rafmacalaba/datause-encoder-data huggingface dataset:
|||(0.184)|(0.182)||(0.177)|(0.176)|
|---|---|---|---|---|---|---|
|Informality rate host||-1.452***|-1.184***||-1.483***|-1.234***|
|||(0.204)|(0.199)||(0.195)|(0.191)|
|Crime incidence origin||-0.253|-0.237||-0.230|-0.214|
|||(0.175)|(0.169)||(0.170)|(0.164)|
|Crime Incidence host||0.114|-0.400**||0.104|-0.394**|
|||(0.211)|(0.196)||(0.209)|(0.199)|
|Perceived corruption origin||1.235**|1.155*||1.288**|1.220**|
|||(0.625)|(0.601)||(0.629)|(0.607)|
|Perceived corruption host||-1.465*|-1.764**||-1.549*|-1.821**|
|||(0.815)|(0.846)||(0.793)|(0.824)|
|Stock of migrants in host|||0.681***|||0.646***|
|State|||(0.117)|||(0.113)|
|Constant|6.769***|8.254***|3.025*|6.401***|7.982***|3.014**|
||(0.550)|(1.580)|(1.633)|(0.563)|(1.485)|(1.530)|
|Observations|992|992|992|992|992|992|
|R-squared|0.700|0.726|0.965|0.660|0.703|0.963|

Notes: The dependent variable is the number of people who migrated from state i to state j in 2015 and still resided in state j in 2020. All explanatory variables are included in logarithmic form and refer to the year of migration (2015). *** p<0.01, ** p<0.05, * p<0.1

# 6.2. Determinants of internal migration at the individual level

**Table 2 presents the results from the multinomial regression model.** All the parameter estimates are displayed in terms of relative risk ratios. The variables associated with characteristics in the state of origin were standardized to correct for differences in the scales.

**The results indicate that age is statistically significant and negatively correlated with the propensity to migrate for economic reasons.** In other words, younger people are more likely to migrate for economic reasons after controlling for other factors. The effect of young age on the propensity to migrate for economic reasons is higher for women than men. The odds that women aged 25-34 years migrate for economic reasons are 42 times higher compared to those aged 65+, while the odds that men in the same age group move for economic reasons are 26 times higher. The odds of women between 35-44 years and 45-55 are 23 and 10 times higher, respectively. Women between 25-44 are also more likely to migrate due to family reunification or due to crime and violence in their state of origin than those aged 65+. However, age is not statistically significant for women who migrated for other reasons. In addition, age differences are not statistically significant for men who moved due to crime and violence in their state of origin or other reasons.

**There are no statistically significant effects of being Afro-descendant on women’s propensity to migrate**

**regardless of the reasons for migration** . On the contrary, Afro-descendant men are more likely to migrate for economic and family reunification reasons than those who are not (1.6 and 1.4 higher odds, respectively).

28
specification are very similar to those that include all children.

The CSP program effects we estimate are remarkable for their magnitude. One way to place them in context is by comparing them with those that have been found for similar programs elsewhere. Schultz (2004) analyzes the impact of PROGRESA, the Mexican conditional cash transfer program, on enrollment. He finds that PROGRESA reduced dropout between 6<sup>th</sup> and 7<sup>th</sup> grade by 7-9 percentage points among boys, and by 10-15 percentage points among girls, with much smaller and insignificant results on enrollment at other school grades. The enrollment effects of other CCTs are also smaller, often substantially so, than those found in Cambodia (see the review by Fiszbein and Schady 2009). The CSP program effects are particularly remarkable given the small magnitude of the transfer.

# C. <u>Program effects on test scores</u>

We now turn to estimates of CSP program effects on learning outcomes. For this purpose, we use data from the household survey, where children were tested regardless whether they were enrolled in school. Note that, because we do not condition on school enrollment, these estimates should be unaffected by any biases associated with selection into school. This is important, as school enrollment could be correlated with both CSP scholarships and the unobserved characteristics of applicants, including latent ability.

As before, we motivate our results with figures of the average test score at each value of the within-school ranking by the dropout score; and the corresponding values from quartic regressions in the rank and an indicator variable for CSP recipients. These results are presented in Figure 4. Unlike the results for school attendance, enrollment, time in school, education expenditures, and school attainment, we find no evidence of discrete jumps in either mathematics or vocabulary test scores at the cut-off that determines eligibility for CSP scholarships.

Estimates of CSP program effects on mathematics and vocabulary test scores from various specifications are reported in Table 3. For neither mathematics nor vocabulary, and for none of the specifications, do we find evidence of significant program effects on learning. Our estimates are 15
- 39 -

Table 8 **(b)** Impact of a 10% !eduction in R&D Tax Allowance on R&D Investments in Pakistani Chemical and Pharmaceutical Industries and Government Revenues Reduction in R&D Expenditures = Rupees (million) 1.8 Increase in Government Revenues - Rupees (million) 1.0 Ratio of R&D Loss to Revenue Gains = 1.75

# Source: Model-based calculations

Table 8(b) quantifies the impact of a 10% reduction in RFD tax allowance on R&D investment in Pakistani chemical and pharmaceutical indus.ries. It shows that if the tax allowance for R&D had been 10% less, R&D expenditures would have been approximately 1.8 million Rupees lower and government revenues higher by a million rupees. Thus R&D tax allowance has fulfilled its policy objectives.

# 4.3 Turkish chemical and petroleum derivatives industries

Before presenting the results of our hypothesis tests, we report the elasticities of the demand for labour and for materials with respect to small changes in the wage rate, the price of materials, and the rental prices of investment in physical and knowledge capital in table (9).

Table **(9)** Turkish chemical and Petroleum derivatives industries Elasticities of the demand for labour and for materials with resoect to chanaes in the input prices. midpoint of the samole (approximate standard errors in brackets)

|GLWL|=|-<br>0.2975<br>(0.7297)|eMW|0.3593<br>(0.2979)|
|---|---|---|---|---|
|eLM|=|0.2970<br>(0.7286)|M|=<br>-<br>97<br>(0.2981)|
|eLRPK|=|0.00028<br>(0.00039)|empK|=<br>0.00007<br>(0.00009)|
|eLRpp|=|0.00019<br>(0.00116)|eMRpp|<br>0.00034<br>(0.00020)|
achievement rates mask disparities between states and regions. What primary school characteristics contribute the most to student learning in a multivariate model? How do these school variables impact on learning achievement? This section addresses these questions, indicating the factors that increase students' success in primary school.

The importance attributed to school factors is not unique, as school-level variables have a stronger effect on students' achievement in LDC's than in developed countries. It is clear that despite the strong contextual variables effect on primary level student achievement in Mexico, school variables are important. Thus, the identification of those school factors that lead to increase student learning is also important. Largely based on quantitative analysis of the EEEP, the impact of _Carrera Magisterial_ and other important school factors on learning achievement are examined using multivariate models.

The hierarchical structure of the data, with students nested within schools, requires a form of regression analysis that weighs the sources of learning achievement variation—students, school and teachers. To analyze the determinants of learning achievement, the models below were estimated. In each model, the school, socioeconomic and teachers ́ characteristics are the same. This estimation strategy allows us to measure the effect of these factors on learning achievement _._ The first model is the variance plus school fixed model, which is the starting point in every multilevel analysis.

The first model fully captures school effects through the use of a complete set of school dummies. The second model uses school variables (instead of dummies) to help analyze the determinants of school factors on learning achievement. Denoting child and household level variables by X, school dummies by D, and school variables by W, the models are: The two models are estimated separately for the urban and rural areas as well as nationally.

This attribute enables us to estimate the overall mean of achievement, and determine the deviations of student scores and school averages around that mean. The second model fully captures the students effects through adding student socioeconomic variables to the empty model. The third model uses school level 12
# **Table 11 – Local banks: Adverse reform spillovers affecting domestic financial market conditions**

_Please indicate the reform areas where you have a reasonable indication that their adoption in other jurisdictions has already had, or is expected to have, a significant adverse impact on the activity in and development of the domestic financial sector (you can select more than one reform area)._

|**N = 20**|**Risk-based**<br>**capital**<br>**reforms**|**Leverage**<br>**ratio**|**Liquidity**<br>**measures**|**Measures**<br>**dealing with**<br>**systemically**<br>**important**<br>**banks**|**Measures**<br>**dealing**<br>**with OTC**<br>**derivatives**|**Any other**<br>**G20**<br>**financial**<br>**reform**|**No**<br>**Significant**<br>**Impact of**<br>**any reform**|**Don't**<br>**know**|
|---|---|---|---|---|---|---|---|---|
|Moneymarkets|5|5|14|3|5|1|5|1|
|Foreign currencymarkets|8|5|7|2|7|1|4|3|
|Retail fundingmarkets|7|6|9|2|0|1|8|1|
|Wholesale funding markets|10|6|14|4|2|1|3|1|
|Corporate debt markets (e.g. pricing, depth,<br>issuance, and liquidity) (e.g. due to<br>reluctance of foreign banks to underwrite<br>new local issuance)|13|8|12|4|2|1|6|0|
|Sovereign debt markets (e.g. pricing, depth,<br>issuance, and liquidity) (e.g. due to<br>reluctance of foreign banks to hold local<br>bonds)|8|6|8|4|2|1|8|2|
|Equity markets (e.g. depth, issuance, and<br>liquidity)|6|5|7|3|1|1|6|4|

33
particular, they cite evidence from other research they have conducted in Zambia which finds that access to extension services can boost productivity by more than eight percent, resulting in an aggregate gain of more than nine percent, when combined with higher cotton prices.

But the largest poverty reduction benefits appear to arise when subsistence households switch to cotton production in the wake of increased demand for exports. Here, a careful matching of subsistence and cotton-producing households shows that, all else constant, subsistence producers could boost their incomes by nearly 20 percent if they switched to cotton production. Such a switch would be greatly facilitated by continued improvement of the out-grower schemes and strong demand for cotton exports. When combined with improved extension services and higher cotton prices, the switch from subsistence production to cotton could boost incomes of some of the poorest households in Zambia by nearly one-third. In sum, Balat and Porto conclude that trade reform alone is not sufficient to raise a large number of poor out of poverty in Zambia, but that when the market opportunities presented by trade reforms are combined with complementary domestic reforms, significant headway in the fight against poverty is possible.

Of course, global trade reforms do not simply alter one single commodity price: rather they potentially affect _all_ prices in the economy – including the prices of nontradeable commodities and services as well as wages and returns to land and capital. So we turn next to a study that seeks to account for the full range of price impacts at a highly disaggregated level. The unusual thing about Joaquim Ferriera Filho and Mark Horridge’s (2005) paper is the very large number of individuals considered in their 17
# **1. Introduction**

Financial education programs have emerged as an integral component of the post-crisis reform agenda around the world.<sup>1</sup> Other macroeconomic and technological trends are also prompting such demand. With the explosive growth of financial inclusion programs and the emergence of mobile money, policymakers and NGOs are urging more widespread financial awareness and knowledge to enable the poor to better understand and utilize the new financial products at their disposal.<sup>2</sup> Thus, a consensus has emerged around the need to expand financial education in the developed and developing world. Such policies may be critical not only to improving household welfare, but also to ensuring a stable financial system. Despite these perceived benefits, early studies on the causal impact of financial education showed limited impact (see Fernandes et al., 2014; and Miller et al., 2015 for literature reviews), from the lack of improvement in bank savings in Indonesia (Cole et al., 2011), to outright failure, such as a video and radio based financial education course that was discontinued due to logistical challenges and low take-up (Chong et al., 2010). Recent research, however, shows more promise with innovative delivery channels (Berg and Zia, 2017), complementarities with goal setting and counseling (Carpena et al., 2017), and a focus on specific target groups (Doi, McKenzie, and Zia, 2014; and Bruhn et al., 2016) all resulting in significant impacts of financial education on real financial outcomes.

This paper combines the instructional insights on curriculum design and delivery from these latter studies and tests a comprehensive financial education program for Savings and Credit Cooperative Association (SACCO) members in a country-wide field experiment involving 200 SACCOs in Rwanda. The program comprised a comprehensive _training-of-trainers_ workshop, whereby staff and community members from 130 SACCOs were trained and then tasked with delivering content to their respective communities. SACCOs were provided with curriculum materials, some financial support, and initial supervision by master trainers.

> 1 According to the 2017 Global Financial Inclusion and Consumer Protection Survey, national financial education strategies are reported to be in place in 44 jurisdictions and under development in a further 27 jurisdictions (World Bank, 2017). The Government of Rwanda itself launched a National Financial Education Strategy in 2013.

> 2 See for example, “Financial Literacy: A Step for Clients towards Financial Inclusion,” Global Microcredit Summit Commissioned Workshop Paper, November 14-17, 2011.

2
# TABLE OF CONTENTS

|||Page|
|---|---|---|
|I.|INTRODUCTION ......................................|......1|
|II.|A THEORETICAL FRAMEWORK FOR ENTERPRISE REFORMS ....|...... 6|
|III.|THE THEORY OF PRINCIPAL AND AGENT .................|...... 9|
|IV.|THE IMPLEMENTATION OF LEASE CONTRACTS .............|..... 19|
|V.|INSTITUTIONALIZING ENTERPRISE REFORMS BASED ON<br>A PRINCIPAL-AGENT RELATIONSHIP ....................|..... 26|
|CONCL|USION ..............................................|..... 29|
|REFER|ENCES ..............................................|..... 32|

I would like to take the opportunity to acknowledge those of my colleagues who have helped me write this paper. Without their advice the quality of this work would have been of a considerably lower standard. I would especially like to thank Gunnar Eskeland, Daniel Kaufmann, Emmanuel Jimenez, Fiona Mackintosh, Zmarak Shalizi and Sudir Shetty.
# <u>Table 6:  The Relationship between Trade Credit Supply and  Market Power and Competition</u>

The reported estimates are from logit (Colums 1-5) and OLS (Columns 6-10) regressions. AR_d is a dummy indicating the use of accounts receivable. AR_per is the percent of monthly sales sold on credit. We transform AR_per in a continuous variable by using the inverse function of the logistic model. See Table 1 for variable definitions. All regressions include 12 sector dummies and 17 city dummies. Robust p-values are shown in parentheses, ***, **, * indicate significance at the 1%, 5%, and 10% level, respectively.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
||AR_d|AR_d|AR_d|AR_d|AR_d|AR_per|AR_per|AR_per|AR_per|AR_per|
|L_Age|-0.24|-0.19|-0.14|-0.17|-0.24|-0.85|-0.67|-0.67|-0.6476|-0.5279|
||[0.01]***|[0.02]**|[0.00]***|[0.03]**|[0.01]***|[0.00]***|[0.00]***|[0.00]***|[0.00]***|[0.06]*|
|L_emp|0.16<br>[0.00]***|0.10<br>[0.01]**|0.10<br>[0.00]***|0.09<br>[0.02]**|0.16<br>[0.00]***|0.51<br>[0.00]***|0.36<br>[0.00]***|0.41<br>[0.00]***|0.3216<br>[0.00]***|0.3123<br>[0.03]**|
|D_Foreign|0.55|0.34|0.14|0.34|0.55|1.49|0.78|1.08|0.8942|0.7311|
||[0.03]**|[0.10]*|[0.24]|[0.09]*|[0.03]**|[0.08]*|[0.27]|[0.13]|[0.21]|[0.37]|
|D_State|-0.11|-0.10|-0.02|-0.12|-0.11|0.01|0.01|0.01|-0.1076|0.0142|
||[0.48]|[0.49]|[0.78]|[0.39]|[0.48]|[0.98]|[0.98]|[0.99]|[0.77]|[0.98]|
|D_Export|0.33|0.35|0.39|0.34|-0.329|0.04|0.06|0.07|0.1368|0.1318|
||[0.16]|[0.07]*|[0.04]**|[0.08]*|[0.16]|[0.96]|[0.93]|[0.91]|[0.84]|[0.86]|
|Saleslargest_5|0.21<br>[0.08]*|||||0.77<br>[0.02]**|||||
|Compmktshare_1||0.21<br>[0.07]*|||||0.86<br>[0.02]**||||
|Lower_price|||0.56<br>[0.00]***|||||1.55<br>[0.00]***|||
|New_product||||0.32<br>[0.00]***|||||0.95<br>[0.00]***||
|Suplcust_5|||||0.21<br>[0.08]*|||||0.51<br>[0.15]|
|Constant|-2.94<br>[0.00]***|-1.92<br>[0.00]***|-2.13<br>[0.00]***|-1.92<br>[0.00]***|-2.94<br>[0.00]***|-11.94<br>[0.00]***|-11.37<br>[0.00]***|-11.89<br>[0.00]***|-10.99<br>[0.00]***|-12.27<br>[0.00]***|
|Observations|1,611|1,974|2,011|2,016|1,611|1,623|2,000|2,038|2,040|1,524|
|PseudoR-squared|0.09|0.08|0.09|0.08|0.07|0.13|0.12|0.12|0.12|0.11|

42
where yt is the logarithm of the firm’s output, most often measured as gross revenue or value added (measured as gross revenue in this paper); lt and mt are the logarithm of the freely variable inputs labor and the intermediate input (materials); and kt is the logarithm of the state variable capital (fixed assets). The demand for the intermediate input mt is assumed to depend on the firm’s state variables kt and ωt; The demand function is assumed to be monotonically increasing in ωt, allowing the inversion of the intermediate demand function, so ωt can be written as a function of kt and mt: Substituting into (1) yields: which is solved using GMM.

# **(ii) Growth and Productivity Decomposition**

**Using standard growth accounting methodology, we assess the impact of each of the factor inputs (TFP, labor, capital) on the growth of firms’ output** . Taking logarithms and differentiating a standard Cobb-Douglas, three-factor production function, we obtain the following growth accounting equation, expressed in value-added form (i.e. output less materials):

This shows that firms’ output growth (first derivative of Y) can be decomposed into three sources of growth: (a) the contribution from growth in the physical capital stock (the derivative of _K_ ), (b) the contribution from growth in the labor force (the derivative of _L_ ), 3
the structure of the expenditures for each decile (panel B). In general, the differences between the three series are not large, but inflation is slightly higher for the bottom income decile. When compared to households in the 10th decile, the annual inflation rate experienced by poor households was higher in six out of the 10 years covered in the analysis. The years with the largest gaps are 2013, 2015 and 2019, whereas the opposite was true during the first phase of the 201819 crisis in 2018 when fast accelerating prices in transportation had a bigger burden on better-off households. The cumulative inflation in the 10-year period was 10.39% for the bottom decile and 10.16% for the top decile, a difference of 0.23 percentage points that is statistically significant at the 5% level. Contrasting the inflation rates of the bottom decile with respect to the average inflation rates or comparing the Bottom 40 vis-à-vis the Top 60 yield similar conclusions (Figure A.1 in the Annex).

Figure 3: Inflation for the 1st Decile, 10th Decile and the overall population (2011-2020) Inflation for 1st vs 10th Deciles (2011-2020) Inflation for 1<sup>st</sup> Decile vs Overall (2011-2020) (Panel A) (Panel B) <!-- Start of picture text --> 18 18<br>16 1st Decile 16 1st Decile<br>10th Decile Average<br>14 14<br>12 12<br>10 10<br>8 8<br>6 6<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> _Source_ : World Bank calculations using data from TUIK We also looked at the contribution of different expenditure categories to examine the sources of variation in the incidence of inflation across the population. Figure 4 shows a breakdown of the overall inflation experience of the 1st and 10th decile across the seven mains groups of expenditures plus a category that combines the remaining items consumed (labeled as “others”). Considering the large weight of food in the total consumption of poor households, it does not come as a surprise to observe that inflation episodes with a heavy burden on this group of the population –such as those that took place in 2013, 2015 and 2019– are chiefly driven by substantial increases 14
23

#  **Rather than amounts in IDRs, should we show percentages as in the WB report?**

# **Table 3**

**Severance pay by employee and job characteristics**

||**Legally entitled**<br>**severance pay**<br>**amount**<br>**IDRs (million)**|**Actual**<br>**severance pay**<br>**received**<br>**IDRs (million)**|**Ratio of actual**<br>**amount received to**<br>**legally entitled**<br>**severance pay**|
|---|---|---|---|
|**Age**||||
|15–18 years|1.274|0.145|11.4%|
|19–24 years|2.335|0.052|2.2%|
|25–34 years|7.862|1.710|21.8%|
|35–49 years|14.172|5.464|38.6%|
|50–64 years|13.201|11.727|88.8%|
|65 years and older|9.125|0.302|3.3%|
|All ages|9.362|3.608|38.5%|
|**Gender**||||
|Female|7.193|3.116|39.4%|
|Male|10.184|3.886|38.2%|
|**Education**||||
|Less than primary|5.136|0.773|14.3%|
|Primary|8.148|1.894|23.3%|
|Junior secondary|7.064|3.085|43.7%|
|Senior secondary|11.249|5.654|50.3%|
|Tertiary|10.577|2.204|208%|
|**Industry**|||.|
|Agriculture|8.702|2.892|33.2%|
|Mining and quarrying|35.383|14.094|39.8%|
|Manufacturing|6.862|1.375|20.0%|
|Utilities|8.861|0.000|0.0%|
|Construction|7.522|0.706|9.4%|