rafmacalaba/datause-encoder-data huggingface dataset:
# lVi INTRODUCnON

Table 13 Population and Percentage Distribution by World Bank RegiQn and Country Department, 1985-2100

|Region and||Po|pulatio|n<br>(mill|ions)||Pe|rcenta|ge<br>of w|ortd p|opula|tion|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|country<br>department|1985|1990|2000|2025|2050|2100|<br>1985|1990|2000|2025|2050|2100|
|World|4,844|5,285|6,204|8,415|10,035|11,330|100.0|100.0|100.0|100.0|100.0|100.0|
|Borrowers and non-borrowers|||||||||||||
|Sub-Saharan Africa|457|534|72A|1,359|1,986|2,634|<br>9.4|10.1|11.6|16.2|19.8|23.2|
|Asia and Oceania|2,574|2,808|3,270|4,186|4,715|5,001|53.1|53.1|52.7|49.7|47.0|44.1|
|Europe, Middle East,|||||||||||||
|and North Africa|1,147|1,223|1,388|1,831|2,187|2,502|23.7|23.1|22.4|21.8|21.8|22.1|
|America|667|721|826|1,040|1,146|1,192|13.8|13.6|13.3|12.4|11.4|10.5|
|Borrowers only|||||||||||||
|Africa<br>Region|415|486|660|1,263|1,858|2,480|8.6|9.2|10.6|15.0|18.5|21.9|
|1Occidental<br>and Central|40|47|63|123|185|250|0.8|0.9|1.0|1.5|1.8|2.2|
|2 Eastern|106|<br>124|<br>169|<br>328|<br>486|<br>654|2.2|2.3|<br>2.7|3.9|4.8|<br>5.8|
|3 South-Central<br>and|||||||||||||
|Indian Ocean|52|<br>61|<br>84|<br>163|<br>245|<br>333|1.1|1.2|1.3|1.9|2.4|<br>2.9|
|4Western|119|<br>139|<br>187|<br>341|<br>472|<br>592|2.5|2.6|3.0|<br>4.0|4.7|<br>5.2|
|5 Sahelian|36|<br>42|<br>56|<br>111|<br>175|<br>252|0.7|0.8|<br>0.9|1.3|<br>1.7|2.2|
|6 Southern|62|73|<br>101|197|<br>295|<br>400|1.3|1.4|<br>1.6|2.3|2.9|<br>3.5|
|Asia Region|2,384|2,608|3,055|3,952|4,483|4,771|49.2|49.4|49.2|47.0|44.7|42.1|
|1 Other South|134|152|190|282|351|401|2.8|2.9|3.1|3.3|3.5|3.5|
|2 Southeast|274|304|364|488|568|609|5.7|5.8|5.9|5.8|5.7|5.4|
|3 China|1,040|1,117|1,269|1,530|1,626|1,672|<br>21.5|21.1|20.5|18.2|16.2|14.8|
|4 India|765|848|1,011|1,361|1,602|1,732|15.8|16.0|16.3|16.2|16.0|15.3|
|5 Indonesia and Pacific|170|187|221|291|335|356|3.5|3.5|3.6|3.5|3.3|3.1|
|EMN Region|474|<br>536|678|1,090|1,451|1,766|9.8|10.1|10.9|13.0|14.5|15.6|
|1Pakistanand Turkey|*146|<br>170|<br>223|<br>375|<br>502|<br>607|3.0|<br>3.2|3.6|4.5|<br>5.0|5.4|
|2 North Africa|55|63|83|131|170|197|1.1|1.2|1.3|1.6|1.7|1.7|
|3Hiddle East|168|<br>195|<br>260|<br>464|<br>657|<br>839|3.5|<br>3.7|4.2|<br>5.5|6.5|7.4|
|4 Europe|105|107|111|119|121|123|2.2|2.0|1.8|1.4|1.2|1.1|
|LAC Region|387|429|512|689|800|850|8.0|8.1|8.3|8.2|8.0|7.5|
|1 Brazil|136|150|178|233|265|278|2.8|2.8|2.9|2.8|2.6|2.4|
|2 Central America|104|116|141|199|237|254|2.1|2.2|2.3|2.4|2.4|2.2|
|3Caribbean and Other|70|78|94|128|150|162|1.5|1.5|1.5|1.5|1.5|1.4|
|4 Temperate and Tropical|78|<br>85|<br>99|129|148|156|1.6|1.6|1.6|1.5|1.5|1.4|

Note: world Bank regions and country departments are defined in Exhibit 2.
4 district to visit. In our approach, by contrast, inefficient units are compared with real-life efficient units. We can tell a policymaker of an inefficient government or a manager of an inefficient delivery unit which real-life country or unit achieves similar outcomes at a lower cost.

The second common shortcoming of DEA and SFA we try to address is their vulnerability to special pleading—policymakers or managers of poorly performing service delivery units claiming that there are legitimate factors explaining their poor performance that are ignored by the analysis. The usual response to date in the DEA and SFA literatures has been to address this issue through a two-stage approach: (1) construct or estimate a frontier to calculate efficiency scores; and (2) regress efficiency scores on factors thought to influence them. This practice has been criticized, not least because the process by which the efficiency scores is generated is ignored in the second-stage regression exercise (Burgess 2006). Instead, we build exogenous constraints into our analysis and allow different groups of units to have different frontiers. This multiple-frontier approach could, of course, be used in DEA and SFA studies too.

We illustrate our approach throughout using examples from the education and health sectors. Our examples are chosen with a view to data quality and variety in terms of level of decision-making. In both education and health, there is growing realization of the need to look beyond the number of people passing through a facility to the difference that the facility makes to the lives of the people passing through it. In the two education examples, therefore, we look at test scores to get at the quality of the education process rather than at student numbers which capture just quantity. One example comes from the level of the local government: our data come from California where school districts control the day-to-day running of public schools. The other education example relates to national education systems and makes use of data from the OECD‟s Program for International Student Assessment (PISA) study; while undertaken by the OECD, the geographic coverage of the study now extends well beyond the OECD countries. Our first health example also uses national data and tries to get at the efficiency of health systems. Looking at patients treated misses the quality of care, and in any case one could argue that if a health system is successful at preventing illness and injuries it ought to be reducing the number of patients requiring treatment. Measures of population health tend to be too broad-brush to be compelling measures of the outcome of a health system: many are affected by factors beyond the health system, and many causes of death are not amenable to medical care at all or only
global production networks—and to allow for much deeper integration by India into global production sharing than has been the case in the past.<sup>3</sup> Duty exemptions were incorporated in the GTAP model and data base following the methodology developed by Ianchovichina (2004). This duty exemption model allows for two separate activities in each industry – production of exports and production for the domestic market. Production of exports is represented as an activity for which imported intermediate inputs are available duty-free. Production for the domestic market uses the same technology as the production for exports, but requires payment of duties on intermediate inputs. Ianchovichina (2004) shows that failure to account for duty exemptions will introduce bias in trade liberalization outcomes in countries using such export processing systems.

The 57 sectors of the GTAP 7 (pre-release 4) database were aggregated into 26 sectors based on their importance in China, India and Russia. Because the 106 regions in this database would provide too much unnecessary detail and slow down our calculations, we aggregated most regions into 11 regional aggregate groups and 14 separate countries of interest—including Russia, China, India and a number of countries in the Asian region.

To examine the implications of more rapid growth in China and India on Russia, we needed first to take account of some of the major reforms that are transforming India’s trade structure, in particular liberalization of non-agricultural tariffs, the introduction of free-trade zones where imported intermediate inputs used in the production of exports are exempt from import tariffs, and improvements in infrastructure needed to support trade. Then, to provide a benchmark against which the effects of higher growth rates of output might be assessed, we undertake a baseline projection to 2020 (See Table 1). This baseline allows us to take account of the much higher expected rates of growth in many developing countries, including China and India, than in the mature industrial economies, and a consequent greater impact of future changes in outcomes in developing countries.

We examine the implications of higher-than-projected growth in India and China in order to assess the direct implications of growth in China and India on Russia. We assumed that growth rates in China and India were 2 percentage points per year higher than under the baseline.

> 3 This was done assuming large-scale liberalization of the non-agricultural sector in India and the introduction of an effective system of duty exemptions for inputs used in the production of exports in India.

11
21 Table 4: Summary statistics for pass-through coefficients for gasoline (percentage of countries in each category except for the median)

|**Parameter** (number of countries)|**Median**|**<0%**|**0–50%**|**50–75%**|**75–100%**|**100–150%**|**>150%**|
|---|---|---|---|---|---|---|---|
|**Income category**||||||||
|Low income (16)|65|0|31|25|13|19|13|
|Lower-middle income (24)|78|13|25|8|21|33|0|
|Upper-middle income (25)|95|12|16|8|20|32|12|
|High income (8)|135|0|0|0|13|63|25|
|**Developing countries only (excluding h**|**igh-income**|**countries**||||||
|Net oil importer in 2009 (45)|99|2|18|11|20|38|11|
|Net oil exporter in 2009 (20)|36|25|35|15|15|10|0|
|East Asia & Pacific (9)|100|0|22|0|22|56|0|
|Europe and Central Asia (4)|92|0|25|25|0|25|25|
|Latin America & the Caribbean (17)|103|6|12|0|24|47|12|
|Middle East & North Africa (8)|9|38|25|13|13|13|0|
|South Asia (5)|59|0|40|40|20|0|0|
|Sub-Saharan Africa(22)|67|9|27|18|18|18|9|

_Sources_ : Tables A2.1 and A2.3, and author’s calculations. _Note_ : The median is that for pass-through coefficients.

Table 5: Summary statistics for pass-through coefficients for diesel (percentage of countries in each category except for the median)

|**Parameter**(number of countries)|**Median**|**<0%**|**0–50%**|**50–75%**|**75–100%**|**100–150%**|**>150%**|
|---|---|---|---|---|---|---|---|
|**Income category**||||||||
|Low income (16)|61|0|31|25|25|19|0|
|Lower-middle income (24)|70|21|25|8|21|25|0|
|Upper-middle income (25)|96|8|16|20|20|32|4|
|High income (8)|114|0|0|0|13|88|0|
|**Developing countries only (excluding h**|**igh-income**|**countrie**|**s)**|||||
|Net oil importer in 2009 (45)|90|2|20|13|29|33|2|
|Net oil exporter in 2009 (20)|18|30|30|25|5|10|0|
|East Asia & Pacific (9)|89|0|22|22|33|22|0|
|Europe and Central Asia (4)|88|0|25|25|0|50|0|
|Latin America & the Caribbean (17)|101|6|6|12|24|47|6|
|Middle East & North Africa (8)|-3|63|13|25|0|0|0|
|South Asia (5)|30|0|80|0|20|0|0|
|Sub-Saharan Africa(22)|70|5|27|18|27|23|0|

_Sources_ : Tables A2.1 and A2.3, and author’s calculations. _Note_ : The median is that for pass-through coefficients.
The differences between education quartiles are very small when analyzing the effect of proximity to the Metro station on housing prices. When only one-year sold projects are considered, proximity to a Metro station does not have any impact on housing prices. In the other two estimations (multiple-year sold projects and all projects in the sample), the negative effect of proximity to a Metro station varies between 11 and 23 percent for low-educated neighborhoods, and between 10 and 23 percent for high-educated neighborhoods.

**Table 6: Heterogeneous effects categorizing the sample by the level of education in each neighborhood where the project is located. The table only shows the coefficients of the interaction between education** **<u>quartiles and private/public accessibility.</u>**

||<br>**(3)**|**(1)**|**(5)**|
|---|---|---|---|
||**One-year**<br>**observations**|**Repeated**<br>**observations**|**Panel all obs**|
|**Regression including the interactio**|**n between private acces**<br>**quartiles**|**sibility (to employment su**<br>|**bcenters) and education**|
|Private accessibility in 1<sup>st</sup>quarter of<br>education|0.0184***<br>(0.00393)|0.0140***<br>(0.00331)|0.0124***<br>(0.00284)|
|Private accessibility in 2<sup>nd</sup>quarter<br>of education|0.0183***<br>(0.00687)|0.0142***<br>(0.00322)|0.00331<br>(0.00339)|
|Private accessibility in 3<sup>rd</sup>quarter|0.0209***|0.00911***|0.0134***|
|of education|(0.00512)|(0.00308)|(0.00252)|
|Private accessibility in 4<sup>th</sup>quarter of<br>education|0.0197***<br>(0.00707)|0.0163***<br>(0.00278)|0.0173***<br>(0.00314)|

**Regression including the interaction between public accessibility (to a Metro station) and education quartiles**

|Public accessibility in 1<sup>st</sup>quarter of<br>education|0.0415<br>(0.0667)|0.235***<br>(0.0730)|0.110**<br>(0.0558)|
|---|---|---|---|
|Public accessibility in 2<sup>nd</sup>quarter of|0.00282|0.233***|0.148**|
|education|(0.0675)|(0.0703)|(0.0586)|
|Public accessibility in 3<sup>rd</sup>quarter of|0.0295|0.254***|0.121**|
|education|(0.0681)|(0.0722)|(0.0555)|
|Public accessibility in 4<sup>th</sup>quarter of|0.0384|0.230***|0.103*|
|education|(0.0681)|(0.0732)|(0.0563)|

Clustered standard errors at the neighborhood level in parentheses. Panel data estimations (column B and C) with random effects. OLS estimation in column 1 has year fixed effects. Different regressions for private and public accessibility. All regressions include the same control variables as in Table 4. *** p<0.01, ** p<0.05, * p<0.1

# **Conclusions**

In this paper, we estimate the effect of accessibility on housing prices in Mexico City. We consider private accessibility as proximity to employment subcenters, and public accessibility as proximity to public transit stations. Furthermore, we estimate heterogeneous effects for households located in neighborhoods with different education levels and 27
# **Table A16.  Participation in Employment and Work by the Proxy Respondent’s Characteristics**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Employed|||||||Working||||
||Women|Men|Women|Men|Women|Men|Women|Men|Women|Men|Women|Men|
|LOA|0.032|0.002|0.051|0.005|0.034|0.005|0.048|-0.014|0.062|-0.012|0.050|-0.013|
||(0.062)|(0.040)|(0.062)|(0.040)|(0.062)|(0.040)|(0.063)|(0.036)|(0.063)|(0.035)|(0.062)|(0.035)|
||[0.650]|[0.956]|[0.450]|[0.907]|[0.625]|[0.919]|[0.484]|[0.715]|[0.356]|[0.773]|[0.467]|[0.744]|
|Proxy is female|-0.090|-0.022|||||-0.089|-0.019|||||
||(0.067)|(0.081)|||||(0.066)|(0.072)|||||
|Proxy has high school<br>|||-0.143*|0.076*|||||-0.086|0.067*|||
|education or higher|||(0.073)|(0.044)|||||(0.077)|(0.038)|||
|Proxy is spouse|||||0.169**|0.120*|||||0.151**|0.074|
||||||(0.077)|(0.065)|||||(0.076)|(0.051)|
|Observations<br>Outcome (Control Group)|261|448|261|448|261|448|261|448|261|448|261|448|
|Mean<br>|0.476<br>|0.749<br>|0.476<br>|0.749<br>|0.476<br>|0.749<br>|0.524<br>|0.835<br>|0.524<br>|0.835<br>|0.524<br>|0.835<br>|
|_Notes:_This table presents the a<br>status. The sample is restricte<br>β1T1hs+ ƟXihs+ bs+ εihs. The<br>include EA (stratification vari<br>the household level are presen|ssociations b<br>d to particip<br>vectorXihs <br>able) fixed e<br>ted in paren|etween th<br>ants assign<br>includes th<br>ffects. The<br>theses, an|e proxy res<br>ed to the L<br>e list of co<br>control m<br>d the rand|pondent’<br>OA and<br>ntrol vari<br>ean refers<br>omization|s characte<br>control gro<br>ables selec<br>to the me<br>inference|ristics and t<br>up. We pr<br>ted using a<br>an of the c<br> _p_-values a|he probabil<br>esent ITT es<br>double-LA<br>ontrol grou<br>re shown in|ity of the<br>timates f<br>SSO proc<br>p for eac<br>brackets|proxy rep<br>rom the fo<br>edure for e<br>h outcome.<br>. ***_p_<0.01|orting em<br>llowing e<br>ach outc<br>Standar<br>, **_p_<0.0|ployment<br>quation:Y<br>ome. All es<br>d errors cl<br>5, *_p_<0.1|and work<br>ihs= α0+<br>timations<br>ustered at|

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- 41 -

Behaviour of Firms

# 7: Condusion

Drawing on a small survey-generated database but checking those findings against information available from more aggregated series, we have attempted to chart the behaviour of some Russian firms over 1991 and 1992. The findings are revealing. Large negative shocks to output for a significant number of firms and branches can be observed in 1992. These shocks came from both demand and supply sides and were fairly common. Aggregate rather than sectoral shocks appear to have dominated. This was not generally associated with a collapse in gross profits. Indeed, the most striking result is that industrial and trade sector firms' profits stayed remarkably buoyant in real terms. There is clear evidence that firms with market power have exercized that muscle by rapid adjustment of producer prices. The inverse association of the changes to output and profits for firms with market power suggests that such firms have tried to maintain or increase their mark-up. Certainly, we find no evidence of a strategic change in the underlying pricing rule.

Reasonably robust financial perfonmance did not characterize the budget-financed entities in our sample. Their implicit call on public financing was probably significant through 1992. But for the bulk of firms in the sample, the main impression is of relative stability in earnings and in the distribution of revenues over the various assigned funds and expenditure titles. We do not find any substantial evidence of decapitalization, at least through greatly enhanced borrowing or predatory wage settlements. Likewise, the shift upwards in interfirm arrears is less large than we might have expected from the aggregate numbers.

Despite the downward pressure on output and the absence of major growth in any of the surveyed firms, employment adjustments were limited. That is not to say that employment transitions were infrequent, but that net separations were relatively restricted given what we know about the size of shocks to output and the continuing presence of labour hoarding. Revealing is the fact that firms continued to hire at significant rates over 1992. This can most likely be explained in terms of fixed factors. It is also obviously related to the continuing high rates of local turnover in Russian firms. In short, firms have discarded little labour and mostly ancillary and female staff.

There is evidence that some firms have chosen to place workers on minimum wages, reducing labour costs quite significantly but with the result that fall-back income is provided within the firm rather than through the labour offices. Increased per capita contributions for the Employment Fund would obviously be one mechanism for breaking this practice. But the larger question relates to the efficiency of fall-back payments through the firm, the implications for job search behaviour and any significance
of fertility levels, and dynamic effects, such as the opening of employment opportunities for complementary workers who would otherwise be unemployed.

These indirect effects are appropriately described as positive and negative externalities. Together with labor market imperfections that lead to wage distortions these factors mean that manpower planners will need to use their informed judgement in adjusting estimates of social returns to education and training. Moreover, these judgements need to be made clear unless the criticisms of precise manpower projections be extended to precise rates of return to education and training. In making these judgements, planners need to get out of their offices and develop a feel for the relevan **t** raining programs and labor markets by keeping in touch with educators, trainers, employers, employees, trainees, jobseekers and observers. They also need to be knowledgeable about trends in technology and international product markets which affect the future structure of the economy. Judgements should not be based solely on rates of return, but a composite of labor market signals. In contrast to most manpower forecasting which tends to be a one report affair, this approach has to be a continuous process with the regular updating of signals.

# D. Sources of Labor Market Information

Manpower planning will monitor labor market conditions and evaluate training programs to produce information guiding the training decisions of individuals, enterprises, and managers of public and private training institutions. In monitoring the operation of labor markets and training incentives, the planner will analyze market imperfections and failures for the purpose of influencing market reforms and helping design appropriate interventions to compensate for the distortion of training incentives. These tasks can best be accomplished where countries have invested in labor market information systems. These systems should include national household and establishment surveys. Examples of these surveys are found throughout industrialized and developing countries. They are conducted annually, quarterly, or even monthly. The information they provide is needed to identify structural changes in the economy 23
|Social Support|2487|0.00<br>(0.98)|0.07<br>(0.07)<br>0.323|0.08<br>(0.07)<br>0.202|
|---|---|---|---|---|
|Social Cohesion|2473|-0.01<br>(0.98)|-0.01<br>(0.08)|-0.02<br>(0.08)|
||||0.870|0.840|

Note: Columns 4 and 5 reflects regression output comparing the outcomes of the villages in which respectively 50% or 75% compared to 25% of the village was treated with either the Personal Initiative and Interpersonal Initiative intervention. Outcomes are standardized to the control condition, at the individual level. Regressions control for randomization strata, and standard errors are clustered at the village level.<sup>†</sup> p<.10, *p<.05, **p<.01, ***p<.001.

**Table S11.** Impacts of Varying Within-Village Saturation of Treatment (Personal or Interpersonal Initiative interventions) on economic outcomes at endline in the Mechanism Experiment

|||**Reference group:**<br>**25% Saturation of**<br>**Program**<br>**Beneficiaries Treated**|**50% Saturation of**<br>**Program Beneficiaries**<br>**Treated vs 25%**|**75% Saturation of**<br>**Program Beneficiaries**<br>**Treated vs 25%**|
|---|---|---|---|---|
|**Outcome**|df|Mean<br>(SD)|**Coefficient**<br>(SE)<br>Cluster robust_p_-value|**Coefficient**<br>(SE)<br>Cluster robust_p_-value|
|**Economic**<br>**Composite Index**|**2474**|**-0.03**<br>**(0.95)**|**0.10**<br>**(0.12)**<br>**0.387**|**0.10**<br>**(0.12)**<br>**0.406**|
|**Food Security**<br>**Index**|**2473**|**-0.03**<br>**(0.97)**|**0.12**<br>**(0.11)**<br>**0.274**|**0.09**<br>**(0.11)**<br>**0.427**|
|Food security|2473|6.86<br>(1.52)|0.11<br>(0.14)<br>0.446|0.06<br>(0.13)<br>0.630|
|Dietary diversity|2473|8.48<br>(8.13)|0.13<br>(0.94)<br>0.232|0.90<br>(1.00)<br>0.370|
|**Business Omnibus**<br>**Index**|**2474**|**-0.02**<br>**(0.96)**|**0.04**<br>**(0.10)**<br>**0.657**|**0.07**<br>**(0.10)**<br>**0.479**|
|**Business**<br>**Engagement**<br>**Index**|**2474**|**-0.02**<br>**(0.95)**|**0.05**<br>**(0.09)**<br>**0.606**|**0.08**<br>**(0.10)**<br>**0.423**|

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