rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: _State level headcount ratio on individual level_

|||O|ur estimat|or|EL|L estimat|or|Surve|y est.|
|---|---|---|---|---|---|---|---|---|---|
||True value|Bias|RMSE|Cov.|Bias|RMSE|Cov.|Bias|RMSE|
|W0(5.5)|0.1259|0.0054|0.0126|0.9600|0.1249|0.1270|0.0000|-0.0026|0.0179|

Another approach would impute the individual poverty measure based on its relationship with the household poverty estimators. This relationship may be hypothesized on the basis of prior knowledge or estimated from the data set at hand. Though, if the relationship between household sizes and income differs between municipalities, the latter two methods do not yield unbiased state-level poverty estimators in general.

In our application, we follow the second approach, i.e. we run the regression on the household level and sample residuals together with household sizes. The results indicate similar conclusions as the analyses at the household-level (Table 5).

# **6 Conclusions**

In this paper we presented a new method to generate poverty maps.<sup>15</sup> While ours is a valid approach to combine simultaneously collected census and survey data, it also allows analysts to obtain up-to-date poverty maps when only a dated census and a more recent survey are available. In contrast to existing approaches, it has low data requirements and weak assumptions. Simulation studies showed an overall good performance. If the distribution of explanatory variables changes over time, our new estimator is superior to the most frequently used method for contemporaneous census and survey collection.

However, our approach is not immune to issues typically encountered in small area estimation techniques that combine census and survey data. In particular, variable selection and adequate modeling of apparent heteroscedasticity and differences in skewness in the residuals can be challenging. Besides, the key assumption, namely that aggregate household characteristics from the old census relate to consumption the same way in clusters covered by the new survey as in clusters not covered by the new survey, may not hold for the specific welfare estimation exercise at hand. For example, the migration pattern between census and survey collection may vary between clusters and may be correlated with the welfare status which is typically not captured by the model.

Violations of the assumptions on the error term may be partly solved by allowing for more distributional flexibility in the response variable or the error term. Rojas-Perilla et al. (2017) and the references therein provide various transformations of the response variable to achieve the validity of the assumption of identically and normally distributed error terms. A more comprehensive approach would be the application of

> 15Software code in Stata and R for the implementation of our proposed method are available on request from the authors. The recently developed Stata package SAE (Nguyen et al., 2018) can be adapted accordingly.

16
**<u>Table 5: Medium-term Effects on Number of Registered Transfers, Mortgages, and Built-up Area</u>**

|<br>|<br>**No. of regi**|<br>**stered sales**|<br>**No. of register**|<br>**ed mortgages**|**Built area**|
|---|---|---|---|---|---|
||**Total**|♀ C**o-owner**|**Total**|♀ C**o-owner**|**Registered**|
|LCSystP,θ3<br>|0.0049***|0.0041***|0.0012***|0.0015***|0.0051***|
||(0.001)|(0.001)|(0.000)|(0.000)|(0.000)|
|LCSpP,θ1|0.0292***|0.0130|0.0103***|0.0075|0.0041***|
|<br>|(0.004)|(0.012)|(0.003)|(0.005)|(0.000)|
|<br>LCSpP #<br><br>|0.0007|0.0178*|0.0044***|0.0078**|0.0005**|
|post-2010,θ2<br>|(0.002)|(0.009)|(0.001)|(0.004)|(0.000)|
|θ3−θ2|0.0042***|–0.0266***|–0.0032***|-0.0138***|0.0047***|
||(0.0018)|(0.0044)|(0.0013)|(0.0024)|(0.0002)|
|<br>**Elasticities**||||||
|LCSystP<br>|0.2869***|0.2943***|0.1457***|0.2551***|0.358***|
||(0.0423)|(0.0417)|(0.0508)|(0.0629)|(0.0067)|
|LCSpP,θ1<br>|0.7945***|0.205|0.5214***|0.2773|0.1439***|
||(0.1170)|(0.1835)|(0.1331)|(0.1872)|(0.0169)|
|LCSpP,(θ1+ θ2)|0.8134***|0.4862***|0.7417***|0.5631***|0.1606***|
||(0.0930)|(0.0679)|(0.1009)|(0.0867)|(0.0131)|
|<br>Mean dep. var.|0.0383|0.0118|0.0299|0.0068|0.0666|
|SD dep. Var|0.2208|0.1169|0.1826|0.0838|0.0925|

Note: All regressions are at for 250m x 250m blocks for 2000–19. The dependent variable is the number of registered sales or mortgages, overall and with female co-owner, or registered area built at block level. LCSystP denotes the lagged cumulative number of parcels systematically registered and LCSRP those sporadically registered, with LCSpP instrumented by its two-period lagged value as described in the text. Population density, share of built-up area of the block, block and year fixed effects, and a constant are included throughout; the coefficients are not reported. Standard errors are in parentheses. Mean and standard deviation for the dependent variable are for the pre-program period. The number of observations is 30,280 (1,514 blocks annually for 20 years) throughout. Mean and standard deviation for the dependent variable are for the pre-program period. Elasticities are calculated at relevant mean values, i.e., for sporadic registration post-2010 means and for systematic post-2011 means restricted to blocks covered by systematic registration are considered. Standard errors are in parentheses. * p<0.10, ** p<0.05, *** p<0.010.

24
sample are, by construction, the same for all migrant households in a given city. Both measures provide estimates of the average school fees that each migrant household is (exogenously) faced with when migrating to the city.

Yet, these school fee measures may still be endogenous at the city level if unobserved citylevel events occur that affect both a city’s school fees and its migrant workers’ decisions regarding child migration. As discussed earlier in the presentation of the empirical model, we address this issue by instrumenting school fees with the one-year lag of unexpected shocks to the city government’s education spending. We gathered the historical city-level education spending as a share of local public spending in the 15 cities (metropolitan areas) covered by RUMiC 2008 for the period 2002-2007 from the China City Statistical Yearbooks. For each city, using different detrending techniques (i.e., Hodrick-Prescott (HP) filter and linear filter), we decomposed the time series records into a trend component and a cyclical component.

We constructed a measure for the trend in city education services with the growth rate of the student-teacher ratio in 2007 based on the number of students and teachers in metropolitan areas in 2006 and in 2007 using the China City Statistical Yearbooks. As a proxy for migration distance between the original and the destination areas, we constructed a dummy variable that equals 1 if the migrant household is from a rural area within the same province and equals 0 if the migrant household is from another province. Since city-level Consumer Price Index data are not available for China, we proxy for living costs with city-level housing prices in 2007 from the China Urban Life and Price Yearbook 2008.

Table 1 shows the summary statistics for household and city characteristics. The average age of household heads in our sample is 36.7, with around one-fourth (26 percent) of households being female-headed. About half of all household heads are primary school graduates and less than one-third (29 percent) of them hold a junior high school diploma or higher. 38 percent of migrant households bring their children with them to the city, and a 22
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representative of each economy’s formal private sector, covering establishments with five or more employees, thus our findings are less narrow than many previous analyses, which have tended to focus only on larger manufacturing firms.

The remainder of this paper proceeds as follows: section 2 outlines the methodology used to measure management practices and to analyze the relationship between management and performance; section 3 presents the data and descriptive statistics; section 4 presents the empirical findings; and, finally, section 5 discusses the results and provides some brief concluding remarks.

# 2. Methodology

## 2.1 Measuring and scoring management practices

Work to systematically quantify management practices has been pioneered by Bloom and Van Reenen (2007, 2010). The United States Census Bureau adapted the survey questions of Bloom and Van Reenen and in 2010 conducted a wide‐scale survey of management practices via the Management and Organizational Practices Survey (MOPS). Based on the MOPS, and in collaboration with Bloom and Van Reenen, these variables have been modified and implemented as part of the standard Enterprise Survey that the World Bank implements throughout the world.

The Enterprise Survey asks about five specific components of management practices, these are as follows: (1) action taken when a problem arose in production or service provision; (2) the number of production or service provision performance indicators monitored; (3) the level of ease or difficulty to achieve production or service provision targets; (4) personnel's knowledge of production or service provision targets; and (5) the basis of managers' performance bonuses. For each component, establishments receive a score between zero and one, where higher scores indicate more structured practices. Take, for example, the component of management practices that examines how establishments respond to problems in production or service provision (action when a problem in production or service provision arose). The least structured practice would be “No action was taken”, while the most structured would be “We fixed it and took action to make sure it did not happen again”. The individual components can be aggregated into a single average management score, where each establishment’s score is an unweighted average of the individual components. A detailed description of how each component was asked and scored can be found in Appendix 1.

6
**Table 6b. Decomposition of concentration index for maternal health services, non-linear**

||**models**|||||
|---|---|---|---|---|---|
||**Measles**<br>**Vaccination**|**Full**<br>**Immuni-**<br>**zation**|**Contra-**<br>**ception**|**ANC4+**|**SBA**|
|**Standardizing (demographic)**<br>**variables**||||||
|Respondent Age (mother’s age)|0.000|0.000|-0.003|0.001|0.001|
|**Subtotal**|**0.000**|0.000|**-0.003**|**0.001**|**0.001**|
|**Control variables**||||||
|Quintiles of wealth index|0.057|0.175|0.061|0.215|0.268|
|Education|0.036|0.036|0.013|0.060|0.085|
|Number of household members|0.001|0.001|0.000|-0.001|-<br>0.002|
|Number of children 5 and under|0.002|0.002|0.014|0.004|0.018|
|Area of residence (Urban=1, Rural=0)|0.013|-0.004|0.041|0.069|0.072|
|Region|0.002|0.017|-0.002|0.041|0.027|
|**Subtotal**|**0.106**|**0.226**|**0.127**|**0.387**|**0.468**|
|Residual: regression error|-0.010|-0.003|-0.004|-0.007|0.057|
|Residual: missing data|0.000|0.000|0.000|0.000|0.000|
|Inequality (total)|0.096|0.223|0.120|0.382|0.526|
|**Inequity/Unjustified inequality**|**0.096**|**0.223**|**0.123**|**0.380**|**0.525**|

Note: ADEPT software (health utilization module) is used to produce the results. The decompositions are based on DHS 2011 data for Measles and Full Immunization and mini-DHS 2014 data for contraception, ANC4+ and SBA.

# **4. Discussion and conclusion**

Ethiopia is making significant progress in MNCH. The trends over the period covered by DHS 2000-2014 period show considerable declines in child undernutrition and mortality levels, as well as increases in health service coverage, such as immunization, contraceptive use, antenatal care visits and assistance from a skilled birth attendant. Although the current levels of services are still low and the health risks are still high compared to similar countries in Africa,<sup>15</sup> and hence much more needs to be done to

> 15 Child undernutrition and mortality levels are among the highest in sub-Saharan Africa. Health services coverage is among the lowest.  For example, in 2011, full immunization

33
a large sample of countries as shown by Figure 5.3. Political and civil liberty indices are from the _Comparative Survey of Freedom_ that Freedom House has provided on an annual basis since 1973.12 The _Survey_ rates each country on a seven-point scale for both political rights and civil liberties (1 representing the most free and 7 the least free). So, it can be argued that countries more open to international capital flows are also open in offering a high degree of political and civil liberty to their citizens. Much current debate probes how best to achieve this "triangle," and balance the benefits with the risks inherent in its pursuit.

**Figure 5.3: Correlations of capital openness with political rights and civil liberties**

<!-- Start of picture text --> Openness to capital account<br>transactions<br>r=0.22 r=0.42<br>Political Civil<br>rights liberties<br>ri=0.91<br><!-- End of picture text --> Note: The estimated correlation coefficients are statistically significant (based on the z-test), and are robust to alternative quantitative measures of capital account openness.

Despite some risks, the benefits of open capital accounts outweigh their costs, for both individual countries and the global economy as a whole. That is generally agreed. Major benefits for developing countries include access to a broader menu of investment sources, options, and instruments. They also include enhanced efficiency of domestic financial institutions, and the discipline of capital markets in conducting domestic macroeconomic policy. And by easing financing constraints, the greater availability of 12 The _Survey_ assesses a country's freedom by examining its record in these two areas: A country grants its citizens **political rights** when it permits them to form political parties that represent a significant range of voter choice and whose leaders can openly compete for and be elected to positions of power in government. A country upholds its citizens' civil **liberties** when it respects and protects their religious, ethnic, economic, linguistic, and other rights, including gender and family rights, personal freedoms, and freedoms of the press, belief, and association. The _Survey_ divides the world into three broad categories: "Free" (countries whose ratings average 1-3); "Partly Free" (countries whose ratings average 3-5.5); and "Not Free" (countries whose ratings average 5.5-7). For more detail see Karatnycky, Adrian. (1999).

14
Inflation in 2019/20 is predicted to be higher than the nominal GDP per capita growth, which makes the population worse off in real terms. The national poverty rate, measured at the 5.5 USD 2011 PPP daily poverty line used by the World Bank for upper middle-income countries, is forecasted to increase slightly from 10.7 percent in 2018/19 to 11.2 percent in 2019/20.

For simplicity we assumed that the GDP per capita growth rate has a one-to-one relationship with growth in household expenditure and is also distribution neutral. The main goal of the current analysis is not to get an accurate poverty rate, but rather to make sure we are using expenditures in the current prices. As such, the 2019/20 estimate is not a precise forecast but rather acts just as a baseline for analyzing the impact of the reforms in present-day Islamic Republic of Iran.

Figure 1. Headcount poverty rates in 2018/19 and forecasted poverty rate in 2019/20 using 5.5 USD 2011 PPP daily <u>poverty line</u> <!-- Start of picture text --> 12.0% 11.2%<br>10.7%<br>10.0%<br>8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>2018/19 Forecasted poverty,<br>2019/20<br>headcount poverty<br><!-- End of picture text --> Source: Authors’ calculation using HEIS 2018/19.

Note: Poverty is calculated using a spatially adjusted welfare aggregate to account for price differences across different regions and rural/urban areas following methodology described in Atamanov et al. 2016. The resulting poverty rate is therefore slightly different from the one reported in PovcalNet.

# 2.2 Reform scenario for simulation

On November 2019, NIOPDC announced a 50 percent price increase in gasoline prices to IRR 15,000 per liter below re-introduced rationed amounts (e.g. 60 liter/month for private cars and 25 liters for motorcycles) and a 200 percent increase to IRR 30,000 per liter for consumption above this. In this note we simulate the impact on poverty and inequality after this change in gasoline prices. For those who have both cars and motorcycles, we assumed the rationed amount to be 85 liters per month.

Initial and final prices are shown in Table 2. Despite the proposed increase, gasoline prices will remain much lower than our estimated cost recovery price of IRR 66,301 per liter. The cost recovery price for gasoline is estimated as follows. We used U.S. regular all formulations retail gasoline price from November 2019 reported by the U.S. Energy Information Administration. We have subtracted from this price federal tax and an average of total state taxes taken from the same website. The resulting cost recovery price is about USD 56 cents per liter. Applying the average November market exchange rate of IRR 118,530 per USD gives us a cost recovery price about IRR 66,301 per liter (Table 3).

4
2 have different success rates depending on household composition, and indicates that special measures may be needed for female-headed households.

The purpose of this study is to analyze the characteristics and behavior of femaleheaded households in urban Brazil and to identify some of the consequences of the growth of these households for child welfare. We do this by identifying the types of female-headed households that are found in Brazil, which ones are more likely than others to be poor, why they are more likely to be poor, and the consequences of poverty and female headship for children in these households. One problem with analyzing these questions in other developing countries has been the representativeness of the data (ICRW, 1988). Headship is often loosely defined at the interview stage, and therefore the meaning of this variable in survey data is sometimes ambiguous, and can represent interviewer bias rather than an actual headship situation from an economic point of view. For this reason, we begin by analyzing whether the economic role of women in households designated as "female-headed" in our data set is actually different from the woman's role in those designated "male-headed." Finding that the gender of the reported head is indeed usually the gender of the person most responsible economically for the welfare of the household (in the case of both male and female heads), we then ask what types of female-headed households are found in Brazil, and why these households are more likely to be in poverty. Do female-headed households have a lower number of earners (or a lower participation rate among the potential earners), earners with less income-earning power (human capital) or more mouths to feed? Are the answers to these questions fundamentally different when discussing male-headed households in poverty?

We find, first, that generalizations about female-headed households are difficult to make in Brazil, as this group is quite heterogeneous, including rich and poor households. However, there is one common theme among Brazilian female-headed households -- those with cl-ldren (less than one-half of the total) have a much higher probability of being poor. Of those female-headed households that are poor, the main reason for this lower income is not because of a lower number of earners per capita, but because of the lower earning power of these earners. As women tend to earn less on average than do men in Brazil, a household
implies that extrapolation to the national level, using the prevalence rates from sentinel data, may be very imprecise. Indeed, even in those districts that have sentinel sites the precision of the prevalence rates cannot be estimated as sentinel survey is not population based, and thus standard errors of the prevalence estimates cannot be estimated.

The only way to overcome the many biases inherent in sentinel data is to draw a nationally (and, ideally, sub-nationally) representative sample of the population and have respondents tested for HIV. National population-based surveys represent a much wider proportion of the population than do sentinel sites, since such surveys include nonpregnant women and also men, and they usually cover a large geographical area. Malawi is one of only a few countries where, for the first time in 2004, a Demographic and Health Survey (DHS) was fielded with the aim of collecting nationally representative data on the prevalence of HIV (NSO, Malawi and ORC Macro, 2005).<sup>4</sup> In Africa, such surveys still remain scarce as they are costly to implement. Moreover, even nationally representative household surveys are not immune from bias because of possibly significant non-response (Mishra et al., 2006). For instance, according to the 2004 DHS, in Malawi 30 percent of surveyed women and 37 percent of men refused to accept HIV testing. Considering the overall rate of participation of 96 percent for women and 86 percent for men, 33 percent of women in the original sampling frame, and 46 percent of men, were not tested for HIV. Nonparticipation in an HIV test due to absenteeism or refusal to be tested creates several associated biases (NSO, Malawi and ORC Macro, 2005). In order to account for the effects of non-response, HIV prevalence estimates could potentially be corrected using statistical procedures. Following application of such adjustments to the 2004 Malawi DHS data the estimate of HIV prevalence increased from 13.3 to 14.4 percent,<sup>5</sup> (NSO, Malawi and ORC Macro, 2005).<sup>6</sup>

> 4 Results from the first eight national surveys implemented during 2001-04 in African counties are discussed in Mishra et al. (2006).

> 5 The adjusted prevalence among women and men age 15-49 was 14.7% for women and 12.5% for men. There was a substantial difference in prevalence between urban and rural areas – 18.3% vs. 11.3%, respectively. Adult HIV prevalence, extrapolated from the 2001 ANC data, was estimated at 15% (25% in urban areas and 13% in rural areas). In line with the overall global trends, the HIV _incidence_ rate in Malawi is estimated to have stabilized since late 1990s, and is estimated at about 1% of the overall population (Bello et al, 2006). HIV prevalence for women in the 2001 ANC data is reported in Table 1.

> 6 Boerma, Ghys and Walker (2003) argue that estimates derived from such surveys are likely to be lower than true population prevalence, but the magnitude of the bias varies between countries.

3