rafmacalaba/datause-encoder-data huggingface dataset:
Table 8: Population growth, 1996-2011

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. Var.: Populationgrowth(1996-2011)|||||
|Homeland (=1)|-1.028|-0.655|-1.32|-5.966|
||[1.189]|[1.163]|[0.955]|[3.941]|
|Share Employed mining1996|0.336|0.205|-0.200|0.029|
||[0.230]|[0.263]|[0.341]|[0.323]|
|Homeland*Share Employed mining1996|-0.255|-0.261|0.014|0.035|
||[0.269]|[0.302]|[0.341]|[0.328]|
|Observations|9,598|9,598|9,598|9,570|
|R-squared|0.132|0.141|0.207|0.514|
|1996 Controls|Yes|Yes|Yes|Yes|
|2011 Controls|-|-|-|Yes|
|Province FE|-|Yes|-|-|
|MunicipalityFE|-|-|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The dependent variable is the difference in log population for 2011-1996. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 4059, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. 2011 controls include: same as 1996 except for per capita income (which is excluded). All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipalityhomeland level. ***p<0.01, **p<0.05, *p<0.1
results. However, in three years, the variations in the degree of attainment between the two types of senior secondary grew even more significant. Controlled for junior secondary school examination score, it is found that attendance at SMK adds much less value in terms of educational attainment as measured by examination results at the end of senior secondary education (figure 5).

**Figure 5: EBTANAS Score by SMA/SMK**

<!-- Start of picture text --> 40.0<br>35.0 34.8<br>35.0 32.7<br>30.0 27.9<br>25.0<br>SMA<br>20.0<br>SMK<br>15.0<br>10.0<br>5.0<br>0.0<br>JSS score SSS score<br><!-- End of picture text --> _Source: IFLS II and III._ A further examination of the determinants affecting EBTANAS score at the end of senior secondary level demonstrates similar results: controlled for EBTANAS score at junior secondary level and other key background variables, attendance at SMK is associated with significantly less added value in terms of test scores. The empirical question is: Is this because unobservable variables determining the choice to attend SMK also determine their time and effort in learning academic content? Or is it because SMK’s specific characteristics, such as curriculum and overall teaching and learning practices, do not encourage the achievement of higher academic attainment? An instrumental variable is also used here to distinguish these two channels of effect. Using predicted value of SMK probability, the estimation still demonstrates a significant negative effect of attendance at SMK on 20
obtained by the common cross-sectional surveys in which the consumption data are based on recall at a single interview. For a six year period 1985-90 the survey was also longitudinal, returning to the same households over time. This was done for administrative convenience (since local SSB offices were set up in each sampled county). The survey was not intended to be a panel survey, but the panel can still be formed. To avoid spurious transience in the data, quite strong conditions were used in defining a panel household ensuring stable household size and composition over time.” We constructed measures of chronic and transitory poverty using the panel data over the six-year period 1985-90 from four contiguous provinces in southern China, namely Guangdong, Guangxi, Guizhou, and Yunan. Three of the provinces (Guangxi, Guizhou and Yunan) form a region of south-west China which is widely regarded as one of the poorest regions in the country. Guangdong on the other hand, is a relatively rich coastal region. Poverty measures are constructed using consumption expenditure per capita as the individual welfare measure. The poverty line is based on a normative food bundle set by SSB, which assures that average nutritional requirements are met with a diet which is consistent with Chinese tastes; this is valued at province-specific prices. The food component of the poverty line is augmented with an allowance for non-food goods, consistent with the non-food spending of those households whose food spending is no more than adequate to afford the food component of the poverty line. The consumption measure is comprehensive, in that it includes imputed values for consumption from own production valued at local market prices, and it also includes an imputed value of the consumption streams from the inventory of consumer durables (Chen and Ravallion, 1995).

Howes and Ravallion (1995) describe how the panel was formed.

11
The next section of the paper describes the data used in our empirical analysis. Section 3 lays out the context, describing the structure of villages, the nature of female seclusion norms, and the salience of caste, following which, in sections 4, 5, and 6 we present, respectively, our empirical framework, results, and policy experiment. We conclude in section 7.

# 2 Data

Our analysis combines representative household survey data with information from a village and school census undertaken in the two most populous provinces of Pakistan, Punjab and Sindh. The household data come from the second round of the Pakistan Rural Household Survey (PRHS-II) conducted in 2004-05, in which 3519 households were randomly drawn from 165 villages. The school census collects detailed information on all schools inside each of these villages as well as schools lying within a 2 km walk of the perimeter of each settlement/habitation of the village.<sup>2</sup> GPS coordinates are available for households and schools, so the distance between each can be calculated. The hamlet in which households and schools are located is also known. The PRHS-II village census provides land ownership data and the caste/clan (zaat/biradari) affiliation of every household in each of the 165 villages.

The school census identifies 1326 schools of which 1112 (84%) have classes at the elementary level (up to grade 5). Of these, 63% are exclusively elementary schools, while the rest had an elementary school attached to a middle or high school. Since three-fourths of elementary schools are public (90% in Sindh province), government school allocation is central in determining access to education (Appendix Table A.1, Panel A). Among elementary schools, 51% are coeducational, 28% are exclusively for boys and 21% exclusively for girls. Public elementary schools are far more likely to be single-sex (62%) compared to private ones (13%).

We consider all children who were age 9-15 in the survey year; practically all of the children who ever enroll in school have already done so by the age of nine. Married girls, which constitute just one percent of females in this age range, are

> 2Given the difficulty in identifying all schools to which children in a village may have access, the school census was conducted in two stages. In the first stage, all schools inside the village, as well as all schools within a 2 km radius of each settlement, were visually identified and a school list was drawn up. This list was provided to all survey teams conducting the household survey. During the household survey, any non-listed schools identified by sample households were added to the initial list. Likewise, any additional schools identified during the community focus group exercise were incorporated into the final school census list.

3
# **Unveiling Multidimensional Poverty: Insights from the EU**<sup>*****</sup>

Monica Robayo-Abril<sup>1</sup> Lucia Echeverria<sup>2</sup> 1 World Bank 2 World Bank and Universidad Nacional de Mar del Plata

**JEL classification** : I32, O52, D63

**Keywords** : Poverty measurement, multidimensional poverty, poverty persistence, material deprivation, social exclusion, EU-SILC; Roma

>  This paper was prepared by staff in the World Bank's Global Poverty and Equity Practice as part of the task of Social Services in the European Union to inform poverty reduction policies in the EU. The authors are grateful for the comments received from Ana Maria Munoz.
52

# **Table 3**

## **Dependent Variable: Growth Rate of Real GDP Information and Communications Technologies (ICT) Pillar Indicators**

|**Years : 1960 - 2000**|**Reg 3.1**|**Reg 3.2**|**Reg 3.3**|**Reg 3.4**|
|---|---|---|---|---|
|**Growth of Capital**|**0.2264*****<br>**(0.0530)**|**0.2251*****<br>**(0.0440)**|**0.2310*****<br>**(0.0464)**|**0.2128*****<br>**(0.0442)**|
|**Growth of Labor**|**0.4614*****<br>**(0.1395)**|**0.4617*****<br>**(0.1297)**|**0.4521*****<br>**(0.1366)**|**0.5694*****<br>**(0.1335)**|
|**Initial GDP (1960)**|**-4.0288*****<br>**(0.7963)**|**-3.4424*****<br>**(0.8767)**|**-3.4300*****<br>**(0.8381)**|**-3.8747*****<br>**(0.8375)**|
|**HC15**|**3.7100*****<br>**(1.3920)**|**4.9953****<br>**(1.9402)**|**5.4972*****<br>**(1.9854)**|**4.3613****<br>**(1.9441)**|
|**Sq of HC15**|**-0.9508*****<br>**(0.3397)**|**-1.1712****<br>**(0.4821)**|**-1.2625****<br>**(0.4920)**|**-1.0208****<br>**(0.4654)**|
|**Log (Educational**<br>**Quality)**|**0.3848**<br>**(0.5157)**|**0.3147**<br>**(0.4825)**|**0.4798***<br>**(0.4936)**|**0.2933**<br>**(0.5121)**|
|**Log (Patents)**|**0.2252*****<br>**(0.0706)**|**0.2366*****<br>**(0.0688)**|**0.2411*****<br>**(0.0711)**|**0.1909*****<br>**(0.0728)**|
|**Computers**|**0.0082*****<br>**(0.0020)**||||
|**Internet Users**||**0.0082*****<br>**(0.0023)**|||
|**Hosts**|||**0.0017****<br>**(0.0007)**||
|**Phones**||||**0.0033*****<br>**(0.0010)**|
|**Constant**|**-2.8603**<br>**(1.7706)**|**-4.1496****<br>**(1.9661)**|**-5.2290*****<br>**(1.9915)**|**-3.666***<br>**(2.1096)**|
|**R squared**|**0.6646**|**0.6114**|**0.6034**|**0.6137**|
|<br>**Number of Countries**<br>*, **, *** denotes significanc<br>Robust standard errors obta|**81**<br>e at the 90, 95 and<br>ined usingWhite's|**85**<br>99 percent confidenc<br>correction for hetero|**85**<br>e level respectively<br>scedasticity.|**85**|
Table 2: Ratio of fenLale to male children who received no treatment for episodes of fever or acute respiratory infection (ARI)

||N|Med-<br>ian|Std.<br>Dev|Highest|Ran|ge<br>Lowest||
|---|---|---|---|---|---|---|---|
|Non-South<br>Asian<br>countries|34|1.02|.29|Colombia (1990)<br>Togo (1988)<br>Ondo State (1986)|2.04<br>1.58<br>1.44|Paraguay (1990)<br>NE Brazil (1991)<br>Ghana (1988)|.57<br>.66<br>.71|
|India|19|1.34|.29|Rajastan<br>Punjab<br>Bihar|1.79<br>1.67<br>1.59|Karnataka<br>Tamil Nadu<br>Kerala|.62<br>.85<br>.88|
|Pakistan|4|1.28<br>_|_.55_<br>______|Baluchistan<br>NWFP<br>__ Punjab|2.01<br>1.38<br>1.18|Sindh|.68|
|South Asia|3<br>I__<br>_|1.19<br> ||.11|India (1992)<br>Bangladesh (1993)|1.27<br>1.19|Pakistan (1990)|1.05|

Notes: A higher ratio indicates that females are LESS likely to get treatment than men.

_Health treatment beiavior._ While mortality outcomes are clearly different between sexes, can we also see differences in the underlying gender discriminating behaviors that produce those outcomes? lVhile it is relatively straightforward to document gender _disparity_ in mortality ratios, it has been quite difficult to create comparable indicators of differential health treatment. In the DIIS, the women surveyed were asked both whether their children (under a certain age, usually five years) had experienced fever, acute respiratory infection (ARI)," and diarrhea. If a mother reported her child had suffered from one of these conditions, she was then asked about various types of treatment the child had received. We The actual survey question refers to a cough with rapid breathing.

10
23 appraising health and population projects.<sup>This section applies the</sup> estimated results (using alternative #2)<sup>reported above to calculate the</sup> cost effectiveness of family planning program and non hospital<sup>health</sup> program expenditures (Table 4). The cost<sup>effectiveness</sup> of the programs are measured with respect to prevented births and<sup>avoided infant deaths. Cost</sup> per unit of effect are calculated for<sup>the total response, including</sup> interaction, and then compared with the unit cost derived<sup>from what might be</sup> called the "naive' model using only the<sup>direct response.</sup> Table 4

# **IMPLIED COST EFFECTIVENESS OF FAMILY PLANNING AND NON HOSPITAL /E&L7 PROGLAWO/ (US$)**

|||**COST**PER||
|---|---|---|---|
||Birth<br>Prevented|<br>Infant Death<br>Averted|Adjusted Infant<br>Death Avertedk/|
|Total Effects||||
|Family Planning Program|80|460|1200|
|Non Hospital Health Programs|1500|1920|2230|
|Direct Effects£/||||
|Family Planning Program|90|**---**||
|Non Hospital Health Programs|---|2500|**---**|

- **A/** Based on reduced form elasticities from alternative #2, Table 3. The Rupee costs were converted to US$ using an exhange<sup>rate of 1US$-</sup> 625Rp.

- **h,** The -unadjusted' infant deaths averted in<sup>the column at the left are derived</sup> from the total response elasticities. These<sup>contain two components (a) change</sup> in infant deaths due to reduced infant mortality<sup>and (b) reduced infant deaths</sup> due to fewer births. The "adjusted' deaths<sup>averted are based only on</sup> component (a).

- **q/** From the structural equations for alternative<sup>#2. This model omits</sup> interaction effects.
Figure 7. Decomposition of inter- and intra-skills components into the composition and structure effects.

<!-- Start of picture text --> 0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>-0.1<br>-0.2 intra comp intra struc<br>inter comp inter struc<br><!-- End of picture text -->

# **VI. Conclusions and policy implications**

This paper analyzes the movements in the gender wage gap in Georgia using the lens of industrial and occupational segregation .and reveals that the gender wage gap in Georgia is present mainly due to the gender wage inequality within industries and skill categories rather than due to the industrial and skill-based segregation.

Industrial and occupational segregation have been shown to be important determinants of gender wage gaps in a range of countries. Using the Duncan index, we establish that industrial and occupational segregation in Georgia during 2004 – 2015 was high. We then quantify the role of industrial and occupational segregation in relation to the within-industry or withinoccupation gender wage inequality by employing the decomposition approach proposed in Brown et al. (1980). This analysis demonstrates that, despite the high degree of industrial and occupational segregation, it is the gender wage gaps within sectors (public and private), industries and skill categories that are the primary factor underlying the presence of the gender wage gap in Georgia. This is in part because female-dominated industries and occupations do not pay much less than the economy–wide average, because women are concentrated in the industries and occupations that have benefitted during expansionary periods and were not as affected by the 2008 crisis as male-dominated industries. Our findings also highlight the growing role of the private sector in contributing to the gender wage gap contraction in Georgia. Importantly, we establish that at least a half or more of the gaps within sectors (public 23
# **9. Recent grain price spikes: A reappraisal**

If international income growth, futures market speculation, and global financial flows do not explain the recent grain price spikes, what does? Why were they so large? Were they caused by the oil price surge shown in Figure 5? Were they irrational bubbles, unrelated to fundamentals, after all?

An important part of the answer is that the spikes, appropriately deflated, were not unusually large. Look again at Figures 3 and 4. There were comparable spikes around 1996—smaller for wheat, larger for maize. Another glance at Figure 5 shows that those spikes were clearly unrelated to oil prices, which were stable around that time. They could hardly have been caused by index fund investment—one of the two major indexes was not even in existence then.

A more promising line of investigation is suggested by Figure 16, which shows world stock-touse ratios for the sum of the three major grains (corn, wheat, and rice).<sup>39</sup> Around 1996, the world aggregate stock-to-use ratio was much higher than recently. But the world figure was distorted by the huge holdings of China, which exported no grain in that period. If China’s effect is removed, the ratio around 1996/97 looks as tight as observed in 2007/08. The lack of stocks in both episodes left the market susceptible to large price spikes from small supply disturbances. One possible objection to this assertion is that the ratio was about as tight around 2002–2004 and yet the price changes observed then were much smaller. But in that period, in contrast to the other episodes, China made substantial exports of maize and rice, increasing available supplies in the global grain market. The recent price spikes are not as unusual as many discussions imply and the balance between consumption, available supply, and stocks seems to be as relevant for our understanding of these markets as it was decades ago.

> 39 This figure and the associated argument draw on the work of Dawe (2009).

– 44 –
# **3 The Data**

For this study, we needed household survey information on supply, demand and net sales of food products—a constraint that narrowed the range of countries we could consider. We sought relatively poor countries from different regions, with different relationships between poverty and location, and between poverty and sources of income household; and with different net trade positions in agriculture. Because of the extremely rapid structural change in the Vietnamese economy, we obtained two observations at different periods.<sup>5</sup> The sample sizes for each of the ten country-periods for which were able to obtain suitable, high-quality survey data are listed in Table 1. These were Bolivia 2005, Cambodia 2003, Madagascar 2001, Malawi 2004, Nicaragua 2001, Pakistan 1999, Peru 2003, Vietnam 1998 and 2004, and Zambia 1998. For our initial poverty rates and poverty gaps, we used the standard “dollar-a-day”<sup>6</sup> expenditure-based measures of poverty from the 2007 World Bank _World Development Indicators_ (WDI). We ranked the households in our surveys by income and placed the poverty line at the income level that reproduced the poverty rate in the (frequently different) year used in the WDI. This process retains the survey information on the sources and distribution of income, while updating the poverty line to the most recent year for which information is available.

Because of the generally higher poverty rates in rural than in urban areas, the greater frequency of net seller households in rural areas, and the focus of this study on food prices, we categorized each household as either rural or urban based on the classification used in the national survey. For lack of information on urban versus rural costs of living, we have

> 5 We would like to thank Carolyn Turk and Martin Rama for making available the 2004 data.

> 6 According to this definition, a person is poor if he/she consumes less that 1.08 USD in 1993 Purchasing Power Parity terms. The years for the poverty lines by country were: Bolivia 2002, Cambodia 1997, Madagascar 2001, Malawi 2004, Nicaragua 2001, Pakistan 2002, Peru 2002, Vietnam 2002, and Zambia 2002-3

7
- Hassan, T. A., S. Hollander, L. van Lent, and A. Tahoun (2020): “The Global Impact of Brexit Uncertainty,” (26609).

- Hollander, S., M. Pronk, and E. Roelofsen (2010): “Does silent speak? An empirical analysis of disclosure choices during conference calls.,” _Journal of Accounting Research_ , 48(3), 531–563.

- Huang, Y., C. Lin, S. Liu, and H. Tang (2023): “Trade networks and firm value: Evidence from the US-China trade war,” _Journal of International Economics_ , p. 103811.

- Popp, D. (2002): “Induced Innovation and Energy Prices,” _American Economic Review_ , 92(1), 60.

- (2022): “Environmental policy and innovation: a decade of research,” .

- Way, R., M. C. Ives, P. Mealy, and J. D. Farmer (2022): “Empirically grounded technology forecasts and the energy transition,” _Joule_ , 6(9), 2057–2082.

15
**Table 4: Econometric Analysis for Arrivals Determinants:** 2000-2016

|**VARIABLES**|**FE**|**Sys-GMM**|**FE**|**Sys-GMM**|**FE**|**Sys-GMM**|
|---|---|---|---|---|---|---|
|**Dep.var.: LN_ARRt-1**|||||||
|**LN_ARRt-1**|0.777***|0.768***|0.778***|0.772***|0.777***|0.740***|
||(0.0321)|(0.0934)|(0.0321)|(0.0919)|(0.0321)|(0.0853)|
|**LN_GDPt-1**|0.00889|0.149***|-0.00592|0.144***|0.00416|0.168***|
||(0.0528)|(0.0562)|(0.0522)|(0.0552)|(0.0524)|(0.0631)|
|**LN_POPt-1**|0.0940|-0.0611**|0.101|-0.0595**|0.0947|-0.0694**|
||(0.169)|(0.0302)|(0.170)|(0.0297)|(0.169)|(0.0308)|
|**LN_Xt-1**|0.0782**|0.0584|0.0779**|0.0597|0.0780**|0.0677|
||(0.0310)|(0.0495)|(0.0311)|(0.0488)|(0.0310)|(0.0470)|
|**LN_SEATSt-1**|-0.00387|0.0484|-0.00161|0.0458|-0.00265|0.0509|
||(0.0246)|(0.0483)|(0.0245)|(0.0483)|(0.0245)|(0.0420)|
|**RER_EUROt**|0.116*|0.110*|||||
||(0.0662)|(0.0644)|||||
|**RER_USAt**|||0.0737|0.0920*|||
||||(0.0555)|(0.0520)|||
|**RER_POUNDt**|||||0.0877|0.0803|
||||||(0.0536)|(0.0506)|
|**Observations**|537|537|537|537|537|537|
|**R-squared**|0.811||0.811||0.811||
|**Number of id**|36|36|36|36|36|36|
|**Long-run effect**|0.520|0.475|0.332|0.404|0.394|0.309|
|**p-value =0**|0.0871|0.0214|0.190|0.0199|0.109|0.138|
|**p-value =1**|0.114|0.0109|0.00826|0.000596|0.0137|0.000893|
|**Arellano-Bond AR(2) stat**||-0.309||-0.324||-0.322|
|**AR(2) p-value**||0.757||0.746||0.747|
|**Hansen stat**||16.59||16.47||16.24|
|**Hansenp-value**||0.219||0.225||0.237|
|**# IV**||37||37||37|

Source and notes: Author ́s calculations. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All specifications include yearly dummies, not reported. FE: fixed-effects model. Sys-GMM: Blundell and Bond (1998) System GMM model using Roodman (2009) collapsing method.

29