rafmacalaba/datause-encoder-data huggingface dataset:
however, that political preferences, beliefs about institutional fairness, as well as (perceived) own past and future mobility, are significantly associated with redistributive preferences in Western European countries and in the new EU member states, but less so in non-EU transition economies.

**Table 6: Assisting the poor should be among the top 2 priorities for extra government spending**

||**Pooled**|**Western**<br>**Europe **|**New EU**<br>**states**|**Non-EU**|
|---|---|---|---|---|
|_Main determinant of success in society (_Effort and hard work<br>|||||
|– baseline)|||||
|Intelligence and skills|-0.107**|0.140*|-0.163**|-0.191***|
||(0.042)|(0.083)|(0.074)|(0.062)|
|Political connections|0.067|0.011|-0.031|0.105|
||(0.053)|(0.149)|(0.087)|(0.073)|
|Breaking the law|-0.034|0.176|0.000|-0.100|
||(0.068)|(0.230)|(0.107)|(0.094)|
|Other|0.055|-0.180|0.151|0.035|
||(0.072)|(0.191)|(0.120)|(0.102)|
|_Main determinant of need in society (_Inevitable part of|||||
|modern life – baseline)|||||
|Unlucky|0.239***|0.282**|0.217*|0.222**|
||(0.065)|(0.125)|(0.111)|(0.098)|
|Laziness|-0.149***|-0.140|-0.109|-0.173**|
||(0.055)|(0.123)|(0.097)|(0.078)|
|Injustice|0.236***|0.379***|0.215**|0.187***|
||(0.047)|(0.107)|(0.085)|(0.067)|
|Other|-0.033|-0.034|-0.173|0.009|
||(0.075)|(0.158)|(0.133)|(0.107)|
|Ladder position 4 years ago|0.013|0.017|0.042*|-0.006|
||(0.013)|(0.027)|(0.022)|(0.018)|
|Ladder position now|-0.050***|-0.092**|-0.046|-0.027|
||(0.017)|(0.039)|(0.033)|(0.023)|
|Expected ladder position 4 years hence|-0.004|-0.008|-0.027|0.001|
||(0.013)|(0.031)|(0.022)|(0.017)|
|Not affected by the financial crisis|-0.031|-0.053|-0.052|-0.027|
||(0.040)|(0.079)|(0.093)|(0.052)|
|_Expressed risk preference (_Would take the risk – baseline)|||||
|Would not take the risk|-0.067*|-0.016|-0.055|-0.077|
||(0.039)|(0.088)|(0.064)|(0.057)|
|Could not answer|-0.048|0.045|-0.078|-0.038|
||(0.059)|(0.216)|(0.093)|(0.077)|
|Market economy preferred in all circumstances|<br>-0.159***|<br>-0.231***|<br>-0.124*|<br>-0.143***|
||(0.039)|(0.084)|(0.070)|(0.054)|
|Asset index|-0.109***|-0.088***|-0.145***|-0.110***|
||(0.012)|(0.021)|(0.023)|(0.018)|
|Individual and HH characteristics|Yes|Yes|Yes|Yes|
|Pseudo R-squared|0.067|0.041|0.072|0.060|
|Obs|28489|5048|8761|14680|

Notes: Estimates from logistic regressions reported. Robust standard errors, clustered at CEA level in parentheses. Dependent variable 1 if the respondent believes that extra government spending to assist the poor is among top 2 government priorities, and zero otherwise. All individual and household characteristics from the baseline model, as well as country dummies are included in all models, but not reported. Significance: * 0.10; ** 0.05; *** 0.01.

21
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estimate that by relaxing liquidity constraints the transfers which averaged roughly 8% of the average household income of recipients led to a net increase of income that was nearly twice the value of the transfer. In a different context Ardington, Case, and Hosegood (2009) show that South African households in which a member receives an old age pension – in this particular example, one that is not financed from past contributions – use a share of the funds to finance migration and job search.<sup>4</sup> Gertler, Martinez and Rubio-Codina (2012) confirm that poor households save out of SN transfers using experimental evidence, also from Mexico. This study based on the initial randomized evaluations of the conditional cash transfer program, Progresa (now called Oportunidades), found that the beneficiaries of the transfers invested 26 percent of the cash received, increasing the value of animals owned as well as participation in micro-enterprises. The propensity to save observed in this study was consistent with a wider literature on saving rates among poor households, but offers a particular insight in that it confronts the presumption that SN are mainly used for current consumption. Similarly, a recent paper based on a carefully designed survey to evaluate Ethiopia’s Productive Safety Net Programme (PSNP), Berhane et al. (2011) find a persistent increase of livestock ownership as well as other assets among program participants.

Additionally, SNs are often designed to shift incentives, thus adding an additional spur to investments that goes beyond the relaxation of a credit constraint. This is motivated, in part, by the assumption that poor households under invest relative to a social optimum. This might be the case if there are information asymmetries<sup>5</sup> or if there is a principal-agent problem in which the investor has a different incentive than the potential beneficiary as may be the case with girls who will leave the natal household (Das, Do, Ozler, 2005)<sup>6</sup> . This issue was raised in the first 4
- 25 -

negative effect on the trade balance. However, with improved confidence in the economy this need not create difficulties since higher investment will contribute eventually to future output and exports.

# 9. Summing Up the Influences on the Exte)nal Position

Table 10 sums up the changes in the import-surplus, following stabilization, in terms of domestic uses. When we compare 1987 with the 1981-1984 average, in terms of current ratios to GNP, we find that private and public consumption roughly offset each other, with the changes being in the order of 5.5t of GNP. This means that the improvement in the trade account by about 3.5Z of GNP is "explained* mainly by the cut in investment.

The foregoing comparisons which are based on ratios of various variables to GNP, measured in current prices, may be problematic when the exchange rate is considered as overvalued (as might be the case in 1987 as a result of the fixed exchange rate policy). In this case the import surplus will be downward biased. In order to check this point we carried out the same comparisons using constant price variables as computed by the Central Bureau of Statistics. The results are suzmmarized in table 11. They show essentially the same picture as the current price ratios and therefore do not require additional comment.

According to one of the earlier interpretation the reduction in public consumption generated an increase in private consumption which offset the effect of the former on the trade account. The improvement in the trade account is then attributable mainly to the tight monetary policy which brought about a reduction in the investment/GNP ratio. Therefore this improvement cannot be considered as being due to fundamental changes.
Table A.6: Descriptives _Garment sector: mid 2000s_

||Mean|St.Dev.|||Percentile|s||
|---|---|---|---|---|---|---|---|
||||10th|25th|50th|75th|90th|
|**Ethiopia**||||||||
|Number of workers|110.70|364.62|5.00|7.00|11.00|50.00|200.00|
|Sales per worker|2452.07|3069.46|389.42|856.68|1522.12|2760.31|4831.53|
|Labor costs per worker|467.84|448.25|89.32|193.26|397.57|618.44|821.36|
|Material costs per worker|1060.75|1575.87|100.21|225.47|445.79|1380.44|2576.81|
|**Kenya**||||||||
|Number of workers|152.17|313.28|10.00|13.00|80.00|150.00|350.00|
|Sales per worker|25943.92|35994.88|5000.61|8673.37|12858.71|30003.66|44123.02|
|Labor costs per worker|3857.40|4597.00|750.09|1393.70|2474.30|4552.53|8182.82|
|Material costs per worker|10016.56|15697.87|1750.21|2500.30|3475.25|9478.43|23574.30|
|**Bangladesh**||||||||
|Number of workers|606.57|714.68|110.00|250.00|400.00|650.00|1350.00|
|Sales per worker|4042.69|3403.03|871.74|1824.57|3303.43|5579.12|7398.13|
|Labor costs per worker|445.87|195.13|203.99|313.83|441.60|564.89|696.07|
|Material costs per worker|2511.09|2398.83|222.76|792.72|1824.44|3536.07|5265.37|
|**India**||||||||
|Number of workers|68.91|116.49|5.00|9.50|28.00|92.50|145.00|
|Sales per worker|21534.18|89165.36|1401.05|3056.85|8253.49|16035.27|22044.57|
|Labor costs per worker|1320.87|3522.43|185.83|509.47|1017.13|1264.13|1728.14|
|Material costs per worker|9823.08|21307.47|407.58|1557.15|4275.95|9486.60|14710.10|

Table A.7: Comparison of data from two surveys _All manufacturing, Addis Ababa_

||Sample size|Mean|Median|
|---|---|---|---|
||(1)|(2)|(3)|
|Abebe et al. survey|90|22708.90|14874.57|
|World Bank Manucturing Survey|393|22462.02|11029.03|

A.20
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27
in a shorter list, may provide a better estimate of the actual cost of diverse recommended diets.<sup>30</sup> Another limitation is that raw price data are not always publicly released. As such, the countries themselves may be in the best position to monitor the cost of nutritious diets, as their governments routinely collect and analyze food price data.

Countries can take several key actions to strengthen and expand monitoring of the cost of nutritious food. On the methodological side, policymakers and experts can use the CoRD in a similar way as NPI to track trends across time, with perhaps a more intuitive interpretation. Time trends are best seen using CPI data, which are collected on a weekly or monthly basis, but they can also be analyzed over longer spans of time using household expenditure survey data, which are collected approximately every five years. International organizations could partner with national statistics offices to conduct cross-country monitoring of these trends. To inform programs at the country level, there may be interest to track the cost of nutritious diets using these straightforward methods.<sup>31</sup>

> 30 Another limitation is that we have not compared the price of healthy foods to all types of unhealthy foods.

> 31 Ministries of food and agriculture, which often have a mandate to assure food security, may also have a stake in tracking these metrics. While not explored in this paper, these ministries often have their own market information system by which they collect food prices.

21
Table **11** Government Size Regressions: Sensitivity of Estimation to Choice of Deflators' (Dependent Variable: Government Consumption/GDP)

|Independent Variables|1987 US<br>OLS|Dollars<br>**IVb**|1985 Internat<br>OLS|ional Prices<br>IV|
|---|---|---|---|---|
|Constant|-2.539***|-2.410***|-1.431***|-1.193***|
||(0.289)|(0.295)|(0.384)|(0.424)|
|Dummy for 1974-83|0.032|0.043|0.024|0.063|
||_(0.051)_|(0.052)|(0.039)|(0.042)|
|Dummy for 1984-93|<br>0.073|<br>0.084|<br>0.071 *|0.106**|
||(0.051)|(0.071)|(0.042)|(0.044)|
|PerCapitaGDP|<br>0.151***<br>(0.036)|<br>0.129***<br>(0.036)|<br>0.082<br>_(0.051)_|0.048<br>_(0.055)_|
|Age Dependency Ratio|<br>0.472***|<br>0.318**|<br>_0.435***_|0.373**|
||(0.152)|(0.153)|(0.155)|(0.157)|
|Urbanization Ratio|-0.022|0.002|<br>-0.025|-0.011|
||(0.048)|_(0.050)_|(0.041)|(0.047)|
|Relative Price of Government|<br>-0.216***<br>(0.068)|<br>-0.225***<br>(0.080)|<br>-0.700***<br>(0.069)|-0.720***<br>(0.092)|
|Openness|<br>0.184***<br>_(0.053)_|<br>0.142**<br>_(0.056)_|<br>0.116***<br>(0.036)|0.076*<br>(0.044)|
|Terms of Trade Changes (TOT)|<br>-2.784<br>(0.949)|<br>-2.572***<br>(0.043)|<br>-2.439***<br>(0.894)|-2.694***<br>(0.968)|
|Openness*TOT|<br>-3.181|<br>-2.986***|<br>-2.750***|-3.062***|
||(1.181)|(1.101)|(0.940)|(0.942)|
|Years Since Independence|-0.004|-0.060|<br>-0.1 16*|_-0.155**_|
||(0.070)|(0.071)|(0.060)|(0.069)|
|Political Freedom|<br>-0.024*|<br>-0.021|<br>-0.031 **|-0.031 **|
|(I high, 7 low)|(0.014)|(0.014)|(0.013)|(0.014)|
|Federal Dummy|0.004<br>(0.059)|-0.021<br>(0.059)|<br>-0.041<br>(0.043)|-0.053<br>(0.043)|
|Latin America Dummy|<br>-0.285***<br>(0.062)|<br>_-0.265***_<br>(0.065)|<br>-0.207***<br>(0.050)|-0.168***<br>(0.057)|
|East Asia Dummy|<br>-0.190**|<br>-0.166*|<br>-0.284**|-0.296***|
|<br>!_________________________________|(0.081)|(0.086)|(0.042)|_(0.053)_|
|Number of Observations|297|290|297|290|
|R-squaredc|0.3675|0.3402|0.5791|0.5404|

* significant at 1% level ** significant at 5% level * significant at 10% level

Standard errors, corrected for heteroscedasticity, are in parentheses.

bFor all IV regressions in Table I1, the instruments for the following variables are the previous five year lag of itself and regional regional and decade dummy variables: per capita GDP; relative price of government consumption; openness; and political freedom. All other variables in the regressioin were treated as exogenous.

The R-squared is not an appropriate measure of goodness of fit with instrumental variable regressions.

_50_
# **Tables**

## **Table 1: HIV/AIDS prevalence for women in Malawi as pictured by the 2001 sentinel (ANC) data**

|Variable|N of obs.|Prevalence, %|
|---|---|---|
|**_region_**|||
|North|2,006|15.95|
|Central|2,633|17.55|
|South|2,719|24.05|
|**_education_**|||
|none|1,326|21.72|
|primary|4,630|17.60|
|secondary+|1,281|23.73|
|unknown, missing|121|23.97|
|**_age group_**|||
|<19|1,572|11.83|
|20-24|2,759|20.19|
|25-29|1,643|24.59|
|30-34|837|22.10|
|35+|475|18.95|
|unknown, missing|72|19.44|
|**_Total_**|7,358|19.52|

22
- 10 -

increase broken rails and safety concerns, as seen in the United Kingdom (Martin, 2002). In a vertically integrated system, possibly under state ownership, track, passenger and freight operations may be coordinated better. A more complex regulatory system is required for safety, coordination of operations, and investment in the vertically separated privatization approach (Kessides, 2004).

_Proposition 4. Private ownership provides the maximum incentive for quality improvements and cost-cutting when the latter has a small adverse effect on the former. Public ownership may have the advantage when cost-cutting deteriorates quality significantly._ The efficiency of in-house provision also depends on the strength of the incentives of government employees as well as the importance to the government of generating quality innovations. In Andhra Pradesh, India, the state government unbundled the electricity sector to the generation, transmission and distribution functions. Each segment was designed to take the responsibility for managing its operations as a corporate entity under the state ownership. Tariffs among the segments are periodically reviewed in an independent manner, and the government also launched an intensive collection campaign while strengthening the anticorruption policies in the utilities. Even with the state ownership maintained, the sector performance improved considerably. The system losses declined from 38 percent in 1999 to 26 percent in 2003. The collection rate increased to 98 percent by 2003 (Bhatia and Gulati, 2004). In the case of the Phnom Penh Water Supply Authority, a new internal incentive mechanism based on a performance-based wage system and a customer satisfaction survey improved its financial and operational performance dramatically (Table 2). The enterprise is still state-owned but was transformed to an autonomous, commercially oriented and financially self-sufficient water company (ADB _et al_ ., 2005).

_Proposition 5. Public ownership ‘can’ have the advantage if quality is considered publicly important and government employees are well incentivized toward cost reduction and quality improvement._
indoor bathroom, a library, and a computer. Given high correlation among these variables, I use principal components analysis (PCA) to combine the first two measures into an index of teacher quality, and the last five into an index of infrastructure quality. I reduce the dimensionality of the data by using the first principal component. In the teacher quality PCA, the first principal component explains 52% of the variation in the quality variables, with an eigenvalue of 1.04. In the infrastructure quality PCA, it explains 60% of the variation, with an eigenvalue of 3.01.

The pre-primary enrollment rate is the number of public pre-primary school students (cr`eche or preschool) as a fraction of the population of children aged 0-6. Starting in 2007, the denominator is the population of children aged 0-5 (the new primary school age range).

# **5.4 Demographic, economic, and political trends**

An additional concern is the potential correlation of income and inequality with demographic, economic, or political trends that affect public investment. I thus account for total population, age 0-6 population, and age 7-15 population (all in 100,000s) in the main specification. As a matter of robustness, I show specifications including three time-varying political factors: the mayor’s vote share in the previous election, the number of political parties competing for mayor, and an HHI of between-party competition for mayor. I also show specifications including timevarying exogenous shocks to the world prices of three important agricultural products in Brazil: coffee, cocoa, and bananas. I multiply the current year world price of each by how many hectares there were in the municipality in 1994.<sup>34</sup> The inclusion of all three sets of variables has virtually no effect on the sign, magnitude, or significance of _γ_ , _δ_ , or _θ_ in any of equations 11 through 14.

# **5.5 Data**

I used several data sources, matched geographically at the municipality level. An observation is a municipality-year. Data are generally available for 1995-2008, and are summarized in Table 5.

School-level data on students, teachers, infrastructure, and amenities are available from INEP’s Census of Schools. The data are of very high quality and detail. From these data, I computed school enrollment by level (pre-primary, primary, secondary) and administrative 34This Herfindahl-Hirschman Index (HHI) takes a municipality to be a market and political parties to be firms.

21
capitalization (MCAP)<sup>2</sup> , ME/BE, dividend yield (DIV)3 and percentage change in the local currency (relative to the dollar, FX). In addition, we also include turnover (TURN, value traded measured in dollars relative to the number of shares outstanding) as an additional explanatory variable. The decision to include turnover as an indicator of liquidity is based on the idea that many international investors have chosen to concentrate on the most liquid stocks in these markets. As a result, tumover might play an important part in determining returns.

# III. Empirical Results

# a. Data

The data come from the Emerging Markets Database maintained by IFC, which contains asset prices, dividends, exchange rates, trading volume and accounting ratios for a number of firms in each of 20 countries. The available sample periods are not the same for all countries, however, and in some cases data is available for only recent years.<sup>4</sup> The sample period chosen covers eight years, 1986-93, which provides 96 monthly observations for each country.' For

**2** In line with the findings of CHL (1991), we normalize market capitalization in order to avoid the effects of overall market growth and to concentrate on relative size. To do that, each month each stock's market capitalization is divided by total market capitalization in order to determine relative market capitalization.

3 Unlike many of the studies of the after-tax CAPM, we choose to ignore the complications involved in estimating expected future dividend yield, although we recognize the importance of that debate. Instead, we choose to include lagged dividend yield as a proxy variable for expected dividends.

4The database covers the period 1976-present, but not all countries are available for the entire sample period. In addition, the local market indices used in calculating , are available only from 1986.

5 Data for Turkey begin in 1987.

8