rafmacalaba/datause-encoder-data huggingface dataset:
Figure 8. Poverty Convergence was Stronger Before the Crisis <!-- Start of picture text --> (a) 2006-2009 (b) 2010-2013<br>TRA1<br>TR10 TR62 TRA2 TRC2<br>TR42 TR63 TRB1 TRC3<br>TR33 TR61<br>TR41<br>TRB2<br>TR82TR90TR71 TRA2 TR31TR10 TR81<br>TR33 TR81TR72TR83 TRA1TRB1 TR32 TR52 TR72 TRC1<br>TR51 TR52TR32 TR22 TRB2TRC3 T RC2 TR71<br>TR63 TRC1 TR51 TR21TR22 TR83<br>TR31 TR21 TR90<br>TR82<br>TR62<br>TR61 TR41 TR42<br>2 2.5 3 3.5 4 2.5 3 3.5 4<br>Log initial poverty rate ($5/day PPP) Log initial poverty rate ($5/day PPP)<br>.15 0<br>.1<br>.05<br>-.05<br>0<br>-.05<br>-.1<br>Annualized growth in the poverty rate 2006-2009 ($5/day PPP) Annualized growth in the poverty rate 2006-2013 ($5/day PPP)<br>-.1<br><!-- End of picture text --> _Notes_ : Initial poverty rate is from 2006. Income based poverty. There are 26 NUTS2 regions in Turkey. No regional population weights used.

_Source_ : SILC and LFS 2006-2013, authors’ calculations.

Poverty convergence was present before the financial crisis. Over the period 2006 to 2009, the poverty convergence elasticity at the $2.5/day PPP line was -0.132 and -0.058 at the $5/day PPP line (Table 3). However, this convergence was also driven by slowing growth in Istanbul and richer regions in 2009. In the pre-crisis period (2006 to late 2007), Turkey experienced successful GDP growth and reduction both in terms of poverty and inequality. Since the period 2006 to 2009 includes both high growth and crisis years, the poverty elasticity effect was also very close to zero. From 2010 to 2013, the poverty convergence elasticity was negative at both the $2.5 and $5/day PPP poverty lines, illustrating that after the crisis, growth was effective in reducing poverty. While the poverty elasticity effect became negative in this period, the mean convergence and direct poverty effects became positive. Poorer regions grew slower, and regions with higher initial levels of poverty reduced poverty at a slower rate or even saw poverty increase.

As the results show, poverty convergence over the period 2006 and 2013 was uneven. Some parts of the country experienced high amounts of poverty reduction while other regions lagged behind. Across time, there are also differences in convergence as growth became less pro-poor after the financial crisis. These particularities all contribute to understanding the patterns of regional poverty convergence and reduction for Turkey.

15
businesses vertically. Through vertical integration contracts outside the firm were minimized- a preferred solution given the cost of enforcement. The government recognized the importance of court development- in the late 1990s a strengthened court enforcement system meant that industry no longer had to devise alternative methods of reducing risk and could diversify business dealings.<sup>10</sup> Another study (Kumar, Rajan and Zingales, 1999) shows that when judicial systems are strong, countries tend to have larger firms. Firms can rely on courts to protect their property rights and enforce increasingly sophisticated contracts, for example property rights associated with intellectual property. Pinheiro and Cabral (1998) discuss how states in Brazil with better performing courts have more developed credit markets. As is intuitively obvious, creditors are loath to advance credit when they lack the means to enforce repayment. For the post communist countries, Johnson, McMillan and Woodruff (2000) discuss how well functioning courts support the development of new relationships so that entrepreneurs are encouraged to contract with new suppliers. Without well functioning courts, firms are forced to rely only on “relationship business”—that is, contracting with those with whom they already have well established relations. In a rapidly changing economic environment such constraints prevent firms from venturing into potentially very profitable opportunities. They also limit the extent to which they are able to protect themselves from relationships that are no longer profitable. Foley (2000) shows how the development of formal bankruptcy proceedings, in which costs play a key role, make both debtors and creditors better off. Bigsten et al (2000) and Collier and Gunning (1999) analyse countries in Africa. They find that ineffective courts and weak legal systems prevented the growth of small firms or limited

> 10 World Bank, 2001, Gow and  Swinnen 2001.

7
describes its key steps and data requirements in more detail.

# **4 Imputation-based Methods**

Calculating unit costs for any economic activity is challenging when there are fixed and joint costs. Hence, even if surveys collect direct information on carriers’ self-reported costs, one can still be skeptical about the degree to which they capture underlying true costs. It is therefore common practice in economics to estimate unit costs assuming that producers minimize costs. As long as there is data on key cost components such as driver wages—which can be proxied by other low-skilled occupational wages—fuel, and vehicle depreciation, a cost minimization routine can be used to impute optimal itineraries and associated costs.

Over the recent past, increased availability and diffusion of Geographic Information System (GIS) data and software enabled researchers to calculate least-cost routing decisions and associated transit times using standard computers. The starting point is a digitized transportation network. Ideally, this should encompass all relevant infrastructure modes (rail vs road) and road types (highway, two-lane, paved, gravel) linking the nodes that represent key centers of economic activity in the economy under study. Information about travel speed along each link helps calculate fastest routes and associated travel times between nodes. A related choice for the determination of the nodes is one of geographic aggregation. Researchers typically use centroids of metropolitan or administrative areas of various size. Another possibility is to impose a high-resolution grid. While doing so at a high geographic detail increases precision, it comes at the cost of steep computational times. Well established algorithms and numerical methods help to resolve the trade-off. Dijkstra’s algorithm and fast-marching method are widely used to find shortest or least-cost paths between nodes in a road transport network (Allen and Arkolakis 2014, Donaldson 2018).

Beyond the technical aspects of route optimization, formulating the relevant cost minimization problem requires making assumptions on how carriers operate: do they chose fastest or shortest routes? What is their willingness to pay tolls if that option is available to reduce travel distance and duration? Do they make these decisions independently of shippers or do they offer a menu of shipping times and prices? Do idiosyncratic demand effects on timeliness matter for routing decisions? For each scenario, an appropriate model of minimization can be used to impute times, distance and routes. Additional external information about variable and fixed operation costs would help transform the imputed units from travel distance and time to actual monetary costs.

Instead of calculating routing and travel times as described above, new GPS 9
GMB for those who manage to find (paid agricultural) work in their home villages in The Gambia.<sup>22</sup> The next two columns of Table 4 examine impacts on internal migration to the capital city of Banjul. This is the most common form of migration, with 41 percent of the control group having spent at least a month in Banjul during our 18-month follow-up period, and 10.7 percent being there at the time of the follow-up survey. The Senegal treatment is found to have reduced the likelihood of having gone to Banjul at some point during the follow-up period by 7.6 percentage points, and the vocational training treatment by 3.6 percentage points. Both these interventions therefore appear to have spurred some longer-term migration to Senegal and crowded out some temporary internal migration. Our wage data show the average monthly earnings of those in Banjul at the time of the endline survey were 5,088 GMB, which is almost 50 percent higher than income in their home villages, but more than 1,000 GMB (20 percent) lower than the income earned by those working in Senegal.

The last three columns then examine whether the treatments changed backway migration. Column 6 examines the number of concrete steps taken towards backway migration, such as saving the money needed, asking someone for help finding a job or accommodation, and mapping out the route and making a travel plan. The control mean is for 1 step out of 5 to be completed, and the vocational training treatment reduces this by 0.15 steps. However, this effect is no longer significant when adjusting for multiple hypothesis testing across all backway migration outcomes. We then see small (0.1 to 0.4 percentage point) reductions, which are not statistically significant, on the likelihood of attempting the backway, and of making it to Europe. However, the control mean is only for 1.1 percent of individuals who have made a backway migration attempt, and only 0.6 percent who have made it to Europe. This very low rate of migration reflects the large drop in aggregate migration from The Gambia seen in Figure 1, and is one-seventh of the 7 percent rate anticipated in our power calculations and stage 1 registered report. The result is that there were very few backway migration attempts taking place in our sample for the treatments to deter. If we had reduced backway migration, we hoped this might lead to an improvement in well-being and a

> 22 Recall that only 28 percent had done any paid work in their village in the past month at baseline, so paid job opportunities are scarce. However, we do not measure the own consumption value of unpaid agricultural work at home, nor differences in the cost of living across locations, both of which would cause the difference in nominal wages across locations to overstate the overall welfare gain from migrating.

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# **Figure A.4. Predicted Headcount Poverty Based on Within-Year Imputation**

<!-- Start of picture text --> Bangladesh 2018/19 Ethiopia 2018/19<br>11 50<br>10<br>45<br>9<br>8<br>40<br>7<br>6<br>35<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Tanzania 2020/21 Malawi 2019/20<br>22 54<br>20 52<br>18 50<br>16 48<br>14 46<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Nigeria 2018/19 Vietnam 2016<br>55 12<br>11<br>50<br>10<br>45<br>9<br>8<br>40<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicates that the estimates are statistically insignificantly different from the true poverty rates. Dashed lines represent the true poverty rates. Dotted lines represent confidence intervals of the true poverty rates. Estimates are obtained using the normal linear regression models.

61
## _Source: Author’s Calculation_

The first quadrant shows countries that are better than the average on both 2010 performance and in terms of progress between 2005 and 2010. The fourth quadrant, on the other, shows countries which are worse than both averages. The second quadrant shows that while countries are better than the average 2010 performance, their progress between 2005 and 2010 is below average. Finally, the third quadrant shows countries that are better than the average in terms of their progress between 2005 and 2010, but their 2010 performance is below average.

All three FCS marked in red and Cambodia (marked blue) fall into the fourth quadrant which means that not only performance in 2010, but also progress between 2005 and 2010 is worse than the average. While the sample size of FCS prevents us from generating a concrete answer to the question, the second hypothesis (i.e., challenges in FCS prevent them from improving aid effectiveness performance between 2005 and 2010) seems to apply for these countries.

# **(4) Development Partner Performance in 2010**

The ten aid effectiveness indicators measure not only partner country performance but also development partner performance by country. As aid effectiveness is a measure of the delivery and management of aid, it is logical to analyze the performance from both the recipient side (partner country) and the provider side (development partner).

Using the methodology introduced in section 3, standardized scores for 32 development partners are calculated by indicator and by country. Table 6 shows the average sum of standardized scores by development partner groups and country groups (i.e., averaged by the number of institution/countries in a group).

**Table** **<u>6: Performance by Development Partner Groups and Country Groups</u>**

|**Group**|**No**|**FCS (Rank)**|**Non-FCS (Rank)**|**FCS + Non-FCS**<br>**(Rank)**|
|---|---|---|---|---|
|Bilateral|22|-15.9 (3)|-40.8(3)|-56.7 (3)|
|MDBs|4|54.0 (1)|150.9 (1)|204.9 (1)|
|Other MOs|4|51.7 (2)|128.2(2)|180.0 (2)|
|Vertical funds|2|-36.2(4)|-113.2(4)|-149.3 (4)|

_Source: Author_ There are two findings. First, there are no differences in ranking among the groups between FCS and non-FCS (e.g. bilaterals are on average the lowest performers in both FCS and non-FCS countries). Second, among the four groups, MDBs perform best followed by other MOs, vertical funds and bilaterals. Table 5 provides us with a general sense that relative development partner performance is quite similar between FCS and non-FCS. However, development partner performance may still differ between FCS and non-FCS on particular indicators.

Table 7-A and B includes the average standardized scores by indicator.

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Table 3B: Robustness check (2), alternative measures of financial development (Df1 & Df2)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Df1|-0.292**<br>(0.120)||||
|**Df1*SIIf**|**7.724*****||**7.668****||
||**(2.841)**||**(3.065)**||
|Df2||-0.206***<br>(0.072)|||
|**Df2*SIIf**||**4.962*****||**5.121*****|
|||**(1.662)**||**(1.863)**|
|log(GDP/capita)|0.175<br>(0.173)|0.097<br>(0.153)|||
|TFP|0.068***|0.061***|0.068**|0.059*|
||(0.022)|(0.023)|(0.030)|(0.030)|
|log(emp)|0.935***|0.937***|0.954***|0.954***|
||(0.094)|(0.093)|(0.101)|(0.100)|
|SKratio|0.629|0.662|0.866|0.845|
||(0.636)|(0.635)|(0.823)|(0.811)|
|K/L|0.066***|0.065***|0.068***|0.066***|
||(0.024)|(0.023)|(0.025)|(0.024)|
|rwage|-0.002|-0.002|-0.001|-0.001|
||(0.007)|(0.007)|(0.009)|(0.009)|
|GVC Participation|2.549***|2.536***|2.668***|2.658***|
||(0.473)|(0.466)|(0.528)|(0.518)|
|Country FEs|Yes|Yes|||
|Sector FEs|Yes|Yes|||
|Year FEs|Yes|Yes|||
|Country*Year FEs|||Yes|Yes|
|Sector*Year FEs|||Yes|Yes|
|Observations|5,758|5,678|5,758|5,678|
|R-squared|0.509|0.511|0.530|0.532|

Notes: The dependent variable is manufacturing export RCA. Df1 refers to the ratio of bank credits to private sectors to GDP. Df2 is the ratio of bank credits to private sectors and stock market capitalization to GDP. SIIf is the ratio of the U.S. embodied domestic financial services in U.S.’ manufacturing value added, averaged over 1995-2007. All WIOD manufacturing sectors 3-16 are covered (not grouped together). Robust standard errors in parentheses, clustered by country*sector. *** p<0.01, ** p<0.05, * p<0.1.

37
incorporates both the direct effect of aging and the correlated income effect: for the reasons given above, the former is believed to dominate.

The figure shown in Box 4.7 also used the fitted (linear) regression line produced by the relationship between population aging and pension spending. The original regression sample, it should be noted, is based on a sample of country/year observations which do not include post-transition period data for the transition socialist economies. The pre and post-1990 data points shown in Box Figure 4.7 are intended to illustrate two points: First, the international fitted line can be used as a yardstick with which to compare the pension spending levels of these demographically diverse countries. Second, the posttransition points demonstrate the inelasticity of pension spending to dramatic declines in GDP. While some of the data have been revised (see for example, Ukraine), the pattern of inelasticity seems robust.

In generating Figure 1. 1O, health spending data were taken from Murray, Govindaraj, and Chellaraj (1993) which documented the sources for the 1993 World Development Report, "Investing in Health", to obtain data on national health spending in a large group of countries. The sample is smaller than the one used in the previous two figures because complete and accurate data was available for only sixty-six countries for which pension spending data was also available. In some cases, countries were eliminated by the author because only partial data on health spending were available or the data quality appeared poor. In many cases, the available year for the health spending ratio was different from the year for the pension spending ratio; however, all data for health and pension spending pertained to the 1982-1992 period.

Murray, Govindaraj and Chellaraj (1993) present a variety of regressions which estimate the income elasticity of health spending over a wide cross-section of countries. They also present a survey of the literature on the income elasticity of health spending. Surprisingly, the influence of demographic factors is ignored. Several studies on the demographic determinants of government expenditures have observed a strong correlation between demographic aging and health spending. Other studies show that, not surprisingly, health spending per person tends to rise dramatically by age cohort (eg., Vukovich 1991, Smeeding et.al, 1988). While an extensive analysis of the demographic determinants of public health spending is beyond the scope of this study, the limited evidence suggests that demographic factors may have an independent effect on health spending ratios. The relationship shown in Figure 1.10 begins to illustrate this independent effect.

14
are _forest committees_ (69.2%), i.e. groups of people dedicating time and resources to avoid forest exploitation.<sup>9</sup> Some of these committees are created by officials of the forest department (35.4%). 26.6% of Other Groups are known as _village help clubs_ . They are formed by young villagers and deal with a wide range of issues related to social violence and public infrastructure. Finally, there are groups formed for cultural activities (3.2%) and farming issues (1.0%). Remarkably, of the 94 Other Groups in our data set, only one consists entirely of women.

We also surveyed a random sample of _Individuals_ to obtain information on villagers who joined neither a SHG nor an Other Group.

Table 1 shows the characteristics of WMs, members of SHGs, members of Other Groups and Individuals who dealt with ward problems at least once in the columns (1) to (5). The Other Group members differ from SHG members in several respects: they are mainly men, are more educated and own about 1 acre more of land. SHG members also differ from WMs and Individuals: the latter are better educated and own more land. 31% of the WMs are women, but 78% of them are elected thanks to reservations for women.<sup>10</sup> Remarkably, there are few women among the Individuals who dealt with ward problems (2.3%). For this reason, we group men and women in column (5).

Columns (6) to (9) show the characteristics of bodies who never dealt with ward problems. Both SHG members and male Individuals are less educated than their counterparts who dealt with ward problems. Female Individuals are slightly more educated and own more land than SHG members, which suggests that SHGs are formed by the more disadvantaged part of the female population.

> 9As most villages are located close to the forest, households depend on it for cooking and as a source of income (e.g., an important source of income is making leaf plates). An increasing population adds more pressure upon the forest. To prevent excessive deforestation, villagers formed voluntary forest committees. Later, the forest department started supporting existing committees and created new ones. They provide training, supplies and introduce new forms of sustainable exploitation of the forest.

> 10During the period under study, it was imposed that one-third of the seats had to be reserved for women. The reservation of seats is allotted by rotation among the different wards (Xaxa, 2010).

7
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Page 37 be able to rely on banks to exert control over enterprises. Initially, banks' control over enterprises has to be indirect through credit allocation and in close collaboration with the semipublic institution in charge of enterprise restructuring and privatization. Only when banks are provided with strong corporate governance will they be able to participate directly in controlling enterprises. Whether banks direct control over enterprises should be performed by special investment banks, like in Hungary and CSFR, or by universal banks, like in Poland, Bulgalia and Romania, will depend on whether banks with strong corporate governance will emerge, assuring banks' independence in their credit decisions.

My personal preference would be that during the transition only investment banks should specialize in controlling enterprises. This should limit the effect of ban-k failure on the rest of the financial system and on d'jpositors, while allowing banks to contribute to the development of the enterprise sector by permitting them to take greater risks. In a way this is the option taken by CSFR. However, to be effective this would need to be a scheme for valuing enterprises and their loans, such as the voucher privatization or by auctioning the bad loans as I have proposed elsewhere.'<sup>9</sup> However, it will be a mistake to postpone the banking system restructuring because it takes time. The key conclusion from the five Eastern Europeain countries' experience is that the role of banks in the transition is of great importance, but its effectiveness will depend on how soon the authorities start with banking restructuring and how they sequence it with the enterprise restructuring and privatization.

# **REFERENCES**

Bruno, Michael i992). 'Stabilization and Reform in Eastern Europe: A Preliminary Evaluation." Miroeo, IMF.1 Washington D.C.: IMF, March.

Business International (1992). 'Hungarian Bankruptcies' Impact on Business." Business Eastern Europe. weekly report. London: Business International, May 11.

Calvo, Guillermo A., and Fabrizio Coricelli (1992). 'Stagflationary Effects of Stabilization Programs in Reforming Socialist Countries: Enterprise-Side vs Household-Side Factors," The World Bank Economic Review. Vol. 6 No. 1: 71-90.

i **Sec Coricelli and Thome (1992)**