rafmacalaba/datause-encoder-data huggingface dataset:
Labor mobility is captured by assuming that L� �L<sup>�</sup> �u�<sup>�</sup> /�∑�∈� L�u�<sup>�</sup> / ∑�∈� L�� , where L<sup>�</sup> � is the base exogenous population in the cell pre any scenario, coming directly from the data. μ�0 implies labor is completely immobile within the country, as in settings (1) to (3). μ�∞ would imply perfect labor mobility. The total national population must equal the sum of all cell populations within the country L<sup>�</sup> � �∑�∈� L� . Results with labor mobility are computed with μ�10 .

In settings (2) ‐ (4), the amount of urban land is able to adjust in response to changes in the urban‐ rural rent differential, so that K�� �K<sup>�</sup> ����r��⁄r̅����r⁄ ��⁄r̅����<sup>�</sup> . We set ν�0.5 .

# **Appendix 3: Parameters**

The assumed parameters in the model are listed in the table below. Each sector of production employs intermediates, land, and labor. Primary good production relies on rural land, whereas manufacturing and non‐tradeable services rely on urban land (the latter has the highest value share of land in production, as this sector includes real estate and therefore all housing production). Primary good production is the most labor intensive of sectors/ Transport costs are calculated using a linear relationship from the transport time (in days) τ�� � 1 � λ<sup>�</sup> time��� , as in Alder (2017) and Roberts et al., (2012). Costs are highest from non‐tradeable services which are produced and sold predominantly in the same location, and lowest for manufacturing, for which longer travel times do not lead to much loss in terms of the value of the good. θ is chosen at 0.75 , reflecting similar assumptions elsewhere in the literature such as for rural goods in China in Roberts et al. (2012). There are other ways of modeling the impacts of transport times on transport costs, such as through an exponential model in Lall and Lebrand (2019).

The GVA data from the UN National Accounts, and the consequent Gross Output values calculated from input‐output matrices, combined with the net trade flows from each country, gives a total value of output of each sector consumed within each country. From these, national consumption value shares for each sector, β�, β�, β� , are calculated.

# _Parameters_

||Primary<br>Goods|Manufacturing|Non‐Tradeable<br>Services|
|---|---|---|---|
|Input Shares||||
|Primary Goodsα�|0|0.2|0.1|
|Manufacturingα�|0.1|0.2|0.1|
|Non‐Tradable Servicesα�|0.1|0.1|0.1|
|Landα�|Rural 0.25|Urban 0.1|Urban 0.3|
|Labourα�|0.55|0.4|0.4|
|Transport Costs||||
|λ<sup>�</sup>|0.18|0.09|1.8|
|θ|0.75|0.75|0.75|
|Elasticities||||
|CES consumptionσ�|6|6|6|
|Export demandη�|10|10|10|
|Returns to scale, _ρ _|0|0(0.2 IRS)|0|
|Mobility; μ= 0(immobile)or 10(m|obile)|Urban Land Suppl|yelasticity, ν =0.5|

36
|Divorced|3.88|4.90|2.88|
|---|---|---|---|
|Separated|9.71|9.80|9.62|
|_Total_|_100_|_100_|_100_|
|**Marriage**||||
|Civil rites only|4.94|6.52|2.86|
|Customary rites only|88.89|89.13|88.57|
|Both civil and customary|6.17|4.35|8.57|
|_Total_|_100_|_100_|_100_|
|**Parental status**||||
|Has children|74.27|87.13|61.9|
|Number living children|1.39|1.57|1.22|
|**Education**||||
|No education|0.48|0.00|0.94|
|Primary, not completed|11.54|7.84|15.09|
|Primary completed|34.62|33.33|35.85|
|Secondary, not completed|26.92|27.45|26.42|
|Secondary, completed|15.87|21.57|10.38|
|High school completed|7.69|5.88|9.43|
|Vocational training|0.48|0.00|0.94|
|Tertiary certificate|1.92|2.94|0.94|
|Other (diploma)|0.48|0.98|0.00|
|_Total_|_100_|_100_|_100_|

Next, from the survey data, five women were selected as case studies for further in‐depth interview based on survey responses regarding current and family household compositions,<sup>7</sup> marital and motherhood status, and work history. Four were textile workers and one was a domestic worker. We contacted these selected women’s family households in three rural locations, where we conducted in‐depth interviews with the migrant‐sending households, with similar non‐migrant‐sending households nearby, and separate focus groups with rural men and women. Because domestic workers had unforeseen difficulties in

> 7 “Current household” consists of those individuals living and eating in the migrant’s home in urban Maseru or Maputsoe at least four days a week. “Family household” consists of those individuals living and eating in the migrant’s family home outside of urban Maseru or Maputsoe at least four days a week.

12
approach of Osili (2007), by surveying both the remittance sender and recipient and comparing the remittance figures obtained to give a better sense of the accuracy of these reports.<sup>10</sup> Finally, the analysis in this paper is by necessity descriptive. Ideally one would like to directly link policy changes to changes in remittance behavior to more definitively answer the policy questions which motivate some of this paper. Thus, for example, as OECD countries change their migration policies, it is of interest to see whether changes in the gender, education, income level, and duration abroad of migrants induced by these policies lead to changes in remittance transfers. Likewise, one would like to know if policy actions to increase remittances, such as lowering remittance costs or setting up matching programs like Mexico’s 3 for 1 program, lead to more remittances. Regular surveys of remittances of Africans abroad would be one first step in creating the necessary data infrastructure needed to carry out such studies.

> 10 Osili (2007) notes that because migrants remit to multiple source country households and that source country receivers receive remittances from more than one migrant household abroad, she cannot directly compare the remittance figures on both side of a transaction. More detailed remittance questionnaires and careful attention to survey timing would allow this to be done.

27
_15_ 3.1 _Thze Government and Corporate Governance_ There _is no sign of the.governnent_ exercising control according _wo cenral_ principles as _azn_ equity holder the way for example the Mexican government took control of its state enterprises in 1986. This was probably due tO the absence of a centralized treasurv function: SOEs that were not yet corporatized remained under the control of their respective line mrinistries. Corporate control could also have bean exercised through other channels. however: we list three. First, the government could through open-ended subsidies or bail-outs have undemuned _efforts by ocher claim holders to establish corporate_ governance. Second. the government through its tax claims became a debt holder and could force adjustment in enterprises: and third, the goi.ernment could and did set general rules and general incentive schemes to induce responsible enterprise behavior. In the Polish context. the third category consisted of an elaborate scheme to induce wage moderation. the effectiveness of which has been questioned by many (eg. Coricelfi e.a. (1992)).

_Subsidkes_ Clearly. unconditional and open-ended subsidies eliminate any chance of other debt holders to control managers since management can simply ask for more subsidies when faced with threats of fimding cut-offs bv. for example. banks. Thus open-ended subsidies destroy any chance of anybody establishing corporate governance. On this score. Polish performance was exemplary: indusil subsidies were sharply reduced over the period 1989-92 (cf Table : what subsidies remained were directed to areas other than manufacturing (e.g.. the railwavs and housing). The absence of open-ended subsidies as a last resort source of funding created the opportunity for other claim holders to exercise control.

Table 7: Government Subsidies to Economic Units

|Zloty trillion|1988|1989|1990|1991|19|
|---|---|---|---|---|---|
|Total Expenditure|8-4|29.6|L72.2|241.9|381.9|
|Subsidies|3.3|9-7|32.9|220|_8|
|Subs/Total Exp.()39||33|19|9|_5_|

Source: GUS Statistical Yearbook 1991 (Table 4 p. 231). 1993 (Table 2 p. 233). Data are noE fUlly comparable because Voivodships (local government authorities) are included from 1991 onwards in the Govermment Budget data: but the trend is obvious.
18 _TFlPD y - ac*k_ - _a,*h_ where 9 is growth of output per worker and the shares of physical and human capital are imputed from non-regression data3.

Table 4 shows the results of regressing TFP growth on the growth of physical and human capital. If the assumed shares are .4 (physical) and .3 (educational) then the growth of educational capital shows a large and very significant negative effect on TFP growth<sup>3</sup> '. Even if I assume that the physical capital share is on the high side at .5 and that the share of human capital in the wage bill is on the low side, at a third, so that the educational capital share imposed is about as low as can be reasonable (.166), it is still the case that educational capital accumulation is strongly negatively related to TFP growth (column 2 of table 4).

Table 4: TFP growth and physical and human capital.

||Assumed aK and_a_<br>and human capita|_H_(shares of physical<br>l)|
|---|---|---|
||aK=.4, aH=.3|_aK_<br>=.5.<br>**aH-.1 **<sup>**6 7**</sup>|
|Growth of physical<br>capital|.126<br>(3.08)|.026<br>(.651)|
|Growth of Schooling<br>capital|-.338<br>(6.91)|-.205<br>(4.19)|
|ln(Initial GDP per<br>worker)|.0009<br>(.625)|.0009<br>(.625|
|R Squared|.419|.205|
|N|91|91|

Notes: absolute value of t-statistics in parenthesis. Physical capital is King-Levine and educational capital is Barro-Lee.

30 By saying Idefine it in this was I am adrnitting to ignoring several whole large and important literatures on what producuvity really is, how aggregate TFP can be rigorously derived from micro production functions, and how it can be consistently estimated. I am simply, for purposes of discussion, associating the concept TFP with the extended Solow growth accounting residuals by fiat.

31 Although the result is only presented for one physical (King-Levine) and schooling (Barro) growth measure the result is robust to alteratives.
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more educated women in recent cohorts are doing a little better. The slope of the marginal effect across cohorts for the urban dummy tends to be quite low and for the case of Bangladesh negative, indicating that, at best, there is no differential progress for urban women with respect to rural ones and, in the case of Bangladesh, urban women have fared slightly worse than rural ones.

In terms of the other norms-sensitive outcomes, the trends for more affluent women have been better in all countries for education, marriage age (with India being an exception), first birth age, son preference, spousal difference in age, and for IPV experience (only in India for this last variable). For some of these outcomes, a better trend for richer women means that the marginal effect amounts to a few percentage points. For example, in the case of marriage age for Pakistan, while the difference in the average marriage age of women in all wealth quintiles born from 1940 to 1965 was not statistically different, this difference became significant, in favor of the richer quintiles, for women born afterwards and reached about 2 percentage point difference for the richest and the poorest quintiles.

Better educated women enjoyed progress only in the case of a) the agency outcome variable, in India and Pakistan and marginally in Nepal, b) differentials in spouses’ education, and c) experience of IPV, apart the case of Bangladesh. For all other outcomes, educated women and not educated women did not experience different trends. Similarly, more recent cohorts of women in urban areas did not experience stronger progress than women in rural areas in any variables with the exception of spousal differences in education for India and in age in Nepal, and experience of IPV in Nepal.<sup>26</sup>

# **Stylized fact 3: Attitudes towards gender roles in South Asia have remained conservative for men and women born across half a century.**

In parallel to the outcome variables, we examine the long-term evolution of attitudes towards gender roles and find no progress in the four attitudes studied. Using various waves of the World Values Surveys, we construct pseudo-panels for three countries in the region, Bangladesh, India and Pakistan, and follow the evolution of attitudes from cohorts born in the late 1940s to cohorts born in the late 1990s. Like Table 3, Table 4 offers a bird’s-eye view of the decadal changes of key beliefs, while Figure 11 shows the detailed trends.

The share of people agreeing with conservative attitudes – such as the priority for becoming breadwinners should be given to men, or women’s preeminent goal is maternity – are remarkably high in the most

> 26 Detailed graphs and comments for all these norms-sensitive outcomes are available in Annex Figures A7 to A26.

22
The inequality possibility frontier: Extensions and new applications Branko Milanovic<sup>1</sup> Key words: inequality, inequality possibility frontier, civil war JEL classification: D31, N3, O1, Q3 Number of words: About 7,700 Sector board: Social protection 1 World Bank, Development Research. Earlier versions of the paper was presented at the conference “Measuring inequality in economic history” held at the Universidad de la Republica in Montevideo in August 2012, economic history seminar at the University of Lund in September 2012, and Poverty and Inequality Measurement and Analysis Practice group seminar at the World Bank, Washington in April 2013. I am grateful to the participants, and in particular to Jorge Alvarez, Luis Bertola, Cecilia Castelnovo, Esteban Nicolini, Javier Rodriguez Weber and Henry Willebald in Montevideo, Ewout Frankema, Christer Gunnarsson and Debin Ma in Lund, and Peter Lanjouw in Washington for very valuable comments. It is written as a part of the KCP-funded research project “Chageable inequalities: facts, perceptions and policies” TF012968. The opinions expressed in the paper are author’s and should not be attributed to the World Bank or its affiliated organizations.
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36
# **Table 3: Natural Hazards and City Demand**

|Dependent variable(logrent)|1|2|3|4|5|6|
|---|---|---|---|---|---|---|
|Vacancyrates(2007)|-0.0291***|-0.0284***|-0.0273***|-0.0320***|-0.0309***|-0.0319***|
|(urban demand)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Urban density (logs)|0.193***|0.478***|0.477***|0.470***|0.484***|0.473***|
|(agglomeration)|(0.06)|(0.08)|(0.07)|(0.07)|(0.07)|(0.07)|
|Urban Population(logs)|0.066|0.128**|0.193***|0.211***|0.223***|0.215***|
|(scale effects)|(0.07)|(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|
|Construction wages(logs)||0.339***|0.251***|0.256***|0.277***|0.261***|
|(developer costs)||(0.05)|(0.06)|(0.06)|(0.06)|(0.06)|
|Average low temperature|||-0.0129***|-0.0128***|-0.0123***|-0.0127***|
|(geography)|||(0.00)|(0.00)|(0.00)|(0.00)|
|Pollution concentration|||-0.00244|-0.00236|-0.00217|-0.00231|
|(environment disamenity)|||(0.00)|(0.00)|(0.00)|(0.00)|
|Cityin earthquake zone||||-0.322***||-0.256|
|(hazard var)||||(0.09)||(0.18)|
|Cityearthquake severity|||||-0.0596***|-0.0152|
|(hazard var)|||||(0.02)|(0.04)|
|Constant|4.657***|1.855|2.692*|2.747*|2.302|2.636*|
||(0.48)|(1.61)|(1.55)|(1.47)|(1.49)|(1.50)|
|Observations|122|119|119|119|119|119|
|R-squared|0.304|0.513|0.57|0.616|0.61|0.617|
|***p<0.01,**p<0.05,*p<0.1|||||||
|Standard errors inparentheses|||||||

Source: Estimates produced for this paper Estimates from regression analysis of office rents are presented in Table 3. These estimates should be considered as indicators of correlation—not precise magnitudes of causal relationships. The first three columns report results of estimating how office rents reflect city demand and supply. Vacancy rates have a negative effect, reflecting lower city demand, while urban density has a positive effect – reflecting economies of agglomeration. Construction wages have a positive effect on rents as construction labor accounts for about half the non-land costs of constructing an office building. Surprisingly, pollution concentration is not associated with office rents.

In column 4, a variable on earthquake risk (whether the city is in an earthquake zone) is introduced into the regression. We find that being in an earthquake zone has a negative effect on office building rents – reflecting lower demand for a city. Similarly, earthquake severity (column 5) also has a negative effect on city demand. These estimates suggest that business owners trade off agglomeration economies with seismic risk in making 17
Table 3a: Transition matrix of specialization/diversity levels

||1989|1994|2000|2005|2010|
|---|---|---|---|---|---|
|A. Specialization index f<br>|or districts - A<br>|verage quinti<br>|le value by tim<br>|e period<br>||
|Lowest initial quintile|1.00|2.57|2.70|2.93|2.86|
|2nd initial quintile|2.00|2.60|2.79|3.01|2.80|
|3rd initial quintile|3.00|3.14|3.12|2.88|2.99|
|4th initial quintile|4.00|3.14|3.03|3.01|3.14|
|Highest initial quintile|5.00|3.53|3.34|3.14|3.19|
|Quintile gap|4.00|0.96|0.64|0.21|0.33|
|B. Diversity index for dis|tricts - Avera|ge quintile val|ue by time per|iod||
|Lowest initial quintile|1.00|2.64|2.83|2.85|2.74|
|2nd initial quintile|2.00|3.03|2.87|2.92|2.81|
|3rd initial quintile|3.00|2.80|3.05|2.94|3.08|
|4th initial quintile|4.00|3.14|3.12|3.05|3.09|
|Highest initial quintile|5.00|3.36|3.12|3.22|3.25|
|Quintile gap|4.00|0.73|0.29|0.37|0.50|

Notes: See Table 1a. Districts are divided into quintiles for each period based upon index values. Each row documents a cohort's evolution based upon initial value in 1989. Moving from left to right along a row, an increase in the value indicates that the average index value for that cohort is increasing from one period to the next.

Table 3b: Initial transition matrix on a period-by-period basis

||1989→1994|1994→2000|2000→2005|2005→2010|
|---|---|---|---|---|
|A. Specialization index for districts -|Average quinti|le value in end|period||
|Lowest quintile in start period|2.57|2.67|2.57|2.41|
|2nd quintile in start period|2.60|2.77|2.49|2.67|
|3rd quintile in start period|3.14|3.06|3.00|2.71|
|4th quintile in start period|3.14|3.15|3.23|3.37|
|Highest quintile in start period|3.53|3.33|3.69|3.82|
|Quintile gap|0.96|0.65|1.12|1.42|
|B. Diversity index for districts - Ave|rage quintile val|ue in end perio|d||
|Lowest quintile in start period|2.64|2.62|2.47|2.20|
|2nd quintile in start period|3.03|2.65|2.92|2.71|
|3rd quintile in start period|2.80|2.97|2.95|3.14|
|4th quintile in start period|3.14|3.23|3.14|3.26|
|Highest quintile in start period|3.36|3.52|3.51|3.68|
|Quintile gap|0.73|0.90|1.04|1.48|

Notes: See Table 3a. The table provides the equivalent of the 1989→1994 transition for each period by reordering districts after each survey into their quintiles at that time.