rafmacalaba/datause-encoder-data huggingface dataset:
such as Japan or the Republic of Korea, where the much smaller land endowments almost ensure that continuing large net imports of food will be required.

One important caveat of this study is that the data used for the analyses rely on FAOSTAT data which in turn are based on official statistics. In particular, several scholars point out that the livestock data for China are flawed (Fuller, Hayes and Smith, 2000; Ma, Huang and Rozelle, 2004). Although we are aware of this potential bias in FAOSTAT data, it turns out to be impossible for us to replace FAOSTAT data with revised data due to the lack of comparable data for other countries. We therefore deal with this issue by conducting a sensitivity analysis in the Appendix.

# **_Some Policy Challenges_**

China’s ongoing shift in demand into more affluent, and particularly animal-based, foods is likely to impose substantial pressure on agricultural resources and environment. In particular, animal production puts pressure on land both directly<sup>24</sup> and indirectly through feedstuff production. Cropland area tends to decrease as it competes for space with urban and industrial uses. Since income growth is associated with both dietary upgrading and economic activities which generate income, economic development is likely to intensify the competition for land. In addition, the quality of China’s cultivated land is reportedly deteriorating due to soil degradation, pollution and desertification (Chen, 2007; OECD and FAO, 2012; Ye and Ranst, 2009). Climate change adds another challenge, as the overall impact of climate change on agricultural productivity is likely to be negative (Ju, van der Velde, Lin, Xiong and Li, 2013).

Given increasingly tight resource constraints, the evolution of China’s net import demand for food depends heavily on its productivity growth in agriculture. This is especially so, as China

> 24 About 20 percent of the world’s pastures and rangelands are degraded to some extent mainly through overgrazing. In China, the shift of production towards a large-scale grain-based industrial system appears to be leading to nutrient overload of soils and water pollution in some geographically concentrated areas (Steinfeld et al., 2006).

25
**Table 7.  The impact of takeover laws on the takeover premium**

||**Mean**<br>**con**|**Dyc**<br>**trol**|**k-Zingales**<br>**value**||**Estmates Nenova**<br>**control value**|**Takeover**<br>**premium**<br>**(R-V 04)**||**All cash offers**<br>**(R-V 04)**|
|---|---|---|---|---|---|---|---|---|
|Numberofobs.|33||31||16|30||30|
|Rsquared|0.1273||0.2365||0.3632|0.1433||0.043|
|Takeover Law|-0.20|<sup>~~d~~</sup>|-0.27|~~b~~|-0.29<br>~~c~~|-9.45||-0.28|
||(0.14)||(0.11)||(0.17)|(24.16)||(0.21)|
|Eff. judiciary|||-0.02||||||
||||(0.02)||||||
|nv. Protection|||||||||
|Ln GNI/capita|-0.01||0.03||-0.04|-7.55|~~c~~|0.00|
||(0.03)||(0.04)||(0.03)|(4.35)||(0.04)|
|Constant|0.38|<sup>~~d~~</sup>|0.20||0.75<br>~~a~~|218.91|~~a~~|0.84<br>~~b~~|
||(0.26)||(0.26)||(0.26)|(42.56)||(0.37)|

_Note: Data from World Bank World Development Indicators, Dyck and Zingales 2004, Nenova 2003, Rossi and Volpin (2004), La Porta et al (1998). GNI is measured by the Atlas method. Takeover premium refers to the bid price as a percentage of the closing price of the target four weeks before the announcement. All cash offers is the share of merges and acquisitions paid for entirely in cash, out of all merger and acquisition deals. Significance levels are as follows: 1% (a), 5% (b), 10% (c), 15% (d). Regressions with robust standards errors._

**Table 8.  Investigating other issues: labor laws or takeover barriers?**

||**Takeover volum**|**e**|**Hostile deals rati**|**o**|**Cross-border ratio**||
|---|---|---|---|---|---|---|
|Numberofobs.|41||41||41||
|Rsquared|0.5501||0.3532||0.4993||
||13.35|<sup>~~c~~</sup>|1.78|~~b~~|-42.86|~~a~~|
|Takeover Law|(6.86)||(0.82)||(10.22)||
||0.08||-0.00||-0.04||
|Labor law|(0.07)||(0.01)||(0.09)||
||8.09|<sup>~~a~~</sup>|0.33|~~a~~|-1.26||
|Ln GNI/cap|(1.48)||(0.13)||(1.87)||
|Constant|-58.29|<sup>~~a~~</sup>|-2.86|~~a~~|76.06|~~a~~|
||(12.39)||(0.97)||(16.67)||

_Note: Data from World Bank World Development Indicators, Doing Business Indicators, and Rossi and Volpin (2004). GNI is measured by the Atlas method. Takeover volume refers to the percentage of domestic traded companies targeted in completed deals ion the 1990s. Hostile deals ratio is the share of attempted hostile takeovers as a percentage of domestic traded companies. Cross-border ratio is the number of cross-boarder deals as target as a percentage of all completed deals. Labor law is proxied by the Doing Business 2006 measure of Difficulty of Firing._ _Significance levels are as follows: 1% (a), 5% (b), 10% (c), 15% (d). Regressions with robust standards errors._ 37
Table 2: Estimates using the raw wage ratio

||_Homi_<br>_(1)_|_cides_<br>_(2)_|_Hospit_<br>_(3)_|_alizations_<br>_(4)_|_Rep_<br>_(5)_|_orts_<br>_(6)_|
|---|---|---|---|---|---|---|
|Raw F-M wage ratio|−.26|−.032|−.76|−.63|−1.1<sup>∗∗</sup>|−1.3<sup>∗∗</sup>|
||(.6)|(.6)|(.87)|(.72)|(.52)|(.5)|
|Controls|N|Y|N|Y|N|Y|
|Municipality FE|Y|Y|Y|Y|Y|Y|
|Year FE|Y|Y|Y|Y|Y|Y|
|Number of municipalities|841|841|841|841|841|841|

_Note:_ The outcome variables are in natural logs; 0.01 is added to each value. The control variables are the natural log of the population of women ages 15–49, the natural logs of male homicides, hospitalizations and reports, the number of households receiving cash transfers, divided by the population of women ages 15–49, the municipal per capita value of cash transfers, the ratio of females to males in secondary education completion, the share of the population ages 15– 49 that is female, the per capita number of health clinics, and the per capita number of hospital beds. Regressions are weighted by the average number of women ages 15–49 in 2011–16. FE = fixed effect. F-M = female to male. Significance levels are reported as * p < .10 ** p < .05 *** p < .01.

Table 3: Main results: estimates using the leave-out wage ratio

||_Homi_<br>_(1)_|_cides_<br>_(2)_|_Hospit_<br>_(3)_|_alizations_<br>_(4)_|<br>_Rep_<br>_(5)_|_orts_<br>_(6)_|
|---|---|---|---|---|---|---|
|Leave-out F-M wage ratio|−1.3|−1.6<sup>∗</sup>|−.59|−.13|−.69|−.43|
||(.89)|(.89)|(1)|(.97)|(1.2)|(1.1)|
|Controls|N|Y|N|Y|N|Y|
|Municipality FE|Y|Y|Y|Y|Y|Y|
|Year FE|Y|Y|Y|Y|Y|Y|
|Number of municipalities|841|841|841|841|841|841|

_Note:_ The outcome variables are in natural logs; 0.01 is added to each value. The control variables are the natural log of the population of women ages 15–49, the natural logs of male homicides, hospitalizations and reports, the number of households receiving cash transfers, divided by the population of women ages 15–49, the municipal per capita value of cash transfers, the ratio of females to males in secondary education completion, the share of the population ages 15– 49 that is female, the per capita number of health clinics, and the per capita number of hospital beds. Regressions are weighted by the average number of women ages 15–49 in 2011–16. FE = fixed effect. F-M = female to male. Significance levels are reported as * p < .10 ** p < .05 *** p < .01.

31
# **Determinants of Property Tax Revenue: Lessons from Empirical Analysis**

## **Rajul Awasthi, Tuan Minh Le, Chenli You**

### **JEL Code: H71**

**Keywords:** Local Government Revenue, Local Government Subsidies, Local Government Taxation, Local Tax, Property Tax, Sales Tax, State Government Subsidies, State Lottery, State Revenue, State Tax, Taxation

Rajul Awasthi is a Senior Public Sector Specialist in Governance and Task Team Leader for the <mark>Strengthening Property Tax Systems in Developing Countries (ID: P169109) project. Tuan Minh Le is a Lead Economist in Macro, Trade, and Investment. Chenli You is an economist and a consultant.</mark>
Table 2: Real exports implications of various scenarios - cumulative impacts (% deviations from the benchmark)

||**Global**<br>**pandemic**|**Amplified**<br>**global**<br>**pandemic**|
|---|---|---|
|China|-3.73|-3.08|
|DevelopingEAP excludingChina|-1.75|-4.07|
|Cambodia|-3.89|-7.40|
|Lao PDR|-3.57|-7.29|
|Malaysia|-2.45|-5.28|
|Thailand|-3.40|-6.81|
|Vietnam|-1.00|-2.82|
|Philippines|-2.94|-6.35|
|Indonesia|-1.38|-3.21|
|HongKongSAR,China|-5.18|-9.80|
|Korea,Rep.|-1.90|-3.95|
|Singapore|-4.39|-8.48|
|Taiwan,China|1.14|1.07|
|Canada|-2.30|-4.73|
|Europe|-2.48|-4.86|
|Japan|-1.04|-2.33|
|United States|-2.37|-4.60|
|Middle East & North Africa|-2.22|-4.87|
|Sub-Saharan Africa|-1.87|-4.29|
|Brazil|-2.03|-4.27|
|Rest of Latin America & Caribbean|-2.21|-4.76|
|Russian Federation|-3.49|-7.44|
|Rest of Europe & Central Asia|-2.89|-5.72|
|India|-1.68|-3.45|
|Rest of South Asia|-1.99|-4.12|
|Oceania|-2.32|-4.98|
|Developingcountries|-2.80|-4.54|
|High-income countries|-2.30|-4.59|
|World Total|-2.50|-4.57|

_Source:_ Envisage simulations

- 10 -
Table 4: Percentage of Population Over 65

|1990|2020|**Percent**<br>**L%ange**|2050|**Percent**<br>**Change**|
|---|---|---|---|---|
|Asapercentage of population|15-65||||
|UK<br>23.1|25.6|10.8%|30.4|18.8%|
|Germany<br>22.5|33.2|47.6%|42.3|27.4%|
|Netherlands<br>18.5|28.9|56.2%|38.1|31.8%|
|Sweden<br>27.4|33.0|20.4%|35.8|8.5%|
|Denmark<br>22.7|30.5|35.6%|39.8|30.5%|
|Swltzerland<br>25.0|48.1|92.4%|46.0|-5.6%|
|France<br>21.0|30.5|45.2%|37.8|23.9%|
|US<br>18.7|25.0|33.7%|31.8|27.2%|
|Japan<br>16.6|33.7|103.0%|37.6|11.6%|
|Canada<br>16.8|29.0|72.6%|36.4|25.5%|

Source: Hagemann and Nicoletti (1989) Table 5: Indicators of the scope of social s*curlty pensions

||Payments/GDP (1985)(1)|<br>SocialsecurLty<br>replacee<br>**rate**<br>for<br>**single**<br>worker|nt<br>(1980)(2)|
|---|---|---|---|
|US|8.2%|44%||
|UK|7.5%|31%||
|Germany|13.5%|49%||
|Japan|5.2%|54%||
|Canada|6.4%|34%||
|Netherlands|11.8%|44%||
|Sweden|13.0%|68%||
|Denmark|9.8%|29%||
|Switzerland|8.5%|37%||
|Memo: France|14.4%|66%||

(1) Source: Mitra (1991) (2) Source: Aldrich (1982) 6b
- 10 -

- (b) While household demand for cooking services may be inelastic, successful stove projects demonstrate that the demand for woodfuels may be made more elastic. Carefully designed cooking efficiency programs, especially for urban areas where consumers are dependent on charcoal, could help consumers lower cooking costs and thus partly offset the impacts of realistic fuelwood pricing policies. And also, these same pricing policies provide consumers incentives to adopt fuel-saving stoves.

1.25 Wastage and Inefficient Transformation. Where fuelwood is converted to charcoal for transport to distant urban markets, underpricing of wood resources encourages inefficient charcoaling practices and wasting of wood. Paying only a fraction of the true cost of his input, the charcoaler has little or no incentive to economize on the use of wood. Similarly, the underpricing of wood from open access forestlands limits the incentives for adoption of improved wood harvesting practices and charcoal kilns in those areas.

1.26 An example from the trial introduction of improved charcoal kilns in Jamaica illustrates the incentive effect economic wood pricing could have. The selected improved kiln type, the Casamance modified earth mound, has a chimney made from three used oil drums. Estimated cost for the chimney is about US$30 (Jamaican $160). This is a significant investment for traditional charcoalers. Demonstrations involving 30 local charcoalers confirmed that the financial rate of return the producer expects to make governs his willingness to make the initial capital investment. Table 1.1 shows the financial incentives to the charcoaler of using the Casamance rather than the traditional kiln. The incentives were estimated by calculating the producer's margin, or producer price less charcoal production costs, at varying wood prices.

Table 1.1: CHARCOAL PRODUCERS' '4ARGIN: JAMAICA (JS/Tonne)

||Kiln Type||
|---|---|---|
|Wood Cost|Traditional|Casamance|
|0|139|205|
|5|105|180|
|10|72|155|
|25|-28|80|
|50|-195|-45|

Source: "Jamaica Charcoal Production Project," UNDP/World Bank report 090/88, September, 1988.

1.27 The table shows that if the producer is paying nothing for wood input, the level of "profit" per tonne is close to 50 percent higher with the use of the Casamance kiln. However, if wood costs just J$10 per
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE For example, Uganda accepts equipment that has been approved in other jurisdictions—such as North America or Europe—but not the EAC.<sup>3</sup> Not all countries are represented by EARPRO. Burundi is a member of the Association des Régulateurs de Télécommunications de l'Afrique Centrale (ARTAC), and Sudan’s regulator is a member of the Arab Regulators Network.<sup>4</sup>

# **Box 4.1 . Regional telephone traffic in Tanzania**

Only Tanzania publishes data on its telephone traffic with the rest of East Africa. Around a quarter of Tanzania’s outgoing international voice traffic is to other East African countries, but only 7 percent of the incoming traffic is from East Africa. This discrepancy suggests that Tanzania has relatively low interregional tariffs. It is cheaper to call from Tanzania than it is to call into Tanzania because interregional telephone tariffs in Tanzania have the lowest average in the region, at $0.44.

**Inbound and outbound East Africa traffic as a share of Tanzania’s international traffic in 2007**

<!-- Start of picture text --> Inbound Outbound<br>7%<br>26%<br>93% 74%<br>East Africa Other East Africa Other<br>Source: TCRA.<br><!-- End of picture text --> Several large mobile groups operate in multiple countries in the region, but no single operator dominates the region. With the exception of Ethiopia, all countries in East Africa allow foreign investment in telecommunications services. Burundi, Sudan, Tanzania, and Uganda do not have a limit on the amount of foreign investment, and each has at least one overseas operator that fully owns its investment. The main strategic investors in the region (in terms of the number of East African countries in which they operate) are MTN of South Africa (three countries) and Zain of Kuwait (four countries). France Telecom and Millicom, both European investors, are each involved in two countries. The Libyan African Portfolio (LAP) owns integrated (fixed and mobile) incumbent operators in Rwanda and Uganda. Practically all of the region’s GSM mobile operators have some degree of foreign investment.

Incumbent operators have been partly privatized in five countries of East Africa. Privatizations in Rwanda, Tanzania, and Uganda have not performed as expected. Problems include renationalization and subsequent resale in Rwanda,<sup>5</sup> strategic investor changes in Uganda,<sup>6</sup> and management issues in Tanzania.<sup>7</sup> Sudan is unique in having released on regional stock markets shares in its incumbent, Sudatel,

> 3 http://www.ucc.co.ug/typeApprovalFAQ.php

> 4 Tanzania is a member of EARPTO and the Communications Regulators’ Association of Southern Africa (CRASA), the telecommunications regulators group for SADC.

> 5 http://www.rwandainvest.com.cn/DetailA.asp?ID=225&intMaxID=9

> 6 http://www.ihsglobalinsight.com/SDA/SDADetail8574.htm

> 7 http://allafrica.com/stories/200909070804.html

50
# **Table 5a. Further results –within group**

||Initiators|||||Receivers|||||
|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|9|10|
|Private<br>university|3.709***<br>(.776)|3.896***<br>(.825)|3.648***<br>(.772)|3.864***<br>(.828)|4.785<br>(3.871)|1.647<br>(1.131)|1.695**<br>(.836)|1.649<br>(1.158)|1.761<br>(1.102)|-7.939**<br>(3.903)|
|Amount||||||.601***|.557***|.600|.579***|.608***|
|received||||||(.052)|(.035)|(.054)|(.049)|(.053)|
|Particularized<br>tt tt i|-1.373**|-1.311**|-1.179**|-1.745**|-1.263**|2.838**|2.619***|2.829**|2.571**|.472|
|rus (rus n<br>friends)|(.560)|(.617)|(.549)|(.747)|(.529)|(1.101)|(.854)|(1.139)|(1.217)|(.507)|
|<br>Benevolent||-.095|||||.929**||||
|self-image||(.494)|||||(.368)||||
|Material<br>||-.986||-4.773|||-3.389***||-6.338||
|utility<br>maximizer||(.880)||(2.922)|||(.937)||(5.029)||
|Socialism||.555|||||1.724**||||
|better||(1.047)|||||(.749)||||
|Authoritarian||-1.202|||||3.377**||||
|better||(1.955)|||||(1.056)||||
|Would likely|||.225|||||.383|||
|<br>know people|||(1.235)|||||(1.702)|||
|Would likely|||||||||||
|know people|||-.799|||||-.138|||
|* trust in|||(.707)|||||(1.115)|||
|friends|||||||||||
|Utility max*<br>||||1.305|||||.966||
|trust in<br>friends||||(1.040)|||||(1.723)||
|Private|||||||||||
|university*|||||-.375|||||3.375**|
|<br>trust in|||||(1.359)|||||(1.422)|
|friends|||||||||||
|Observations|53|53|53|53|53|53|53|53|53|53|
|R squared|.391|.422|.432|.430|.392|.822|.887|.822|.855|.839|
|F statistic|10.93|6.19|9.25|8.63|8.77|54.70|78.85|41.31|96.95|46.62|
|RMSE|2.789|2.839|2.752|2.757|2.816|3.510|78.85|3.587|3.231|3.365|

Note: *** (**) [*] denote significance at p<.01 (p<.05) [p<.10]. All estimates include the full baseline specification and a constant term.

39
Unfortunately, however, growth in HFCE tends to substantially exceed mean consumption growth in household surveys.<sup>3</sup> One study, for example, concludes that on average only about half of the growth in HFCE was reflected in growth in survey consumption.<sup>4</sup> This is partly because official surveys face severe challenges obtaining accurate measures of welfare for the very wealthy. Because there are few very wealthy households, they may not be included in the sample. Wealthy households are more likely than poor households to refuse to participate in the survey, and tend to underreport their income or consumption. However, there are also good reasons why national accounts data may overestimate growth in mean income or consumption, including the use of outdated ‘rates and ratios’ based on old survey data.<sup>5</sup> Because of the systematic discrepancy between growth in national accounts data and household surveys, using HFCE to adjust survey data is prone to overstating poverty reduction for countries that lack a recent survey such as India. In fact, past analysis from India demonstrates how the typical line up adjustment procedure can greatly overestimate poverty decline, using data from 2004-05 and 2009-10.<sup>6</sup> Estimates were generated for 2009-10 by lining up the 2004-05 data to 2009-10, applying the HFCE growth rate during that period. These line-up estimates for 2009-10 were then compared with measurements from actual survey data from that year. The line-up procedure generated an estimated reduction in poverty from 41.6 to 13.4 percent, whereas the actual data from 2009-10 yielded a poverty rate of 32.7%.<sup>7</sup> In other words, the line-up method overstated the decline in poverty by 19 percentage points. However, virtually all of the discrepancy between the lineup estimate and the actual data resulted from consumption expenditure growth in the national accounts exceeding mean welfare growth in the survey. In contrast, assuming that the 2004-05 welfare distribution remained constant except for a scale factor created negligible error. In this case, assuming that the distribution of welfare changed by a common scale factor was fine; the real problem was determining what factor should be used to scale up measured welfare in the initial year.

The concern that growth in the national accounts exceeds growth in survey mean welfare motivates the use of a survey-to-survey imputation method to determine an appropriate scale factor or “pass-through rate” to apply to HFCE. The major portion of the exercise involves estimating poverty at the $1.90 line in 2014-15, using

> 3See Edward and Sumner (2013), Hillebrand (2008), Korinek, Mistiaen, and Ravallion (2006), Deaton and Kozel (2005), Ravallion (2003) and Minhas (1988).

> 4Ravallion (2003).

> 5Deaton and Kozel (2005).

> 6Jolliffe (2014) p.253.

> 7These figures are based on the older, $1.25 poverty line (in 2005 PPP).

3
open sun. Nobody is likely to enjoy this work, and that is undoubtedly part of the reason that relatively rich people appear to rarely turn up for work on the scheme. Yet, while this “selftargeting” mechanism is a key aspect of the rationale for such schemes in fighting poverty, the fact that the type of work is unpleasant has never (to our knowledge) been used in assessing the welfare of actual or potential participants.

This paper has offered a simple correction for this deficiency in past assessments of the performance of such programs. Our adjustment to measured household consumption to allow for the disutility of casual manual labor involves a single preference parameter that can be readily calibrated to available data assuming that labor supply choices are utility maximizing. We have also proposed a robustness test given the possibility of involuntary unemployment in casual labor markets. The empirical implementation has been for India’s NREGS.

The proposed adjustment of the welfare measure for the disutility of casual manual labor entails a marked difference to standard measures of poverty and inequality. The distributional change implies a lower incidence of poverty, but higher measures of its depth and severity; for example, the squared poverty gap index roughly doubles. For rural India as a whole, the Gini index of inequality rises from 0.31 to 0.43.

The adjustment also affects the assessment of targeting performance of NREGS. Whereas the choice between the unadjusted consumption and our adjusted version does not make a significant difference among the 30% richest, the assessed targeting performance is changed appreciably among the poorest half of the distribution when we allow for the disutility of doing casual manual work.

However, allowing for the disutility of work also devalues the benefits to participants. Using survey data for Bihar, we find that this effect dominates the better targeting performance implied by allowing for the disutility of the type of work done. The scheme reaches even poorer people, but that it does less to raise their welfare. On balance the poverty impact of the scheme is lower, representing a decline of 0.8 percentage points in the overall poverty rate in Bihar, as compared to 1.2 points when the disutility of work is ignored.

15
# **Table S1.6: Replacing country unemployment rate shocks with individual unemployment status**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|A: Physical Violence|||||
|Own unemployment|-0.026*|-0.032**|-0.036***|-0.042***|
||(0.014)|(0.013)|(0.011)|(0.007)|
|Spousal unemployment|-0.002|-0.011|-0.005|0.004|
||(0.023)|(0.018)|(0.017)|(0.018)|
|Observations|88080|88080|88080|74704|
|B: Sexual Violence|||||
|Own unemployment|-0.033***|-0.025***|-0.026***|-0.026**|
||(0.009)|(0.006)|(0.008)|(0.009)|
|Spousal unemployment|0.024**|0.018*|0.011|0.012*|
||(0.010)|(0.010)|(0.006)|(0.007)|
|Observations|75611|75611|75611|74920|
|C: Any Violence|||||
|Own unemployment|-0.042**|-0.042***|-0.048***|-0.050***|
||(0.015)|(0.012)|(0.009)|(0.007)|
|Spousal unemployment|0.016|0.005|0.008|0.012|
||(0.027)|(0.022)|(0.020)|(0.018)|
|Observations|75356|75356|75356|74669|
|Year FE||Y|Y|Y|
|Country FE|||Y|Y|
|X controls||||Y|

_Source:_ Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

## _Notes:_

* p<0.10, ** p<0.05, *** p<0.01. Standard errors are robust to arbitrary forms of heteroscedasticity and are clustered at the country level. The dependent variable in each regression is a binary dummy variable taking the value one if the individual female respondent was a victim of violence, defined as physical (panel A), sexual (panel B) or either (panel C) in the last twelve months. X is a vector of controls for age and education of respondent, education of her spouse, and urban location. Relative to Table 1, female and male unemployment rate shocks are replaced with an indicator for the individual unemployment status of the woman and her partner. Information on employment status of partner is available in only 18 of our 32 countries.
<!-- Start of picture text --> Environment, Incentives and<br>Opportunities<br>-* — _<br>7 — —— N<br>-7<br>ra - “ / 1 ° ‘ N N<br>Early Childhood )=—> Adolescence —> ( Post-Basic Schooling) _,, Older Age<br>& Working Age<br>Sy ‘ } 7<br>‘ XN \ / /<br>N \ U 4<br>N a - 3 7<br>x<br>\. é& 4<br>sC External Shocks » 4<br><!-- End of picture text -->