rafmacalaba/datause-encoder-data huggingface dataset:
progress in education but little contribution from TFP.<sup>13</sup> Tahari et al. (2004) focus exclusively on 30 countries in Africa between 1960 and 2002. Their results suggest that TFP has improved since the 1980s, although the overall GDP growth in SSA during the entire period was primarily due to factor accumulation similar to the results from Bosworth and Collins (2003). Recently, Zelleke et al. (2013) extended the analysis to 31 African countries through 2008, providing insights on the source of the region’s recent economic spurt. Their findings emphasize the increases in investment after 1995 but provide little details on the dynamics of other factors.

In line with these studies, our research examines the sources of growth, paying particular attention to changes in the pattern of growth since the mid-1990s. We use data ranging from 1960 to 2010 and cover 32 SSA countries. We first conduct the standard growth accounting exercise by decomposing GDP growth, and unlike previous studies, delve deeper into the underlying dynamics and sources of changes. More specifically, we examine the determinants of each factor including demographic transition, capital accumulation, and TFP growth. In doing so, we investigate the heterogeneity of the patterns across different types of countries by fertility level and resource richness.<sup>14</sup> Our findings suggest that the overall economic growth in recent years in SSA is largely due to increases in the share of the working age population, capital accumulation, and TFP growth.<sup>15</sup> Increases in the share of the working age population due to fertility declines, capital accumulation with an inflow of foreign direct investment (FDI), and progress in TFP are a clear deviation from earlier patterns in the region. Resource rich countries (both oil and non-oil resource producing countries) experienced significant inflows of FDI. Contrary to common conjecture, however, this is only a partial explanation for increased FDI, because FDI growth is observed in resource poor countries as well. Reduced political instability and structural transformation of the economy from agriculture to other sectors since the mid-1990s seem to have contributed to TFP growth.

> 13 Ndulu and O'Connell (2003) used Bosworth and Collins' data set and re-estimated the sources of growth for the 19 SSA countries using a different functional form and confirmed the main findings. 14 See Appendix Table 1 for the list of countries by type.

> 15 Arbache and Page (2013) also found that growth accelerations in the region are associated with increase in investment.

4
25 benefited most. A much larger share of the population in Uganda grows cash crops (21 percent) than in Ghana (6 percent) which may explain the larger drop in poverty amongst food crop producers in Uganda. Reviewing the existing evidence on the experience with agricultural reforms in sub-Saharan Africa, Kherallah _et al_ (2000) arrive at a similar conclusion-export-crop producers seem to have benefited more than food crop producers. What needs to be better understood is the _transmission_ mechanism that led to economic gains of households not producing for export.

**Table 11: Poverty incidence by rural activity, Ghana and Uganda in the 1990s.**

|||_Uganda:_||||_Ghana._|||
|---|---|---|---|---|---|---|---|---|
||Population<br>share(2000)|1992|2000|Percent<br>reduction|Population<br>share(1998)|1992|1998|Percent<br>reduction|
|Foodcrop|45.9|63.3|45.7|-27.8|43.9|68.1|59.4|-12.8|
|Cashcrop|21.3|62.7|29.7|-52.6|6.3|64.0|38.7|-39.5|

Source: World Bank, _Poverty Dynamics_ studies.

Potential pathways include rural labor markets, with higher export crop prices stimulating export crop production leading to increased demand for agricultural wage labor and ultimately higher agricultural real wages. Abdulai and Delgado (2000) find that in Ghana a 1 percent change in the domestic terms of trade between agriculture and non-agriculture leads to a 0.83 percent change in the real agricultural wage rate in the long run, underscoring the importance of labor markets in transmitting the effects of economic reforms. Increased liquidity in rural economies from agricultural exports can also have important spin-off effects, through an expansion of both investment in export and food crop production, and increased consumption of goods and services produced with previously underutilized local labor, land or capital. As a rule of thumb Delgado et al. (1998) posit that any policy enhancing producers' income from agricultural exports increases local rural income by twice the amount of the increased exports.

To understand the different evolution in poverty among food- and cash-crop producers, it is important to keep in mind that the former group tends to be much more heterogeneous than the latter. In export-crop growing zones, the effects of favorable export crop prices were also transmitted to the food-crop growing households-either through the labor market or the input and product markets, or both. Transmission of such benefits to areas unsuitable for export crop production, especially when they are also remote, is much harder. For example, in Ghana food producers in more remote and less integrated regions (in the north) did not experience a similar reduction in their poverty as food growers in cash-crop (and better integrated) areas. Similarly,
- 76 -

institutional effectiveness in changing economies. It seems reasonable to think, moreover, that it has been achieved through the combined influence of the other eight characteristics.

5.19 It is important not to over-generalize from this kind of analysis. Nevertheless, it does illustrate how these developing countries have developed training systems which have the characteristics necessary to adjust to changing economies. Significantly, these systems involve a variety of training modes, including secondary vocational schools that are often comparatively successful.

5.20 We know very little, of course, about the economic effectiveness of the different kinds of training institutions in these systems, although the Korea data demonstrate at least the possibility of good external efficiency for secondary vocational schools, and there is stronger evidence for the external efficiency of nonformal training in several regions. Post-secondary institutions have been reported to have high placement rates for graduates (Malaysia, Korea, Jordan, Algeria). Teacher training institutions have been able to place gradutates in teaching posts where salaries and conditions of work have been attractive.

5.21 Secondary vocational schools in middle income countries have been more effective than those in low income countries, particularly those in Africa. The explanation lies in the fact that many of the effective schools have acquired characteristics -- such as linkages with enterprises, incentives to attract and retain qualified instructors and students, good 'feedback" systems -- which are most often thought characteristic of nonformal modes. They exhibit, in short, what Dougherty (1988) has identified in his review of the literature as the "convergence of modes." Thus they may no longer be "public vocational secondary schools" in the stereo-typical sense, but rather be evolving towards a new institutional form. Further -- investigation into this hypothesis, of course, is needed particularly to see the extent to which these institutions have been freed of the more constricting features of traditional secondary education.

# C.onstraints on Effective Systems Evolution

5.22 In implementation terms, at least, investments in the development of national training systems in middle income countries have been successful. In contrast, less success has been found in the sample of components in low income countries, especially in Africa. These have been small investments, typically supporting a few vocational training institutions; most of these have been secondary vocational schools.
The analysis relies on primary household survey data on financial knowledge and behaviors that was collected over two rounds, six months and twelve months after the intervention. The paper also utilizes administrative data on savings balances obtained directly from SACCOs, for up to eighteen months after the intervention.

The data analysis finds stark differences in financial knowledge and behavior change among the two experimental groups. First, we find that the program was successfully disseminated by the trainers in both the autonomous selection (T1) and fixed selection (T2) groups to 68,000 community members over a twelve-month period (approximately 500 community members per SACCO, on average). Responses from the household surveys show that the take-up rate among SACCO community members in both groups was not statistically different (21 and 23 percent, respectively), however, the number of sessions attended varied, with recipients in T1 being 15 percent more likely to attend three or more sessions of the program. Hence, there were statistically significant differences in program take-up intensity.

On content effectiveness, our results show that while SACCO members in T1 were significantly more likely than the control group to score higher on composite scores of financial attitudes, financial rules of thumb, and financial planning, the coefficients for T2 are indistinguishable from zero. Furthermore, for both rules of thumb and financial planning scores, the differences between the coefficients of T1 and T2 are statistically significant (F-test p-value of 0.041 and 0.065, respectively).

Turning to financial behavior, the analysis finds that SACCO members in T1 were 20 percent more likely than the control group to have a written budget, and 6 percent more likely to adhere to the budget. The coefficients for T2 are less than half in magnitude and not statistically significant. For savings, we do not find any significant treatment effects for either T1 or T2 on the extensive margin – having a bank, SACCO, or VSLA account; or borrowing money from a financial institution. However, on the intensive margin, analysis from both survey and administrative data finds that T1 members saved almost 10 percent more than the control group as per their financial plan, and held 5 percent more deposits in their savings accounts. None of these behaviors are significant for T2 4
**Table 3. Discrete time hazard analyses of first‐migration for both men and women jointly with testing for male and female differences in age effects, wealth effects, education effects, and year effects, Albania LSMS 2005 (control variables not shown)**

|**First Migration**|**OR**|**SE(b)**|**P>|z|**|
|---|---|---|---|
|**_Female – Age_**|**_interaction_**|**_s_**||
|**Female x 20‐25**|1.219|0.141|0.086|
|**Female x 25‐30**|1.163|0.150|0.242|
|**Female x 30‐35**|1.304|0.209|0.097|
|**Female x 35‐40**|1.370|0.275|0.116|
|**Female x 40‐45**|2.417|0.623|0.001|
|**Female x 45+**|3.455|1.636|0.009|
|**_Female – Wealt_**|**_h interactio_**|**_ns_**||
|**Female x Wealth Index 1990**|1.111|0.025|0.000|
|**_Female – Educati_**<br>|**_on interact_**|**_ions_**||
|**Female x At least some HS**|2.109|0.205|0.000|
|**Female x At least some Univ**|2.943|0.440|0.000|
|**_Female – Year_**|**_interaction_**|**_s_**||
|**Female x 1991**|1.190|0.421|0.622|
|**Female x 1992**|1.442|0.501|0.293|
|**Female x 1993**|1.902|0.652|0.061|
|**Female x 1994**|2.430|0.776|0.005|
|**Female x 1995**|1.335|0.460|0.402|
|**Female x 1996**|2.499|0.754|0.002|
|**Female x 1997**|1.868|0.550|0.034|
|**Female x 1998**|1.661|0.497|0.090|
|**Female x 1999**|1.735|0.507|0.059|
|**Female x 2000**|1.923|0.566|0.026|
|**Female x 2001**|2.089|0.613|0.012|
|**Female x 2002**|1.310|0.395|0.371|
|**Female x 2003**|1.309|0.402|0.380|
|**Female x 1991**|1.190|0.421|0.622|
|**Female x 1992**|1.442|0.501|0.293|
|**Female x 2003**|1.309|0.402|0.380|
|**Number of Cases**||63162||

25
<!-- Start of picture text --> 10 0.5<br>8<br>0.4<br>6<br>0.3<br>4<br>0.2<br>2<br>0 0.1<br>2006 2007 2008 2009 2010 2011 2012 2006 2007 2008 2009 2010 2011 2012<br>Year Year<br>baseline no-navy no-navy-no-response baseline no-navy no-navy-no-response<br>(a) Average Ability (b) Success rate<br>500<br>150<br>400<br>100<br>300<br>200<br>50<br>100<br>0 0<br>2006 2007 2008 2009 2010 2011 2012 2006 2007 2008 2009 2010 2011 2012<br>Year Year<br>baseline no-navy no-navy-no-response baseline no-navy no-navy-no-response<br>(c) Total number of attacks (d) Total number of hijacks<br>250 3.5<br>3<br>200<br>2.5<br>150 2<br>100 1.5<br>1<br>50<br>0.5<br>0 0<br>2006 2007 2008 2009 2010 2011 2012 2006 2007 2008 2009 2010 2011 2012<br>Year Year<br>baseline no-navy no-navy-no-response baseline no-navy no-navy-no-response<br>(e) Total ransom (f) Average ransom<br>4.5 0.7<br>4 0.6<br>3.5<br>0.5<br>3<br>0.4<br>2.5<br>0.3<br>2<br>1.5 0.2<br>1 0.1<br>2006 2007 2008 2009 2010 2011 2012 2006 2007 2008 2009 2010 2011 2012<br>Year Year<br>baseline no-navy no-navy-no-response baseline no-navy no-navy-no-response<br>(g) Average size (h) Average duration of captivity<br>Average Ability Success Rate<br>Number of Attacks Number of Hijacks<br>Total Ransom<br>Average Ransom<br>Average Size<br>Avg. Duration of Captivity<br><!-- End of picture text --> Figure 11: Counterfactual: No navy patrols Note: The figures plot the entry costs, average ability, total number of attacks, average ransoms, success rates, average size attacked, the average size hijacked, and relative delay in the counterfactual analysis where we shut down navy patrols (dash line) and benchmark model (solid line). All other parameters are kept the same as in the benchmark model.

36
These are arguably rather extreme alternatives to the poverty-gap scheme discussed above, in that the basic income scheme essentially requires no information for identifying the poor (though it assumes perfect information on who is rich). Yet a great deal of information is available in practice. As a result of this assumption, the implied MTRs are clearly going to be higher; indeed, the ratio of the tax rate on the rich needed to cover the poverty gap exactly to the tax rate required for a basic income set at the poverty line is identically equal to the poverty gap index, which is 7.6% for the developing world as a whole in 2005 using $1.25 a day (Chen and Ravallion, 2008). The required tax rates for a “global” basic-income scheme will be 13 times higher, though differences in administrative costs may well reduce this differential.

# **4. Estimates for developing countries**

Two “poor-country” poverty lines are considered. The first is $1.25 (converted using 2005 PPPs for consumption); this is the average national poverty lines found in the poorest 15 countries (Ravallion et al., 2009). The second is $2.00 a day at 2005 PPP, which is the median poverty line for all developing counties with the available data. In setting the “rich-country” poverty line I shall use $13 a day at 2005 PPP, which is the poverty line per person for a family of four in the US; see <u>US Department of Health and Human Services.</u> So the hypothetical redistribution is from those living in each developing country who are not poor by US standards to those who are poor by the standards of either the poorest countries (giving the $1.25 line) or developing countries as a whole (giving the $2 line). For notational brevity, the MTR required to reduce the poverty gap for the $1.25 line when<sup>_z_</sup> _r_  $13 is denoted  _k_ )1(   _k_ .1(25,13) , while  _k_ (2) is the corresponding value for the $2 line.

The data are from _<u>PovcalNet</u>_ <u>, which facilitates estimation of</u>  _k_ in the form of equation (3). The estimates were possible for 89 countries with at least some poverty assessed by the $1.25 a day line and at least two surveys over time (to allow inter-temporal comparisons). The underlying surveys provide distributions of household consumption expenditure or income per person. (The difference between consumption and income is ignored for the present purpose.) Table 1 gives the poverty gap indices and the implied values of<sup></sup> 1)1( and<sup></sup> 1(2) . Since marginal tax rates cannot reasonably exceed 100%, I truncate them at this figure. (This also helps deal with concerns about how well the upper tail of the income distribution is measured in 12
break down (i.e., to lose applicability) in periods of stress. Many deal structures were so new and untested that real-world data on default frequency and loss given default had to be drawn entirely from an unrepresentative period of soaring house prices and needed to be supplemented by synthetic data that could incorporate reasonable estimates of effects that might unfold in times of price decline and market stress.

# _CROs need to take responsibility for their mistakes..._

Formally, a CRO’s aggressive declaration that an adequately documented “true sale” of a particular loan pool had taken place was a key step in moving the assets off originator and securitizer accounting balance sheets. But CROs apparently felt no duty to describe how fully the ownership of the pool could be documented and CRO judgments on this matter could have had no legal standing in any case.

The quality of CRO analysis was and is further undermined by CRO efforts to avoid legal responsibility for any mistakes. Despite their intense and critical involvement in designing securitization structures, CROs claim only to be expressing an “opinion.” They insist that the constitutional right of free speech protects them from lawsuits for damages suffered by investors who chose to rely on what might turn out to be incompetent or negligent opinions. To create a foundation for this defense, CROs routinely incorporate language into their reports stating that it is “unreasonable” for anyone to rely on their “mere opinions,” which should not be construed by anyone as “investment advice.” Ironically, for investors and regulators, the reputational damage CROs have absorbed from massively over-rating structured securitizations has imparted to these weaselly disclaimers an element of unintended truth that has undermined the value of their brands and is forcing them to rebuild confidence in the value of their work. They will find that this can only be done by taking ex post responsibility for future mistakes.

Because CRO fees were so large and because synthetic securities could not legally have been sold in large quantities to trusteed and municipal investors without the blessing of high ratings, the courts might impose this result on CROs in any case. Whenever someone (say, a lawyer) collects a large fee for communicating his or her 19
|Table9:Mem<br>_(Italicized_item<br>andeither_zero_|bership <br>sareco<br> _sum_or|in_desa_gove<br>nsistentwith<br>_crowdout_co|rnmentorgan<br> the hypothe<br>mmunityeff|izationsan<br>sisofpositiv<br>ects).|dten proxies <br>eprivate effe|forgovernan<br>ctsof_desa_|ce.<br> government|
|---|---|---|---|---|---|---|---|
||Pred.<br>prob.|<br>Marginal<br>effects<br>(p-level)|<br>Percentage<br>change|Marginal<br>effects<br>**(p-level)**|Percentage<br>change|Sumof<br>marginal<br>**effects**|Percentage<br>change|
|||_Pr_<br>**(Hou**|_ivate_<br>**sehold)**|_Co_<br>(Villagele|_mmunty_<br>sHousehold)|**(Sumo **<br>|_Net_<br>**fthetwo)**<br>|
|HHinformed<br>about3types|.327|_.041_<br>_(001)_|_12.5%_|**_(009)_**<br>|_-20 _<br>**_-26.0%_**|_-04_<br>_-.044_|_13S_<br>_-13.5%_|
|HHU-<br>reports all3<br>"moreopen"|0.186|_.2_<br>_(029_|_15.6%_|_.3_|_-19.4%_|_-.007_|_-3.8%_|
|Someparticipation<br>inplanning_desa_<br>programs|0.344|_0.067_<br>_(000)_<br>**_00(58_**<br>|**_19.5%_**|_-0.066_<br>|_-19.2%_|**0.001**|0.3%|
|Someparticipation<br>indetermining<br>sanctions|0.138|_0.031_<br>**(**|_22.7%_<br>___<br>__<br>___<br>|_-0.039_<br>_(103)_<br>_(03_<br>|_-28.3%_|_-0.008_|_-5.6%_|
|HHinvolvedin<br>protest|0.089|_0.0074_<br>**_(303,)_**|_8.3_<br>_8.%_|<br>_-0.045_<br>**_(021)_**|_-506%_|_-0.0376_<br>__50_6__0_037_|_-42.2%_<br>__42__2_|
|Noexpressionin<br>spiteofproblem<br>(positiveisless<br>voice)__|0.174|_-0.026_<br>_(013~)_|_-14.9%_|**_0.094_**<br>_(DOG,)_<br>_<br>_ _<br>_<br>_|_540%_<br> <br>_<br>_ _<br>_<br>__|_0.068_<br>|_39.1%_|
|Most effective<br>expression<br>(problem,voice,<br>solution)|026<br>**0.236**|_0.0427-0_<br>_(000)_<br>|_47_<br>**_18.1%_**|_-0.047_<br>_(124)_<br>|_-19.9%_<br>|_-0.0043_<br>|_-1.8%_<br>|
|Economic<br>Problems|.076|_.013_<br>_(.145)_|_166%_|**_-.083_**<br>_(001)) _|**_-109.5%_**<br>05__71|_-.071_|_-92.9%_<br>_929_|
|SocialProblems<br>|.389|**-.013**<br>**1303**<br>**_609_**|**-3.4%**<br>|**_1340_**|_-103%_|_-.053_|_-13.7%_|
|Envioronmental<br>Problems|.523|**_.)_**<br>_.028_||**_._**<br>_45.4_<br>**(004)**|_%_<br>**25.7%**|.163|31.1%|

Notes: Bolded items are based on probit coefficients statistically significant<sup>at</sup><sup>**a**p-levelof10%level or lower.</sup> a) see Annex 2 for a description of the reporting of the probit results.

_Net impact._ With positive private and negative community effects the net impact of greater involvement by an additional household could go either way. What is truly striking about the empirical results is that, for eight of the ten indicators, the _net_ impact is negative.21 For example, the estimates for information awareness suggest that households who are members of one additional _desa_ government organization are<sup>4.1</sup> percent more likely to report knowing all three types of information (and are also more likely to report improvements in transparency). But the community impact is negative, 21 Note that the positive sign for one of the voice indicators (no expression in spite of existing problems) indicates a negative _(i.e.,_ detrimental) impact.

27
**Tables**

**Table 1: Estimation results based on Equation (1)**

||(1)<br>Salesper worker|(2)<br>Sales(log)|(3)<br>Employment(log)|
|---|---|---|---|
|Energy intense|0.240**|0.255***|0.235***|
||(0.0955)|(0.0964)|(0.0320)|
|Price oil, log|11.64***|11.20***|-2.044***|
||(0.450)|(0.452)|(0.140)|
|Price electricity, log|-1.818***|-1.978***|-0.734***|
||(0.295)|(0.302)|(0.0987)|
|Explicit petroleum subsidy (% GDP)|1.468|1.469|-0.587**|
||(1.010)|(1.031)|(0.298)|
|Explicit electricity subsidy (% GDP)|8.011***|7.852***|-0.463*|
||(1.132)|(1.131)|(0.269)|
|Constant|-54.12***|-50.26***|7.677***|
||(2.350)|(2.364)|(0.731)|
|Observatons|21,629|21,629|21,629|
|R-squared|0.225|0.331|0.471|
|Country FE|YES|YES|YES|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Weighted by country sample size Controls for size clusters<sup>[1]</sup> , age, wave, and subsector [1] Constructed using employment in columns (1) and (2), and sales in column (3) 18