rafmacalaba/datause-encoder-data huggingface dataset:
Table 6.5 Health Care Utilization by 'lan Subscribers nd Non-subscribers at Bokoro Health Center *

|I<br>1989|<br>Jan|Feb|Par|Apr|May|Jun||AVERA|GE RATh|O#I|
|---|---|---|---|---|---|---|---|---|---|
|lPopulatfon sze<br>|I|||||||||
|<br>Non-subscribe<br> <br>|rs<br>3817<br>|3797<br>|3794<br>|3819<br> <br>|3799<br>|**2966** <br>||<br>3669<br>||||
|Subscribers<br>|I156|176|179|<br>174|174<br>|176<br>|173<br>|||
|Totat|3973|3973|3973|<br>3993|3973|3162 <br>|I3841<br>|||
|-----------<br>----------<br>|<br> <br>Now cases|-------<br>| ANNU|---<br>AL<br>UTIL|----<br>--<br>IZATI|------<br>ON<br>RATE.|-------|--<br>-1|-------1|- -----||
|Non-subscribe<br>|rs<br>I0.45|0.38|0.46|0.48|0.46|0.59|0.47|||
|Subscribers|4.59|1.69|1.86|2.25|1.79|2.30|I2.36|I5.0||
|tAntibiotic<br>prescr|.18|||||||||
|Non-subscribers|10.03|0.05|0.05|0.08|0.07|0.06|0.06<br>|||
|Subscribers|10.49|0.31|0.27|0.31|0.49|0.72|0.44|I7.3||
|linjections|||||||I<br> <br>|||
|INon-subscrib<br> <br>|ers<br>I0.14<br>|0.08|0.15|0.32|0.33|0.19||<br>0.20||||
|Subscribers<br>|I1.98|0.92|0.27|1.24|1.51|3.11|I1.72|88.6||
||AVE|RAGE<br>RAT|E PER N|EW CAS|E|||||
|fAntibiotic<br>Rx<br>|I|||||||||
|Non-subscribers|I0.06|0.06|0.06|0.09|0.09|0.10 <br>||<br>0.07<br>|||
|Subscribers|0.11|0.18|0.14|0.14|0.20|0.31|j0.18|I2.61||
|lInjections<br> <br> <br>|I<br> <br>|||||<br>|I<br> <br>|I<br>||
|<br>INon-subscribe<br>|rs<br>I0.32|0.21|0.31|0.66|0.70|0.33 <br>|I0.42 <br>|I<br>|I|
|Subscribers|0.43|0.55|0.62|0.55|0.84|1.35|0.73|1.7||

*) Data from the *BORDEREAU DE CONTROLD **E** CAISSEn pert A, Nber 14 - 16 wnd 19. ?) Ratio of average for subscribers divided by average for non-subscribers. *) Projected annual utiLization rates, calcuLated **as:** nimber of services in month * 12 / population size.

# insurance plan.

Table B.6 Sokoro Survey: Ouestions Asked Separately of Insured Respondents (N=21) <!-- Start of picture text --> QUESTIONS Respondents<br>Can you pay 100 Zeares for an in urance scheme 21<br>which wfil take care of all of your fmily? Wien?<br>Now 52X<br>In a week 19X<br>In a month 10X<br>kewer 19X<br>Can you pay 200 Zaires for an Insurance scheme _ich<br>wIll take care of all of your fteily? 21<br>Now 38X<br>In one day 5X<br>In a week 10X<br>In a month 29X<br>Never or no response 19X<br><!-- End of picture text --> 34
Therefore, we find no support for our second hypothesis (H2) that the effect of political conflict on total FDI flows depends on the geographic scope of the conflict **.**

**Table 1 Effect of Political Violence on Total, Resource, and Non-resource Greenfield FDI**

||Depend|ent Variable: Lo|g greenfield FD|I (in USD milli|ons), LSDVC Es|timation|
|---|---|---|---|---|---|---|
||Tota|l FDI|Resource-R|elated FDI|Non-Resourc|e-Related FDI|
||(1)|(2)|(3)|(4)|(5)|(6)|
|BRDt-1(ln)|-0.095+|-0.111|0.009|0.046|-0.125*|-0.225**|
||(0.052)|(0.072)|(0.074)|(0.102)|(0.051)|(0.070)|
|BRD (ln)*localizedt-1||0.033||-0.075||0.202*|
|||(0.096)||(0.137)||(0.094)|
|Greenfield FDIi, t-1(ln)|0.190***|0.189***|0.095*|0.095*|0.119**|0.109*|
||(0.042)|(0.043)|(0.043)|(0.043)|(0.044)|(0.044)|
|GDPt-1(ln)|-0.427|-0.440|-1.484+|-1.461+|0.126|0.061|
||(0.594)|(0.597)|(0.848)|(0.851)|(0.582)|(0.583)|
|Populationt-1(ln)|8.696**|8.734**|14.547***|14.528***|6.432*|6.583*|
||(2.831)|(2.838)|(4.027)|(4.035)|(2.763)|(2.759)|
|WGI regulatory qualityt-1|-0.069|-0.084|1.616+|1.655+|-0.819|-0.916|
||(0.647)|(0.651)|(0.925)|(0.929)|(0.635)|(0.637)|
|Polity Indext-1|0.006|0.002|0.014|0.022|-0.012|-0.033|
||(0.049)|(0.049)|(0.070)|(0.070)|(0.048)|(0.048)|
|Exchange Ratet-1(ln)|-1.515+|-0.272|-1.447|-1.438|-0.893|-0.870|
||(0.783)|(0.367)|(1.024)|(1.026)|(0.741)|(0.741)|
|Exchange Rate Volatilityt-1|-24.344**|-24.497**|-12.680|-12.378|-12.606+|-13.489+|
||(7.480)|-7.476|(10.638)|(10.626)|(7.331)|(7.317)|
|Control of Corruptiont-1|1.495*|1.487*|0.784|0.804|1.306*|1.259*|
||(0.627)|(0.629)|(0.893)|(0.896)|(0.611)|(0.612)|
|Inflationt-1|-0.002|-0.002|0.011|0.011|0.001|0.002|
||(0.008)|(0.009)|(0.012)|(0.012)|(0.008)|(0.008)|
|Long-term BRD (ln)|-0.117*|0.137|0.010|0.051|-0.142*|-0.253**|
||(0.065)|(0.088)|(0.083)|(0.113)|(0.059)|(0.079)|
|Observations|707|707|707|707|707|707|
|Number of countries|90|90|90|90|90|90|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Note: Political conflict proxied by battle-related deaths. Bootstrapped standard errors are in parentheses. *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 With respect to the control variables, the lagged FDI term is highly significant across both specifications. Exchange rate volatility and level are negatively associated with FDI flows. The effect is particularly strong for exchange rate volatility. Control of corruption and large population size are positively and significantly associated with FDI flows, whereas regulatory quality, level of democracy, 22
**<u>Table 5: Poverty Changes and Sectoral Employment/Productivity Intensity</u>**

||(1)<br>Benchmark|(2)<br>Controls|(3)<br>Non<br>Upper<br>Middle<br>Income|(4)<br>Non LAC|(5)<br>Positive<br>Growth|(6)<br>Negative<br>growth|
|---|---|---|---|---|---|---|
|Employment-intensive growth in|||||||
|Agriculture (Primary Sector)|2.003|3.393|8.155***|-0.428|4.780|-19.75|
||(0.37)|(0.68)|(3.30)|(-0.04)|(1.41)|(-0.85)|
|Employment-intensive growth in|||||||
|Secondary Sector|-6.578***|-5.487***|-4.898***|-5.987***|-4.845***|0.554|
||(-4.60)|(-3.89)|(-2.87)|(-3.16)|(-4.78)|(0.07)|
|Employment-intensive growth in|||||||
|Tertiary Sector|-2.887|-4.529*|0.721|-7.414*|-2.250|-24.88|
||(-1.17)|(-1.87)|(0.31)|(-2.02)|(-1.53)|(-1.68)|
|Productivity-intensive Growth in|||||||
|<br>Agriculture (Primary Sector)|-4.504|-3.344|0.292|-5.345|-3.325|-12.88|
||(-1.17)|(-0.95)|(0.16)|(-0.81)|(-1.15)|(-1.04)|
|Productivity-intensive Growth in|||||||
|Secondary Sector|-1.753|-1.287|1.254|1.507|1.523|-5.187|
||(-1.16)|(-1.00)|(0.81)|(0.43)|(1.09)|(-1.05)|
|Productivity-intensive Growth in|||||||
|Tertiary Sector|-0.621|-1.651|0.219|-2.843|-1.804|-5.394|
||(-0.52)|(-1.62)|(0.18)|(-1.44)|(-1.29)|(-0.89)|
|Change in Gini (%)||2.893***|1.693***|2.869***|2.211***|4.030|
|||(3.24)|(4.15)|(5.05)|(4.98)|(1.38)|
|Constant|0.0778|0.121|-0.0303|0.173|0.0400|0.232|
||(1.10)|(1.66)|(-0.46)|(1.06)|(0.58)|(0.73)|
|Observations|98|98|58|37|68|30|
|AdjustedR-squared|0.140|0.275|0.342|0.311|0.420|0.134|
|*p<.10,**p<.05,***p<.01.T-s|tatisticsinpar|entheses.|||||

The main difference in the results is that productivity-intensive growth in agriculture is robustly correlated with poverty reduction and employment-intensive growth in agriculture is robustly correlated with poverty increases. It is therefore difficult to determine whether they are capturing different phenomena or the same phenomena. An alternative explanation is related to traditional sector dualism, where output in agriculture is fixed and earnings in this sector are equal to average output per worker and therefore more workers means less mean output and thus lower earnings. Therefore, employmentintensive growth in agriculture would be equivalent to the negative of productivityintensive growth in the sector, and productivity growth in agriculture might be reflecting outflows of workers from agriculture. The positive correlation between poverty and increases in employment in agriculture might also be a response to urban crises in which poverty increases and therefore workers seek agricultural employment as a safety net, in 21
# **III. The Survey**

The survey was sent to IFC client firms in developing and transition countries, and asked both firm-specific questions and questions about the perceived quality of the locally trained MBAs hired by the firms. In the survey, the term ‘local MBA’ was applied to individuals with graduate business administration degrees from schools within the country in which the firm was located. The survey also specified that local graduate degrees in business administration may not be called ‘MBAs’ and the firm should rate the equivalent local degree. The survey itself is attached in Annex A.

The firm-specific information included the name of the firm, the sector in which the firm operates (obtained from IFC databases), the size of the firm based on number of employees, and the average number of local MBAs the firms hire each year. If firms responded that they do not hire MBAs, they were asked to explain why they did not.

The questions pertaining to the perceived quality of locally trained MBAs began with questions about the technical, communication and language skills of locally trained MBAs. Respondents were asked to rate locally trained MBAs on the following scale: (1) Lacking, (2) Inadequate, (3) Adequate, (4) Good and (5) Not Applicable. These questions were followed by open-ended questions on the perceived strengths and weaknesses of local MBAs. The final question about the quality of local MBA graduates asked for an overall evaluation of local MBA graduates, and answers were chosen from the scale: (1) MBAs are inadequately prepared, (2) MBAs are fairly well prepared, (3) MBAs are well prepared, and (4) Not Applicable.

The survey also asked from which local schools the firms usually hired MBAs, and which schools produce the best MBAs in the country. Usually, the firms mentioned the same schools for both questions.

The firm-specific information was used to classify opinions about the quality of local MBA graduates based on firm size, sector and number of MBAs hired each year. Such categorization of responding firm information was done to determine whether these factors are correlated with responses regarding MBA quality. In the sections that follow, the main results are discussed.

6
10 PHN 1991 Sector Review nation of the country's program and uncertain government commitment. There, too, integration of fimify planning into a broader array of serices designed to inprove health is seen as the most effective way to stimulate the demand for family planing that provides the long-term conditions for program success.

2.24 _Nutn._ Bank support of nutridon is clearly manifest mx the large growth in number of operadons, which support nutrition activity--from thirteen in FY89 to eighteen in FY90 and twenty-eight In FY91, and in lending for nutrition (including PHN, education, Women in Development (WID) and food security operatlons)--from $33 million in FY89 to $200.9 million in FY90 and $252.7 million in FY91. Estimates of overall project resources for Bankassisted nutrition activities have risen in three years from $54.8 million to $337.7 million to $470.4 million (Annex 1, Figures 2 and 3). As with population, the Bank may sometimes finance only a modest share of total project resources for nutrition while raising substantially more funds from other agencies. (For example, because of the nature of nutrition operations, large portions of overall project costs are sometimes met by food aid agencies).

micronutrient components, including the pioneering Mali project for Iodizing water by equipping pumps with an iodine module. 'he Bankes support for micronutrient projects has grown considerabl, projecs under preparation demonstrate Bank interest in fortfication programs. Although there were additions to the technical nutriton staff this year to handle the grwng nutridon programs, the Bank continues to rely heavily on consultants for fieldwork assinments. The Bank needs to increase institudonal capacty to respond adequately to growing demands and opportunities for Bank-assisted nutrition operations.

2.25 These figures for nutrition do not reflect adjustment operations, which gave considerably more attention to nutrition in FY91. Venezuela's targeted food and nutrition progams went up more than sevenfold in conjunction with adjustment operations--from $102. million in 1989 to $761 million in 1991. And a significant portion of the Agrficultural Adjustment Project for Mexico ($400 millon loan plus $200 millon from the Inter-American Development Bank was directed to nutridon. Overall, nine of sixteen adjustment loans approved in FY91 address nutrition; four include specific nutrition actions as conditions for tranche release and three led to sizeable sodal funds to improve nutrition conditions. Three of the four agriculture sector adjustment operations include nutrition activities, and in two of them nutrition actions are a condition for tranche release (Annex 1, 'bble 10). Projections for FY9294 lending for nutrition projects and projects with significant nutrition components point to a continuadon in growth.
Table 4

# SENSITIVITY ANALYSIS IN TIlE EQUILIBRIUM NElGIIBORIIOOD Witlh a Nested CES Welfare Function for the Imiporter

|Elasticities oftheEndogen<br>Variables with respect t|ous<br>o|Initial||Importer's P|arameters<br>|.|Exchan<br>in Tran<br>Curr|ge Rates<br>siter's<br>ency|**_Gas_Tr**<br>Co|**ansil**<br>st|Gas Supp|ly Cost|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|I% change in<br>theExogenous<br>Variabl<br>.____________|es<br>_____|Values<br>___**___**<br>.<br>____|Expendi-<br>ture on<br>Foreign<br>_G as(Y)_|Substilu-<br>lion<br>Elasticity<br> <br>_(°)_|Russia's<br>Mfarket<br>Share<br>_(a)_|Price of<br>"Other"<br>Gas<br>(PO)|DhI<br>(en)|**Rbl**<br>(er)|Unit<br>(S)|Fixed<br>(FT)|Unit<br>(C)|Fixcd<br>(FR)|
|Benchmark Value|||$20.6 bn|7.25<br>m-|25.0%|$86/tcm<br>_.|1.0|1.0|$5/tcm|S100n|_$SSO/lc_|_mS2OOm_<br>-|
|Supplier'sPrice|Pft|$65.9/tem|0.00|.0.10|+0,08|+0.40|+0.40|-0.38|-0.01|0.00|+0.62|0.00|
|Transit Fee|T|$20.1 /tem|0.00|-0.66|+0.18|+0.86|+0.86|-0.09|+0.24|0.00|-. 09|0.00|
|Demandfor RussianGas|G|60bcm|+1.00|+1.32|+0.39|+1.79|+2.81|-2.55|-0.26|0.00|-2.55|0.00|
|Importer's<br>Price|P.|$86.0/tcrn|0.00|-0.23|+0.11|+0.51|-0.49|+0.45|_+0.05_|0.00|+0.45|0.00|
|Demand for'Other'Gas|0|180bcm|+1.00|-0.36|-0.16|-1.75|-0.77|+0.70|+0.07|0.00|+0.70|0.00|
|Importer'sWelfare Index|U|100.0|+1.00|+0.06|-0.03|-0.87|+0.12|-0.11|-0.01|0.00|-0.11|0.00|
|Unit Rent|R|$3 1.0/tem|<br>0.00|-0.63|+0.30|+1.41|+1.41|-0.36|-0.04|0.00|-0.36|0.00|
|_Russia's_Profit|lilt||+ 1.27|+1.15|+0.93|+4.40|+5,71|-5.15|-0.40*|0.00|-3.93|-0.27|
|Transiter's<br>Profit|IT||+ 1.12|+0.48|+0.71|+3.33|+4.48|-3.00|-0.30|-0.12|<br>-3.00|0.00|
|Revenue<br>'|w|.<br>23.4%|0.00|-0.43|+0.08|_+0.35_|+0.35|_-0.54_|+0.19|0.00|-0.54|0.00|
|Transiter.<br>Share in<br>Unit Rent|wit||0.00|-0.25|-0.05|-0.26|-0.26|+0.23|+0.02|0.00|+0.23|0.00|
|Total Profit|wll.||0.07|-0.32|-0.11|-0.50|4-.56|+1.08|+0.05|-0.06|+0.47|+0.13|
-86-

Table 7.3 compares our labour productivity results for Brazil, Mexico and the USA with those of analogous studies for other countries. The studies of Paige and Bombach for 1950, and Smith, Hitchens and Davies for 1967/8, and Smith for 1977 all found the UK/US productivity ratio (value added per person employed) to be rather similar to what we found for Mexico/USA in 1975. However, the overall averages in some of the studies were not always derived in exactly the same way. For example, Paige and Bombach used blow-up procedures relying predominantly on quentitative parallelism for their covered and uncovered sectors, and there is a negligible difference between their sample result and their overall result for manufacturing.

----------------------------------------------------------

__----------------

|TAB<br>Results<br>of Analogous<br>Studies<br>and O<br>in Manufacturing<br>as a<br>---------------------------------------------------------------------|LE 7.3<br>ur Study of Output per Person<br>Whole, as a % of the USA<br> <br> <br>|Engaged<br>_------<br>|
|---|---|---|
||at local<br>at US<br>g<br>prices<br>prices|eometric<br>average|
||UK/USA (1950)||
|Paige and Bombach (1959)|34.2<br>39.1|36.6|
||UK (1968)/US<br>(1967)||
|Smith, Hitchens and Davies (1982)|36.2<br>39.7|37.9|
||UK/USA (1977)||
|Smith (1985)|38.3<br>41.5|39.9|
||USSR/USA (1963)||
|Kudrov (1969)|33.6<br>36.8|35.3|
||Japan/USA (1972)||
|Yukizawa (1978)|78.2<br>62.1|69.9|
||Canada/USA (1963)||
|West (1971)|64.4<br>68.5|66.4|
||Brazil/USA (1975)||
|Present study|41.6<br>58.5|49.3|
||Mexico/USA (1975)||
|Present study|33.0<br>45.0|38.6|
||Brazil/UK (1975)||
|Van Ark (1988)|82.1<br>1 0 4 <sup>. </sup>1 <sup>a</sup>|92.5|
|a) at UK prices<br>Sources: see our bibliographic refer|ences.||
included other HEIs. Since the local HEI supply is likely to affect students’ enrollment choices, we use as instruments the availability of SC HEIs and other HEIs.

# **3 Data**

We use individual-level administrative data on student background characteristics, high school exit exam test scores, higher education enrollment choices and trajectories, and formal labor market outcomes for the universe of high school graduates in 2005. Individual records from the high school exit exam ( _Saber 11_ ) contain test scores for math, reading, science, physics, history, chemistry, geography, and philosophy, as well as student age, gender, and socioeconomic characteristics (e.g., mother’s education, household income level, and number of siblings) at the time of the test. Higher education enrollment, trajectories, and completion come from the System for Dropout Prevention of Higher Education (SPADIES, in Spanish).

The initial sample includes the universe of Saber 11 test-takers in 2005, restricted to those who were 14-24 years old (93 _._ 5% of the full set of test takers). We further restrict the sample to students with data on all socioeconomic variables and test scores. We match this sample with higher education enrollment data from SPADIES, and set an enrollment window of five years after high school graduation. We restrict graduation to take at least two and four years for those in SC programs and bachelor’s programs, respectively. In addition, we restrict graduation age to the 19-30 year-old range. We remove students who enrolled in more than four programs between 2005 and 2015 (less than one percent of students).

Wages and formal employment for higher education graduates are available in the Labor Market Observatory for Education (OLE, in Spanish) for 2008-2013.<sup>7</sup> For higher education graduates, we restrict entry age in the labor market to be at least 20 years. Our datasets do not include labor market information for students who did not enroll in higher education, enrolled but dropped out, or enrolled but were still in college by 2013. For these students, we use Colombia’s Integrated Household Survey (GEIH, in Spanish) from the Socioeconomic Database for Latin America and the Caribbean (SEDLAC) to impute labor market participation and wages between 2008-2013. We estimate regressions of formal labor market participation and wages as a function of gender, age, household characteristics, an indicator of urban area, and region fixed effects for the cohort of individuals who were 14-24 years old in 2005, and use predicted labor market outcomes for the imputations (see Appendix A.3 for further details).

> 7About 3% of students who enroll in higher education graduate after 2013 and we therefore lack OLE data for them. We classify students who graduate after 2013 as having incomplete higher education. Dropping them from the estimation sample does not affect our results (not shown).

7
# Figure 10 Impulse Response functions: Oil Price Shock

<!-- Start of picture text --> Response to Cholesky One S.D. Innovations ± 2 S.E.<br>Response of GAP_DR_BK to OIL Response of GAP_HT_BK to OIL<br>.015 .015<br>.010<br>.010<br>.005<br>.005<br>.000<br>.000<br>-.005<br>-.005<br>-.010<br>-.010 -.015<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Response of DLINFL_DUS_DR to OIL Response of DLINFL_DUS_HT to OIL<br>.06 .06<br>.04<br>.04<br>.02<br>.02<br>.00<br>.00<br>-.02<br>-.02<br>-.04<br>-.06 -.04<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Response of RER_DR to OIL Response of RER_HT to OIL<br>.10 .06<br>.04<br>.05<br>.02<br>.00<br>.00<br>-.05<br>-.02<br>-.10<br>-.04<br>-.15 -.06<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text --> 39
periods than counties with local banks, which are more sensitive to local credit supply conditions.

Table 6.5 Evidence for credit constraints

||||<br>|Change in n<br>|umber of no<br>|n-tradable<br>|jobs<br>|||
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|All|National|Local|All|National|Local|All|National|Local|
||[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|[9]|
|△tradable jobs|1.682**|1.477*|3.031|2.833**|3.929**|0.303|3.011***|3.148***|2.646|
||[0.846]|[0.895]|[2.837]|[1.199]|[1.655]|[1.795]|[0.618]|[0.681]|[1.794]|
|Constant|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Time fixed effect|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|County fixed effect|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Observations|9,018|4,280|4,729|12,013|5,692|6,309|15,029|7,131|7,883|
|Number of county|3,106|1,494|1,609|3,111|1,498|1,610|3,123|1,507|1,613|
|Year||2011-2013|||2011-2014|||2011-2015||

Robust standard errors in brackets, *** p<0.01, ** p<0.05, * p<0.1 The findings in this section have significant policy implications. Since job losses propagate greatly during the Great Recession, the quantitative finding suggests an argument for government interventions, in particular, job-stabilizing and job-stimulating policies. Without such policies in place to assist the hardest hit population and sectors, negative shocks would spread through other healthier sectors of the economy, worsening the impacts of a recession.

On the other hand, the relatively small job gain multiplier during the first phase of the Recovery suggests that stimulus programs, while necessary, might not be sufficient. Complementary policies, such as those that alleviate uncertainty and credit constraint issues, might be needed to ensure the spillover channels of job creations are operating favorably.

29
(up from 5.11 percent between 1960 and 1971). In contrast, housing investment has been gradually declining in high income/ OECD countries, having peaked in the 1960s. In fact, urban growth was rapid for OECD countries through the 1960s (Table 1). For example, annual urban growth in Norway during 1960-70s was on average 3.24 percent, before gradually tapering down in the following decades. During the period of high urban growth, Norway’s housing investment was 14.6 percent of GDP. Similarly, the Republic of Korea maintained housing investments of around 5 percent of GDP when its urban growth was more than 6 percent. Countries like USA, Germany, Canada, Australia and Netherlands had already urbanized by that time. They however maintained a steady share of housing investment, which ranged from 4.5 to 6 percent of GDP during this period despite gradual declines in urban growth.

**Table 1 Decadal average of housing supply (% of GDP) across income categories**

||De<br> <br>|cade<br> <br>|||
|---|---|---|---|---|
|Income category|1960-70<br>19|71-80<br>1981-90|1991-00|2001-11|
|Low income|2.51|3.00<br>3.58|4.79|4.56|
|Lower middle income|4.38|4.97<br>5.41|6.10|6.06|
|Upper middle income|5.11|8.49<br>9.14|8.86|9.12|
|High income: OECD|11.29|9.87<br>8.06|6.03|5.71|

While housing investment has been increasing for developing nations across income groups, we observe pronounced regional differences. For example, table 2 shows that low income East Asia invested 7.2 percent of GDP between 2001 and 2011 in comparison with low income SubSaharan Africa, where countries invested 4.9 percent of GDP in housing during the same time span. Among upper middle income countries, the MNA region has been investing 12.9 percent of GDP in housing, followed by LAC (10.3 percent), ECA (9.5 percent), SSA (6.6 percent) and EAP (4.9 percent).

**Table 2  Decadal average of housing supply (% of GDP) across regions and income categories**

||1960-70|1971-80|1981-90|1991-00|2001-11|
|---|---|---|---|---|---|
|Low income countries||||||
|East Asia & Pacific||||6.34|7.16|
|Europe & Central Asia|||2.62|1.70|1.47|
|Sub-Saharan Africa|2.51|3.00|3.61|5.53|4.94|
|Lower middle income||||||
|East Asia & Pacific|6.88|6.75|5.90|8.44|8.64|
|Europe & Central Asia||||2.27|3.36|
|Latin America & Caribbean|3.32|4.50|4.61|4.42|3.87|
|Middle East & North Africa|3.77|2.85|2.26|2.70|2.76|

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