rafmacalaba/datause-encoder-data huggingface dataset:
With increasing mobility in the future, the capacity for local areas to make these decisions will likely diminish. New pressures will be put on the Center to allocate according to non-geographic characteristics. Although geographical effects on poverty will remain important, the ability to achieve national objectives through local intervention will diminish because of mobility. However, this could still be a long way off. In the meantime, responses should aim to reduce the costs of migration and make registration simpler. High costs of migration are probably not in the interest of the poor nationally.

Once well-defined rules for delineating recipients have been instituted for all programs and regions, it will be easier to coordinate between programs and integrate objectives so as to maximize the impact and coverage of resources. The criteria for allocating relief across provinces remains haphazard. Each program is subject to different rules and many are implemented by different ministries. Outcomes are unlikely to be optimal from the point of view of reducing poverty. Methods used for determining provincial needs and transfer amounts need to be standardized across ministries or the responsibility relegated to a specific ministry.

Implementing an integrated, national safety net will probably entail greater spatial redistribution. Resources are extremely limited. One objective of poverty and safety net program proponents must be to increase budget allocations for promoting and protecting the poor. Another equally important and perhaps more feasible option in the short term is to ensure that the little there is goes to the neediest households. There are currently clear gaps in coverage. Vulnerable households in poor areas are less well served than others by the existing arrangernents. The national HEPR also provides hope for provinces worst hit by natural 28
acquisitions (rather than greenfield investments). We found that foreign ownership seems to be particularly detrimental for credit under five-years’ maturity, while more government bank ownership is associated with lower credit share to GDP or longer-term credit (over five years). The negative association between government ownership and long-term credit is particularly large for LAC.

Table 7 repeats the correlation analysis focusing this time on the share of credit by purpose. The results confirm the patterns of association discussed earlier between macroeconomic, governance variables, and financial variables and credit to GDP. Overall effects seem similar for all types of credit, albeit higher government ownership is negatively associated with consumer and mortgage credit but not to commercial credit. Overall, relationships in LAC are similar to those elsewhere with the exception of depth of credit information, which is negatively correlated with all types of credit. In addition, NPLs appear particularly detrimental to credit levels in LAC.

# **6. Conclusions**

Despite their importance, data on the structure of bank credit by maturity are scarce. For LAC in particular, data are particularly difficult to obtain as few banks report loan maturity data to commercial data sets such as Bankscope. With the support of ASBA, we assembled a novel data set on the structure of bank credit allocation in LAC covering 21 countries during 2004–2014. Exploiting this information and combining it with Bankscope data for the rest of the world, we constructed a data set on shares of credit by maturity, which we then applied to IFS data and normalized by GDP to obtain a data set on IFS credit to GDP by maturity and loan purpose.

The lack of long-term credit for investment purposes has long been identified as a factor hampering growth in LAC. However, we found that LAC is financially underdeveloped because short-term credit to GDP is lower than in countries with similar income levels, while long-term credit is at par. In terms of loan by purpose, the shares of consumer and commercial loans to GDP are lower in LAC than other regions.

Certainly, more long-term credit, particularly for commercial purposes, could foster higher growth in LAC. However, while it does not appear that overall access to long-term credit is worse in LAC than in peer countries, short-term credit seems to be more limited. Reflecting on this pattern, consumer and commercial lending are lower in LAC but mortgage lending appears in line 19
**Appendix 1. Descriptive analysis of male youth, male adults, female youth and female** **<u>adults</u>**

||Male<br>youth|Male<br>adults|Female youth|FemaleAdults|
|---|---|---|---|---|
|Age|18.9|40.7|19.0|39.4|
|Married|13.1%|87.3%|32.2%|88.7%|
|No any formal education|24.1%|40.7%|45.8%|73.5%|
|Below primary education|5.5%|4.4%|3.1%|2.1%|
|Middle level|20.5%|15.0%|15.0%|8.4%|
|Matric level|23.3%|11.7%|13.6%|4.3%|
|Inter level|17.8%|14.5%|13.8%|6.2%|
|Bachelor’s|6.5%|5.9%|5.8%|2.5%|
|Post-graduation level|2.3%|7.7%|3.0%|2.9%|
|Migration within past 10 years|3.7%|5.0%|6.4%|5.0%|
|Household Characteristics|||||
|# of members, 0-5, female|0.66|0.88|0.76|0.90|
|# of members, 0-5, male|0.69|0.91|0.81|0.93|
|# of members, 6-14, female|1.07|1.07|1.03|1.16|
|# of members, 6-14, male|1.17|1.18|1.15|1.26|
|# of members, 15-24, female|1.20|0.95|2.04|0.87|
|# of members, 15-24, male|2.15|0.92|1.25|0.91|
|# of members, 25-65, female|1.40|1.54|1.38|1.77|
|# of members, 25-65, male|1.40|1.83|1.50|1.53|
|# of members, 66+|0.20|0.21|0.22|0.25|
|Household size|9.93|9.48|10.13|9.59|
|# work/# not work|0.73|0.60|0.61|0.54|
|Urban|37.1%|36.0%|36.1%|33.8%|
|Punjab|53.3%|54.0%|55.4%|54.9%|
|Sindh|26.9%|26.4%|23.4%|23.9%|
|NWFP|15.6%|14.6%|18.0%|16.7%|
|Balochistan|4.3%|5.0%|3.2%|4.6%|
|Number of observations:|22797|37743|21514|37500|

29
date was September 30, 2008. Some additional payments were made after the formal due date; MRFC reports that there is typically no additional loan repayment two months past the due date for agricultural loans. In the empirical analysis we obtain our dependent variables from the August 2008 survey data as well as administrative data from MRFC on loan take-up, amount borrowed, and repayment.

# **_Balance of baseline characteristics across treatment vs. control groups_**

To confirm that the randomization across treatments achieved balance in terms of pre-treatment characteristics, Table 2 presents the means of several baseline variables for the control group as reported prior to treatment, alongside the difference vis-à-vis the treatment group (mean in treatment group minus mean in control group). We also report statistical significance levels of the difference in treatment-control means. These tests are presented for both the full baseline sample and the loan recipient sample.

Overall, we find balance between the two groups in both the full baseline sample and the loan recipient sample. In the full baseline sample, the difference in means for the treatment and control groups is not significant for any of the 11 baseline variables. In the loan recipient sample, for 10 out of these 11 baseline variables, the difference in means between treatment and control groups is not statistically significantly different from zero at conventional levels, and so we cannot reject the hypothesis that the means are identical across treatment groups. For only one variable, the indicator for the study participant being male, is the difference statistically significant (at the 10% level): the fraction male in the treatment group is 6.6 percentage points lower than in the control group.<sup>16</sup>

# **3. A Simple Model of Borrower Behavior**

To study how dynamic incentives affect borrower behavior, Online Appendix D develops a simple model that incorporates both moral hazard and adverse selection. We provide here some

> where the follow-up survey was administered, but as we discuss below in Section 5.C. (see Online Appendix Table 3), there is no evidence of selective attrition related to treatment status. For the full sample as well as the borrower subsample, in no regression is fingerprinting or fingerprinting interacted with predicted repayment statistically significantly associated with attrition from the survey.

> 16 It will turn out, however, that the regression results to come are not substantially affected by the inclusion or exclusion in the regressions of a large set of control variables (including the “male” indicator).

9
with binding coverage below 35%<sup>11</sup> are required to bind [70-90]% of lines if their binding coverage is currently below [12]%; [75-90]% if their binding coverage is between [12] and [25]%; and [80-90]% if their binding coverage is between [25] and 35%.

One group of small and vulnerable economies (SVEs), with average bound tariffs of 50% or higher, is to bind at an average level not exceeding [22-23]%, or to cut average bound tariffs by an average of 40%. The second group, with an average bound tariff between 30 and 50%, bind at [18-28]% or reduce average bound tariffs by 30%. A third group, with average bound tariffs below 30%, binds at an average level of [14-20]% and applies a minimum line-by-line reduction of [5-10]% on [90-95]% of tariffs.

The NAMA proposal includes provision for Sectoral Initiatives, for which participation is not mandatory, but agreement is to be reached when 90% of world trade is included. In most cases, it is proposed to move to zero tariffs on these products.

The assumptions used to assess the implications of the Modalities for NAMA are set out in Annex Table 5.

**Annex Table 5. Assumptions made in assessing the NAMA Modalities**

||**Developed**|**Developing**|**LDCs**|**SVEs**|**Para 6**|
|---|---|---|---|---|---|
|**NAMA**||||||
|Formula|Swiss 8|_x_(i) Swiss 20+0 cuts on 6.5 8% of<br>|No libn|No libn|No Libn|
|Flexibility|None|lines/imports<br>x (ii) Swiss 20+ 1⁄2 cuts on 13/15.5% of<br>lines/ imports<br>y (i) Swiss 22+ no cuts on 5/5% of<br>lines/imports<br>y (ii) Swiss 22+ 1⁄2 cuts on 10/10% of<br>lines/imports<br>zSwiss24.5% with noflexibilities||||
|Unbound|MFN 2001 +<br>+20%i|30 % if the tariff is below half the coefficient;<br>fthe tariff is abovehalfthe coefficient||||

The political welfare costs associated with each of the potential five choices of formula (x(i), x(ii), y(i), y(ii) or z) were evaluated using the Jean, Laborde and Martin (2008) methodology. Based on these results, each of the 22 developing countries undertaking tariff reduction under the standard Swiss formula (i.e. non SVE, RAM or low binding coverage members) was assigned to the regime that minimized the political “pain” of tariff changes. Economies with high and uniform bindings, such as Singapore and Hong Kong chose regime z, and other countries were distributed widely across the other four regimes, suggesting that their boundaries have been placed in a way that offers useful choices for countries with different tariff regimes.

> 11 Frequently called Paragraph 6 countries because of the paragraph in the 2004 Framework Agreement that introduced this provision.

22
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Table 7. Financial Development (M3/PIB), Property Rights and Poverty (Income of the poor)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|M3/GDP (log)|0.077|0.128|1.051|1.125|
||(1.12)|(2.00)**|(2.97)***|(2.97)***|
|GDP per capita ($US, log)|0.236|0.275|0.322|0.239|
||(5.11)***|(5.76)***|(7.58)***|(4.79)***|
|Property Rights (log)|||0.677|0.559|
||||(1.97)**|(1.55)|
|M3/GDP (log) * Property Rights (log)|||-0.247|-0.205|
||||(2.51)**|(1.99)**|
|Inflation (log)||-0.599||-0.406|
|||(6.17)***||(3.35)***|
|Trade Openness (log)||-0.072||-0.142|
|||(1.05)||(1.78)*|
|Constant|1.051|0.993|-2.140|-1.350|
||(5.05)***|(3.00)***|(1.64)|(1.02)|
|Observations|68|65|64|59|
|Number of countries|36|34|34|32|
|Chi square (Wald test)|55.58|164.49|166.67|217.41|
|Pseudo R2|0.38|0.43|0.40|0.38|

Notes: Data are averaged over five years. Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.

Table 8. Financial Development (M3/PIB), Property Rights and Poverty (Poverty gap)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|M3/GDP (log)|-0.306|-0.352|-1.791|-1.526|
||(2.84)***|(3.19)***|(4.43)***|(3.44)***|
|GDP per capita ($US, log)|-0.583|-0.616|-0.661|-0.713|
||(8.32)***|(7.28)***|(9.12)***|(8.28)***|
|Property Rights (log)|||-1.089|-0.763|
||||(2.92)***|(1.84)*|
|M3/GDP (log) * Property Rights (log)|||0.383|0.295|
||||(3.25)***|(2.27)**|
|Inflation (log)||0.905<br>(5.91)***||1.074<br>(5.65)***|
|Trade Openness (log)||0.037||-0.011|
|||(0.33)||(0.09)|
|Constant|6.661|6.764|11.372|10.667|
||(22.69)***|(12.85)***|(8.56)***|(7.09)***|
|Observations|70|67|66|64|
|Number of countries|36|34|34|33|
|Chi square (Wald test)|254.52|180.44|336.40|160.53|
|Pseudo R2|0.39|0.45|0.43|0.48|

Notes: Data are averaged over five years. Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.

16
(23) Alternating the removal of summations over i, s, and r in equation (23) produces MAPE estimates on shipments by commodities, shipping regions, and receiving regions respectively. For regional intermediate transactions, the aggregate MAPE index is defined as: Alternating the removal of summations over i, j, and r in equation (24) produces MAPE estimates on intermediate transactions by inputs, using sectors, and regions respectively. The model and all test experiments are implemented in GAMS and the complete GAMS program and related data set are available from the authors upon request.

# **_Test Results_**

Table 2 summarizes all the eight measurement indexes from the seven experiments listed in Table 1. The accuracy of the estimates is judged by their closeness to the true interregional trade and individual region’s input–output flows aggregated from the GTAP database.

# **_(Insert Table 2 here)_**

Generally speaking, the model has remarkable capacity to rediscover the true interregional trade flows from the highly distorted data. The estimated shipment data are very close to the true data, as judged by the eight MAPE measurements, in all testing experiments except the Batten model. Most of the mean absolute percentage errors are about 4-7 percent of the true data value, which implies the model has great potential in the application of estimating interregional trade flows. In contrast, recovering the individual region’s input-output flows from weighted average national values only obtained limited success, indicating national IO 18
would be expected from the GDP numbers, because non-wage jobs tend to be much lower productivity.<sup>10</sup>

**Figure 3: Incomes sources of households in Uganda, 1992/93-2005/06, showing that share of non-agricultural income sources grew,**

<!-- Start of picture text --> Uganda National Rural areas<br>20.9 24.2<br>Agriculture wage Agriculture wage<br>10.7 11.9<br>Public non-agriculture 6.0 Public non-agriculture 5.3<br>wage 8.4 wage 6.7<br>Private non agriculture 21.2 Private non agriculture 15.1<br>wage 12.8 wage 8.9<br>41.4 38.5<br>Non farm enterprise Non farm enterprise<br>27.7 24.3<br>77.3 86.9<br>Farm Farm<br>82.0 91.6<br>0.0 20.0 40.0 60.0 80.0 100.0 0.0 20.0 40.0 60.0 80.0 100.0<br>2005/06 1992/93 2005/06 1992/93<br><!-- End of picture text -->

**Source** : Authors‘ calculations based on the IHS 1992/93 and UNHS 2005/06

Moving to household livelihoods (which covers all employment, not just primary employment) _we see a clear trend toward income diversification into incomes sources outside-subsistence agriculture as expected._ A comparison of household sources of income in 1992/93 and 2005/06 in Figure 3 shows that the proportion of households with an income from non-farm sources increased dramatically. The proportion of households with a private non-agricultural income almost doubled while that of households with non-farm household enterprise increased by 50 percent. The number of NFEs has been rising at an annual average rate of 6 percent in the past decade. A

> 10 We do not have the data to estimate the productivity of wage and non-age jobs by sector. Most analyses find that informal enterprises have lower labor productivity.  To the extent that earnings reflect productivity, Uganda is no exception; median earnings are lower in non-wage jobs (see Fox, 2009).

17
3 calculated directly from the surveys, with little information available to adjudicate the choice.<sup>3</sup> In response, poverty economists have been developing a series of different poverty prediction methods, exploiting the comparability of subsets of data within and across surveys.<sup>4</sup> The methods differ in the predictors and prediction techniques used, but they generally share the critical and largely untested assumption that the estimated relation between the predictors and their welfare measure is stable over time. This cannot be taken for granted and has become a stumbling block in furthering the use of poverty prediction techniques to overcome data constraints in tracking poverty over time.

The need for comparing and validating poverty prediction methods is perhaps best illustrated by the ―Great Poverty Debate‖ in India (Deaton and Kozel, 2005). Following market liberalization in the early 1990s, the official poverty numbers for India showed a drop in poverty incidence from 36 percent in Round 50 of the National Sample Survey (NSS) (1993/1994) to 26 percent in Round 55 (1999/2000), or about a reduction in the number of poor people by 60 million.

However, these official numbers were received with skepticism. There was a widespread view that the underlying data were not comparable because reporting periods for various consumption items had changed between the two survey rounds. There were also lingering doubts about the accuracy of the price indices used to update the poverty lines (Deaton, 2008). Different poverty calculations were proposed, each of them

> 3 For instance, while households in Tanzania faced price increases of 93 percent between 2000/01 and 2007 according to the national Household Budget Surveys, the official CPI recorded only a price increase of 45 percent (Hoogeveen and Ruhinduka, 2009).  Adjustment of the CPI, which was largely based on urban consumption baskets, also proved to go a long way in remedying the 1994-2003 Growth-Poverty paradox in Burkina Faso (Grimm and Günther, 2006).

> 4 See for example, Ravallion (1996), Sahn and Stifel (2000), Kijima and Lanjouw (2003), Azzarri et al. (2006), Stifel and Christiaensen (2007), Tarozzi (2007),  Mathiassen  (2009), and Grosse, Klasen, and Spatz  (2009).
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Pietrovito, F. (2020). The Impact of Credit Constraints on International Quality and Environmental Certifications: Evidence from Survey Data. _Journal of Risk and Financial Management_ 13(12): p.322.

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Riillo, C.A., I. Mijatovic, and H.J. de Vries (2022). Certification to Compensate Gender Prejudice – Analysis on Impact of Management System Certification on Export. _Applied Economics_ 54(33): 3777-3794.

Romano, P. (2000). ISO 9000: What is its Impact on Performance? _Quality Management Journal_ 7: 38–56.

Rothenberg, A.D., A. Gaduh, N. E. Burger, C. Chazali, I. Tjandraningsih, R. Radikun, C. Sutera, and S. Weilant (2016). Rethinking Indonesia’s Informal Sector. _World Development_ 80(April): 96– 113.

Rozo, S., and H. J. Winkler (2021). Is Informality Good for Business? The Impacts of IDP Inflows on Formal Firms. _Journal of Human_ Resources 56(4): 1141-1186.

Sampaio, P., P. Saraiva, and A. G. Rodrigues (2009). ISO 9001 Certification Research: Questions, Answers and Approaches. _International Journal of Quality and Reliability Management_ 26: 38– 58.

Schumpeter, J. A. (1942). **Capitalism, Socialism and Democracy** . Harper. New York. NY.

35
- 22 -

Table 2.4: Growth in Agricultural Production and Agricultural Exports, 1971-1981 and 1980-1984

|Country|Average Rate<br>Agricultur<br>(pe|of Growth in<br>al Production<br>rcent)|Average Rate<br>Agricultur<br>(perc|of Growth in<br>al Exports<br>ent)|
|---|---|---|---|---|
||1971-1981|<br>1980-1984|1971-1981|1980-1984|
|Southeast Asia|||||
|Indonesia|3.7|4.6|24.3|-13.5|
|Malaysia|4.7|3.4|21.3|- 5.9|
|Philippines|<br>4.9|2.1|13.2|- 5.4|
|Thailand|6.0|3.4|24.5|3.9|
|South Asia|||||
|Bangladesh|2.9|2.5|6.0|3.3|
|Burma|3.4|6.9|12.9|-14.2|
|India|3.4|1 <sup>.</sup>7<sup>a</sup>|15.1|0.2|
|Nepal|1.2|3.1|-3.1|6.6|
|Pakistan|3.2|2.3|2.8.0|- 7.9|
|Sri Lanka|3.8|2.1|9.1|8.6|

a1976-1983 Source: V.S. Vyas and W.E. James, 'Agricultural Development in Asia: Performance, Issues and Policy Options* (Washington, D.C.: World Bank, 1987; typescript).