rafmacalaba/datause-encoder-data huggingface dataset:
# **6. Conclusions**

Despite growing efforts directed to assessing the impacts of CCTs, the most popular type of safety net applied in developing countries, on education, gaps in knowledge exist as to whether the largely documented positive effects on enrollment and attendance are sustained over time and result in higher school attainment. The evidence on the relationship between higher utilization of school inputs due to CCTs and learning outcomes is equally scant. This paper seeks to help fill these gaps by empirically investigating the schooling trajectories and academic performance of various cohorts of participant children who have different levels of program exposure ranging from one to nine years. We find robust evidence that the FA program increases school attainment by helping participant children to finish high school. Indeed, treated children are on average between 4 and 8 percentage points – equivalent to an increase of 8-16 percent – more likely to graduate from high school relative to those in the control group. If at present the program supports nearly 3.5 million poor children and only about 36 percent of the children in Colombia who start primary school are expected to graduate from high school<sup>35</sup> , a conservative extrapolation of program impacts to this population would be equivalent to around 100,000-200,000 additional high school graduates.<sup>36</sup> . Moreover, the size of these program impacts is in the same range of magnitude as the effects found in similar CCT and education fee waiver programs in Colombia and Pakistan.

By encouraging participant children to finish high school, the FA program is expected to have other positive effects on further human capital gains (increasing the probability of entering into higher education), future employability, and income growth. As is the case in most developing countries, finishing high school is a critical achievement for lowincome individuals and may lead to significant positive externalities in various dimensions. The first obvious channel is eligibility to apply for college or formal

> 35 Reference about work from Sanchez

> 36 If households classified in SISBEN 1 have on average 1.37 children between 7 and 18 years and the number of families covered by the program is about 2.8 million, _FA_ supports nearly 3.8 million children. The effect of the program in terms of the number of students is proxied by the increment in the high school completion rates induced by the program (from 36 percent to 40-44 percent) with respect to the 3.8 million that are currently participating in FA. Estimates based on the high school completion rates observed for the control group from the PSM analysis (children between grades 4 and 10 at baseline) suggests that approximately 50 percent of these children would graduate from high school. Applying the effects of the program to this completion rate implies that the aggregate effect would be in the order of 140,000-280,000 additional high school graduates.

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Table 1. ODA for Sector Programs, General Budget Support and Debt Relief (Commitments, US$ million at 2004 prices, 2001-2004) <!-- Start of picture text --> eeType a % of total commitments<br><!-- End of picture text --> <!-- Start of picture text --> sector Programs |__a| nana sans] 1g066| 12a] oxe| ts<br><!-- End of picture text -->

<!-- Start of picture text -->
|UnalincatedBOOOMG] TH<br><!-- End of picture text -->

<!-- Start of picture text --> WnaliocatedHT<br><!-- End of picture text --> <!-- Start of picture text --> (7) Excluding debt refef and general budget support to avoide double counting. Only commitments with no investment or TC components.<br><!-- End of picture text -->
# ANNEX 3. Outcome Measures

## Primary Indicators

**Depression and anxiety:** The primary outcomes are the prevalence and intensity of depression and anxiety symptoms, as measured using the Patient Health Questionnaire Anxiety‐Depression Scale (PHQ‐ADS), which combines the 9‐item Patient Health Questionnaire (PHQ‐9) and the 7‐ item Generalized Anxiety Disorder (GAD‐7) scales (Kroenke and others 2016; Chilcot and others 2018). The PHQ‐9 incorporates Diagnostic and Statistical Manual of Mental Disorders (DSM)‐ Version IV depression diagnostic criteria with other major depressive symptoms (Kroenke and others 2001). Participants rate their responses on a 4‐point Likert scale ranging from ‘not at all’ to ‘nearly every day’ (0‐3), with the score ranging from 0 to 27. It measures whether a person had difficulty in sleeping, felt hopeless/down, felt tired, had trouble falling asleep, had poor appetite, felt bad about him/herself, had difficulty concentrating, had been slow or restless, and had suicidal thoughts. The GAD‐7 is based on the DSM IV diagnostic criteria for generalized anxiety disorder and has 7 items (Spitzer and others 2006). As with the PHQ‐9, each item is scored on a 4‐point Likert scale ranging from ‘not at all’ to ‘nearly every day’ (0‐3), with the score ranging from 0 to 21. The GAD‐7 measures whether a person has not been able to control worrying, has trouble relaxing, feels nervous and on the edge, is easily irritable, feels afraid that something terrible might happen, etc. The PHQ‐ADS score is calculated as the sum of the PHQ‐9 and GAD‐7, with the total score ranging from 0 to 48. Higher scores indicate higher intensity. The cutoff scores of 10, 20, and 30 indicate mild, moderate, and severe levels of depression and anxiety, respectively.

The Cronbach’s Alpha scores of PHQ‐9 and GAD‐7 scores show strong internal consistency, with scores above 0.8 across all three rounds (see Table 9). The overall PHQ‐9 and GAD‐7 scores also appear to be highly correlated.

Table 9. Reliability of PHQ‐ADS score

||**Cronbach’s Alpha***|**Correlation**|
|---|---|---|
|Baseline|0.8449|0.7402|
|5 weeks|0.8232|0.7009|
|3 months|0.8348|0.7184|

Source: Saraf, 2019

## Secondary Indicator

**Well‐being:** This was measured using the WHO‐5 Well‐Being (WHO‐5) index (Topp and others 2015). The WHO‐5 is a short, generic global rating scale measuring subjective well‐being. The respondent is asked to rate how well each of the 5 statements applies to him or her when considering the previous 14 days. Each of the 5 items is scored from 5 (‘all of the time’) to 0 (‘none of the time’). The raw score ranges from 0 (an absence of well‐being) to 25 (maximal well‐being). Scores are then converted to a percentage scale from 0 (absent) to 100 (maximal). The WHO‐5 measures whether a person reports feeling cheerful and in good spirits, calm and relaxed, active and vigorous, fresh and rested, and values their daily life.

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Table 7: Effect of savings default on planned vs. unplanned expenditures

||(1)<br>F|(2)<br>ood|(3)<br>n|(4)<br>on-durables|
|---|---|---|---|---|
||Planned|Unplanned|Planned|Unplanned|
||Panel A:|One week after|transfer||
|Savings Default|652.018<br>(432.842)|54.034<br>(52.608)|-275.008<br>(213.848)|31.277<br>(31.092)|
|Observations|472|472|472|472|
|Mean of dependent variable in Cash group|4711.85|278.37|2315.56|64.89|
|SD of dependent variable in Cash group|5085.93|620.60|2764.69|303.39|
|R-squared|0.21|0.28|0.23|0.05|
||Panel B:|Two weeks afte|r transfer||
|Savings Default|158.904<br>(425.306)|-10.275<br>(40.799)|297.821<br>(235.307)|23.619<br>(33.923)|
|Observations|312|312|312|312|
|Mean of dependent variable in Cash group|3748.37|185.56|1334.38|39.54|
|SD of dependent variable in Cash group|4540.24|454.46|1998.36|228.99|
|R-squared|0.32|0.19|0.26|0.04|

Categorization into planned and unplanned purchases is based on respondents’ answers during itemized elicitation of expenditures. For each item respondents were asked if they had planned to purchase the item before they arrived at the market or store, or if the decision was made on the spot. Panel A shows regressions with outcome variables measured one week after the transfer, Panel B uses the same outcomes measured two weeks after the transfer. The sample for Panel B is smaller since two-week follow-up data were only collected for respondents who received transfers immediately or with one day delay. All specifications include village and week-of-first-survey fixed effects, and the value of the outcome measured at survey 1. Exchange rate: MK 420 per USD. Robust standard errors in <u>parentheses.</u> * <u>p</u> _<_ 0.10, ** <u>p</u> _<_ 0.05, *** <u>p</u> _<_ 0.01 35
where **βj,i** is the adult equivalent for individual _j_ in household _i_ . We use a single calorific equivalence scale based on OECD recommendations and we assign to each child in the age group 0- 18 years a value of β= 0.3 of an adult, and a value of β= 0.5 to each other adult in the household which is not head of the household. Against this background, the total consumption variable to be used in the econometric specification is constructed as the sum of household food and non food consumption scaled by equivalents. Consumption is expressed in March 2007 prices using the annual inflation rate for rural areas provided by the Malawi Statistical Office.

Quantile regression models were used to quantify outcomes at the median and at the .25 and .75 quantile: this choice allows for a type of robust regression analysis which reduces the influence of outliers. The dependent variable used in the analysis is given by the ratio of the variation of consumption between the final round and the baseline round over the average total consumption measured at t0 and t1. This is an alternative measure of consumption variation used in place of the more traditional difference in logarithmic consumption at t1 and t0. The econometric specification can be written as: where the dependent variable, defined as [(c1-c0)/( c1+c0)]*2, is regressed on a constant and a set of taste shifters Zi like the household composition as well as age, sex, health and marital status of the head of the household. Moreover, a set of variables Xi measuring idiosyncratic shocks at household and village level affecting the level of income, such as crop pests, livestock stolen and price shocks, was used. The transfer T is also part of the regression. Under the perfect risk sharing hypothesis, the coefficients on idiosyncratic shocks and on the transfer should be equal to zero. If this is not the case, then it means that transfers and other shocks to income have an impact on the household’s level of consumption and therefore the perfect risk sharing hypothesis is ruled out. In fact while usually negative shocks are considered good candidates for testing the effect of idiosyncratic shocks to consumption, we use a positive shock in the form of an unconditional cash transfers given to some households in the village, as idiosyncratic shock. Positive shocks should in fact also be shared and not affect household consumption directly, but only through aggregate village resources.

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Surma and Concsl n A key source of Pakistan's impressive GDP growth (6 percent annual growth sustained for two decades) has been the agriculture sector, which grew at about 3.6% annually during last 25 years. This paper analyses whether such a growth rate sustainable in the future.

Sources of past impressive growth have been different in different periods -- seed, fertilizer and irrigation package in 1960s, intensification of water and fertilizer use in the 1970s and improvement in crop management and incentives in 1980s. In the last decade cotton has been the main source of growth.

A careful look at the sources of growth suggests that the past momentum may be running out. There is no chance of a significant increase in total cultivable land or in irrigation. At best, a 10 percent expansion in water resources can be expected, and 3nly at a large cost. Cotton also has run into problems in recent years.

With past sources weakening, future growth will have to come predominantly fromi productivity growth. And overall productivity growth will come from such sources as allocation of resources to crops in which the country has a comparative advantage, improvement in technical efficiency of inputs of each major crop, and increases in cropping intensity. On all counts, Pakistan potentially can do much better than it does now. However, future growth through productivity increase will require major changes in systems, policies and institutions for agriculture.

- Broadly speaking, there are two sets of constraints facing Pakistan's agriculture resource constraints and policy distortions. Resource constraints can be described under 4 categories. First, soil erosion and land degradation are causing problems. Inappropriate incentive policies has often led to inappropriate use of land and hence to problems of soil erosion and land degradation. Second, distribution of land resources and systems of land tenure are also constraints. Land distribution is concentrated - at one extreme are very large, absentee landlords not optimally using land and at the other extreme are the very small and ill-equipped peasant farms. Insecurely of tenure, creating disincentives for investment in land, is another problem. Third, serious problems have plagued irrigation, which accounts for more than three-fourths of agricultura **l** and in Pakistan. Fourth, human resoures and infrastructure are comparatively weak in Pakistan.

This report is drawn from the Bank report, Pakistan: A Strategy for Sustainable Agricultual Growth (Report No. 13092 Pak). The Bank report was prepared by Rashid Faruqee with assistance from Kevin Carey. Messrs Moazam Mahmood, Nadeem Ilahi, A.R. Saleemi, Tayyeb Shabbir, Derek Byerlee, Omar Noman and Yusuf Choudhry prepared background notes and papers for the Bank report.
Source: Author’s elaboration from Enterprise Survey (2014) <u>Table 16. Main Reasons for Process Innovation (% of process innovation firms)</u>

|**Reasons**|**Bangladesh**|**India**|**Nepal**|**Pakistan**|**Africa**|
|---|---|---|---|---|---|
|Increase quality products|95%|81%|66%|92%|81%|
|Increase production|87%|66%|56%|63%|85%|
|Increase flexibility|65%|58%|65%|46%|78%|
|Increase speed production|89%|68%|62%|64%|78%|
|Increase speed delivery|60%|42%|45%|63%|71%|
|Decrease production costs|44%|41%|27%|41%|47%|
|Reduce waste|74%|54%|66%|63%|56%|

Source: Author’s elaboration from Enterprise Survey (2014)

# 5.6 Characteristics of the Innovation Process

In the South Asia region, most of the product and process innovations are in-house. However, process innovations are related to more collaboration than product innovations. External cooperation is in most of the cases linked to other firms, although cooperation with the private sector plays a more important role for process innovation than for product innovation (Figure 3). The primary sources of information for leaders are in-house or the private sector, while customers dominate in laggard countries (Figure 4).

Figure 3. In-house versus Collaboration <!-- Start of picture text --> How was product innovation developed?<br>How was process innovation developed?<br>(% of product innovation firms)<br>(% of process innovation firms)<br>Nepal 51% 8% 10% 31%<br>0.6%<br>1.2%<br>India 84% 15%0.6% Nepal 28% 63% 8%<br>2%<br>1.7% India 64% 35% 1.1%<br>Pakistan 94% 6%0% 0.4%<br>Pakistan 68% 32% 0.8%<br>0.4%<br>1.3%<br>Bangladesh 83% 16%1.0% Bangladesh 66% 33% 0.5%<br>0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%<br>All In House Cooperation other Firms All In House Cooperation other Firms<br>University or Government Don't Know University or Government Don't Know<br><!-- End of picture text --> This very high reliance on in-house innovation development - larger than in Africa and much larger than in the ECA region – significantly constrains the ability to increase the novelty of innovation either due to the lack of internal resources or the inability of exploiting synergies with the rest of the private sector or the scientific community. Reliance on internal capabilities only implies limited scope to introduce more novel products, and it is likely to influence the large imitation rates observed in India and Bangladesh.

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16 Table 5, which includes NRAs for non-farm tradable sectors, reveals that for the full sample the RRA too is lower than for the K/S/V sample of 17 countries in the period to 1984. Latin America and Asia had very high rates of manufacturing protection in that period, and they were especially high in China and India which were not included in the K/S/V study. Since those two are large economies, the weighted average NRA for all developing country producers of non-farm tradables is estimated to be 47 percent for the 1960-84 period, generating a weighted average RRA of -49 percent compared with the unweighted average rate of -29 percent for the K/S/V sample (or -30 percent based on K/S/V’s TRA).

Together these new findings suggest the broad qualitative conclusions drawn from the K/S/V study of two decades ago would not have altered had they included more products and more countries in their sample. However, with a bigger sample they would have been able to stress their policy implications even more forcefully, as the estimated magnitudes of the antiagricultural and anti-trade bias indicators both would have been larger by two-thirds.

# **Distortions to Agricultural Incentives in High-Income Countries pre-1985**

Tables 3 to 5 also show the new project’s estimated weighted average NRAs and RRAs for high-income countries, which include all the significant economies of Western Europe plus Australia, Canada, Japan, New Zealand and the United States, from which several points are worth stressing. First, the agricultural NRAs were already more than 20 percent by the latter 1950s, and they doubled over the period to 1984 (dipping only slightly in the mid-1970s when international food prices spiked upwards). This contrasts markedly with the developing country average NRA of below -20 percent in that era. Second, even exporting farmers in high-income countries were assisted, although much less so than import-competing farmers who enjoyed an NRA average that was more than three times that of import-competing farmers in developing countries. And third, with declines in manufacturing protection in high-income countries, their RRA average rose even more than their agricultural NRA average, from 14 percent in 1955-59 to 38 percent in 1980-84 and 51 percent in 1985-89. Together these estimates mean that farmers in developing countries were harmed in the K/S/V era not only by their own countries’ agricultural and non-farm policies but also – and increasingly from the latter 1950s to the latter 1980s – by competition in world markets from high-income countries that was enhanced by those countries’ pro-agricultural policies.
constrain income growth? The characteristics of these households can support the design for better policies to strengthen sustainable and inclusive economic growth.

The rest of the paper is organized as follows. The following section presents a brief conceptual underpinning of the analysis of mobility to be presented here. This will be followed by two separate sections on economic and social mobility where a separate discussion of methodology will be presented along with the main results. In addition to estimating the mobility in each case, the paper will also attempt to benchmark the South Caucasus countries with other countries in the region wherever appropriate and also attempt to present a fuller characterization, including profiles of these mobility metrics to the extent that it is allowed by the data. The final section will offer some concluding remarks on the summary of the key results and the possible implications for policy.

Specific points about data will be made in the subsequent methodological sections but it is necessary to mention at the outset that our best efforts to present a South Caucasus level narrative faces considerable challenges due to the heterogeneity in the availability of appropriate data across the sub-region. Azerbaijan, in particular, does not have comparable data for two points in time making it difficult to construct a credible poverty trend, let alone a systematic assessment of economic mobility. Technically the survey carried out by the statistical agency to monitor household consumption could be used, but the data from these surveys are not accessible. Thus, for the economic mobility part of the analysis, the focus will be entirely on Georgia and Armenia. Specifically, the data from Georgia allows us to follow the same household over time and conduct a significantly richer analysis than what we are able to for Armenia where we have to rely on the “synthetic panel” methodology. For the social mobility part of the analysis, however, we rely on data from the Life in Transition Survey (LITS) which implemented the same questionnaire in all three countries. This will allow us to cover all three countries and also present the results in a manner in which it is comparable across the three of them.

# Conceptual Underpinning

The overall conceptual framework for the analysis of economic mobility derives from the assetbased framework of the kind used by Bussolo and Lopez-Calva (2014). This approach embeds the household within the social and macroeconomic context of the country. On the one hand, the long-term production capacity of households will affect their ability to contribute to overall growth. On the other hand, the overall macroeconomic environment (e.g. demand for labor in various sectors, relative prices, i.e. the returns to assets and the intensity of use of assets over a life cycle) will in turn affect the households productive and income generation capacity. There may also be social influences on how the initial distribution of assets and the returns on these assets is determined (e.g., poor human capital endowments among minorities and lagging regions and any discriminatory practices).

5