rafmacalaba/datause-encoder-data huggingface dataset:
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29 Informal Employment and Worker’s Wellbeing in Russia / June 2019
# Table 4.2.

# Estimate of Phillips Relation

Specification: Johnson & Layard - OLS

|Wage<br>rate|Labor market2<br> <br>|Estimat<br>with (<br>|ed coeffi<br>t-stati<br>|cients<br>stics)<br>|<br> <br>|<br>|Stati<br>|stics<br>|<br> <br>|Number of<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|<br> <br>Wage rate|<br>indicator,<br>L|Constan|t (p-w)1|Log L|<br>t|adj.R|2 F|D.W.|<br>S.E.|observations|
|Manufacturing|SPO est. of U|_38.59_<br>(0.96)|0.64)<br>(2.30)|-32.07<br>(-0.80|1.87<br>)<br>(0.67)|0.12|2.01|2.14|16.70|24|
|,~nAppl./P|lacem.|9.41<br>(1.10)|0.45<br> (1.59)|-32.63<br>(-1.63|1.65<br>)<br>(1.29)|0.12|1.92|1.98|15.63|21|
||(Appl.-Pl.)/App|l-<br>71.57<br>(0.81)|0.4'<br>(1.41)|-20.10<br>(-0.79|0.75<br>)<br>(0.56)|0.02<br>|1.88<br>|1.89<br>|16.50<br>|21<br>|
||Registr.<br>unempl.|1.57<br>(0.05)|0.64<br>(2.28)|-2.12<br>(-0.16)(-0|-3.02<br>.01)|0.09|1.75|2.14|16.95|24|
|Manuf.&mining|SPO est. of U|42.76<br>(1.14)|0.57<br>(2.37)|-36.1?<br>(-0.97|2.14<br>)<br>(0.82)|0.14|2.27|2.03|15.57|24|
||Appl./Placem.<br>|9.89<br>(1.21)<br>|0.47<br>(1.91)|-29.19<br>(-1.52|1.36<br>)<br>(1.13)|0.13|2.02|1.87|14.98|21<br>|
||(Appl.-Pl.)/App|l.<br>64.34<br>(0.79)|0.44<br>(1.69)<br>|-17.68<br>(-0.74<br>|0.56<br>)<br>(0.45)<br>|0.05<br>|1.32<br>|1.77<br>|15.72<br>|21<br>|
||Registr.<br>unempl.|4.29<br>(0.14)|0.58<br>(2.15)|-1.09<br>(-0.09|-0.20<br>)(-0.10|0.10<br>)|1.87|2.03|15.92|24|

Specification see G.E.Johnson and P.R.G.Layard, "The Natural Rate of Unemployment: Explanation and Policy" in Handbook of Labor Economics, Vol. UI, ed.s O.Ashenfelter and R.Layard, pp. 921-999, Amsterdam 1986.
<!-- Start of picture text --> Taiivanta 2008-09 2010-11 2012-13<br>(wave 1) (wave 2) (wave 3)<br>Malawi 2010-11 2012-13<br>(wave 1) (wave 2)<br><!-- End of picture text --> <!-- Start of picture text --> Taiivanta 2008-09 2010-11 2012-13<br>(wave 1) (wave 2) (wave 3)<br>Malawi 2010-11 2012-13<br>(wave 1) (wave 2)<br>Uganda 2009-10 (wave 1) 2011-12 (wave 3)<br>2010-11 (wave 2)<br>ae 2011-12 2013-14<br>Nigeria 2010-11 2012-13<br>(wave 1) (wave 2)<br>Niger. (wave2011-121)2011-121)1)<br>2008 2009 2010 2011 2012 2013 2014<br>ETHIOPIA<br>(n=110) 50<br>MALAWI! =<br>(n=43) 50<br>-<br>NIGERIA<br>(n=104) = 6<br>-<br>TANZANIA<br>(n=101) _ og<br>UGANDA<br>(n=54) 7<br>Oo 20 40 60 80<br>% of listed crops<br>CTI Fruit Trees Trees NA | Plant/Herb/Grass/Roots<br>WO tree Cash Cropa§§9§9§) “= trees for timber and fuelwood<br><!-- End of picture text -->

<!-- Start of picture text --> 2010-11 (wave 2)<br>ae 2011-12 2013-14<br>Nigeria 2010-11 2012-13<br><!-- End of picture text --> <!-- Start of picture text --> (wave 1) (wave 2)<br>.<br>Niger. (wave2011-121)2011-121)1)<br>2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> <!-- Start of picture text --> (n=101) _ og<br>UGANDA<br>(n=54) 7<br>Oo 20 40 60 80<br>% of listed crops<br>CTI Fruit Trees Trees NA | Plant/Herb/Grass/Roots<br>WO tree Cash Cropa§§9§9§) “= trees for timber and fuelwood<br><!-- End of picture text -->
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76
**40 |** P a g e

# **References:**

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|p<l|49.18**|99.03|
|---|---|---|
|_p<2_|25.28|49.86|
|p<3|13.31|24.58|
|p_<4|7.455|11.27|
|p<!~5|3.814|3.814|

** Rejection at a 1 percent level of significance. _Source:_ Authors' calculations.

**Table** 7. **Misspecification Tests for Co-Integration Vector Auto Regression for Ghana, Sub-sectoral Specification**

|Industry||||
|---|---|---|---|
|_Single-equation analysis_<br>Manufacturing|F4(1,_15)_<br>0.081|X<sup>2</sup>nor.wiv (2)<br>1.298|Fh(l,<br>14)<br>0.000|
|Agriculture|4.021|5.369|1.423|
|Services|0.003|2.396|0.146|
|Mining|0.141|0.706|0.001|
|Construction|0.290|1.723|0.476|
|Gas and water|2.372|1.699|0.185|
|_Vector analysis_|F,,,(36, 24)<br>2.155*|**X**<sup>**2 **</sup>**.,ali,3,(12)**<br>13.222||

* Rejection at a 5 percent level of significance. _Source:_ Authors' calculations.

The Johansen test for this alternative specification reveals the existence of two distinct co-integrating relationships. This result supports our earlier hypothesis of subsectoral dynamics possibly vanishing in the course of aggregation to the industry level.

The co-integrating rank is restricted to one for Cote d'Ivoire, one for Zimbabwe, and two for Ghana. Tests of overidentifying restrictions on the _a_ and ,B vectors may now be carried out. For a, the main test of interest is that of weak exogeneity of a particular variable with respect to ,B. This test is carried out by restricting the adjustment coefficients of the variable in question to zero. This amounts to testing whether the variable in question adjusts to deviations from equilibrium (which is the interpretation of the co-integrating relation). If all the a coefficients of a particular variable can be restricted to zero, then we may condition on this variable in the further analysis. That is, we may remove it from the left-hand side of the equation and it becomes exogenous to the remaining system. If this is the case, this particular variable drives the system of equations. Regarding ,B, there is no "right" size of the intersectoral dependency from a 13
domestic food prices. The average pass-through effect for the region as a whole is 0.25, only slightly lower than the „average‟ estimate of the effect in the literature (0.3). The slightly lower average effect is consistent with the high prevalence of policies intended to cushion consumers and limit movements in domestic food prices in nearly all MENA countries.

**Table 3. Food Price Pass-Through Coefficients**

|Countr and rou|6-month foodpricepass-through|12-month foodpricepass-through|
|---|---|---|
|y gp|Worldprice increase Worldprice decreas|e<br>Worldprice increase Worldprice decrease|
|Bahrain|0.050<br>0.057<br>(0.057)<br>(0.036)|**0.349**<br>0.051<br>(0.113)<br>(0.034)|
|Kuwait|0.107<br>0.016|**0.279**<br>0.020|
|s|(0.081)<br>(0.029)|(0.128)<br>(0.029)|
|Oman<br>trie|**0.341**<br>0.079|0.213<br>0.075|
|oun|(0.142)<br>(0.063)|(0.130)<br>(0.074)|
|Qatar<br>C c|**0.286**<br>**0.182**|**0.355**<br>**0.220**|
|GC|(0.125)<br>(0.085)|(0.161)<br>(0.099)|
|Saudi Arabia|0.144<br>0.033|0.266<br>0.023|
||(0.278)<br>(0.024)|(0.232)<br>(0.021)|
|UAE|0.355<br>**0.298**|**0.413**<br>0.315|
||(0.178)<br>(0.143)|(0.202)<br>(0.163)|
|Algeria|0.065<br>0.066|0.059<br>0.048|
|ers|(0.077)<br>(0.048)|(0.072)<br>(0.037)|
|Iran<br>ort|0.103<br>0.003|**0.282**<br>0.052|
|exp|(0.081)<br>(0.026)|(0.116)<br>(0.043)|
|Iraq<br>oil|0.122<br>0.062|**0.497**<br>0.055|
|ing|(0.131)<br>(0.083)|(0.158)<br>(0.100)|
|Syria<br>lop|**0.163**<br>0.052|**0.261**<br>0.100|
|eve|(0.097)<br>(0.053)|(0.114)<br>(0.067)|
|Yemen<br>D|**0.393**<br>0.147|0.311<br>0.234|
||(0.161)<br>(0.177)|(0.166)<br>(0.192)|
|Djibouti|**0.583**<br>0.037|**0.464**<br>0.129|
||(0.180)<br>(0.078)|(0.183)<br>(0.106)|
|Egypt|**0.336**<br>0.041|**0.441**<br>0.034|
||(0.124)<br>(0.032)|(0.140)<br>(0.031)|
|Jordan<br>s|**0.219**<br>0.054|**0.392**<br>0.130|
|ter|(0.102)<br>(0.047)|(0.118)<br>(0.069)|
|Lebanon<br>por|0.080<br>0.145|0.180<br>0.132|
|im|(0.172)<br>(0.096)|(0.209)<br>(0.093)|
|Morocco<br>Oil|0.044<br>0.061|**0.394**<br>0.052|
||(0.063)<br>(0.050)|(0.121)<br>(0.042)|
|Tunisia|0.070<br>0.004|0.058<br>0.005|
||(0.092)<br>(0.022)|(0.092)<br>(0.025)|
|WBG|**0.475**<br>0.015|**0.658**<br>0.017|
||(0.123)<br>(0.034)|(0.134)<br>(0.040)|

_Note:_ Bootstrapped standard errors in parenthesis; bold numbers indicate significance at the 5 percent level or better.

The strongest pass-through effects are found in the West Bank and Gaza (WBG), Djibouti, Iraq, Egypt, and the United Arab Emirates (UAE). In these countries the pass-through coefficients are above 0.4, indicating high vulnerability to world food price shocks. The large majority of countries have food price pass-through coefficients in the order of 0.2-0.4. This indicates some degree of vulnerability to

-18-
# **2.5. Population Growth and Urbanization**

The Asian countries included in this study alone currently account for over half of the world’s population and are projected to keep growing to comprise 3.6 billion out of the 7.2 billion total world population in 2015 (World Bank, 2008). Aside from population growth, the other notable demographic change in Asia is that it is also going through a period of rapid urbanization. Table 4 lists the population of Asian countries in 1990 and 2006, the percentage of urban dwellers, and the growth rates of total and urban populations. Urban population growth can be seen to be significantly higher than total population growth in all of the countries except Mongolia and Sri Lanka.

**Table 4: Total and Urban Population Size and Growth Rate**

||19<br>|Popul<br>90<br>|ation<br>20<br>|06<br>|Avg. Annual<br>(1990-20<br>|Growth<br>06)<br>|
|---|---|---|---|---|---|---|
||Total<br>(millions)|Urban share<br>(%)|Total<br>(millions)|Urban share<br>(%)|Total<br>(%)|Urban<br>(%)|
|Bangladesh|113|20|156|26|2|3.6|
|China|1,135.2|27|1,311.8|41|0.9|3.5|
|India|849.5|26|1,109.8|29|1.7|2.5|
|Indonesia|178.2|31|223|49|1.4|4.4|
|Korea, Rep.|42.9|74|48.4|81|0.8|1.3|
|Malaysia|18.1|50|26.1|68|2.3|4.3|
|Mongolia|2.1|57|2.6|57|1.3|1.3|
|Pakistan|108|31|159|35|2.4|3.3|
|Philippines|61.2|49|86.3|63|2.1|3.8|
|Sri Lanka|17|17|19.9|15|1|0.2|
|Thailand|54.3|29|63.4|33|1|1.6|
|Vietnam|66.2|20|84.1|27|1.5|3.3|

Source: World Bank (2008).

While developed and developing countries alike continue to urbanize, the rate of urbanization is especially fast in developing countries where the majority of people have not been city-dwellers traditionally. The year 2007 was historic in that it commemorated the first time that half of the world’s population lived in cities, a share that is anticipated 20
the measure proposed in a country like Morocco and, more generally, in North-Africa and the MiddleEast where the gender divide in the labor market is very important and still little understood.

# **4. Data**

This paper uses data from the Morocco Household and Youth Survey (MHYS) of 2009–2010. This survey was undertaken by the World Bank to study economic and civic exclusion of the young population in Morocco. The survey collects information on all household members with a stronger focus on the youth (aged 15-29) within the households.<sup>4</sup> The MHYS 2009-2010 is a nationally representative survey of 2,000 households, of which 1,216 households are urban and 784 are rural. The survey collected information on household members’ demographic and educational characteristics, as well as their economic activities. Information was also collected on household assets and expenditure, the ability of households to cope with shocks, their use of social assistance, and migration behavior. In addition, the MHYS administered a separate instrument with additional questions on aspects of employment and civic participation to 2,883 young individuals aged 15 to 29 (representing about 90 percent of the youth in the surveyed households).

The paper first looks at income inequality and relative deprivation using the entire sample of 2,000 households that covers 10,769 individuals. The expenditure module of the MHYS was used to construct a household level expenditure aggregate, which was adjusted for cost of living differences across rural and urban areas.<sup>5</sup> As it is standard in the measurement of welfare in middle income and poor countries, expenditure is preferred to income as income is often underreported and more volatile than expenditures. Hence, the expenditure aggregate is used as the household level indicator of wellbeing.

We then shift to the analysis of relative labor deprivation where the sample is limited to those individuals aged between 15 and 64 who are not enrolled in school. This restricts the sample to 6,451 individuals – 3,339 females and 3,112 males. In the last stage, we further restrict the sample to the 2,128 youth (aged 15-29) – 1,085 females and 973 males – who are not enrolled in school.

> 4A World Bank (2011) report “Promoting Youth Opportunities and Participation” is based on this survey, as well as on a qualitative study.

> 5 To adjust nominal expenditure across rural and urban households in the absence of CPI data that covers prices in rural areas or information on unit values from the household survey, we take a simpler approach. We use the ratio of the official urban and rural poverty line of 2007 as reported by the HCP to adjust nominal expenditures across rural and urban households. Urban prices are assumed to be 7.4 percent higher than rural prices (the ratio of the urban and rural poverty line or 3834 / 3569).

12
**Figure 2b: Budget share of millet and sorghum expenditure**

<!-- Start of picture text --> 0.18<br>0.16<br>0.14<br>Rural<br>0.12<br>0.1<br>0.08<br>National<br>0.06<br>0.04<br>Urban<br>0.02<br>0<br>10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5<br>Log of per eq adult expenditure<br>National Urban Rural<br>Rice Share in Total Expenditure<br><!-- End of picture text --> Source: Authors’ estimation using ELIM 2006.

**Figure 2c: Budget share of corn expenditure**

<!-- Start of picture text --> 0.1<br>0.09<br>0.08<br>0.07<br>0.06<br>0.05<br>Rural<br>0.04<br>0.03<br>National<br>0.02<br>Urban<br>0.01<br>0<br>10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5<br>Log of per eq adult expenditure<br>National Urban Rural<br>Rice Share in Total Expenditure<br><!-- End of picture text --> Source: Authors’ estimation using ELIM 2006.
|||1952-1998|||1978-199|8|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|YRD|0.972***|0.684**||1.961**|1.545**||
||(0.351)|(0.309)||(0.782)|(0.635)||
|Guerrilla||0.468*|||1.374**||
|||(0.277)|||(0.670)||
|YRD×GuerrillaNo [_b_0]|||0.465|||0.980|
||||(0.309)|||(0.657)|
|YRD×GuerrillaYes [_b_1]|||1.058***|||2.645***|
||||(0.364)|||(0.820)|
|Other Control Variables|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs.|15|51|51|15|51|51|
|Adjusted-_R_<sup>2</sup>|0.233|0.589|0.598|0.424|0.662|0.669|
|_H_0 :_b_0 = _b_1|||0.071|||0.037|

Table 13: Effects of Power Structure on Growth Rates in Fujian, Border-Sharing Counties Only. Notes: White standard errors are in parenthesis. Intercept not reported. Columns (1) and (4) use subsample of FA3 counties and their borders sharing counties. Columns (2) and (5) use subsample of guerrilla counties and their borders sharing counties. Other control variables include Ln_GVOPC_52 (Ln_GVOPC_78), Ln_pop_52 (Ln_pop_78), Share of Plain (%) and Distance to Xiamen.

# **5.4 Robustness Checks: Border Counties**

Some may argue that, given the uneven distribution of YRD and FA3 counties over the province, the difference in development outcomes we have so far documented may reflect difference in other unobserved differences in geography or aspects not captured by our limited controls such as the share of plain in the county and the distance to Xiamen (and therefore Taiwan). That is, the concern is that such controls are unlikely to be sufficient.

A useful robustness check would thus be to hold these unobserved geography or other aspects as constant as possible. To this end, when we estimate the effect of YRD, we only keep FA3 and YRD counties that are neighbors. Since counties are geographically small—there are almost 3000 counties in China now—neighboring counties tend to be similar in geography (and culture). This stringent control results in a much smaller sample of 15 counties (versus 57 counties in the full sample). When we estimate the effect of guerrilla (and its interactive effect with YRD), we only keep counties that had guerrilla and their neighboring counties. The restricted sample has 51 counties.

Since our results are quite robust across various sets of controls, we present the specification with the comprehensive controls. The results on annual growth rates when restricted to the border counties are presented in Table 13, and those on famine control and death rates are in Table 14.

In Columns (1) and (4) in Table 13, we examine the effect of factions (measured by the YRD dummy) on border-sharing counties with observations from only 15 border-sharing counties. We find that the results, both 38