rafmacalaba/datause-encoder-data huggingface dataset:
manufacturing sector continues to be dominated by low value-added industries, in particular in Central America (such as the maquillas in Mexico) (Figure 3). In fact, while the Washington consensus policies led to the demise of old and unsustainable industries, new ones did not emerge to a sufficient extent.<sup>7</sup> The absence of development of new industries also undermined export performance (Figure 4). The lack of LAC‘s structural transformation is illustrated in figures 3 and 4 which show the evolution of East Asia‘s versus LAC‘s economy. East Asia has been increasing the share of high-tech and manufacturing commodities in its exports and in its economy. Latin America to the contrary has experienced a declining share of high-tech and manufacturing exports. Without diversification and structural upgrading, Latin America is less likely to sustain growth and is more exposed to downside risks in the global economy.

<!-- Start of picture text --> Figure 3: LAC's Structure of the Economy<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Agriculture, value added Manufacturing, value added<br>Services, value added Total natural resources rents<br>% of GDP<br><!-- End of picture text --> Source: World Development Indicators.

**Figure 4: LAC and East  Asia: Structure of Exports**

<!-- Start of picture text --> 35<br>LAC 1995<br>30<br>LAC 2000<br>25<br>20 LAC 2005<br>15 LAC 2010<br>10<br>EAP 1995<br>5<br>EAP 2000<br>0<br>High-technology exports (% of Food and agricultural raw Trade in services (% of GDP) EAP 2005<br>manufactured exports) materials exports (% of EAP 2010<br>merchandise exports)<br>Source: World Development Indicators.<br><!-- End of picture text --> 7 Some new industries have emerged, for example in Peru in the areas of agro-processing or pharmaceutical products.

8
<u>Table A2. Labor force participation rate and Employment rate by gender and age groups</u>

|||LFP|rate|||Employ|ment rate||
|---|---|---|---|---|---|---|---|---|
||All|15-24|25-44|45-64|All|15-24|25-44|45-64|
|_Women_|||||||||
|Albania|47.73|21.68|62.59|50.18|39.07|12.40|51.97|0.45|
|Argentina|58.97|41.72|67.43|56.59|53.79|30.67|62.75|0.54|
|Bangladesh|19.58|13.46|23.29|18.68|19.28|12.76|23.03|0.19|
|Colombia|41.60|26.44|52.69|39.11|39.22|22.81|49.90|0.38|
|Ecuador|45.13|24.96|54.75|51.02|43.57|22.82|52.79|0.51|
|Ghana|11.78|15.56|9.57|14.02|9.42|9.19|8.02|0.13|
|Guatemala|36.80|28.05|42.91|39.55|33.36|24.19|39.25|0.37|
|Iraq|13.85|7.39|17.82|16.17|11.38|4.31|14.82|0.16|
|Mexico|49.54|35.85|58.64|48.18|48.84|34.54|57.97|0.48|
|Moldova|67.08|32.10|70.10|76.80|67.08|32.10|70.10|0.77|
|Mongolia|55.49|24.99|69.60|64.66|48.34|18.42|61.24|0.59|
|Pakistan|22.14|19.65|24.45|21.95|20.64|17.76|23.11|0.21|
|West Bank and|19.22|8.69|24.16|22.51|15.21|4.47|19.05|0.22|
|Peru|n.a.|n.a.|n.a.|n.a.|51.74|34.46|59.47|0.58|
|South Africa|44.89|21.94|60.06|45.41|36.28|12.85|48.81|0.43|
|Timor|34.21|24.32|32.79|49.95|27.77|15.26|26.43|0.47|
|Turkey|26.95|25.14|31.38|20.24|25.27|22.16|29.58|0.20|
|Uruguay|68.10|43.95|81.61|67.72|61.75|31.99|74.29|0.66|
|USA|71.95|63.73|78.54|69.68|64.83|48.83|72.21|0.66|
|_Men_|||||||||
|Albania|77.71|46.85|93.24|86.70|67.26|30.23|83.65|0.81|
|Argentina|87.20|67.75|94.89|87.99|82.80|57.79|91.42|0.86|
|Bangladesh|86.24|62.19|95.70|94.16|85.64|61.28|94.97|0.94|
|Colombia|77.33|47.89|91.39|84.64|71.85|41.01|85.84|0.81|
|Ecuador|79.55|49.04|95.31|90.61|77.86|46.39|93.99|0.89|
|Ghana|29.77|21.95|32.72|28.90|21.96|12.27|24.45|0.23|
|Guatemala|84.05|66.96|95.82|92.13|80.48|62.51|92.47|0.90|
|Iraq|77.86|58.03|95.82|75.93|70.14|46.28|89.64|0.73|
|Mexico|86.20|64.51|97.50|90.82|84.36|62.16|95.97|0.89|
|Moldova|74.30|47.68|81.60|80.03|74.30|47.68|81.60|0.80|
|Mongolia|66.70|31.90|87.14|73.17|57.55|23.26|75.99|0.67|
|Pakistan|83.27|66.15|95.54|89.29|81.92|63.95|94.48|0.89|
|West Bank and|84.40|57.32|97.64|80.13|76.79|48.15|89.51|0.76|
|Peru|n.a.|n.a.|n.a.|n.a.|80.82|56.26|92.29|0.90|
|South Africa|59.23|27.15|79.53|65.78|51.13|18.99|70.12|0.61|
|Timor|69.86|35.27|90.54|87.87|58.46|23.85|76.37|0.81|
|Turkey|79.15|56.24|97.31|65.44|73.74|46.53|92.17|0.64|
|Uruguay|84.21|58.55|97.58|86.56|80.22|49.61|94.42|0.85|
|USA|80.95|64.33|91.84|79.07|74.79|51.79|86.92|0.75|

Source: Authors’ elaboration based on World Development Indicators (2017).

51
6

# **2. The SHI reforms of Europe and Central Asia: a brief history**

Under communism, health care in the ECA countries was financed out of general revenues (and out-of-pocket payments) and delivered through a centrally-planned “Semashko” model consisting of a tiered system of health providers, each allocated budgets according to population-based norms, with health workers paid by salary.<sup>13</sup> In the early 1990s, as most countries shifted away from communism, several looked to SHI to solve several emerging problems.

The most pressing one was the dramatic decline in government revenues as a share of GDP, caused by a variety of factors, including the growth of the private and informal sectors where tax compliance was lower, a shrinking of traditional tax bases such as state-owned enterprises, and pressures for tax cuts from a population experiencing declines in real income. With falling GDP and revenues falling as a share of GDP, health sectors experienced substantial cuts in government spending. SHI was seen, rightly or wrongly, as a way of protecting spending levels in the health sector, the presumption apparently being that earmarking would help ensure the health sector did not have to compete with other sectors in government spending allocation decisions, and that earnings in the economy as a whole would fall less than government revenues and be more stable. Providers were especially enthusiastic about SHI which they saw as a way to increase their salaries.

SHI was also perceived as having other advantages vis-à-vis both the financing and delivery of health care. One was the perceived potential to reduce the grip of finance and health ministries over the finance and delivery sides of the system, the vision being that payroll tax

> 13 This section draws heavily on Langebrunner et al. (in press).
In contrast, the impact of an increase in rail fares is much smaller. An increase of 1 percent in rail fares causes a loss in workers who commute by rail ranging from 0.07 to 0.08 percent, the majority of whom switch to bus. It should also be noted that raising rail fares by one percent reduces the numbers of workers who commute by rail+bus by 0.07 percent.

If one is willing to assume that the elasticities computed in Table 13 for commute mode choice apply to all trips, then Table 13 can be used to estimate the impact of fare increases on passenger volumes and revenues. This is done for separate increases in bus and rail fares in Table 14 and for joint increases in bus and rail fares in Table A.5 of the Appendix, using the elasticities for the Multinomial Logit (Cost/Wage) model in Table 13.

**Table 14: Impact of Fare Increases on Passenger volume and Revenue**

|**Impact**<br>**f**|**Cha**|**nge in Pa**|**ssenger Vol**|**ume**||**C**|**hange in R**|**evenue**||
|---|---|---|---|---|---|---|---|---|---|
|**o...**|**Bus**|**Rail**|**Bus**|**Rail**|**Bus**|**Rail**|**Bus**|**Rail**|**Bus+ Rail**|
||**(Per**|**cent)**|**(Mill**|**ion)**|**(Per**|**cent)**||**(Rs. Millio**|**n)**|
|**Bus fare**||||||||||
|**increase**||||||||||
|1%|-0.36|0.10|-5.37|2.30|0.64|0.10|54.44|9.87|64.32|
|2%|-0.71|0.20|-10.75|4.60|1.27|0.20|108.28|19.74|128.03|
|3%|-0.71|0.30|-10.75|6.90|2.27|0.30|192.67|29.62|222.29|
|4%|-1.42|0.40|-21.50|9.20|2.52|0.40|214.14|39.49|253.63|
|5%|-1.78|0.50|-26.87|11.50|3.13|0.50|266.17|49.36|315.53|
|10%|-3.56|0.99|-53.74|23.00|6.09|0.99|517.21|98.72|615.93|
|15%|-5.34|1.49|-80.62|34.50|8.86|1.49|753.13|148.08|901.21|
|20%|-7.12|1.99|-107.49|46.00|11.46|1.99|973.92|197.44|1171.37|
|25%|-8.90|2.48|-134.36|57.50|13.88|2.48|1179.60|246.80|1426.40|
|30%|-10.68|2.98|-161.23|69.00|16.12|2.98|1370.14|296.16|1666.31|
|**Rail fare**||||||||||
|**increase**||||||||||
|1%|0.01|-0.08|0.20|-1.77|0.01|0.92|1.10|91.70|92.80|
|2%|0.03|-0.15|0.39|-3.54|0.03|1.84|2.20|183.24|185.44|
|3%|0.04|-0.23|0.59|-5.31|0.04|2.76|3.30|274.63|277.93|
|4%|0.05|-0.31|0.78|-7.08|0.05|3.68|4.40|365.87|370.27|
|5%|0.06|-0.38|0.98|-8.85|0.06|4.60|5.50|456.96|462.46|
|10%|0.13|-0.76|1.95|-17.69|0.13|9.16|11.00|910.13|921.13|
|15%|0.19|-1.15|2.93|-26.54|0.19|13.68|16.50|1359.50|1375.99|
|20%|0.26|-1.53|3.91|-35.39|0.26|18.17|22.00|1805.07|1827.07|
|25%|3.24|-1.91|48.85|-44.23|3.24|22.61|274.95|2246.84|2521.80|
|30%|0.39|-2.29|5.86|-53.08|0.39|27.02|32.99|2684.82|2717.81|

Source: Authors’ calculations 19
_Table A 12_ : Malawi: Regression of self-reported drought exposure on measured rainfall intensity and controls.

|VARIABLES|(1)<br>IHPS13|(2)<br>IHPS16|(3)<br>IHPS19|
|---|---|---|---|
|12-month total rainfall (mm)|-0.000582***|-0.000305**|-3.88e-05|
||(0.000217)|(0.000128)|(0.000241)|
|Soil fertility index|-0.0269|0.0110|0.0142*|
||(0.0239)|(0.0304)|(0.00834)|
|Plot Potential Wetness Index|0.00874*|0.0123*|0.00105|
||(0.00512)|(0.00631)|(0.00201)|
|Plot Slope (percent)|0.000432|-0.00328|-0.000594|
||(0.00243)|(0.00382)|(0.000946)|
|Intercropped|0.0523|0.114***|0.0321***|
||(0.0390)|(0.0411)|(0.0122)|
|Plot has any erosion control structure|-0.0226|0.0461|-0.0115|
||(0.0240)|(0.0335)|(0.0189)|
|Any organic fertilizer|0.0147|0.0340|0.0346*|
||(0.0256)|(0.0239)|(0.0187)|
|Inorganic fertilizer was applied on plot|-0.0613*|0.0105|0.0117|
||(0.0350)|(0.0322)|(0.0162)|
|Plot has improved maize variety||0.0130|-0.000186|
|||(0.0221)|(0.0119)|
|Constant|0.639***|0.158|0.0379|
||(0.225)|(0.172)|(0.301)|
|Observations|925|1,004|1,490|
|Adjusted R-squared|0.052|0.370|0.111|
|District FE|YES|YES|YES|

Linear Probability Model regressing self-reported drought exposure on 12-month rainfall intensity and a number of controls that are potentially relevant for drought exposure. Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 60
- **Venter, Zander S, Kristin Aunan, Sourangsu Chowdhury, and Jos Lelieveld** , “COVID-19 lockdowns cause global air pollution declines,” _Proceedings of the National Academy of Sciences_ , 2020, _117_ (32), 18984–18990.

- **Wallace, Jeremy L** , “Juking the stats? Authoritarian information problems in China,” _British Journal of Political Science_ , 2016, _46_ (1), 11–29.

- **Wang, SW, Qiang Zhang, DG Streets, KB He, RV Martin, LN Lamsal, D Chen, Y Lei, and Z Lu** , “Growth in NO _x_ emissions from power plants in China: bottom-up estimates and satellite observations,” _Atmospheric Chemistry and Physics_ , 2012, _12_ (10), 4429–4447.

- **Wei, Zigang, Shobha Kondragunta, Kai Yang, Hai Zhang, and Brian McDonald** , “Correlating Economic Activity Indicators and Tropospheric Column Nitrogen Dioxide during COVID-19 Pandemic in the United States,” 2020.

**Zhao, Yanbin, Kun Zhang, Xiaotian Xu, Huizhong Shen, Xi Zhu, Yanxu Zhang, Yongtao Hu, and Guofeng Shen** , “Substantial changes in nitrogen dioxide and ozone after excluding meteorological impacts during the COVID-19 outbreak in mainland China,” _Environmental Science & Technology Letters_ , 2020, _7_ (6), 402–408.

42
Table 1: Summar statistics <u>y</u>

||Mean|Std. dev.|Min.|Max.|
|---|---|---|---|---|
|Per capita GDP (PPP-adj.) 2008|15207|12807|802|49711|
|Log per capita GDP|9.144|1.134|6.687|10.814|
|No secondary schooling (proportion)|.088|.131|0|.643|
|Morality (%)|21.27|10.72|5.02|48.64|
|Correlation morality-schooling|.1387|.076|-.130|.268|
|Geography (degrees latitude from equator)|32.99|17.93|1|64|
|Institutions (“Rule of law” from WGI)|.150|.967|-1.38|2.00|
|Trade (exports + imports as share of GDP)|89.54|58.25|25.83|456.65|
|Logof trade|4.359|.498|3.252|6.124|

36
Migration numbers, on the other hand, are not exogenous and we can test whether they reflect distance at all. First, consider figures 5A and 5B. They report for the developing country regions the average distance migrants have travelled to North America and Western Europe and the number who have migrated in thousands. It is clear that there is a negative relationship between these, especially for Europe. In addition, the last row and column of Table 11 report the correlations (at regional level) between the migration stock reported in table 7 and the distance reported here. They are all negative and half are significantly different from zero (critical value at 5 percent is 0.582). This suggests quite strongly that distance is an important determinant of migration behavior throughout the world.

<!-- Start of picture text --> Figure 5A Migrants in North America and Distance<br>SAS<br>EAP<br>AFR<br>no in '000<br>MENA distance<br>in km<br>ECA<br>LAC<br>0 5,000 10,000 15,000 20,000<br><!-- End of picture text --> <!-- Start of picture text --> Figure 5B. Migrants in Western Europe and<br>Di t<br>SAS<br>EAP<br>AFR<br>no in '000<br>MENA distance<br>in km<br>ECA<br>LAC<br>0 2,000 4,000 6,000 8,000 10,000 12,000<br><!-- End of picture text --> A third influence on migration is income. We expect most migrants to have higher incomes as a result of migration, although in particular instances non-pecuniary benefits may dominate. Income is, of course, an individual or household matter, dependent on the individual’s circumstances before and after migration. We cannot gauge that with country-level data, but it is still interesting to ask whether migration is predominantly from 29
increasing the non-financial costs of schooling, _ki,s,_ leading households to lower _siU_ , their unconstrained level of education.

A further effect which we wish to consider is the possibility that due to information and network effects, having a migrant parent increases the likelihood that the children themselves will become migrants. This may have an _immediate substitution effect_ , whereby as a result of the opportunity cost of staying in school increasing due to higher potential earnings abroad, children drop out of school in order to migrate to work. Again this can be viewed as increasing _ki,s,_ leading _U_ households to lower _si_ .

Even if children do not migrate at the age when they would be attending school, the possibility they may migrate in the future can influence the expected returns to education, changing _ri,s_ in our framework. As returns to schooling appear to be higher in Mexico than in the United States, the possibility of migration in the future will _lower_ the expected returns from migration. Since children of migrants are more likely to migrate in the future than children of non-migrants, we would therefore _U_ 15 expect this _incentive effect_ to lower _si_ in migrant households.

Each of these three additional channels through which migration may affect child schooling (disruption of household structure, direct substitution of schooling today for migration today, and the change in expected future returns to education) all _U_ act to lower _si_ . Assume first that this reduction in the unconstrained desired level of schooling occurs equally across wealth and maternal education levels. Coupling this _P_ with the increase in _si_ arising from remittances gives an overall effect of migration as seen in Figure 4. Two possibilities arise. Figure 4a shows the case where the effect of

> 15 For example, in a survey of students in Zacatecas, Kandel and Kao (2001) find that living in a migrant household is negatively associated with directly elicited university aspirations.

18
# Table 10: Table 9 with urban and rural distinctions

||DV: Log|growth in|district man|ufacturing|employm|ent from 19|89-2010|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Urban district specialization index (1989/94)|0.052||0.071||||0.077|
||(0.029)+||(0.033)*||||(0.031)*|
|Urban district diversity index (1989/94)||0.021|0.050||||0.083|
|||(0.029)|(0.033)||||(0.030)**|
|Rural district specialization index (1989/94)||||0.107||0.108|0.066|
|||||(0.035)**||(0.037)**|(0.034)+|
|Rural district diversity index (1989/94)|||||-0.028|0.004|-0.014|
||||||(0.032)|(0.033)|(0.029)|
|State fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Weights|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|272|272|272|277|277|277|265|

Notes: See Table 9. + significant at 10% level; * significant at 5% level; ** significant at 1% level.
**Table A.23:** Citizen Attitudes and Trust Towards the Village Government

||**Citizens survey**||
|---|---|---|
||Interactions withgovt<br>Perceivedgovtquality|Trust ingovt|
||(1)<br>(2)|(3)|
||**Panel A: Full Sample**||
|New village head|0.177<br>-0.018<br>(0.159)<br>(0.142)|0.027<br>(0.126)|
|Observations|8815<br>8790|8789|
|Control mean|-0.12<br>-0.034|-0.029|
|Robust p-value|0.223<br>0.949|0.724|
|<br>MSE-opt. bandwidth|17.1<br>17.7|17.5|
|Effective obs.|3752<br>3892|3812|
|<br>|**Panel B: First-Degree Connection**<br><br>||
|New village head|0.294<sup>_∗_</sup><br>-0.034|0.169|
||(0.179)<br>(0.161)|(0.160)|
|Observations|2638<br>2631|2630|
|Control mean|0.21<br>0.23|0.23|
||**Panel C: Second-Degree Connection**||
|New village head|0.398<sup>_∗_</sup><br>-0.105|-0.128|
||(0.218)<br>(0.179)|(0.169)|
|Observations|2201<br>2196|2196|
|Control mean|-0.098<br>-0.012|-0.035|
||**Panel D: Third-Degree or More Connection**||
|New village head|-0.016<br>0.033|-0.000|
||(0.179)<br>(0.171)|(0.151)|
|Observations|3976<br>3963|3963|
|Control mean|-0.24<br>-0.14|-0.11|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). The dependent variable is: in column 1, a z-score of the frequency of interactions with village officials, as reported by citizens; in column 2, a z-score of self-reported satisfaction with the village government; in column 3, a z-score of self-reported trust in the village government. Panel A reports results for the full sample of citizens. Panels B through D report estimates from subsamples of citizens at varying levels of social distance from the village government, analogous to Table A.21. See Section 5 for details.

* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors clustered by village in parentheses.

59
_TREATi,t_ takes value one if unit _i_ has been assigned to receive treatment by time _t_ (that is for t=1,2,...r), and zero otherwise. The treatment effect of interest is then given by γ.

Note that γ does not contain a _t_ subscript: either the treatment is assumed to have a constant level effect, or the treatment effect of interest is taken to be the average treatment effect over the _r_ post-treatment rounds. Clearly a further, very important, use of multiple postintervention surveys is to collect information on the trajectory of treatment impacts (e.g. Woolcock, 2009), since the short and long-run impacts of some policies may differ dramatically. However, unless one imposes some structure on the form these impact trajectories can take, collecting more rounds of data merely increases the number of points in time at which these impacts can be measured, but does not improve the power of an experiment for measuring these impacts. Even in this case where impacts at different points in time are of interest, one can consider multiple measurements located around each time horizon of interest, and thereby improve power. For example, if a study is interested in knowing whether the impact of microfinance differs at a one year horizon versus a three year horizon, taking measures of microenterprise profits at 11, 12 and 13 months after treatment, and at 35, 36, and 37 months after treatment can be used to improve the accuracy of impacts at around these horizon points, by assuming the treatment effect is constant or considering an average treatment effect in the neighborhood of the horizon of interest. The analytics and ideas of the remainder of the paper can then be applied to estimating the impact at a particular horizon by taking more measurements in neighborhoods around these horizons.

# **2.1.The case of a single baseline and single follow-up**

Consider first the case of a single baseline and single follow-up. Then we see that the least squares estimator of γ is simply the difference in means between the treatment and control groups at follow-up less the difference in means between the treatment and control groups at baseline: Then sample size to detect a given treatment effect at a specified power level and significance level is a linear function of the variance of this estimator. Assume the are independent across individual units with cross-sectional variance σ<sup>2</sup> , but may be autocorrelated over time for the 5
<!-- Start of picture text --> Figure 1: Under-5 mortality rate Figure 2: Maternal mortality ratio<br>250 1,600<br>Low income Low income<br>1,400<br>Lower middle income Lower middle income<br>200<br>Upper middle income 1,200 Upper middle income<br>Congo<br>1,000<br>150<br>Congo 800<br>100 600<br>400<br>50<br>200<br>0<br>0<br>0 5,000 10,000 15,000 20,000<br>0 5,000 10,000 15,000 20,000<br>GNI per capita (PPP, current USD) GNI per capita (PPP, current USD)<br>Source: WDI Source: WDI<br>Under-5 mortality rate (per 1,000 live births)<br>Maternal mortality ratio (per 100,000 live births)<br><!-- End of picture text --> According to the latest household survey from 2005 (ECOM, Republic of Congo 2006), about half of the population lives below the poverty line and meeting most MDGs is a challenge. Health indicators are particularly low and infant and under-5 mortality have even increased in recent years (Table 2). In the education sector, school enrolment rates have increased significantly during the last decade in all school segments, following the introduction of free schooling in 2007/08, but qualitative indicators remain low. In addition, educational progress is highly unequal, with which much better outcomes in urban areas and for high-income households.

**Table 2:** Key social indicators, 1990 - 2009

|**Indicator**|**1990**|**2000**|**2006**|**2007**|**2008**|**2009**|**Lower middle**<br>**income**<br>**countries**<sup>**a**</sup>|**Sub-**<br>**Saharan**<br>**Africa**<sup>**a**</sup>|
|---|---|---|---|---|---|---|---|---|
|**_Education_**|||||||||
|Gross primary school enrolment rate (%)|123<br>|85|116|115|114|120|107|100|
|Net primary school enrolment rate (%)|81<sup>b</sup>|-|59|-|-|-|87|75|
|Primary school completion rate|59|55<sup>c</sup>|77|77|73|74|90|64|
|(% of relevant agegroup)|||||||||
|**_Health_**|||||||||
|Life expectancy at birth (years)|59|54|53|53|54|54|68|53|
|Infant mortality rate (per 1,000 live births)|67|74|78|79|80|81|43|81|
|Under-5 mortalityrate(per 1,000 live births)|104|116|124|125|127|128|57|130|
|**_Memorandum item_**|||||||||
|Population(millions)|2.4|3.0|3.5|3.6|3.6|3.7|-|-|

_Source:_ World Development Indicators. Note: a = data from 2008 or 2009, b = data from 1991, c = data from 2001.

Unemployment has been a long-standing problem in Congo with particularly high unemployment rates during the years of the conflict (Figure 3). The situation improved after the end of the conflict, although unemployment levels remained higher than in the 1970s and 1980s, especially in urban areas. Unemployment rates in 2005 and 2009 were estimated at 19.4 percent and 16.1 percent (for urban areas), respectively. Based on the 2005 household survey, the urban unemployment rate reached 32.6 3