck 2005). By 2002, they had reached 5.1 (Patrinos, Ridao-Cano and Sakellariou 2009).

More recent studies have found greater returns to higher levels of education. In relation to over time estimates, Liu (2006), Pham and Reilly (2007) and Oostendrop and Doan (2013) found the returns to increase over the period 1998 to 2006 (Figure 1). However, Phan and Coxhead (2013) reported that returns to education in Vietnam declined between the periods 1993 to 2002 and 2002 to 2008. The mixed findings that have been derived to date are most likely due to the employment of different estimation techniques across the various studies (e.g., Ordinary Least Squares versus Instrumental Variables), along with differences in the defined sample and control variables included in the estimated models (Phu Viet Le 2014; Tien 2014; Sakellariou and Fang 2010; Imbert 2010). Wigren and Nilsson (2015) estimate the returns to schooling in 2012 at 9 percent. The male and female education returns displayed a linear pattern in both 2002 and 2010, with earnings rising with increased levels of education. Relative to males with no qualifications, the returns to those with a vocational training qualification or below fell between 2002 and 2010, while the economic returns to a college education and above increased. Similar results were observed for females (McGuinness, Kelly, Thu and Thu 2015). Diep and Coxhead 2016 also find rising returns but not as high as in other studies. Returns soared in the 1990s but fell after the crisis (Doan, Tuyen and Quan 2018).

<!-- Start of picture text --> Figure 1: Returns to Schooling in Vietnam since the early<br>1990s<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1992 1997 2002 2007 2012<br><!-- End of picture text --> Source: Annex 1 4
located. The subsample upon which this report is uased upon is comprised of 9,954 nonagricultural workers ranging from 16 to 65 years of age, with positive labor market earnings.

Although the data used in this paper does possess a variable indicating union status, it does fall short in regard to some issues relevant to union wage gap analysis. One shortcoming is EYe lack of detailed information on non-monetary remuneration. A study by Freeman (1978) found that union workers receive a higher proportion of compensation in nonpecuniary forn than do nonunion workers. Therefore, it should be kept in mind that the wage gap may be greater than indicated since fringe benefits cannot be included in the analysis. Freeman's (1981) analysis indicates that previous estimates of the union wage differential understated the union compensation differential, but only by about two percent (Hirsch and Addison 1986).

Although the Mexican data do not allow for an adequate examination of nonpecuniary compensation, information on the incidence of health insurance coverage among workers is included. DJot surprisingly, the percentage of those with health care coverage among the union subsample is significantly higher than the percentage with coverage in the nomnmion subsample, at 86 and 56 percent.

The mean values for those variables used in the analysis for the entire sample, and for the union and nonunion subsamples separately, are listed in Table 1.

6
_c= w+b= pY -_ **_+sb_** _= P-'x-(l-[)[-'sb_ and from _PN ,-'x - 0 -'sb N_ we get employment, We can see that with subsidies, employment is clearly larger and compensation is smaller than in the case without subsidies. This is because they influence employment and compensation through the utility maximization and the constraint. Subsidies shift up the average product curve and allow for larger employment and/or compensation. But they also shift the relationship between inside and outside opportunities (x to _x-sb)_ which will increase employment. Given the production function and the fact that , < 1, this channel acts more strongly on compensation resulting in lower compensation and larger employment than in the case without subsidies.

Let us see what will happen if subsidies are cut. Employment will decrease and compensation (and wages if social benefits are fixed) will increase in the short run. If we introduce a demand shock also (p(t) < p(O)) we will have the same increase in total compensation and a larger decrease in employment. Therefore, a demand shock influences employment only and leads to its further decline.

We should emphasize that all conclusions concerning the dependence of wages on subsidies are closely related to the assumption of Cobb-Douglas technology in the state sector with an elasticity of output with respect to employment of less than one. If we assume another form of production function, for instance, with linear average and marginal products; _Y = N(Tj- aN)_ where **rl** = maximum of average product and a = the slope of the average product curve, we get another solution of the maximization problem 13
# **Table 6: Robustness**

|_De_|_pendent Varia_|**R**<br>_ble:Log Eva_<br>Alter|**obustness Check**<br>_sion Value Gaps _<br>native Proxies<br>|**s**<br>_by NSH6-sourc_<br>|_e country-ye_<br> <br>|_ar_<br>Alternative Sam|ples<br>Excl. top|
|---|---|---|---|---|---|---|---|
||||Products for|Products for|All||<br>|
|_Sample_|Main<br>Sample|Main<br>Sample|<br>which import<br>shares are<br>predicted|<br>which import<br>shares are<br>predicted|products<br>and<br>countries|Germany,<br>Netherlands,<br>Belgium only|and bottom<br>10% gaps<br>by product<br>class|
||Q|N|Predicted|Predicted|V|V|V|
|_Proxy_|||Import share|Import share||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Log (Tariff+1)|0.048|0.044|-0.085|0.063|0.084***|-0.167|-0.012|
||(0.055)|(0.055)|(0.055)|(0.078)|(0.014)|(0.12)|(0.037)|
|Ben Ali %|-0.791*|-0.677|4.304**|2.601|-0.443|-2.029|-0.242|
||(0.469)|(0.684)|(1.788)|(6.146)|(0.329)|(1.342)|(0.338)|
|Offshore %|0.142|0.144|0.138|1.180|0.225**|0.406|-0.131|
||(0.185)|(0.207)|(0.162)|(1.039)|(0.109)|(0.557)|(0.131)|
|Public %|-0.753*|-0.655|-0.784***|1.358|-0.228|-0.277|-0.126|
||(0.43)|(0.532)|(0.220)|(0.827)|(0.230)|(0.847)|(0.226)|
|Ben Ali %*Log (Tariff+1)|0.271**|0.281||0.151|0.182**|0.685*|0.165*|
||(0.129)|(0.19)||(1.687)|(0.090)|(0.379)|(0.097)|
|Offshore % *Log (Tariff+1)|-0.146***|-0.103*||-0.266|-0.083**|-0.195|-0.035|
||(0.052)|(0.059)||(0.259)|(0.032)|(0.158)|(0.038)|
|Public%*Log (Tariff+1)|0.105|0.115||-0.596**|-0.039|-0.016|-0.039|
||(0.13)|(0.158)||(0.252)|(0.067)|(0.262)|(0.066)|
|Log (NTB + 1)|-0.072|-0.067|0.065**|0.055*|0.025*|-0.033|-0.047|
||(0.049)|(0.049)|(0.302)|(0.294)|(0.137)|(0.086)|(0.029)|
|Country*year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Country*product FE|Yes|Yes|||No|Yes|Yes|
|<br>N|49347|49347|10395|10395|107299|9442|39477|
|Number of products|1386|1386|421|421|4095|1149|1372|
|R2|0.626|0.625|0.068|0.070|0.0421|0.592|0.542|
|Note: Standard errors are c<br>AdjustedR2|lustered bypr<br>0.533|oduct. *** p<br>0.531|=<0.01, ** p<0.0<br>0.018|5, * p=0.10. For<br>0.030|sample rest<br>0.0361|rictions for the<br>0.494|main<br>0.403|

Note: Standard errors are clustered by product. *** p=<0.01, ** p<0.05, * p=0.10. For sample restrictions for the main see notes to Table 4; these apply to columns (1), (2),(6), and (7); note that columns (6) and (7) impose additional restrictions, notably restricting the sample to Germany the Netherlands and Belgium only (column 6) and excluding the top and bottom 10% largest gaps by product class (column 7). For construction of the predicted import share, see the Appendix.

43
**Figure 1: Outsourcing by the legal status of Survey respondents**

<!-- Start of picture text --> 35<br>22<br>21<br>42<br>34<br><!-- End of picture text --> <!-- Start of picture text --> 35<br><!-- End of picture text --> <!-- Start of picture text --> 45<br><!-- End of picture text --> <!-- Start of picture text --> 42<br><!-- End of picture text --> <!-- Start of picture text --> 13<br><!-- End of picture text --> <!-- Start of picture text --> 61<br><!-- End of picture text --> <!-- Start of picture text --> 31 8<br><!-- End of picture text --> <!-- Start of picture text --> 57<br><!-- End of picture text --> <!-- Start of picture text --> 37 5<br><!-- End of picture text --> <!-- Start of picture text --> 5 21<br><!-- End of picture text --> <!-- Start of picture text --> 33<br><!-- End of picture text --> <!-- Start of picture text --> 53 13<br><!-- End of picture text --> <!-- Start of picture text --> 43<br><!-- End of picture text --> <!-- Start of picture text --> 46 11<br><!-- End of picture text -->

**Table 1: Analysis of variance: Outsourcing index by the respondents legal status**

|Analysis of varian|ce|F = 21.65***|||
|---|---|---|---|---|
|Scheffe|Sole proprietors|Partnership|Company|Close corporation|
|Sole proprietors||MD = 12.215<br>St. err.=5.118|MD = 13.451 (***)<br>St. err.=3.491|MD = -7.199<br>St. err.=3.092|
|Partnership|||MD = 1.236<br>St. err.= 4.909|MD = -19.414 (***)<br>St. err.= 4.634|
|Company||||MD = -20.650 (***)<br>St. err.= 2.733|

MD – Mean Difference; St. err. – Standard error; ***, **, * - statistical significance at 1%, 5%, and 10% level, respectively.

8
demand system (described in a section below), which determines welfare. As the supply of other goods and services is exogenous, we assume that they grow at the overall rate of total factor productivity (TFP) growth, which is roughly equal to the world economy's TFP growth rate.<sup>10</sup> Because the production of other goods and services does not draw on the land resource, we assume without loss of generality that their cost of production is zero.

In the technical appendix, section C.1, we discuss parameterization of other goods and services production.

# 2.12 GHG Emissions

The GHG emissions ows, _zt,_ in the model result from a number of sources: (a) combustion of petroleum products, (b) the conversion of unmanaged and managed forests to agricultural land (deforestation), (c) non-CO 2 emissions from use of nitrogen fertilizers in agricultural production, (d) non-CO 2 emissions from the livestock sector (which include emissions from enteric fermentation and manure management) and (e) net GHG sequestration through forest sinks (which includes the GHG emissions from harvesting forests). We dierentiate between the emissions resulting from combustion of petroleum products and the emissions resulting from land use, _z_<sup>_L_</sup> _,_ because the price path (and therefore the bulk of the combustion path) for fossil fuels is predetermined, whereas the other sources of GHG emissions are endogenous.

We assume that GHG emissions from the rst three sources are linearly related to the use of fossil fuels, and the allocations of commercial lands. A ton of oil equivalent (toe) of fossil fuel combusted emits _μ_<sup>_f,e_</sup> tons of CO 2 equivalent (tCO 2 e). A ton of oil equivalent (toe) of fossil fuel converted to fertilizer emits _μ_<sup>_f,n_</sup> tCO 2 e. The conversion of natural forest land to commercial land entails emissions of _μ_<sup>_L_</sup> tCO 2 e per hectare of land deforested. A ton of fertilizer applied to agricultural land emits _μ_<sup>_n_</sup> tCO 2 e.

The livestock emissions are calculated as a sum of emissions per hectare of pasture land (e.g., due to manure left on pastures), _μ_<sup>_P_</sup> , and emissions per ton of livestock produced (e.g., due to enteric fermentation), _μ_<sup>_l_</sup> .

GHGs can also be reduced by carbon forest sequestration.<sup>11</sup> A hectare of

> 10The economy's output has a small fraction of endogenously determined output from landuse. We ignore this complication in this partial-equilibrium model.

> 11GHG emissions ows are also sequestered by atmospheric and ocean sinks. We ignore this complication as our model does not provide comprehensive accounting of all GHG emissions ows, and focuses on understanding emissions from land use and related sectors.

16
transactions not aligning with the period for which entrepreneurs are surveyed (de Mel, McKenzie and Woodruff 2009). Moreover, the shorter the recall period, the more acute is the mismatch between when items are bought and sold (Samphantharak and Townsend 2006). This is particularly problematic for small firm surveys that typically ask for data over a one-month window to minimize the risk of recall error.

Attempts to mitigate mismeasurement – such as encouraging entrepreneurs to maintain accounting records (de Mel, McKenzie and Woodruff 2009), using radio frequency identification tags to objectively measure stock turnover and profits (de Mel, McKenzie and Woodruff 2016), or using Personalized Digital Assistants to conduct consistency checks on sales and profits (Fafchamps et al. 2012) – find that alternative approaches do not perform better than simply asking for selfreported sales and profit estimates, which can yield reasonable ordering in outcomes. That said, asking entrepreneurs to enumerate profits directly runs the risk of being too complex and combines disparate elements of the business that are better remembered separately (Beatty 2010). Thus, entrepreneurs often find it difficult to respond to single-question proxies for profits or net worth, resulting in a large number of cases that cannot be estimated (Daniels 2001), high levels of item nonresponse and null values (Bruhn and Zia 2013), and significant underreporting of true values (de Mel, McKenzie and Woodruff 2009). In fact, de Mel, McKenzie and Woodruff (2009) find that small firms underreport outcomes by as much as 30% when a simple recall (or self-report) approach is used. In addition, the genuine volatility in the data caused by seasonality of production (Fafchamps et al. 2012) or variations in income (Collins et al. 2009) makes it challenging to assess the impact of firm growth programs, as well as efficacy of business partnerships, using simple selfreported profits or sales.

One solution for addressing concerns about volatility in outcomes is to collect firm-level data through multiple surveys over longer periods of time (McKenzie 2012; Abebe 2013; de Mel, McKenzie, and Woodruff 2009). Although traditionally constrained by the large budgetary and logistical demands of collecting such detailed longitudinal data, technological advancements are making this approach more feasible. For example, Garlick et al. (2016) find that high-frequency mobile phone surveys offer comparable data quality, measure dynamics better, and cost less than conventional low-frequency in-person surveys. However, mobile phone surveys are limited in the 3
# **Economic Environment in the Philippines**

Over the past decade the Philippines has made significant progress in establishing a marketoriented economy. But since 1997 annual GDP growth has been moderate, around 5 percent, and inflation has been about 4 percent (figure 1). A growing budget deficit, high interest rates, and poor investment sentiment have constrained economic development, keeping borrowing costs high and putting pressure on the peso (figure 2).

Because a large portion of the deficit is financed by domestic borrowing, it is crucial that the government develop an effective domestic debt management policy and efficient ways to distribute and service its domestic debt at the lowest possible costs. In addition, the increasing budget deficit and rising debt are undermining the government’s creditworthiness. Though this is more of a concern for foreign borrowing, it could also lower domestic investment.

**Figure 1. GDP growth and inflation in the Philippines, 1997–2002** (In Percentage Points)

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>1997 1998 1999 2000 2001 2002<br>-2<br>GDP growth CPI inflation<br><!-- End of picture text -->

**Figure 2. Budget balance in the Philippines, 1995–2001**

<!-- Start of picture text --> 1995 1996<br><!-- End of picture text --> <!-- Start of picture text --> 2000 2001<br><!-- End of picture text --> <!-- Start of picture text --> 1998 1999<br><!-- End of picture text --> _Source:_ World Bank, _World Development Indicators, 2002._ 22
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33
|**Variable**|**Per**|**centage (weight**|**ed) **<sup>**n(raw)**</sup>|
|---|---|---|---|
||Poorest|27.24|421|
||Second|24.28|420|
||Third|19.51|421|
||Fourth|16.07|420|
||Richest|12.90|420|

# _Note._

*Denominator is out of those who experienced some flooding impact in either 2023 or 2022 **Denominator is out of those who reported experiencing some level of discrimination

Refugee households, households with a mother with less than a primary education, and households in rural areas on average had children with significantly lower levels of child development for children 0-35 months, measured by CREDI Z-scores, compared to a global population (Table 3). As such, the average non-refugee child in this population would be considered to be developmentally on track (CREDI Z-score= -1.69 SDs) while the average refugee child in this population would not be considered to be developmentally on track (CREDI Z-score= -2.35). In terms of child development for children 36-72 months, measured by percentage correct on the AIM-ECD, the same differences were seen but only some relationships were statistically significant. Specifically, there were no significant differences by refugee-status, but mothers with less than a primary education had children with significantly lower AIM-ECD scores on all domains except for executive functioning, where the same trend was seen but it was not a statistically significant difference. Mothers living in rural areas had children with significantly lower AIM-ECD scores on all domains of development.

Across both age groups, mothers in refugee households experienced significantly higher levels of depression, on average, and no significant differences were seen for depression levels by maternal education status (Table 4a, Table 4b). Mothers in urban areas with a selected child 0-3-years-old experienced significantly higher levels of maternal depression. Mothers in refugee households experienced significantly higher levels of anxiety, on average, and no significant differences were seen for anxiety by area of residence. Mothers with less than a primary education experienced significantly higher levels of maternal anxiety in households with a selected child 3-6-years-old. Mothers in refugee households experienced significantly higher levels of parenting stress, on average, and no significant differences were seen for parenting stress by maternal education or area of residence.

18
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39
WPS 3238

# MOVING PEOPLE TO DELIVER SERVICES: HOW CAN THE WTO HELP?

Sumanta Chaudhuri* Aaditya Mattoo** Richard Self*** _Abstract_ : The previous General Agreement on Trade in Services (GATS) negotiations produced little liberalization of the movement of individual service providers (mode 4), and the potentially large global gains from such movement remain unrealized. In the current negotiations, as part of the Doha Development Agenda, developing countries are seeking greater openness in their area of comparative advantage: the movement of providers unrelated to commercial presence abroad. At the same time, many multinational firms would like easier intra-corporate movement of their personnel. We describe how this coincidence of interest could be harnessed to deliver greater openness at least for skilled service providers.

## World Bank Policy Research Working Paper 3238, March 2004

_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._

*Permanent Mission of India to the WTO, **World Bank, ***Nathan Associates. The comments of  Crawford Falconer, Alejandor Jara, Hamid Mamdouh, Deepak Mishra, Julia Nielson, Anya Oram, Anirudh Shingal, B.K. Zutshi, participants in seminars in India, Geneva and Paris, and an anonymous referee are gratefully acknowledged.

1