rafmacalaba/datause-encoder-data huggingface dataset:
7

# _Pure public goods_

If the good's benefits are both nonrival and nonexcludable, then the good is a pure public good. A good's benefits are nonrival among users when one agent's consumption or use of the good does not detract, in the slightest, from the consumption opportunities still available to other agents from the same unit of the good. For example, reducing CFCs or greenhouse gas (GHG) emissions helps stem either the depletion of the ozone layer or else the heating of the atmosphere, respectively, that affects all nations. If a provider of a public good _cannot_ keep an agent (e.g., an individual, a firm, or a nation) from receiving the good's benefits, then its benefits are nonexcludable. The provider cannot, therefore, keep a _nonpayer_ from taking advantage of the good's benefit, and this inability limits incentives on the part of users to finance the good's provision. Again consider the reduction of CFCs and GHGs. Nations engaged in these reductions cannot deny other nations from receiving the benefits that result. Thus, curbing global warming and improving the protective stratospheric ozone shield are purely public, because both activities' benefits are nonrival and nonexcludable.

Two additional examples of pure public goods at the transnational level are limiting the spread of contagious diseases (e.g., AIDs and Ebola) and uncovering basic research findings. Efforts to forestall the spread of a contagious disease benefit all those at risk regardless of whether or not they supported the containment. Additionally, the reduced risk experienced by one person from prophylactic measures does not limit the safety afforded to others. Once made public, basic research findings will diffuse rapidly among those with the capacity to understand them. Even before being released, information about basic research findings tends to leak out to the scientific community. The findings are nonrival and can be exploited to advantage by countless teams of researchers without diminishing their benefits. For example, the discovery of
# **4. Financial Access around the World**

In this section, we use the Financial Access Survey data to roughly estimate the distribution of rates of deposit account ownership and access to credit around the world. We first use regression models and other data sources to fill in missing values for the numbers of deposit accounts and loans in different countries. From the filled in estimates we can generate worldwide totals for deposit accounts, and the breakdown by country (expressed as maps). We then use household surveys to estimate the average number of accounts per account holder which allow us to estimate the number of banked and unbanked individuals worldwide and broken into developing and developed countries.

There are two main difficulties which we face in these exercises. First, our estimate of the number of accounts per active accountholder comes from a very limited set of household surveys and so is quite rough (our error bands are very wide). This is a fundamental limit imposed on us by the availability of household survey data and our estimate should be treated as only a rough guess. Second, there is significant bias in the reporting of non-bank data relative to commercial bank data which we were not able to overcome when filling in the missing data with estimates or outside data sources. While we were fairly confident in our ability to generate a reasonable econometric model to predict rates of commercial bank account ownership, there were a number of factors which made us less confident in any similar attempt with the other categories, including the fact that the development these other categories tend to be influenced by specific factors in each country. We tried to fill in the missing values from other data sources, especially for the two main non-bank categories of MOFIs and SSFIs, but were not confident that the data we found were very comprehensive. Thus we expect that our estimates of non-bank accounts are extreme lower bounds and should be interpreted as such. Our estimates of commercial bank accounts are generated by statistical models and are as likely to be too high as too low for any given country, so our estimates of commercial bank account numbers are not lower bounds but best guesses. This implies that where we estimate a ratio of non-bank to bank accounts this ratio is also an extreme lower bound on the true ratio.

15
<!-- Start of picture text --> China<br>25<br>oe2 e<br>>gS°°<br>it2%so é ° ee ee Ad e<br>g<br>Ss bd e ° e 6<br>5 eo * ~¢<br>e8 10 o 2<br>° * « °<br>e-* . e ¢@<br>Sy Daipecemeernsnenseseonsnessenarsessnreners snes eoteassnaroronnecnssneeseneestaned)smeenansrsnnestsneoa seein vere este<br>t) 5 10 15<br>Father's Schooling Years<br>@ Mean Years of Schooling Fitted values<br>@ Mean Residual Squared — Fitted values<br><!-- End of picture text --> <!-- Start of picture text --> India<br>25<br>g ® °<br>5S>S 20 .e 7° °<br>215 °<br>=[og eHe<br>5 nia:<br>3S 10 . s o-< e e<br>io) ee @ ry ° * 2©<br>5<br>©<br>t) 5 10 15<br>Father's Schooling Years<br>@ Mean Years of Schooling Fitted values<br>©@ Mean Residual Squared — Fitted values<br><!-- End of picture text --> <!-- Start of picture text --> Indonesia<br>20<br>2 e<br>5 4° ° °<br>= ee Se «@ eo *° @ e<br>=c3>e e °2<br>= 10 2<br>ro]g ee?ee” * J ° e ° ; °<br>2 5<br>S<br>oO<br>°<br>0)<br>0 5 10 15 20<br>Father's Schooling Years<br>@ Mean Years of Schooling Fitted values<br>@ Mean Residual Squared — Fitted values<br><!-- End of picture text -->
**Table A.10 Adult Hygiene Awareness** **<u>and Behavior—DID-Adjusted Model</u>**

||**N**|**No. of**<br>**Clusters**|**Adj.**<br>**R^2**|**(Treatment1 ***<br>**Midline)Beta5**|**(Treatment2 ***<br>**Midline)Beta6**|**(Treatment3 ***<br>**Midline)Beta7**|**Beta5 =**<br>**Beta6#**|**Beta5 =**<br>**Beta7#**|
|---|---|---|---|---|---|---|---|---|
|**Handwashing kn**|**owledge**||||||||
|Knowledge<br>before eating|10,550|251|.0615|.0645<br>(.0465)|.0654<br>(.0422)|.0935*<br>(.0504)|0<br>(.981)|.35<br>(.552)|
|Knowledge<br>afterdefecation|10,550|251|.0672|.0692*<br>(.0409)|.091**<br>(.0405)|.102**<br>(.0458)|.24<br>(.623)|.45<br>(.503)|
|**Practice**|||||||||
|Treatment of<br>drinking water|10,550|251|.113|-.00565<br>(.021)|.0304<br>(.0241)|.0061<br>(.0225)|2.23<br>(.136)|.27<br>(.602)|
|Soap used<br>before eating|10,550|251|.0712|.0799<br>(.0645)|.0805<br>(.0608)|.0693<br>(.0662)|0<br>(.991)|.03<br>(.868)|
|Soap used after|10,550|251|.019|.0711**|.0483|.016|.38|2.29|
|defecation||||(.0301)|(.0302)|(.0293)|(.54)|(.132)|
|Safe disposal of|1,768|247|.0936|-.0208|-.117|-.146|1.39|1.87|
|feces||||(.0803)|(.0835)|(.0935)|(.24)|(.173)|

_Note:_ * significance at the .1 level, ** significance at the .05 level, *** significance at the .01 level. # F-values and P-values (reported in parentheses). DID = difference-in-differences.

55
<!-- Start of picture text --> Proportion of the Global Increase in Poverty<br>India 85.2<br>Bangladesh [J ss<br>Mexico fj 2.1<br>South Africa fj 2.0<br>Ethiopia J 2.0<br>Morocco J 0.8<br>Pakistan | 0.8<br>Kenya | 0.7<br>Sri Lanka | 0.4<br>Turkey | 0.4<br>Rov [2.0<br>it) 20 40 60 80<br><!-- End of picture text -->

<!-- Start of picture text --> Proportion of the Global Reduction in Poverty<br>Philippines 20.5<br>Brazil 19.2<br>Nigeria 15.4<br>Vietnam 94<br>Peru a4<br>Indonesia 46<br>Thailand 37<br>Ecuador 3.4<br>Yemen 29<br>Colombia 28<br>Rov 92<br>it) 5 10 15 20<br><!-- End of picture text -->
- 8 -

In addition to ODA as measured above, some of the SSA countries have also received substantial amounts of foreign assistance in the form of debt reschedulings at the Paris Club since the reform process began. While the initial reschedulings were on non-concessional terms, they have become increasingly coi,cessional since 1988. Of the five countries being compared in this paper, Tanzania has received by far the largest amount of debt relief under t6e Paris Club. During the four Paris Club meetings that it has attended, it was granted debt relief (in cash flow terms) of about US$2.5 billion. By comparison, the total debt relief granted to Uganda is only US$352 million, and to Malawi it is even smaller at US$78 million. Ghana and Kenya have not received any debt relief through the Paris Club.

Thus, by all conventional measures, Tanzania receives a very large amount of foreign assistance. Even before the beginning of the structural adjustment programs in 1986, Tanzania ranked among those countries receiving the largest amount oi foreign assistance. Since then, higher levels of foreign assistance in Tanzania have been closely correlated with the adoption of stabilization and structural adjustment programs. To some extent then, foreign assistance has cushioned the impact of economic adjustment measures on the economy. Balance of payments support has allowed Tanzania to maintain relatively high levels of imports despite the sharp devaluations of the Tanzanian shilling, which could have been expected to dampen demand for imported commodities, including raw materials and capital goods. To determine whether Tanzania became more dependent on external assistance, or whether the assistance was used productively and has contributed to a more sustainable financial position in Tanzania, we will look, qualitatively and quantitatively, at its prograrn of structural adjustment in sections IV and V, respectively. Before we do thac, however, we explain the data adjustments made in the next section.

# **III.** Data Deficiencies and Adjustments

# _111.1 Data_ **_Deficiencies_**

It is difficult to make an accurate assessment of the performance of the Tanzanian economy because of the severe problems with its official data. This sectioL describes three major problems in Tanzania's macroeconomic data: (i) the flawed compilation of the national accounts data; (ii) the under-recording of foreign aid in the government budget; and (iii) the under-recording of exports in
# **VIII. Cost-Benefit Analysis**

Campos (2001) and Carnoy and others (2000) provide a very detailed discussion on the unit costs of CONALEP, the _general bachillerato,_ and the _media superior_ schools. Unit cost data are provided for 1992, 1994, 1995, and 1998. Cost items are divided into two classes: investment in infrastructure and equipment, and operational expenses. Operational expenses include, among other things, salaries of teachers and administrators, security services, and utilities (electricity, telephone, water, etc.). The cost data refer to the three year program. The control group's unit cost per year is $11,512.90, or 7.4 percent higher than CONALEP's unit cost of $10,719.98 (in 1998 pesos). As shown in Section V, the control group's average earnings are lower than CONALEP's average earnings ($26,504.40 vs. $22,684.8, 1998 pesos). It follows that CONALEP's present value is always positive. An alternative scenario was estimated assuming that the control group's unit cost is unknown, that there is a discount rate of _5_ percent, and that earnings differences remain constant over the next 30 years. The breakeven year, when the discounted present value of accumulated benefits equals costs, is 12 years in the alternative scenario. If opportunity costs are added, the breakeven year is 18 years.

# **IX. Conclusions**

The Mexican government introduced CONALEP as an alternative technical education system to the traditional upper-secondary education. CONALEP has undergone significant structural changes in the past decade. A major transformation took place in 1991, when CONALEP reduced the number of careers offered from 146 to 29 careers and introduced modular courses, the forerunner of the competency based education and training model (CBET) now adopted in Mexico.

45
level wage equation, as leaving out significant variables will cause omitted variable biases."<sup>4</sup>

# **6.1.8 Differentials by sectors**

Does the sector in which an employee is employed imake a difference to wages? Eniployees in the metal and textile sectors earn more and less, respectively, than their colleagues in the food sector, controlling for other firm and individual characteristics in the regression. The sectoral wage premium is 7 percent for metals and minus 30 percent for textiles, compared to the food and wood sectors.

**Table 9**

**Sectors**

||Product<br>Coef. S|ivity<br>td. Err.|T<br>P|>Itj<br>|Wages<br>Coef.<br>S|td. Err.|t<br>P|>jt)|
|---|---|---|---|---|---|---|---|---|
|metal<br>-|0.232<br>|0.077 -3|.026 0|.003<br>|0.069|0.037<br>|1.840 0|.066|
|<br> <br>textile<br>-|<br> <br>0.921|<br>0.375<br>-2|<br>.454 0|<br>.014<br>-|0.358|0.183 -|1.957 0|.051|
|<br> <br>textiexp<br>-|<br>0.015|<br>0.004 -4|.139 0|.000<br>-|0.006|0.002<br>-|3.226 0|.001|
|<br> <br>metatrai<br>-|<br>0.024|<br>0.076 -0|<br>.313 0|<br> <br>.754<br>-|<br>0.120|<br>0.037<br>-|3.245 0|.001|
|<br> <br>texttrai<br>|<br>0.118<br>|<br>0.299<br>0|<br>.395 0|<br> <br>.693<br>-|<br>0.484|0.146<br>-|3.324 0|.001|
|<br> <br>woodtrai<br>-|<br> <br>0.083<br>|<br> <br>0.469 -|<br>.178 0|<br> <br>.859<br>-|<br>0.454|0.229 -|1.987 0|.047|
|<br> <br>textasia<br>|<br> <br>1.725<br>|<br>0.536<br>3|<br>.216 0|<br> <br>.001<br>|<br>1.045|<br>0.261<br>4|<br>.000 0|<br>.000|
|<br> <br>woodasia<br>0|<br> <br>.812<br>|<br> <br>0.665<br>1|<br>.222 0|<br> <br>222<br>|<br>0.753|<br>0.324<br>2|.327 0|.020|
|<br> <br>apprmeta<br>0|<br> <br>.027<br>|<br> <br>0.056<br>0|<br>.478 0|<br> <br>.633<br>|<br>0.107|<br> <br>0.027<br>3|<br>.939 0|<br>.000|
|<br> <br>nexpmeta<br>|<br> <br>0.152<br>|<br> <br>0.039<br>3|<br>.874 0|<br> <br>.000<br>|<br>0.078|0.019<br>4|.061 0|.000|
|<br> <br>emplmeta<br>|<br> <br>0.042<br>|<br> <br>0.016<br>2|<br>.565 0|<br>.010<br>-|0.009|0.008 -|1.083 0|.279|
|<br> <br>folimeta<br>|<br> <br>0.328<br>|<br> <br>0.058<br>5|<br>.674 0|<br> <br>000<br>|<br>0.110|0.028<br>3|.897 0|.000|
|<br> <br>nexptext<br>0|<br> <br>.100<br>|<br> <br>0.092<br>1|<br>.092 0|<br> <br>.275<br>|<br>0.074|<br> <br>0.045<br>|<br>1.642 0|<br>.101|
|<br> <br>empltext<br>|<br> <br>0.240<br>|<br> <br>_0.055_<br>4|<br>.397 0|<br> <br>.000<br>|<br>0.045|<br> <br>0.027<br>|<br>1.706 0|<br>.088|
|<br> <br>fountext<br>-|<br> <br>0016|<br> <br>0003 -|<br>038 0|<br> <br>000<br>|<br>0.003|<br>0.002<br>2|.071 0|039|
|<br> <br>nexpwood -|.<br>0.496|.<br>0.286<br>-|.<br>1.733 0|<br> <br>.083<br>|<br>0.405|<br> <br>0.139<br>2|<br>.909 0|<br>.004|
|<br>emplwood -|<br>0.122|<br> <br>0.026<br>-|<br>4.783 0|<br>.000<br>-|0.032|0.012<br>-|2.545 0|.011|

4The introduction of enterprise-level covariates reduces the estimated relationship between wages, and, for example, completed university education consistent with the controls being correlated with employee quality. Nevertheless, a significant positive relationship is still present.

24
fiscal position. However, the exchange arrangement is not mostly significant in all regressions but column [3] of Table 19.

Table 20 uses the misalignments calculated with the PMG coefficient estimates of the long-run RER equation. The lagged RER misalignment again shows a negative and significant coefficient. Capital account openness variables such as _TL_ and _TAL_ are positive and significant while the _Chinn-Ito index_ of financial openness is significant in regressions that do not control for fiscal discipline. On the other hand, fiscal discipline and liability dollarization have a negative and significant coefficient when trade openness is positive and significant. Intervention is significant only while controlling for fiscal discipline while exchange rate regime has a robustly positive and significant coefficient estimate.

# _Composition Effects in Financial Openness_

Tables 21 and 22 attempt to disentangle the effects of financial openness and investigates whether the structural of foreign liabilities helps determine the size of RER undervaluations. In Table 21 we present the findings of RER misalignments using the time series Johansen estimates whereas Table 22 uses those of PMG estimates. Analogously to the _Probit_ analysis, we find that equity-related liabilities have negative and significant coefficient while loan-related liabilities have positive and significant coefficient in almost all specifications reported in Table 21.

Again, fiscal policy has a negative and significant coefficient, whereas intervention in the foreign exchange market is significant only when we exclude the fiscal position of our analysis. The coefficient is positive though, supporting the idea that active policies in the FOREX market may also influence the size of the undervaluation. Finally, we find that the exchange rate regime indicator –either measured by the coarse or find classification- has a positive and significant coefficient estimate in most regressions. Hence, countries with more flexible arrangements are able to sustain and also affect the magnitude of the RER undervaluation.

Table 22 shows that loan-related liabilities are positive and significant while the coefficient of equity-related liabilities is not significant for a 5% threshold in RER undervaluations. Trade openness is positive and significant while liability dollarization is negative and significant. Fiscal discipline is positive significant while intervention is always positive significant with or without fiscal discipline. Exchange rate regime is always significant under any classification.

26
The most important variables used in our analysis follow in Table 10; this includes notably the variables outlined in Equation (1) on p.12. In the Appendix, we document the considerable regional variation in the sample, showing the descriptive statistics of the variables included in the analysis for each of the four World Bank regions.

**Table 10: Climate, livelihoods and control variables: descriptive statistics**

|**Variable**||**Units**|**Range**|
|---|---|---|---|
|Total|Cash and subsistence annual income from all sources<br>transformed byinversehyperbolic sine (ihs).|ihs 2005<br>USDppp|0.0-12.2|
|Crop|Cash and subsistence annual income from cultivating<br>crops, transformed byinversehyperbolic sine (ihs).|ihs 2005<br>USDppp|0.0-11.6|
|Livestock|Cash and subsistence annual income from livestock<br>(animal husbandry) transformed by inverse hyperbolic<br>sine (ihs).|ihs 2005<br>USD ppp|0.0-11|
|Forest|Cash and subsistence annual income from forest<br>extraction, including processed forest products but<br>excluding plantation products, transformed by inverse<br>hyperbolic sine (ihs).|ihs 2005<br>USD ppp|0.0-10.9|
|Environment|Cash and subsistence annual income from non-forest<br>extractive sources -- grass- and bushlands, rivers, lakes,<br>including small-scale mining, excluding aquaculture,<br>transformed byinversehyperbolic sine (ihs).|ihs 2005<br>USD ppp|0.0-12.2|
|Wage|Cash annual income earned from wages and salaries,<br>transformed byinversehyperbolic sine (ihs).|ihs 2005<br>USDppp|0.0-11.6|
|Other|Cash and subsistence annual income from other sources<br>such as businesses, pensions and remittances,<br>transformed byinversehyperbolic sine (ihs).|ihs 2005<br>USD ppp|0.0-11.9|
|temp_mean|Annual mean temperature between 1981 and 2010,<br>linearextrapolationbetweengrids.|°C|9.1-28.5|
|temp_mean.2|Annual mean temperature between 1981 and 2010<br>squared,linearextrapolationbetweengrids.|°C<sup>2</sup>|82.0-814.3|
|prec_mean|Mean annual precipitation between 1981 and 2010,<br>linearextrapolationbetweengrids.|m/year|0.7-3.7|
|prec_mean.2|Mean annual precipitation between 1981 and 2010<br>squared,linearextrapolationbetweengrids.|(m/year)<sup>2</sup>|0.4-13.4|
|temp_anomaly|Temperature deviation in the survey year from<br>temp_mean divided by standard deviation of<br>temp_anomalyinthe period1981to2010.|standard<br>deviation|-2.5-1.4|
|prec_anomaly|Precipitation deviation in the survey year from<br>prec_mean divided by standard deviation of<br>prec_anomalyinthe period1981to2010.|standard<br>deviation|-1.3-3.6|
|temp_interaction|temp_anomaly × temp_mean|standard<br>deviation<br>× °C|-57 -33|
|prec_interaction|prec_anomaly × prec_mean|standard<br>deviation<br>× m/year|-3 -6|
|distance_road|Distance to nearest road|Km|0-186|
|distance-city|Distance tonearest city|Km|1-247|

26
property rights and absence of corruption depends on its tax base, its ability to collect revenue, and the level of external aid received. Furthermore, the model predicts that external aid affects the growth rate of GDP per capita through its effects on political institutions and state capacity. Empirical confirmation of these effects have important implications for aid policies towards fragile countries.

# 3.1 Growth regressions

First, we assume that It is large relative to <u>c, and kt</u> > kL. Second, we apply a log transformation on both sides of equation (2.3.4) to derive an empirical model that is used to test our fragility hypothesis.<sup>15</sup> where subscript i indicates country, and t indicates year. The dependent variable is ln(Kt+1/Kt); and the independent variables are property rights, Git; political stability and absence of violence, (1− λit); absence of corruption, (1 − dit); investment ratio (to proxy the stock of physical capital), (Iit/Yit); tax ratio, (Tit/Yit)); and various control variables. Because some of our variables contain zero values, we keep all variables in level form; hence we test the following simple linear model: where one of the control variables is the initial GDP per capita (I_GDPpc), yt−1.

We organize our data on growth rates and on the other variables for which measures are available into six half-decade observation periods, for 1980-84, 1985-89, etc. The data span from 1980 through 2010 for about 120 countries. All data are from the World Bank (WDR 2011). In all regressions, we use the growth rate of real GDP per capita as our dependent variable and a set of independent variables in which we include initial GDP (IGDP), investment ratio (I/GDP), tax ratio (T/GDP), total Offi cial Development Aid (ODA), Political stability and absence of violence (Polit. Stab.), Latitude, Latitude Square, and a set of dummies, including intercept and slope

> 15 We assume that <u>c</u> is very small relative to It and can be droped from equation (2.3.4).

18
15 Table 2. (con.t) Relative Change Model

|Socioeconomic|||Regressi|on coeffici|ents for||R2|
|---|---|---|---|---|---|---|---|
|category|Constant|Base<br>value|1980-84<br>|<br>1985-89|Pre-<br>Adjust<br>ment|Post-<br>Adjust-<br>inent||
|Total|0.93<br>(6.7)|*|-0.007<br>(-0.2)|<br>-0.033<br>(-1.1)|-0.024<br>(-0.4)|-0.017<br>(-0.3)|0.20|
|Urban|0.86<br>(3.4)|*|0.001<br>(0.5)|<br>0.039<br>(0.0)|<br>-0.069<br>(-0.6)|0.055<br>(0.6)|0.04|
|Rural|0.93<br>(6.2)|*|-0.011<br>(-0.2)|<br>-0.033<br>(-1.0)|<br>-0.020<br>(-0.3)|-0.025<br>(-0.4)|0.19|
|No education|0.90<br>(4.5)|*|0.022<br>(0.4)|<br>-0.036<br>(-0.8)|<br>-0.050<br>(-0.6)|-0.017<br>(-0.2)|0.19|
|Primary educ|1.02<br>(5.7)|*|-0.035<br>(-0.7)|<br>-0.028<br>(-0.7)|<br>-0.020<br>(-0.3)|0.012<br>(0.2)|0.04|
|Secondary<br>+ ed|uc 1.50<br>(2.6)|*|-0.134<br>(-0.8)|<br>-0.050<br>(-0.4)|<br>0.241<br>(1.0)|-0.053<br>(-0.2)|0.12|
|Rural, no educ|0.91<br>(4.4)|*|0.016<br>(0.3)|<br>-0.034<br>(-0.7)|<br>-0.051<br>(-0.6)|-0.023<br>(-0.3)|0.19|
|Urban, no educ|0.77<br>(2.8)|*|0.064<br>(0.7)|<br>-0.007<br>(-0.1)|<br>-0.029<br>(-0.2)|0.032<br>(0.3)|0.08|

Model specification: 5<sup>qo(i)/</sup> 5<sup>qo(base)-</sup> constant + years of period i in 1980-84 + years of period i in 1985-89 + years of i in 3 years before start of adjustment + years of period i in 3 years after start of adjustment.

In the absolute decline model, the R<sup>2</sup> 's are about 0.8 or 0.9 (except the equation for secondary and higher education, for which the R<sup>2</sup> is only about 0.5, perhaps because of the small numbers of women and children in this group). In the total equation, both 1980-84 and 1985-89 have large negative coefficients. But the coefficient for 1985-89 is not much larger than that for 1980-84, indicating again some slowdown of the pace of decline in child mortality in the late 1980s. Both the pre-adjustment and the post-adjustment periods have negative