rafmacalaba/datause-encoder-data huggingface dataset:
Using household survey data matched with administrative data for 2007‐2009, we have examined the relationship between China’s rural dibao program and rural poverty and conducted targeting analysis using conventional and propensity score approaches. We find that during these years the rural dibao program provided sufficient income to poor beneficiaries, but the poverty impact of the program overall was small. Although total dibao expenditures are fairly large relative to the poverty gap, the program did not substantially reduce the poverty gap.

Conventional targeting analysis reveals large inclusionary and exclusionary targeting errors. Propensity score analysis of targeting reduces the targeting errors, which suggests that the program has been implemented in reference to an unobserved latent income variable and observable correlates of income. Nevertheless, even using the propensity score targeting approach, the targeting errors remain quite large.

These findings are subject to some limitations of our data. One limitation is the lack of household‐level information on dibao transfers. Another is potential understatement of dibao participation. Dibao participation in the CHIP sample is considerably lower than in the official statistics. This discrepancy could be because the official statistics are biased; however, if it reflects bias in the CHIP survey sample, then our findings could understate the dibao program’s effectiveness.

Our analysis indicates that during these years a central reason for the program’s modest poverty impact was that the proportion of the population covered by the program as measured by both the CHIP and official data was relatively small. Since 2009 government spending on the rural dibao program has expanded rapidly. Most of the budget increase, however, has been used to increase transfer amounts. The number of recipients has changed relatively little.

Using simulations, we investigate whether expanding the dibao program would increase its impact on poverty. Our findings indicate that expanding the program could be beneficial if the expansion mainly takes the form of expanding coverage rather than increasing transfer amounts per recipient. We also use simulations to explore whether adopting a uniform transfer and uniform threshold will improve the program’s poverty impact. We find that standardization of transfers and thresholds has the potential to substantially reduce poverty, but the extent to which that potential is realized depends critically on targeting. For example, a uniform transfer 27
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tertiary degree. In column (2), the sample is restricted to persons (in age group 18-23) who already have completed the higher secondary level, and the dependent variable is an indicator for attending tertiary education or having already completed a tertiary degree. In column (3), the dependent variable is an indicator for having completed the higher secondary level or above (in age group 18-23). So column (1) explains participation in tertiary education, column (2) explains participation conditional on completion of a higher secondary degree, and column (3) explains completion of at least the higher secondary level.

Table 1: Determinants of tertiary education access for age group 18-23

||(|1)|(|2)||(3)|
|---|---|---|---|---|---|---|
||Attending o<br>tert|r completed<br>iary|Attending o<br>tertiary co<br>complet<br>seco|r completed<br>nditional on<br>ed higher<br>ndary|At least com<br>seco|pleted higher<br>ndary|
||**2004**|**1993**|**2004**|**1993**|**2004**|**1993**|
|sc*|‐0.205***|‐0.403***|0.052|‐0.103|‐0.248***|‐0.413***|
|st*|0.008|‐0.371***|0.407***|‐0.089|‐0.151**|‐0.391***|
|muslim*|‐0.364***|‐0.533***|‐0.033|‐0.157*|‐0.406***|‐0.548***|
|female*|0.057|‐0.019|‐0.138*|0.016|0.110***|‐0.029|
|rural*|‐0.780***|‐0.755***|‐0.593***|‐0.420***|‐0.650***|‐0.686***|
|female*Rural*|‐0.328***|‐0.547***|‐0.089|‐0.331***|‐0.346***|‐0.517***|
|quintile 2*|0.332***|0.227***|0.177|0.005|0.294***|0.232***|
|quintile 3*|0.644***|0.530***|0.346***|0.221**|0.585***|0.485***|
|quintile 4*|0.955***|0.759***|0.518***|0.278***|0.860***|0.716***|
|quintile 5*|1.565***|1.214***|0.957***|0.572***|1.394***|1.128***|
|Number of|||||||
|Observations|68,894|65,722|14,956|11,699|68,894|65,722|

Notes: (1) * p<0.05; ** p<0.01; *** p<0.001 (2) The coefficients are marginal effects.

(3) * denote dummy variable and marginal impact is for discrete change of dummy variable from 0 to 1. . (4) Non-SC/ST is the social group of reference.

(5) The model also includes state dummies (full results in Annex III) Most of the explanatory variables considered are statistically significant at the 1 percent level. In general, the results confirm the descriptive statistics from the previous section. The estimations suggest that: `o` <u>Income seems to be a decisive factor for participation in tertiary education. There is a strong positive association between expenditure quintile and participation in tertiary education. Income is strongly and statistically significantly associated with both completion of secondary education, and</u> 24
# I. THE CONTEXT: EGYPT'S DEVELOPMENT STRATEGY FOR THE 1990s'

1. The role and development of Egypt's higher education system must be placed in the context of the country's current economic development ;roblems and the strategy which the government is implementing to confront these difficulties. Accordir. gly, this report begins with a brief overview of problems and strategies, with a special focus on the la 'or market. The second section provides historical background to the development and current problems of the higher education system. The principal quantitative and qualitative developments of higher education are then presented; internal and external efficiency and equity of the current system are briefly analyzed. The paper concludes with a review of the principal responses to the problems by government, employers, students, and universities.

2. The Egyptian economy is characterized by serious macro-imbalances and microdistortions. These have not only generated a large foreign debt, but have also seriously impeded the rate of growth of the demand for labor. This, in turn, has driven down real wages and increased unemployment. Both have contributed to a probable increase in poverty since the mid-1980s; unemployment is increasingly concentrated among young people with education, including university graduates. The employment problem is particularly severe since the labor force is growing by 2.7% per year.' Given Egypt's resource endowment, a development strategy which focuses on laborintensive manufactured exports has the best chance of remedying this serious situation. One should ask how the higher education system can the system provide the kind of human capital which such a strategy will require.

3. The core of Egypt's macroeconomic problems is the "twin gaps' between investment and domestic savings, and imports and exports. These difficulties are compounded by a third gap, that between government spending and revenue. The savings/investment gap grew throughout the 1980s, reaching 12% of GDP in 1986/87. S. ings, in particular, have collapsed: public sector savings declined and oil revenues dwindled, and public sector companies, enjoined to hire and forbidden to fire for roughly two decades, accumulated losses, which in the late 1980s were about 8% of GDP. Recorded private sector savings available to the economy were and are reduced by negative real interest rates on Egyptian pound deposits.

4. Desp.te the high investment ratio, most investment went into (admittedly badly det?riorated) infrastructure, rather than traded goods production which received perhaps 45% of investment from 1982-87. The relative neglect of investmen: in traded goods, especially manufacturing (which received less than 20% of all investment) did little to foster exports and to create jobs. More seriously still, investment had a strong capital-intensity bias: 'COR (incremental ' Unless otherwise stated, all numbers In this section ar^ official Central Bank statistics.

***** I would like to thank Jamil Saimi for his helpful comments on an earlier draft.
22 Anderson, K. and E. Valenzuela. “Do Global Trade Distortions Still Harm Developing Country Farmers?” _Review of World Economics_ 143(2007) (forthcoming). Accessible at www.worldbank.org/trade/wto Balaoing, A. and J. Francois. “Deconstructing EU Trade Policy.” Invited Paper presented at the Global Economic Analysis Annual Conference, Lubeck, 9-11 June 2005.

Binswanger, H. and K. Deininger. “Explaining Agricultural and Agrarian Policies in Developing Countries’, _Journal of Economic Literature_ 35(1997): 1958-2005.

Blalock, G. and P. Gertler. 2004. “Learning From Exporting Revisited in a Less Developed Setting.” _Journal of Development Economics_ 75: 397-416.

Byerlee, D. and G. Sain. “Food Pricing Policy in Developing Countries: Bias Against Agriculture or for Urban Consumers?” _American Journal of Agricultural Economics_ 68(1986): 961-69.

de Gorter, H. and J. Swinnen. “Political Economy of Agricultural Policy.” Ch. 36 in _Handbook of Agricultural Economics Vol. 2,_ edited by B. Gardner and G. Rausser, Amsterdam: Elsevier, 2002.

Easterly, W. _Global Development Network Growth Database,_ 2006. Accessible at http://www.nyu.edu/fas/institute/dri/global%20development%20network%20gro wth%20database.htm Ebrill, L., M. Keen, J.-P. Bodin and V. Summers. _The Modern VAT._ Washington DC: International Monetary Fund.

Ethier, W.J. “Selling ‘Protection for Sale’.” PIER Working Paper 06-014, University of Pennsylvania, Philadelphia, June 2006.
- 28 -

Gross Fixed Private **and** Public Sector Investment. These series were obtained from National Accounts, INCI.

Implicit Price Deflators. Both the implicit price deflator for investment and GDP were obtained from **the** National Accounts, _IGCI._ Output-Capital Ratio. It is the ratio of CDP and capital stock lagged one year. The data on CDP **was** obtained from National Accounts, _INEGI._ The Real Interest Rate. Is the ex-post real interest rate calculated **as** the ratio between one plus the nominal interest rate and one plus the inflation rate in the implicit CDP deflator. The nominal interest rate is a weighted average of after tax yields of banks instruments. The source was: Gil Diaz F., lexlicot Macroeconomic Policies Adjustments and Growth in the Long-Rung, 1988, Table A56.

The Real ExchanRe Rate. It is a trade-weighted real exchange rate. World Bank staff estimates.

The Index of Quantify Restrictions on Imports. It reflects the percentage of imports subject to quantity restrictions out of total imports. The source wass Gil Diaz F., Ibidem.

The Rate of Net Domestic Credit Creation. Net domestic credit is obtained from the consolidated banking system balance sheet. Its rate of change is obtained as the annual change in the nominal stock of net credit divided by the nominal stock of monetary assets at the beginning of the year. The source **was** IFS, IMF,
The evolution of inflation is very similar to that of growth: years of low growth were also the years of high inflation, and vice versa. The average unweighted inflation rate for the transition countries decreased from its peak of more than 1,000 percent in 1992, and just below 1,000 in 1993, to around 6 percent in both 2004 and 2005. Again, from 1993, the decline in the unweighted inflation rate was monotonical: each successive year saw a lower average rate. The evolutions of unweighted growth rate and inflation are shown in Figure 2.

Figure 2. Average (unweighted) growth rate and inflation during the transition, 1990-2005 <!-- Start of picture text --> inflation<br>growth<br>1990 1995 2000 2005<br>Year...<br>10<br>1000<br>800<br>0<br>600<br>400 annual inflation<br>-10<br>annual GDP growth rate<br>200<br>0<br>-20<br><!-- End of picture text --> Note: GDP growth in percent per annum (broken line; left axis). Inflation in percent per annum (right axis). Data are unweighted average of 26 transition economies.

The situation is just slightly different with government expenditures as a share of GDP. Government spending was inelastic, both when incomes severely dropped at the onset of the transition and when they kept on increasing later. Thus, the unweighted 9
Columns 1-3 report estimates of the impacts on highway provision when the presidential party status is interacted with a dummy equal to one if the PAN is in power, and zero otherwise (i.e. the PRI is in power). The coefficient estimates on the interaction term are statistically insignificant at standard confidence levels, regardless of the order of the spline approximation. In other words, by focusing only on closely contested elections, there is no statistical evidence of political bias in highway construction when we contrast a PAN presidency with a PRI presidency. This is not surprising given that both parties expressed similar taste for public spending on infrastructure as evidenced by their large infrastructure investment programs. What our results show is that they both resort with the same intensity to pork-barrel politics.

Table 6: Potential Motives: Divided vs.

||LLR|with bandw|idth <sup>ˆ</sup>_h_|Poly|nomial of or|der_p_|
|---|---|---|---|---|---|---|
||ˆ_h_1= .3|ˆ_h_2= .2|ˆ_h_3= .1|<br>_p_= 2|<br>_p_= 3|<br>_p_= 4|
||(1)|(2)|(3)|(4)|(5)|(6)|
|A. Divided|||||||
|President|1.364*|1.972**|3.979***|1.593*|2.779***|3.107***|
||(0.729)|(0.879)|(1.342)|(0.822)|(1.023)|(1.131)|
|Observations|2,751|1,930|1,037|4,496|4,496|4,496|
|R-squared|0.402|0.371|0.407|0.397|0.398|0.398|
|B. Unifed|||||||
|<br>President|0.245|-0.204|-0.085|0.034|0.322|0.306|
||(0.497)|(0.614)|(0.864)|(0.491)|(0.586)|(0.671)|
|Observations|2,126|1,402|678|4,409|4,409|4,409|
|R-squared|0.336|0.430|0.448|0.173|0.173|0.173|
|Covariates|Yes|Yes|Yes|Yes|Yes|Yes|
|Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ The table shows the regression discontinuity estimates of the presidential party status in legislative elections on multi-lane road (federal highway) outcomes during periods of divided (panel A) and unified (panel B) government. The estimation methods include: local linear regression in columns 1-3 with two MSE-optimal bandwidth choices ( _h_<sup>ˆ</sup> 1 and _h_<sup>ˆ</sup> 2) and one CER-optimal bandwidth choice ( _h_<sup>ˆ</sup> 3), calculated according to Calonico et al. (2017); spline polynomial approximation in columns 4-6 with second, third, and fourth polynomial, respectively. Estimations in panel A has no covariates while estimations in panel B include the following covariates: state capital dummy, US border dummy, log area size, log population size, and proxies for higher education and employment. Robust standard errors clustered at the municipality level are reported in parentheses. All specifications include state, time, and state-interacted-with-time fixed effects. ** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

23
the private factory area in the first two models and the coefficients on the private reserve area and private non-reserve area in the latter two models are predicted to be positive. Additionally, the coefficient on the private non-reserve area type is predicted to exceed that for the private reserve areas.

The factory price is announced prior to planting (Siddiqui 1997), and the analysis assumes that farmers have rational expectations regarding the price of gur. To provide some perspective on the expected price difference, the average cane prices in the regulated and unregulated markets for the 2001–2 through 2007–8 cropping seasons are reported in table 3.<sup>15</sup> On average, the floor price is 43 percent higher than the gur price less gur manufacturing costs.

The model predicts that an increase in the expected difference between the floor price and the cane price in the unregulated gur market increases the cultivation costs of farmers selling to private factories that use ex post permits because farmers compete more aggressively to obtain permits as the expected price difference increases. The second set of empirical models tests this hypothesis by estimating the per unit cultivation cost, replacing the factory-type dummy variables with interaction variables for the difference between the cane price in the sugar and gur markets and the factory area type. Farm size and regional fixed effects are also included in these estimations (district-specific fixed effects are not viable in these models because five of the nine factories are the only factory from their district in the sample).

15 Most farmers manufacture gur themselves, so the effective cane price received by the farmers in the unregulated market is equal to the gur price less all other costs. The NFCSF reports state-level gur prices. The Ministry of Food Processing Industries of India (MOFPI) reports projected costs per unit of gur manufacture, including packing and other non-cane raw materials, utilities, wages, processing and marketing expenses. The average recovery of gur from cane is reported to be 10 percent by the MOFPI. Using this information, the effective price for cane used to manufacture gur is ( gur price - non-cane processing costs ) × ( average recovery rate of gur ) .

19
samples suggest that persistence is about 33 percent higher for daughters, while the correct estimate from full samples is 45 percent. In contrast, the gender gap estimates based on IGC are similar across full (4.5 percent higher for daughters) and coresident (4.8 percent higher for daughters) samples. Thus, consistent with the cross-country comparisons discussed above, when working with coresident samples, it is preferable to use IGC as a measure of intergenerational persistence to understand gender gap in educational mobility.

# **5. Additional Evidence**

# **5.1 Alternative Age Ranges for the Children**

The age range used so far in Tables 1-3 is 13-60 years. This is motivated by the fact that the average schooling attainments in rural Bangladesh and India remain low in the survey years, so that a 13 years lower threshold may not be binding for most of the rural children. In Bangladesh data, the average years of schooling is only 4.43 years; for sons it is 5.5 years and for daughters 3.4 years. The average schooling in India is 5 years, and for sons it is 7 years and for daughters 3.7 years. To explore the sensitivity of the conclusions with respect to the age range of children, we estimate the IGRC and IGC across a number of different age ranges. For the sake of brevity, we report estimates from the following age ranges: ( _i_ ) 13-50 years, ( _ii_ ) 16-60, and ( _iii_ ) 20-69 years.

Since many children start first grade at age 6, a 13 years age cut-off implies 7 years of potential schooling as the minimum threshold in our sample (primary schooling is 5 years). The observed schooling attainment, however, may vary across 13 year old children for a variety of reasons. For example, children from poor households may start schooling later than usual, and they may also have to interrupt schooling because of negative economic shocks.<sup>22</sup> The variations in schooling attainment at age 13 (or even younger) can thus provide us useful evidence on the role played by family background. However, one might worry that some children at age 13 have not yet completed schooling, and it is important

> 22According to one estimate for India, 53 percent of students drop out before completing primary (5 years). Among every 100 girls enrolled, only 40 progress to 4th grade, 18 reaches 8th grade, and only 1 is lucky enough to go up to 12th grade (India Education Report, 2005, pp. 6-7).

18
to corporations and foreigners, and the “popular” market for domestic tenants. We are mainly interested in the popular market in addressing this question.

Both private and public sector data are hard to come by for this market.<sup>60</sup> Evaluations of new contract rents start with surveying rental ads and broker listings or holding interviews. Anecdotal evidence from such sources obtained during the study indicates new contract rents for one-room (studio) apartments starting from US$50 per month in Yerevan (Armenia), through US$100 in Vilnius and Bucharest, to US$250 in Warsaw and Moscow. More representative analysis in this direction was beyond the scope of this study.

**Table 9. Rents, Rent-to-Income and Cost-to-Income Ratios in Studied Countries**

||**Armenia**|**Poland**|**Romania**|**Russia***|**Serbia**|
|---|---|---|---|---|---|
|Rentsin LCU||||||
|- public|11000|154|200188|381|nil|
|- private|15044|124|799487|646|4645|
|Private/public|136.8%|80.5%|399.4%|169.6%|n.m.|
|Rent-to-income**||||||
|- public|23.1%|8.3%|4.7%|5.7%|nil|
|- private|27.1%|7.3%|8.9%|9.7%|18.8%|
|Private/public|117.5%|87.7%|188.1%|172.4%|n.m.|
|Housing costs-to-income***||||||
|- owners|11.0%|n/a|13.2%|17.4%|9.1%|
|- public rental|34.3%|n/a|16.8%|17.6%|10.1%|
|- private rental|35.5%|n/a|21.6%|19.4%|27.6%|
|Private/public|103.4%|n/a|128.9%|110.1%|273.4%|

_Source_ : Household surveys (Table 1). _Note_ : Urban apartments only, housing bill excluding energy/heating. **Average rent payment divided by average income proxy value. ***Average housing costs (rent/condominium fees + utilities) divided by average income proxy value.

However, the results from the surveys (Table 9) provide a useful snapshot of average existing contract rents in the popular market. Since rent levels are derived from interviews with sitting tenants holding seasoned contracts, they reflect different contract originations (vintages) and will differ from current new contract rent levels. The difference between new and existing contract rent levels will be influenced by locally specific tenure durations and the presence or absence of rent controls. However, in cross-country comparisons, average rents do not seem to go too far astray from the anecdotal data on new rents.

The exception is Poland, which is the only studied country exhibiting both lower private than public rents and significantly lower average rents than marginal (new contract) rents (Table 9). Private rents reflect a market-mimicking grading scale used by many municipalities for calculating rent ceilings below the overall ceiling of 3 percent of replacement costs, set by the central government. These grading scales are set less sensitive to location factors and more sensitive to the physical conditions of apartments and their buildings. Since the restituted prewar

> 60  Brokers and investment advisers offer rent information for the upscale market. This segment is left out of the study, as it is characterized by very specific and not representative supply and demand conditions.

38