rafmacalaba/datause-encoder-data huggingface dataset:
- 18 -

more-general connectivity, including not only Monrovia, but also secondary cities. Therefore, other secondary cities can also grow when their intercity connectivity is improved.

# **<u>Table 6 Instrumental Variable Spatial Autoregressive Model</u>**

||<br>(1)||<br>(2)||<br>(3)||<br>(4)||(5)||(6)||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|ln_RAI_|<br>0.115<br>(0.192)|<br>|||||||||||
|ln_RDDEN_|||–0.035<br>(0.385)|<br>|||||||||
|ln_GDRD_|||||–0.123<br>(0.216)|<br>|||||||
|ln_TCMK_|||||||–0.312<br>(0.132)|**<br>|||||
|ln_TCPT_|||||||||–0.775<br>(1.087)|<br> <br>|||
|ln_MAI_||||||||||<br>|<br>0.503<br>(0.189)|***<br>|
|ln_N15_|0.788|***|0.828|***|0.845|***|0.752|***|0.788|***|<br>0.741|<br>***|
||(0.088)||(0.114)||(0.083)||(0.074)||(0.075)||(0.075)||
|_POPDEN_|0.1660||0.1780||0.1840||0.2390||0.1880||0.1860||
||(0.1190)||(0.1440)||(0.1260)||(0.1140)||(0.1390)||(0.0877)||
|_INFO_|–0.003||–0.006||–0.009||0.001||0.000||0.003||
||(0.025)||(0.026)||(0.027)||(0.024)||(0.024)||(0.024)||
|_UNEMP_|0.002||–0.003||–0.009||–0.006||0.003||–0.003||
||(0.014)||(0.013)||(0.017)||(0.013)||(0.016)||(0.013)||
|_EDU_|0.021||0.010||0.006||0.003||0.007||0.001||
||(0.091)||(0.091)||(0.091)||(0.089)||(0.092)||(0.089)||
|Constant|<br>–15.960|<br>***|<br>–14.630|<br>***|<br>–13.820|<br>***|<br>–14.260|<br>***|<br>–14.540|<br>***|<br>–13.560|<br>***|
||(4.991)||(5.260)||(5.124)||(4.662)||(4.778)||(4.703)||
|Observations|134||134||134||134||134||134||
|Number of|||||||||||||
|region dummy|||||||||||||
|<br>variables|5||5||5||5||5||5||
|Autoregressive c|oefficients||||||||||||
|λ|–0.372|**|–0.349|*|–0.335|*|–0.330|**|–0.376|***|–0.353|**|
||(0.152)||(0.193)||(0.200)||(0.160)||(0.136)||(0.157)||
|ρ|<br>0.526|<br>|<br>0.759|<br>|<br>1.057|<br>|<br>0.711|<br>|<br>0.390|<br>|<br>0.702|<br>|
||(1.181)||(1.160)||(1.103)||(1.118)||(1.183)||(1.120)||

Note: Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively.

1 The instrumental variable estimation is performed with three instruments in logarithm: _SLOP_ , _ELEV_ , and _DIST_ .
**Table II – Estimates of the impact of treaty dummy on cooperation**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|Treaty dummy|4.953<br>(3.302)|6.502*<br>(3.508)|5.794*<br>(3.380)|5.199<br>(3.321)|
|CoV|538.802***<br>(171.631)|558.177***<br>(168.783)||568.414***<br>(170.548)|
|(CoV)<sup>2</sup>|-1299.975***<br>(465.388)|-1388.05***<br>(471.344)||1397.947***<br>(454.859)|
|Ratio Basin<br>(control)|-0.202<br>(0.163)||-0.123<br>(0.172)|-0.161<br>(0.175)|
|Sum Basin<br>(control)|0.036<br>(0.051)||0.067<br>(0.051)|0.023<br>(0.053)|
|Ratio Basin<br>(importance)|-0.004<br>(0.003)||-0.004<br>(0.003)|-0.006*<br>(0.003)|
|Sum Basin<br>(importance)|-1.932<br>(6.460)||-1.925<br>(7.133)|-0.0291<br>(7.468)|
|Alliance||-1.178<br>(3.341)|-2.325<br>(3.824)|-0.708<br>(3.776)|
|Democracy||-1.321***<br>(0.362)|-1.336***<br>(0.380)|-1.338***<br>(0.365)|
|GDP Ratio||-0.014**<br>(0.006)|-0.021***<br>(0.007)|-0.013**<br>(0.006)|
|GDP Cap Ratio||-0.541<br>(0.803)|-0.465<br>(0.797)|-0.350<br>(0.759)|
|Constant|-30.672***<br>(14.564)|-30.214**<br>(13.659)|16.892***<br>(4.410)|-30.928**<br>(15.089)|
|Observations|320|314|314|314|
|R-squared|0.069|0.110|0.0680|0.1189|

Notes: Robust standard errors are in parenthesis, where *** is significance at the 1% level, ** significance at the 5% level, and * significance at the 10% level.

15
If the intensity of climate change events, such as extreme temperatures, precipitation, and floods, declined from 2016 to 2023, we might expect a corresponding reduction in public demand for climate actions and willingness to support such policies. That support would also change if climatic events affected people differently in the periods covered by the two survey rounds.

To test the personal experience hypothesis, we use administrative data on several environmental risks at the primary sampling unit (PSU) level.<sup>11</sup> The top panel of Table 5 shows changes in the means of these risk factors between 2016 and 2023. The levels of precipitation remained relatively constant over that period. The temperature, however, increased across all three dimensions. The level of air pollution and flood severity index were higher in 2016 than in 2023. The bottom panel of <u>Table 5</u> shows the coefficients on the risk factors when we add them to the specification in <u>Table 4. None of the factors was significantly different from zero in both years. Only the flood</u> severity index significantly but negatively affects the willingness to pay in 2016. Our results fail to confirm the personal experience hypothesis, in terms of both differences in levels and changes in the effects of risk factors on willingness to pay for climate change actions.

# **_Extended hypothesis tests_**

Does the Oaxaca-Blinder decomposition of the changes in willingness to pay higher taxes to combat climate change confirm our hypothesis results? <u>Table 6</u> shows the decomposition in which we extended our baseline specification in Table 2 by adding indexes of right-wing populism and economic conservatism and the set of the PSU-level variables representing the environmental risks. The results are consistent with our findings. The right-wing populism index is the only factor that significantly affects the difference in willingness to pay between the third and fourth rounds of the LiTS (Specification 2). The effect of this variable comes from the changes in “endowment,” or, in other words, from the increased share of respondents who report embracing a right-wing populist ideology. Neither the economic conservatism index nor the environmental risk indicators have a meaningful effect on that difference (Specifications 3 and 4).

> 11 We have administrative data for all countries in our sample except nine countries in the Middle East, North Africa, and Western Europe.

14
**Table 6:  Village radio access and total village bed nets**

|Dependent<br>variable:|Total bed<br>nets|Free Gov‘t<br>bed nets|Paid Gov‘t<br>bed nets|Total pre-<br>treated bed<br>nets|Free Gov‘t<br>pre-treated|Paid Gov‘t<br>pre-treated|
|---|---|---|---|---|---|---|
|**Non-commercial**|**0.924**|**-0.766**|**1.690**|**1.227**|**-0.465**|**1.692**|
|**private stations**|**(0.36)**|**(0.28)**|**(0.07)**|**(0.21)**|**(0.55)**|**(0.02)**|
|Public stations|-3.843|-2.524|-1.319|-2.052|-1.900|-0.152|
||(0.10)|(0.13)|(0.50)|(0.29)|(0.24)|(0.92)|
|Commercial private|2.046|0.984|1.061|0.298|0.574|-0.275|
|stations|(0.04)|(0.28)|(0.31)|(0.81)|(0.63)|(0.73)|
|Religious stations|0.0890|0.486|-0.397|-0.128|0.488|-0.616|
||(0.96)|(0.78)|(0.82)|(0.95)|(0.79)|(0.68)|
|Distance to closest urban|-0.0433|0.00834|-0.0517|-0.0233|0.00149|-0.0248|
|center|(0.24)|(0.78)|(0.05)|(0.55)|(0.96)|(0.26)|
|Distance to closest|0.139|0.117|0.0217|0.137|0.124|0.0132|
|bus/train stop|(0.00)|(0.00)|(0.49)|(0.00)|(0.00)|(0.60)|
|# functional private|-4.923|0.148|-5.071|-3.688|-1.159|-2.529|
|schools|(0.05)|(0.95)|(0.06)|(0.12)|(0.69)|(0.17)|
|Literacy center in village|1.497|3.241|-1.745|3.293|3.664|-0.371|
||(0.46)|(0.03)|(0.23)|(0.09)|(0.02)|(0.79)|
|Health or maternity|-0.406|-1.023|0.617|-1.291|-1.337|0.0456|
|center in village|(0.83)|(0.49)|(0.65)|(0.51)|(0.40)|(0.97)|
|Village Chief with|5.291|3.618|1.673|4.712|3.389|1.323|
|Secondary Education|(0.02)|(0.01)|(0.37)|(0.02)|(0.02)|(0.41)|
|Average > 45K CFA|-9.606|-5.476|-4.129|-3.114|-4.505|1.391|
|income|(0.05)|(0.37)|(0.43)|(0.71)|(0.59)|(0.76)|
|Average brick walls|6.638|7.564|-0.926|12.52|10.67|1.853|
||(0.27)|(0.14)|(0.90)|(0.03)|(0.04)|(0.74)|
|Average with more than 5|30.71|11.06|19.65|7.363|2.099|5.264|
|rooms|(0.00)|(0.15)|(0.00)|(0.58)|(0.86)|(0.32)|
|Average with own TV|24.40|8.357|16.04|10.16|2.004|8.156|
||(0.00)|(0.29)|(0.10)|(0.25)|(0.78)|(0.30)|
|Average number of HH|1.689|1.547|0.143|2.880|2.546|0.334|
|<br>adults|(0.26)|(0.25)|(0.91)|(0.02)|(0.10)|(0.74)|
|Average number of HH|2.539|1.384|1.155|2.708|1.344|1.364|
|<br>children|(0.01)|(0.08)|(0.18)|(0.00)|(0.11)|(0.08)|
|Observations|199|199|199|199|199|199|
|Adj._R_<sup>_2_</sup>|0.58|0.46|0.36|0.39|0.33|0.37|

Note: Observations are village-level averages. Estimates based on ordinary least squares controlling for commune fixed effects. Robust _p_ -values clustered on communes in parentheses. Coefficient estimates of the following controls are insignificant and not reported: village population; presence of a paved road in village; no. of potable water sources built in 27
**Table 2: Cross‐Sectional Associations of Business Practices with Sales and Profits**

||Log Sales|Log Sales|Log Sales|Log Sales|Log Profits|Log Profits|Log Profits|Log Profits|
|---|---|---|---|---|---|---|---|---|
|Business Practices Score|1.211***|0.965***|0.833***|0.795***|1.010***|0.830***|0.697***|0.655***|
||(0.047)|(0.046)|(0.048)|(0.048)|(0.045)|(0.045)|(0.047)|(0.046)|
|Log(Paid Workers)|0.653***|0.475***|0.464***||0.496***|0.365***|0.357***||
||(0.026)|(0.026)|(0.027)||(0.026)|(0.026)|(0.026)||
|Log(Owners Hours)|0.351***|0.313***|0.302***||0.260***|0.232***|0.221***||
||(0.020)|(0.019)|(0.019)||(0.019)|(0.018)|(0.018)||
|Log(Capital Stock)||0.094***|0.085***|||0.072***|0.065***||
|||(0.007)|(0.006)|||(0.006)|(0.006)||
|Log(Inventories)||0.200***|0.198***|||0.148***|0.146***||
|||(0.010)|(0.010)|||(0.009)|(0.009)||
|Owner is male|||0.096**||||0.130***||
||||(0.045)||||(0.045)||
|Age of owner|||‐0.005***<br>(0.001)||||‐0.006***<br>(0.001)||
|Years of education of owner|||0.012***||||0.011***||
||||(0.003)||||(0.003)||
|Digitspan recall|||0.036***||||0.034***||
||||(0.007)||||(0.007)||
|Firm age (years)|||0.008***<br>(0.001)||||0.007***<br>(0.001)||
|Raven test score|||0.019***<br>(0.004)||||0.013***<br>(0.004)||
|Control coefficients vary by country|No|No|No|Yes|No|No|No|Yes|
|Sample Size|18146|18146|18146|18146|18135|18135|18135|18135|

Notes: Robust standard errors in parenthese ** p<0.05 *** p<0.01" Regressions also include dummies for zero or missing values of each control. All regressions include country dummies and country*industry sector dummies.

Columns 4 and 8 allow the coefficients on all control variables to vary by country. 32
accumulate savings and make investments.<sup>2</sup> There is also evidence that financial inclusion supports many of the Sustainable Development Goals (Klapper, El-Zogbhi and Hess, 2016). Well-functioning financial systems serve a vital purpose, offering savings, credit, payment and risk management products to people with a wide range of needs. Inclusive financial systems – where financial services are affordable and readily accessible to everyone who wants them – are especially likely to benefit poor adults and other disadvantaged groups. Without inclusive financial systems, poor people must rely on their own limited savings to invest in their education or open a business – and small enterprises must rely on their limited earnings to pursue growth opportunities. Financial systems that exclude poor adults, women, and other underserved populations can contribute to persistent income inequality and slower economic growth.<sup>3</sup> Informed by a fast-growing body of knowledge and experience, policy makers and regulators are beginning to make universal financial inclusion a priority in financial sector development. A survey of bank regulators in 143 countries found that 67 percent have a mandate to promote financial inclusion (World Bank, 2014a). More than 50 countries have set formal targets and ambitious goals for financial inclusion in the last few years.<sup>4</sup> International organizations such as the G20 and World Bank have put financial inclusion at the center of their development strategies.<sup>5</sup> While financial inclusion begins with having an account, the benefits only come from active use of accounts to make payments, save money and manage risk. Moving cash payments into digital channels has many potential advantages for both senders and receivers, including higher account ownership and usage.<sup>6</sup> For example, digitizing transfers of government wages or social benefits can increase the number of adults with an account. It can also lower the cost of

> 2 See, for example, Aportela (1999); Ashraf, Karlan and Yin (2010); Beck, Demirguc-Kunt, and Martinex Peria (2007); Bruhn and Love (2014); Burgess and Pande (2005); Dupas and Robinson (2013a, 2013b); Prina (2012); and Ruiz (2013).

> 3 See, for example, King and Levine (1993); Beck, Demirguc-Kunt, and Levine (2007); Beck, Levine, and Loayza (2000); Demirguc-Kunt and Levine (2009); Klapper, Laeven, and Rajan (2006); and World Bank (2008).

> 4 For example, the “Maya Declaration Commitments" made by the Alliance for Financial Inclusion, http://www.afiglobal.org/maya-declaration-commitments.

> 5 For example, the G20 Global Partnership for Financial Inclusion (http://www.gpfi.org) and the World Bank Universal Financial Access 2020 goal (http://ufa.worldbank.org).

> 6 See World Bank (2014b) for a more detailed discussion of the benefits and challenges of digitizing payments.

3
is believed to be subject to constant returns to scale — predominates in the rural areas, the di ff erences in the returns for these well-o ff households located in rural areas would re fl ect the di ff erences in the productive public capitals as well as access to markets across regions. Migration costs a ff ect the mobility of the households belonging to the lower to middle part of the distribution. As proximity to the destination facilitates short-term migrations and formation of the migration networks, the returns di ff erences especially for the lower quantiles are likely to be smaller in magnitude in regions which are close by. In this paper, we examine the di ff erences in the rates of return to observed household attributes over the entire welfare distribution and across regions with di ff erent levels of infrastructure development, market access and proximity.

The empirical evidence, based on the quantile decomposition technique pioneered by Machado and Mata (2005) and on two rounds of household level data from Bangladesh, uncovers substantial di ff erences in the returns between an integrated (I) region contiguous to the growth poles (Dhaka and Chittagong metropolitan areas) and a less integrated (LI) region which is cut-o ff from the growth poles by two main Asian rivers (Ganges and Brahmaputra). The returns e ff ect measuring di ff erences in the rates of return to observed household attributes across rural areas in the I and LI regions is quite substantial for the upper quantiles of the welfare distribution. This result suggests an important role of the di ff erences in public capitals and market access in sustaining the di ff erential returns across regions. Comparison of the returns e ff ects across di ff erent areas (rural vs overall I-LI gap) also indicates the sorting of households with unobserved but better attributes in the I region and in the urban areas within the I and LI regions respectively. The signi fi cant returns e ff ects for the I-LI gap for the households at the lower end of distribution is consistent with the view that the poor face higher costs of migration. However, there is virtually no di ff erence in the returns to observed household attributes across urban and rural areas within each region at the lower half of the distribution. This result along with the substantial returns e ff ect in the case of the I-LI gaps imply that while within each region, migration seems to equalize the returns for the households belonging to the lower half of the distribution, the physical barriers created by the rivers do impose signi fi cant migration costs by hindering short-term migrations and 22
One, Two, and Three 8

|With SGRY control<br>1.426_∗∗∗_<br>1.428_∗∗∗_<br>[0.320]<br>[0.338]<br>0.349_∗∗∗_<br>0.313_∗∗_<br>[0.125]<br>[0.146]<br>156,133<br>156,133<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>With SGRY control<br>-3.129_∗∗∗_<br>-3.040_∗∗∗_<br>[1.042]<br>[1.110]<br>-0.874<br>-0.705<br>[1.017]<br>[1.116]<br>156,133<br>156,133<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>RY from 2004 to 2007. Program expenditure<br>ssions include district and year-quarter fxed<br>July 2004 to June 2005 and from July 2007<br>dual spends in public works. The dependent<br>ll other controls are the same as in table 2 in<br>e clustered at the district level. SGRY: Sam-|
|---|
|**Panel A. Dependent Variable: Public Employment**<br>Without SGRY control<br>1.174_∗∗∗_<br>1.161_∗∗∗_<br>1.157_∗∗∗_<br>1.442_∗∗∗_<br>[0.316]<br>[0.315]<br>[0.329]<br>[0.321]<br>0.231_∗∗_<br>0.222_∗_<br>0.183<br>0.360_∗∗∗_<br>[0.115]<br>[0.115]<br>[0.135]<br>[0.125]<br>156,133<br>156,133<br>156,133<br>156,133<br>No<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>No<br>No<br>No<br>No<br>Yes<br>**Panel B. Dependent Variable: Private Employment**<br>Without SGRY control<br>-2.164_∗∗_<br>-2.237_∗∗_<br>-2.137_∗∗_<br>-3.104_∗∗∗_<br>[0.982]<br>[0.956]<br>[1.011]<br>[1.070]<br>-0.321<br>-0.445<br>-0.270<br>-0.773<br>[1.076]<br>[0.960]<br>[1.062]<br>[1.131]<br>156,133<br>156,133<br>156,133<br>156,133<br>No<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>No<br>No<br>No<br>No<br>Yes<br>ed on NSS 2004 – 2008 (Rounds 61 and 64) and program expenditure data for SG<br>ural Development, Government of India.<br>e order10_−_2. Each column presents results from a separate regression. All regre<br>es are in logs. The sample is composed of all men aged 18 to 60 interviewed from<br>variable in panel A is the percentage of days in the past seven days that an indivi<br>entage of days in the past seven days that an individual spends in private works. A<br>s are computed using sampling weights. The standard errors in square brackets ar<br>na. NREGA: National Rural Employment Guarantee Act.<br>_<_0_._01|
|_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_Source_: Author’s analysis bas<br>data is from the Ministry of R<br>_Notes_: All estimates are of th<br>effects. All dependent variabl<br>to June 2008. The dependent<br>variable in panel B is the perc<br>the main article. All estimate<br>poorna Grameen Rozgar Yoja<br>_∗p <_0_._10_,∗∗p <_0_._05_,∗∗∗p _|
more to household chores and featuring men proudly showing their domestic contributions. More broadly, as Krafft, Selwaness, and Sieverding (2022) argue with respect to the MENA region as a whole, “the pandemic has underscored gender inequality in care work, but also offers an opportunity to rethink care responsibilities and ultimately recognize, reduce, and redistribute care work” (3).

In prior work (Barnett 2022, 2023), I collected the first evidence available on the relationship between personal attitudes and perceived norms on a range of gender issues in Morocco. One striking, unexpected finding was a large gap between what Moroccans say they value and what they believe others value when it comes to men’s contributions to unpaid domestic and care work. In a nationally representative survey, 75.6% of Moroccans agreed or strongly agreed that men are just as responsible for taking care of the household and children as women are, but only 54.7% believed most of their neighbors similarly agreed. To the extent that behavior aligns more with perceived norms than personal attitudes, such misperceptions may represent an unnecessary constraint on more equal household distributions of labor and ultimately on female labor force participation.

Previous research in OECD contexts has highlighted gender gaps in perceptions of how others value unpaid labor and the extent to which they see them as fair (see e.g. Cerrato and Cifre (2018)). We know little, however, about individuals’ perceptions of others’ attitudes toward and time spent on unpaid labor and care work in the MENA region, with some notable recent exceptions in studies of Egypt (Zeitoun et al. 2023) and Jordan (Gauri, Rahman, and Sen 2019). At the same time, recent research has shown that people in the MENA region and South Asia, especially men, generally tend to overestimate others’ embrace of patriarchal attitudes (Bursztyn, González, and YanagizawaDrott 2020; Gulzar, Sonnet, and Khan 2020; Minardi, Akmal, Crawfurd, and Hares 2021). One policy implication of these findings is that providing more accurate information and more positive narratives about existing changes in attitudes might correct misperceived norms, in turn altering how individuals behave (Bursztyn, González, and YanagizawaDrott 2020; Gauri, Rahman, and Sen 2019), although the long-term effects of information interventions remain uncertain (Prentice and Paluck 2020; Tankard and Paluck 2017).

5
# **Table 5:  Poverty Transition Matrix, 1993 to 2000, from IFLS Panel Data (raw numbers of individuals, not weighted)**

||||Ru|Po<br>ral|or<br>Urb|<br>an|2000<br>Ru|Non<br>ral|Poor<br>Ur|ban|
|---|---|---|---|---|---|---|---|---|---|---|
|||#<br>individuals|Agriculture|Non<br>Agriculture|Agriculture|Non<br>Agriculture|Agriculture|Non<br>Agriculture|Agriculture|Non<br>Agriculture|
|Rural|Agriculture<br>Non|713|276|41|4|3|307|68|8|6|
|oor|Agriculture|375|69|68|7|10|51|151|1|18|
|P<br>|Agriculture|69|2|0|25|9|3|4|14|12|
|Urban|Non||||||||||
|3|Agriculture|296|0|2|16|132|0|4|8|134|
|9|||||||||||
|Rural<br>19<br>r|Agriculture|1,053|162|38|3|0|648|174|19|9|
|n Poo|Non<br>Agriculture|1,389|61|117|0|23|238|864|17|69|
|No<br>|Agriculture|112|3|0|13|7|23|6|33|27|
|Urban|Non<br>Agriculture|1,301|4|4|9|148|16|69|42|1009|
||Total|5,308|577|270|77|332|1,286|1,340|142|1,284|

Note: Data are numbers of individuals in the category in 1993 who end up in the 2000 category. The individuals are those who were aged 15-55 in 1993 and were working.

23
**Figure 13: Trust in the Military Police in Northeastern States (positive responses), 2012**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>percentage<br><!-- End of picture text --> _Source_ : AmericasBarometer, LAPOP, 2012. _Note_ : Blue line represents regional average.

**Figure 14: Trust in the Military Police in Southeastern States (positive responses), 2012**

<!-- Start of picture text --> 60<br> 50<br> 40<br> 30<br> 20<br> 10<br> -<br>Espírito Minas Rio de São Paulo<br>Santo Gerais Janeiro<br>percentage<br><!-- End of picture text --> _Source_ : AmericasBarometer, LAPOP, 2012.

Likewise, over half of the interviewees reported that the quality of education has improved in the states of Ceará, Pernambuco, and Minas Gerais (Figure 15 and Figure 16). These states have averages that are above those of their respective region. However, other states that were not implementing performance management at the time that the survey was conducted, such as Bahia, Piauí, Maranhão, and Espírito Santo, also scored high marks.

**Figure 15: Perceptions of Quality of Education in Northeastern States (percentage), 2012**

<!-- Start of picture text --> Sergipe<br>Rio Grande do Norte<br>Piaui<br>Pernambuco<br>Paraíba<br>Maranhão<br>Ceará<br>Bahía<br>Alagoas<br>0 20 40 60 80 100<br>percentage<br>Improved No change Worsened<br><!-- End of picture text --> _Source_ : System of Social Perception Indicators (SIPS), IPEA, 2012.

**Figure 16: Perceptions of Quality of Education in Southeastern States (percentage), 2010**

<!-- Start of picture text --> São Paulo<br>Rio de Janeiro<br>Minas Gerais<br>Espírito Santo<br>0 20 40 60 80 100<br>percentage<br>Improved No change Worsened<br><!-- End of picture text --> _Source_ : System of Social Perception Indicators (SIPS), IPEA, 2012.

The state of Pernambuco has the highest percentage of respondents reporting good quality in health services. Bahia, which has used private-public partnerships in the health sector, also scores high, along with Alagoas. Nevertheless, health sector results are less differentiated in the Southeast region.

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