rafmacalaba/datause-encoder-data huggingface dataset:
# **BOX m4:** Per student =nditures: Intrasectoral trends in Costa Rica and Ghana

In Costa Rica, public per student expenditures dropped drastically between 1980 and 1982. Although they rose gradually after 1982, in 1986 they remained below 1980 levels. Furthermore, the breakdown by level of education _shows_ that the tertiary level did not suffer as much as the primary level: per student spending declined less, and rose faster. Although primary and secondary spending were reduced by the same proportion through 1982, per student secondary spending increased more rapidly after 1982.

|Costa Rica|1980|1981|1982|1983|1984|1985|1986|
|---|---|---|---|---|---|---|---|
|Primary and Preschool|100|71|48|53|58|59|65|
|General Secondary|100|70|47|_55_|62|67|74|
|<br>Vocational Secondary<br>|100<br>|70<br>|46<br>|54<br>|60<br>|68|77<br>|
|Tertiary|100|86|61|65|76|77|90|

## Soure: World Bank (1989c).

Real public expenditures per student also declined at all levels in Ghana from 1970 to 1980, but declines were greater for secondary and tertiary education. Primary education was relatively protected. Severe declines date from well before the first World Bank adjustment loan in 1983. In fac, per student expenditures (particularly primary) have improved since 1983, partly as a result of enrollments growing less rapidly than the school-age population.

|Ghbana*|1970|1975|1980|1983|1985|1988|
|---|---|---|---|---|---|---|
|Primary|161|100|51|39|61|98|
|<br>Secondary|76|100|50|30|44|58|
|<br>Tertiary||100|33|17|52|61|

Source: World Bank (1988c). 'Because of the difficulties in combining figures from different sources, these figures are not exact and are intended only to indicate likely trends.

3.13 Capital versus recurrent spending: Whether or not capital and recurrent spending levels are adequate is determined in part by how well existing facilities serve the current population and meet future needs. Gallagher (1990) finds that the share of investment in total education spending declined between the mid-1970s and the mid-1980s in 19 of 25 developing countries. However, donors played an increasing role in financing capital investments in Africa, and capital spending in countries such as Botswana and Thailand declined after universal primary education was achieved (when budgetary needs were limited to construction and equipment necessary to keep pace with population growth).

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along a continuum, which ranges from authorized user to claimant, proprietor and ultimately owner. In this spirit, Kilic and Moylan (2016) distinguish between reported owners, economic owners, documented owners and holder of various bundles of rights. These different ownership and use rights do not necessarily fall together (Slavchevska et al 2017; Doss et al 2017). In many parts of Africa, village chiefs, kinship groups and extended families engage in specific aspects of property transactions. For example, land allocations within the family and heirship may be within the purview of senior male family members whereas sales and/or alienations outside of the lineage require the permission of village chiefs or elders (Pande and Udry 2005; Doss et al 2015). Based on data for Senegal, Lambert et al (2014) show that even though 17 percent of women inherited some land from their parents, only 2 percent have land to bequest to their heirs, suggesting that women’s land ownership rights are often revocable. This is further complicated by cross-country differences in land tenure systems, with some African countries recognizing individual property rights and others vesting land ownership in the state (granting only individual use rights) or recognizing multiple systems (Slavchevska et al 2017). The DHS questions capture a concept closest to that of reported ownership (sometimes described as perception-based ownership) but cannot distinguish between different forms of ownership or provide information about the security of ownership. Moreover, even though the DHS are fairly standardized, regional differences in tenure systems may affect the interpretation of questions about individual ownership.<sup>13</sup> Across Africa, reported property ownership is much more prevalent than documented ownership, though relatively more men than women hold registered deeds and other types of formal documentation. This can be illustrated for Malawi and Tanzania, where the DHS (under the 7<sup>th</sup> round) collect additional data on title deeds (Figure 4).

**Figure 4: Female landowners are less likely to hold title deeds**

<!-- Start of picture text --> Title deeds for land owned (sole and joint ownership)<br>Malawi, female Malawi, male<br>2% 5%<br>Tanzania, female Tanzania, male<br>8% 17%<br>has title - with respondent's name has title - w/o respondent's name<br>does not have title DK<br>Graphs by country and sex<br><!-- End of picture text --> _Note:_ Only women and men who report sole or joint land ownership. _Source:_ DHS.

In Malawi, less than 2 percent of women reporting to own land have title deeds registered in their name, compared with 5 percent of men. These numbers are higher in Tanzania, but male landowners remain twice as likely as women to have a title deed in their name (17 vs. 8 percent). Similar evidence is reported for

> 13 Likewise, because the DHS data ask about land and housing in general and do not refer to specific assets (e.g. a specific parcel of land, or a specific residential unit) they do not allow ‘reconciling’ intra-household discrepancies in perceptions about ownership (see Doss et al 2017 for a discussion).

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# Table 13: Under 5 child health

||Diarrhea||Hemoglobin|Anemic||Anthropo|metric z-scor|es|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|in past 7<br>|Diarrhea in past 14<br>|level (g/L)|(Hb < 110<br>|weight-for-<br>|height-for-|weight-for-<br>|head<br>|
||days|days (Listing Data)||g/L)|age|age|height|circumference|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|HWWS (β1)|-0.004|-0.013|0.089|0.007|0.015|0.030|-0.006|0.229|
||(0.012)|(0.011)|(0.754)|(0.023)|(0.043)|(0.057)|(0.048)|(0.141)|
|TSSM ( β2)|-0.001|-0.010|-0.772|0.024|-0.044|-0.006|-0.061|0.092|
||(0.012)|(0.012)|(0.713)|(0.022)|(0.035)|(0.059)|(0.048)|(0.129)|
|HWWS + TSSM (β3)|-0.011|-0.021*|-1.652**|0.060**|-0.075**|-0.008|-0.097**|0.227|
||(0.013)|(0.013)|(0.772)|(0.023)|(0.038)|(0.057)|(0.045)|(0.154)|
|p-values β1= β2|0.744|0.813|0.208|0.398|0.137|0.515|0.247|0.263|
|for F-test β1= β3|0.594|0.428|0.023|0.020|0.039|0.476|0.050|0.991|
|β2=β3|0.399|0.360|0.214|0.091|0.396|0.961|0.445|0.351|
|_Observations_|5,768|34,045|5,203|5,203|5,203|5,208|5,202|5,208|
|_R-squared_|0.053|0.051|0.194|0.159|0.062|0.084|0.064|0.223|
|_Control Mean_|0.086|0.168|111.441|0.414|-1.033|-1.946|0.055|-0.511|

Notes: Robust standard errors in parentheses clustered at the ward level.*** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated using a linear probability model including block fixed effects, child gender and age (month) dummies. Column (1) reports on the symptom-based diarrhea measure capured in the household survey. Column (2) reports on direct diarrhea reports captured in the listing survey.

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of wage labor activities into self-employment activities in agriculture as peace was restored. Similarly, since the consumption data for 2011 and 2012 are not reliable and it is then when conflict declined the most, it is not possible to capture the effect on this welfare measure.

Thus far, all of the regression results presented have relied on a monetary dependent variable and so prices have both been part of the construction of the dependent variable and the explanatory variable included in the analysis. As a robustness check on the findings, we conduct a similar exercise using non-monetary measures of welfare that are correlated with consumption: z-scores (standard scores) of weight for age and weight for height among children less than 5 years of age in the household. The results are presented in Table 11. These data were only collected from 2010 onwards and only collected for children, making the sample size available for these regressions much smaller. For this reason only one price—the price of beans—is considered. Although the results are not consistently significant across specifications, they do show that weight for height is positively impacted by rainfall, and that weight for age is positively affected by higher prices, as suggested by the regressions on income and consumption.

Overall the results show that while income diversification is an effective strategy to manage some sources of risk—such as weather—the amount of diversification undertaken by Ugandan households is not able to fully insure total income and consumption from fluctuations in the external environment. These findings suggest that it is desirable for households to be more fully insured against shocks than they currently are.

**Table 9: Impact of weather,** **<u>prices and peace on income and consumption</u>**

||(1)<br>Crop<br>income|(2)<br>Livestock<br>income|(3)<br>Agricultural<br>wage<br>income|(4)<br> <br>Non-<br>agricultural<br>wage income|(5)<br>Non-farm<br>self-<br>employment<br>income|(6)<br>Consumption<br>(2006, 2010)|
|---|---|---|---|---|---|---|
|Log of rainfall (percent of<br>needs measured by|1.886***|-0.198|-4.853***|-3.627***|-2.796***|0.478***|
|WRSI)|(0.343)|(0.833)|(0.706)|(0.701)|(0.750)|(0.147)|
|Log of maize price|0.492***|-0.0671|-1.130***|-0.0973|-0.401|-0.218**|
||(0.0840)|(0.264)|(0.338)|(0.339)|(0.371)|(0.0975)|
|Log of beans price|1.091***|1.213**|-1.453***|4.263***|1.175**|0.729***|
||(0.155)|(0.516)|(0.506)|(0.422)|(0.506)|(0.125)|
|Log of number of fatalities|-0.146***|-0.227|0.451***|0.323**|0.177|-0.00909|
||(0.0515)|(0.142)|(0.135)|(0.134)|(0.145)|(0.0143)|
|Constant|-6.619***|-0.196|36.68***|-12.84**|9.026|5.127***|
||(2.010)|(5.792)|(5.804)|(5.190)|(6.101)|(1.172)|
|Observations|6,852|6,986|6,497|6,497|6,497|3,154|
|Number of HHID|2,044|2,046|2,045|2,045|2,045|1,946|

Source: Staff calculations using UNPS. Notes: Household fixed effects estimation with robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

**Table 10: Impact of weather, prices and peace on income and consumption: bottom 40 percent**

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# **Acknowledgements**

We are grateful to Mitra & Associates and Afra Chowdhury for their contribution during the data collection and preparation process. Md. Abir Hasan provided excellent research assistance. Helpful comments on earlier version of the paper were received from Pablo Gottret, Tekabe Belay, Celine Ferre and Yoonyoung Cho. Financial support for this research was provided by the Trust Fund for Environmentally & Socially Sustainable Development, Gender Trust Fund and the Rapid Social Response MDTF of the World Bank.

# **Acronyms**

|BDT|Bangladeshi Taka|
|---|---|
|BISP|Benazir Income Support Program|
|DiD|Difference in Difference|
|EGPP|Employment Generation Program for the Poorest|
|HIES|Household Income and Expenditure Survey|
|MP|Member of the Parliament|
|PFDS|Public Food Distribution System|
|PSM|Propensity Score Matching|
|PSU|Primary Sampling Unit|
|UNO|Upazila Nirbahi Officer|
|BDT|Bangladeshi Taka|

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## **6.3 Examining Topic Drift**

Figures 10-14 illustrate the potential for obtaining a deeper understanding of topic drift over time by breaking down the coefficient plots of the above section by time period during which the tweets appeared. We would like to draw your attention to two aspects of these graphs. First, August 2011 and August 2012 demonstrate sparse features for each – category except for the “other” category. This could mean one of two things people simply were not talking about the subsidy reform during these months on Twitter, or we are no longer capturing the tenor of the conversation expressed in our search taxonomy. Given that we observe similarly dense features for each month across the categories, however, it seems that the former is more likely to be true. For example, our taxonomy still captures partisan discussions taking place in May 2012 in the aftermath of the March 2012 congressional elections in El Salvador, but we do not see many partisan tweets during August 2011 or 2012. Furthermore, the “other” category does not demonstrate this same sparsity. This suggests that there is still news being reported about the subsidy, but that people are no longer discussing it. We may tentatively take this as evidence that people do not have anything further about which to complain, so they have ceased to tweet about it.

The second aspect to which, we would draw attention is the rise of the term _distribuidor_ in the May 2011 and August 2011 tweets. It seems that though it may be the case that the gas distributor strikes of May 2011 were inconsequential [Calvo et al., 2014], perhaps they influenced public perception in a very negative way. In the next section we will offer some avenues for further improvements on this study and then conclude.

# **7 Future Work and Improvements**

We begin this section with some caveats, then offer some next steps for making potential improvements to the accuracy of our classifier and gaining additional insights in to the subject matter of the tweets about the reforms. For the first caveat, it is worth noting that such novel sources as Twitter data are not yet fully understood. In particular baseline information must be sought and representativity more fully investigated [Tufekci, 2014]. However, we believe the effect of the bias of Twitter towards more affluent demographics is somewhat mitigated since the reform, according to an incidence analysis, was particularly “pro-poor” and benefited the great majority of the population [Tornarolli and Vazquez, 2012].

In addition to the potential sampling bias, the subjectivity bias of the labeling exercise 32
# **Inputs**

The sum of the within elasticities of both types of capital is 0.37, with fixed capital accounting for most of the impact on productivity. Turning to the between regressions, the elasticity on fixed capital is particularly high (0.59) in the between-time regression. This suggests that the pace of the implementation of changes in the available technology was strongly constrained by the level of the fixed capital stock in agriculture. The between-time estimate of capital of agricultural origin is significantly positive, though more modest in value (0.24). While the between-time coefficient on fixed capital is robust to different specifications of the model, the other between-time coefficients are difficult to interpret in light of the limited time series (31 years) and the concern over multicollinearity.

The between-country estimate of fixed capital is smaller than the within estimate, while the between-country coefficient on livestock and treestock is larger (0.14). These estimates are more similar to those obtained from cross-country studies, in which the impact of capital on the changes in technology over time is not considered.<sup>30</sup>

# **State variables**

State variables are included in the analysis to eliminate the bias of the estimated input coefficients that is caused by the jointness property of choosing inputs and implemented technology simultaneously. Looking first at the within estimation, the technology variables give mixed results. The development indicator is quite robust, indicating that the more productive is the economy as a whole, the higher is the productivity of agriculture. Schooling and peak yield have unexpected negative coefficients, though the coefficient on peak yield is not significant. As expected, the price coefficient is positive, and that of the price variability is negative. Macroeconomic instability (in particular, inflation) does not have a significant effect. The institutional measures do not seem to be able to capture any effect beyond what was already reflected by the development indicator.

The between-country results reflect the determinants of cross-country variability at a given point in time, where countries implement different technologies, but presumably operate under the same set of available technology. Schooling is positively significant, suggesting that education was conducive to the techniques used by the more productive countries. Again, peak yield and the institutional measures are not significant. Development has changed to being significantly negative.

> 30 The early literature on cross-country studies on agricultural production was reviewed in Mundlak, Larson, and Butzer (1999).

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11 1988 (Table 4). Nigeria, whose exchange rate had appreciated during the 1970s as a result of oil revenues, initially resisted reform in the 1980s, but was forced to take dristic action to counteract mounting deficKs. The Naira was devalued by 64 percent in the last quarter of 1986, and another 16 percent In 1987.

Table 4

||Indices of Real<br>|Effective Exchange Rates<br>(1980-100)|
|---|---|---|
|Country|1980-86|1987<br>1988|
|Egypt|133.4|156.4<br>159.0|
|Malaysia|105.4|87.8<br>79.6|
|Nigeria|128.7|29.0<br>29.7|
|Souith<br>Korea|98.8|80.2<br>88.9|
|Thailand|100.7|79.9<br>77.3|
|Tunisia|96.0|71.2<br>69.8|

Source: World Bank

# Impact **on** Factor Markets

Agarwala (1983) studied market price distortions, including factor markets, in the **early** 1980s for 31 developing countries. His findings confirm the link between trade regimes and factor market price distortions. He developed criteria for ranking price distortions in fiscal, monetary, and exchange rate policies; product markets; finance and credit markets; labor markets; and the public sector. The distortion rankings given for each of these features were low = 1, medium = 2, and high = 3. The rankings were averaged across all features for each country. Price distortions in the six comparator countries show a correlation with the trade regimes described above (Table 5).
Policy Research Working Paper 5902

# **Abstract**

Measurement of the middle class has recently come to the center of policy debate in middle-income countries as they search for the potential engines of growth and good governance. This debate assumes, first, that there is a meaningful definition of class, and second, that thresholds that define relatively homogeneous groups in terms of pre-determined sociological characteristics can be found empirically. This paper aims at proposing a view of the middle class based on vulnerability to poverty. Following this approach the paper exploits panel data to determine the amount of comparable income—associated with a low probability of falling into poverty—which could define the lower bound of the middle class. The paper looks at absolute thresholds, challenging the view that people above the poverty line are actually part of the middle class. The estimated lower threshold is used in cross-section surveys to quantify the size and the evolution of middle classes in Chile, Mexico, and Peru over the past two decades. The first relevant feature relates to the fact that the proposed thresholds lie around the 60th percentile of the distribution. The evidence also shows that the middle class has increased significantly in all three countries, suggesting that a higher number of households face lower probabilities of falling into poverty than they did in the 1990s. There is an important group of people, however, which cannot be defined as middle class from this perspective, but are not eligible for poverty programs according to traditional definitions of poverty.

This paper is a product of the Poverty, Equity and Gender Unit, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at lflopezcalva@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|**Source**<br>WDI_†_<br>WDI<br>WDI<br>WDI<br>WDI<br>Chinn & Ito (2008)<br>WDI<br>Beck _et al._ (2000)<br><br>ICRG_†_<br>ICRG<br>ICRG<br>WDI<br>WDI<br>WDI<br>Bloomberg, WDI<br>WDI<br>Beck _et al._ (2000)<br>Beck _et al._ (2001)<br>Doing Business<br>WDI and GEM_†_<br>Laeven & Valencia (2012)<br>itor. ICRG indicators are measured<br> for depreciation, as implied by the<br>ntries with insufcient data in the<br> available investment data.|
|---|
|**Variable**<br>**Defnition**<br>_Economic variables_<br>Fixed investment<br>Gross fxed capital formation in constant 2000 U.S. dollars<br>Output<br>Gross domestic product (GDP) in constant 2000 U.S. dollars<br>Output growth<br>Growth in real output_‡_<br>Cost of capital<br>Real interest rate (lending rate adjusted for infation)<br>Trade openness<br>Imports plus exports divided by GDP<br>Financial openness<br>Index of restrictions on capital account openness<br>_Structural and institutional variables_<br>Financial development<br>Domestic credit to private sector<br>Financial structure<br>Stock market value traded divided by domestic credit<br>Institutional quality<br>Simple average of rule of law and control of corruption indices<br>Institutional structure<br>Index of democratic accountability<br>Business environment<br>Index of strength of investment protection<br>Tax structure<br>Highest marginal corporate tax rate<br>_Alternative measures_<br>Gross investment<br>Gross capital formation in constant 2000 U.S. dollars<br>Investment rate<br>Gross fxed capital formation as share of GDP<br>Alternative cost of capital<br>Diference in domestic and exchange-adjusted risk-free rate*<br>Alternative fnancial development<br>Domestic credit by banking sector<br>Alternative fnancial structure<br>Stock market capitalization divided by domestic credit<br>Alternative institutional structure<br>Herfndahl index of government parties<br>Alternative business environment<br>Commercial contract enforcement index<br>Capital stock<br>Stocks of capital in constant 2000 U.S. dollars_§_<br>Financial crisis<br>Indicator variable for occurrence of fnancial crisis_¶_<br>_†_ WDI = World Development Indicators, ICRG = International Country Risk Guide, GEM = Global Economic Mon<br>such that higher values indicate lower risk (better outcomes).<br>_‡_ Since the depreciation rate is (assumed) constant across countries, the diference between adjusting output growth<br>theoretical model, is trivial.<br>* Computed as the U.S. real interest rate, multiplied by the change in the exchange rate.<br>_§_ Computed using the perpetual inventory method, with an assumed constant depreciation rate of 5 percent. Cou<br>constant investment series were backcasted using a regression of the investment defator on the GDP defator and <br>_¶_ Financial crisis defned as the coincident occurrence of a banking and currency crisis within the 5-year period.|

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