rafmacalaba/datause-encoder-data huggingface dataset:
Table 13: Poverty Gap Equations (Tobit): East European Countries

||_Bulg_|_aria_|_Hung_|_ary_|_Pol_|_and_|
|---|---|---|---|---|---|---|
|_Dependent Variable: In (Erpenditure per_<br>_Equivalent Adult) Right-Censored at Poverty_<br>_Line_|_Parameter_<br>_Estimates_|_Standard_<br>_Error_|_Parameter_<br>_Estimates_|_Standard_<br>_Error_|_Parameter_<br>_Estimates_|_Standard_<br>_Error_|
|Intercept|8.559*|0.184|9.273*|0.060|7.417*|0.055|
|Number of children|-0.085*|0.020|-0.078*|0.007|-0.093*|0.005|
|Number of male adults|-0.098*|0.023|-0.009|0.011|-0.027*|0.007|
|Number of female adults|-0.056*|0.023|0.003|0.011|-0.004|0.007|
|Number of elderly|-0.077*|0.031|-0.018|0.014|-0.02l*|0.011|
|Education of head: primary|-0.285*|0.039|-0.252*|0.018|-0.225*|0.014|
|Education of head: vocational/technical|0.142*|0.071|-0.095*|0.020|-0.106*|0.013|
|Educationofhead: university|0.171*|0.057|0.164*|0.032|0.207*|0.028|
|Age of head|0.024*|0.007|0.022*|0.002|0.012*|0.002|
|Age of head squared|-0.000*|0.000|-0.000*|0.000|-0.000*|0.000|
|Female head|-0.173*|0.038|-0.038*|0.014|-0.042*|0.010|
|Household owns enterprise|0.355*|0.090|0.132*|0.028|0.236*|0.026|
|Household owns land|0.280*|0.035|-|-|0.055*|0.013|
|Household is renter|-0.346*|0.055|-0.207*|0.015|-0.025*|0.011|
|Share of wages in household income|0.528*|0.060|0.199*|0.029|0.220*|0.018|
|Number of unemployed in household|-|-|-0.115*|0.012|-0.114*|0.010|
|Head is unemployed|||-|-|-|-|
|Head is inactive|-|-|0,007|0.024|0.045*|0.016|
|Location: non-capital city|-0.035|0.046|0.055*|0.016|-0.031|0.022|
|Location: village|-0.231*|0.053|-0.005|0.017|-0.142*|0.024|
|Log-likelihood|-1178.5||-2580.1||-5345.9||
|Chi-squared|595.31||1501.56||2368.6||
|Prob > chi-squared|0.0000||0.0000||0.0000||
|SER|0.527||0.351||0.390||

_Note:_ * Indicates that coefficient is significantly different from zero at 90 percent confidence level.

67
details on DGD. In order to investigate the effect of CEFTA countries adopting the EU common external tariff, we make use of MFN tariff data that come from https://wits.worldbank.org/WITS/ WITS/ Restricted/Login.aspx. The original tariff data are at the HS17 8-digit level for Albania, Bosnia and Herzegovina, Moldova, Montenegro, and Serbia, while the tariff data for Macedonia are at the HS17 6-digit level. We aggregate all tariff data to the HS17 6-digit level, as correspondence tables to HS07 and SITC Reve. 3 & 4 are only available at the 6-digit level. We aggregate the tariff data in two different ways: (i) unweighted by taking simple averages, (ii) weighted by total import shares of the respective sectors. We then merge SITC Rev. 3 & 4 correspondences and HS07 correspondences in order to be able to match with our mining, manufacturing, and agricultural trade data.

# **4 Empirical Analysis and Findings**

This section presents our empirical findings. Subsection 4.1 offers an analysis of our partial equilibrium estimates, while Subsection 4.2 describes the results from our general equilibrium analysis.

## **4.1 On the Uneven Impact of EU and CEFTA Borders**

The estimation results that we report and analyze in this section are based on econometric specification (2). We start with a discussion of our findings across the four main sectors in the sample, including Manufacturing, Agriculture, Mining, and Services. Then, we zoom in on the determinants of trade flows across eleven manufacturing categories, as described in the data section. Following the best estimation practices from the structural gravity literature, which we summarized in Section 2, we obtain our main results with the PPML estimator. Estimates obtained with the OLS estimator are also consistent with our main findings.<sup>17</sup> Several findings stand out from the estimates for the four main sectors in our sample, which appear in Table 1. First, overall, we note that the estimates of the effects of the standard gravity

> 17The corresponding OLS results can be found in Tables 3 and 4 of the Supplementary Appendix, where, as mentioned earlier, we also offer estimates that are obtained after replacing the missing values in the ITPD-E with zeroes (see Tables 5 and 6) and estimates that are based on panel data (see Tables 7 and 8).

15
|**Oaxaca decomposition of log v**|**Table 8**<br>**alue of output per h**|**ectare: South (exc**|**luding the west)**|
|---|---|---|---|
|_A. Mean Gender differential_||||
|Mean Male Managedplot||12.53***||
|Agricultural Productivity||(0.0802)||
|Mean Female managedplot||12.29***||
|Agricultural Productivity||(0.0963)||
|Mean Gender Differential||0.239*||
|in agricultural Productivity||(0.125)||
|_B. Aggregate Decomposition_|Endowment Effect|Male Structural<br>Advantage|Female Structural<br>Disadvantage|
|**Total **|0.287***|-0|-0.0484|
||(0.111)|(0.0144)|(0.130)|
|Share of the Gender Differential|120%|0%|-20%|
|_C. Detailed Decomposition_|Endowment Effect|Male Structural<br>Advantage|Female Structural<br>Disadvantage|
|Age|0.0148|-0.480|-2.037|
||(0.0366)|(1.034)|(1.453)|
|Age Squared|-0.0160|0.510|1.424*|
||(0.0386)|(0.534)|(0.764)|
|Years of schooling|0.00647|-0.102*|-0.0793|
||(0.0219)|(0.0591)|(0.0762)|
|Manager is nonmuslim†|0.0104|0.130|-0.380|
||(0.00955)|(0.157)|(0.336)|
|Manager has nonfarm activity†|0.00134|-1.21e-06|-0.00301|
||(0.00392)|(0.0309)|(0.0390)|
|Household female adult size(age 12-60)|-0.0249*|-0.0236|0.0536|
||(0.0131)|(0.0692)|(0.0854)|
|Household male adult size(age 12-60)|0.0189|0.0790|0.114|
||(0.0125)|(0.0626)|(0.0726)|
|Child dependencyratio|-0.00365|0.0924*|0.0724|
||(0.00880)|(0.0526)|(0.0565)|
|Log [GPS-Land Area(HA)]|-0.215***|0.141|-0.225|
||(0.0742)|(0.310)|(0.508)|
|Log [GPS-Land Area(HA) Squared]|0.0268|-0.148|-0.00854|
||(0.0321)|(0.183)|(0.322)|
|Plot distance to household|0.00293|-0.00120|-0.00272|
||(0.00333)|(0.00298)|(0.00291)|
|Access to extension†|0.00875|-0.00121|-0.000208|
||(0.00758)|(0.00800)|(0.0114)|
|Grows cash crop†|-0.000211|-0.00132|-0.0104|
||(0.00166)|(0.0166)|(0.0214)|
|Onlyone croponplot†|-0.0193|0.0119|0.00573|
||(0.0160)|(0.0199)|(0.0305)|
|Log [Qtyof fertilizer(kg)/ha]|-4.88e-05|-0.0391|-0.0339|
||(0.0164)|(0.0295)|(0.0481)|
|Log [Herbicide(kg)/ha]|0.0310**|-0.00129|-0.000517|
||(00150)|(00111)|(000805)|
|Log [Pesticide(kg)/ha]|.<br>-0.00356|.<br>-0.00856|.<br>-0.00649|
||(0.00727)|(0.00997)|(0.0112)|
|Usedpurchased seed†|0.00171|-0.0350|-0.0254|
||(0.00357)|(0.0519)|(0.0710)|

38
potential in Bangladesh, Indonesia, and Nepal, among others (Lobell et al., 2009). Insufficient soil health is commonly used to explain, at least partially, said yield gaps (Cassman, 1999; Lobell et al., 2009; Tittonell et al., 2008).

Several methods for closing yield gaps have been identified by the scientific community. According to the FAO, the use of sustainable soil management techniques, such as zero tillage and agroforestry, could boost food production by as much as 58 percent (FAO, 2015). The use of improved crop varieties and chemical input use have been shown to improve productivity and/or resilience exponentially (Cassman, 1999; Duflo et al., 2008). Additionally, Kumar and Quisumbing (2011) draw positive linkages between improved varieties and nutritional status. However, the uptake of such practices has been unenthusiastic, particularly in Sub-Saharan Africa. Marenya and Barrett (2009) suggest that farmer demand for fertilizer use is variable on soil carbon level, with higher carbon content plots achieving greater marginal product of fertilizer, suggesting that soil quality has implications for adoption of fertilizer use. As will be illustrated in this paper, relying on subjective farmer assessments of soil quality as a proxy for carbon content may provide data unsuitable for use in targeting of fertilizer adoption programs.

Productivity has also been observed to vary with farm size. Soil quality has long been argued to explain the inverse farm-size productivity puzzle, which suggests that small farms are more productive than larger farms (Bhalla and Roy, 1988; Barrett et al., 2010; Carletto et al, 2013; Carletto et al., 2015; Lamb, 2003; Tatwangire and Holden, 2013). Despite the results from Barrett et al.’s (2010) experimental study which concluded that soil properties did not explain away the inverse productivity relationship, much research suggests that omitted high quality data on soil properties is at least partially responsible for the inverse relationship (for example, Bhalla and Roy (1988)).

Yield gaps and the _quantity_ of crop production are not the only concerns related to soils, food security, and nutrition. The _quality_ of food produced can vary, and lack of micronutrients can lead to hidden hunger (Cakmak, 2002; FAO, 2015). With a direct link between the micronutrient content found in crops and the soils from which they grew, soil health measurement and monitoring could lead to the identification and, ideally, prevention of micronutrient malnutrition.

Agricultural analysis is multidimensional. Knowing the quantity of production alone, or even productivity, is not sufficient to analyze determinants of strong yields, estimate adoption of sustainable or improved farm management practices, or establish causal links between agriculture and nutrition. These data, however, are most readily available in household-level surveys with a focus on agriculture, such as 3
is associated with a 2% drop in income. Converting all variables into standard-deviation units, each one standard deviation increase in the correlation between morality and ability is associated with an increase in the log of per capita income of one-fourth of a standard deviation, approximately equal to $3600 for the median-income country in our sample.<sup>15</sup> This standardized coefficient of .25 is comparable to that for schooling (-.23 standard deviations), and larger than for geography or morality (both at .16), and openness (.08). Only rule of law (.38) has a larger standardized coefficient).

The ability and morality variables are computed from survey data, with different sample sizes in each country’s surveys. Values estimated from larger samples will tend to be more precise, so the observations for those countries contain more information. In equation 2-5, we report estimates using weighted least squares, with observations weighted by the number of WVS respondents. Results from WLS estimation differ only trivially from the OLS results in equation 2-4.

Equation 2-6 shows how results change when the most influential observation in favor of our key hypothesis is dropped. Figure 1 depicts the partial correlation between the dependent variable and the morality-ability correlation from equation 2-4. Thus, the slope of the least-squares line imposed on the figure equals the regression coefficient 3.616. Tanzania is clearly the most influential case in our favor. When this observation is dropped in equation 2-6, the coefficient of the morality-ability correlation declines from 3.62 to 2.85, but remains significant at the .01 level. Two other relatively influential cases are Uganda and Kyrgyzstan.<sup>16</sup> When they and Tanzania

> 15 The standard deviation of (log of) per capita income in the sample is 1.13, so a standard deviation increase in the morality-ability correlation will be associated with an increase of about .28 (=1.13 x .25) in the log of per capita income. This .28 estimate coincides with the difference in log of per capita income between the two median-income countries in our sample, Bulgaria (9.33) and Russia (9.61). In dollar terms, the difference in incomes between these two countries is $3658 (= 14, 917 - 11, 259). Because income is in logs, the marginal effect in terms of dollars will be greater (lower) for countries at higher (lower) income levels. For countries at the 25<sup>th</sup> percentile of the income distribution in our sample (such as Georgia or Guatemala), the marginal effect is about $1400, and for countries at the 75<sup>th</sup> percentile (such as Israel or Korea) it is about $8000.

> 16 Mali is also a negative outlier on the ability-morality correlation, but it lies above the regression line.

21
|Code|Household head:<br>education<br>(grade completed)|Household head:<br>main language spoken<br>at home|Region of<br>residence|Average<br>probability of<br>attending school:<br>ages 5–17 (%)|Average probability of<br>accessing/using health<br>services when required:<br>ages 0–17 (%)|
|---|---|---|---|---|---|
|11|7th to 9th|||89|80|
|12|10th or more|||94|88|
|13|5th or less|||89|83|
|14|6th|Spanish only or||94|87|
|15|7th to9th|mixed||91|85|
|16|10th or more|||96|90|

_Source_ : Author’s estimates from INEC’s EPH (2009).

After sorting these 16 circumstance groups by their probability of access/use to education (from least to most likely) and their probability of access/use to public health care, respectively, figure12 and figure 13 report the gap between each group’s share of population and share of benefits. For access to secondary education, groups that have a lower than average probability to access and receive a share of public resources lower than their population shares are children in households whose heads speak only Guarani or, if speaking Spanish or both, live in rural areas. These groups are coded as 13, 11, 3, 2, and 1 in figure 12. Interestingly, groups with household heads that speak only Guarani and reside in urban areas (and are well educated)—that is, groups 10 and 14—opt out of the public education system to a considerable extent: between 20 and 36 percent of children in those circumstance groups are enrolled in private education, compared to between 1 and 5 percent of children in the other mentioned groups. This procedure would then only select five very specific circumstance groups to target with additional resources following the integrated outcome and opportunity criteria.

Similarly, children with the least probability of accessing public health care (when sick) and having the largest gap—in relative terms—between their population and benefit shares live in rural households whose heads speak only Guarani and have low levels of education (sixth grade or less), as seen in groups 1 and 5. Also in this category—of groups receiving proportionally less benefits than their population shares—are children in households whose heads speak Spanish or both languages, reside in rural areas, and typically have household heads with low education. Those groups are coded 14, 13, 10, 11, 7, and 3 in figure 13. These groups represent a small fraction of the population—20 percent of the population for the eight groups identified—but their gaps in terms of population-benefit shares are very large in proportional terms. These groups—most of which do not appear to opt out of the public health system in favor of private attention<sup>32</sup> —would constitute obvious candidates for targeted interventions. It

> 32 Only groups 10 and 14 appear to substantially seek private health providers when demanding medical attention. In effect, these groups use private providers in one out of three cases of sickness and/or accidents, according to EPH (2009).

25
Future research on carbon supply from households should prioritize estimating carbon supply functions that incorporate both intra-household and inter-household marginal costs. Both are valid elements of carbon supply functions. We have included the latter, which we see as a contribution to a literature that has generally estimated at most a few points rather than anything like a “function.” People who are part of a formal collective action institution, the CF program, react very differently to REDD+ and perhaps other PES schemes than those without such affiliations. We see this point as important, because even in Nepal where the CF program is very large, it only covers about 35% of the rural population and those who are unaffiliated may be able to offer lower-cost carbon sequestration. Additional work on the sources and implications of differences is therefore likely to be very useful.

# **References**

Adamowicz, W., Boxall, P., Williams, M. and Louviere, J. 1998. Stated preference approaches for measuring passive use values: choice experiments and contingent valuation. _American Journal of Agricultural Economics_ . 80, 64-75.

Adamowicz, W., Swait, J., Boxall, P., Louviere, J. and Williams, M. 1997. Perceptions versus objective measures of environmental quality in combined revealed and stated preference models of environmental valuation. _Journal of Environmental Economics and Management_ . 32, 65-84.

Agrawal, A., Chhatre, A., & Hardin, R. (2008). Changing Governance of the World's Forests. _Science, 320_ , 1460-1462.

Alpizar, F., F. Carlsson and P. Martinsson. 2003. Using choice experiments for non-market valuation, _Economic Issues_ , 8, 83-110.

Angelsen, A. 2010. The 3 REDD ‘Is’ _Journal of Forest Economics_ 16 (2010) 253–256 Bailis, R, R. Drigo, A. Ghilardi, O. Masera. 2015. The Carbon Footprint of Traditional Woodfuels. _Nature Climate Change_ 5: 266- 272.

Baland, J-M, P. Bardhan, S. Das, D. Mookhjerjee and R. Sarkar. 2010. The Environmental Impact of Poverty: Evidence from Firewood Collection in Rural Nepal. _Economic Development and Cultural Change_ . Vol. 59: 23-61.

Blaeij, A.T. de, Nunes, P.A.L.D. and J.C.J.M. van den Bergh. 2007. ‘No-choice’options within a nested logit model: one model is insufficient. _Applied Economics_ , 39: 1245-1252.

Bluffstone, R. 2013. Economics of REDD+ and Community Forestry. _Journal of Forest and Livelihood_ , 11(2), 69-74.

35
# **I. INTRODUCTION**

The FSAP program was launched as a joint Bank-Fund initiative nearly a decade ago to assess on a regular basis the stability and developmental challenges of financial systems around the globe. The number of completed FSAPs has now reached 119, covering 103 countries in all regions of the world and at all levels of income (Table 1). FSAPs cover all aspects of financial systems, from both a stability perspective and a developmental perspective. Drawing “lessons” from this program is a formidable challenge in view of its size (simply reading the thousands of pages of FSAP reports would already a major undertaking) and the variety of its content. At the same time, however, the broad crosscountry coverage of the FSAP program as well as its breadth and depth of analysis provide a unique opportunity to peek deep down into financial system issues in a systematic and wide ranging manner.

This paper uses a simple statistical approach to exploit some of this wealth of information. We classify FSAP recommendations along a logical grid that organizes FSAPs’ content in accordance with the fabric of financial activity and the ways in which states organize their policies in support of financial development. Counting recommendations along such a grid provides a simple yet fairly rich and flexible way to measure FSAPs’ focus and orientation. The FSAP studies can thus be compared and contrasted according to their date, their nature (first assessment or update), the region and main structural characteristics of the assessed country (income level, size, etc.), and any other relevant characteristics of the study.

**Table 1.   FSAPs Completed and Projected, 1999-2008**

||||||**Actual**<br>|||||**Proje **<br>|**cted**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
||**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2007**|**2008**|
|**AFR**||1|3|4|5|2|3|2|1|3|6|
|**EAP**||||1|1|1|||1|2||
|**ECA**||2|7|4|3|5|4|5|2|6|8|
|**LCR **||2|4|1|2|6|5|4||1|3|
|**MNA**|1|1|3|1|3|2|1|1||4|3|
|**SAR**|||1|1|1|1||||1|1|
|**High Income**||2|||5|4|1|5||1|2|
|**Monetary Areas**||||||1||1|||1|
|**Total **|1|8|18|12|20|22|14|18|4|18|24|
|**Total Updates**|||||1|4|4|6|1|6|16|
|**ROSCs per FSAP**|1.0|6.3|6.1|7.2|6.7|5.0|3.4|3.0|2.0|||

With some caveats reflecting the inherent limitations of the exercise (including difficulties in sorting out supply and demand effects), this analysis provides a simple monitoring tool to help understand better the nature and evolution of the FSAP program. At the same time, it throws light on the _nuts and bolts of the process of financial development and its inter-linkages with economic development_ , i.e., as countries develop and their financial systems deepen, what policies matter when. While many of the findings conform well to what one would expect (set up basic frameworks and rules before focusing on how well they are applied, the more basic naturally comes before the 2
> Figure 2. Transparency Index and Regulatory burden (KKZ)

<!-- Start of picture text -->
1.24478  0 0, o  o<br>8800  0008  0  0  00<br>6 800  00.0  00  00<br>e0  . O  o0  0  0So  o  o<br>0 °o°° 0  00  °  0  o  0°  ° 0 00<br>9  °  000 °  °°  0  8  o 0<br>Sa  w  0 8~~~~~~ 0  0  00800  0 0<br>m  0  000  0  0  ~~~~~~~0  0<br>00  0  0<br>0  0<br>0~~~~~~~~~<br>0~~~~~~~~~<br>-3.14219  -__<br>1  ~~~~~~~~~~~~~~~~6<br>transparmncy<br>Figure 3.  Transparency  Index  and Graft  (KKZ)<br>2.12902  - o0 0<br>0<br>0  0<br>o  0 0<br>8<br>90<br>0  0<br>o  0  00  0  0  00  0<br>8  0  0  0  0  : 0  00  0<br>0  o  0  0  0  00<br>000  0 008  0 0  8  0 0  8 0  0 0~~~~~~~~~~~~~~~~ 0o<br>o  o  8§~  o  000°<br>0  0  o<br>-1.56681  0<br>6.0125 800  8  o  0~0  transpoarency o  0  0<br><!-- End of picture text -->

Figure -1.5668 **4.** 000Transparency 0 Index 0 and Corruption **(ICRG)** <!-- Start of picture text --> 000<br>6.015 - 00 8 0 00 0 0 0<br>0 0 0 0<br>0 0 8 o 0<br>CD 0 0 0 0 0 00 0 0 0 0<br>E~~~0 0 0 3 . 00 0 o 0 0 000<br>00 00 000<br>0~~~~~<br>0 0 0 0 0<br>0<br>.68125 0 0<br>transparency<br><!-- End of picture text --> 20
- 70 -

Second, related to the above, private financing in transport infrastructure has been limited. Private participation in infrastructure continues to play an important role even in developing countries. It has reached close to $160 billion per annum in 2009. However, private participation remains selective by sector and country. The transport sector accounts for about 15 percent of total private investments in infrastructure for the past two decades. The vast majority were invested in the ICT and energy sectors (Figure 43).

It is noteworthy that private financing cannot substitute for public financing. Governments’ strong commitment and other legal and institutional frameworks in the infrastructure sector are important to catalyze more private investments, as experienced in Latin America during the 1990s. While Chile and Colombia, in which public investment has remained high, attracted many private investments, private participation in infrastructure also tends to be marginalized, as in Argentina and Mexico where the public spending was also tightened under the fiscal pressure ( Calderón and Servén, 2004; Fay and Morrison, 2007).

**Figure 43. Private participation in infrastructure in developing countries**

<!-- Start of picture text --> 200<br>Water<br>180 Telecom<br>160 Energy<br>Transport<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>-<br>Source: PPI database.<br>Private participation in infrastructure in<br>developing countries ($ 2009 constant billion)<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> However, the current public procurement systems may not work well to improve efficiency in spending resources in the infrastructure sector. Public procurement is normally sizeable in the economy. The public sector often account for 20 to 25 percent of GDP in developing countries. A large portion of this goes through public procurement systems (OECD, 2005c).