rafmacalaba/datause-encoder-data huggingface dataset:
# **2 Context and Program Design**

## **2.1 Context**

Youth in Ghana, as elsewhere, often face unique challenges transitioning into the labor market. The overwhelming majority of jobs (almost 90 percent) are in the informal sector (Honorati & Johansson de Silva, 2016). Most of these jobs are low productivity and require limited use of cognitive skills (Honorati & Johansson de Silva, 2016). Thus, obtaining a job with growth and earnings potential is especially challenging. Recent data from Ghana show that youth ages 15-24 are much less likely than adults ages 25-65 to be working: while just over half of young people (52 percent) are working, the majority of other adults (89 percent) are (Ghana Statistical Service, 2014). The lower labor force attachment among youth reflects in part the fact that many young people are still in school. But policy makers and researchers are increasingly concerned by the growing share of young people who are neither in school nor at work.

There are important gender differences in the data. Among those aged 15-24 the female unemployment rate, at 24 percent, is 50 percent higher than the male unemployment rate (World Bank, 2019). In the age group 15-35 the female unemployment rate is 80 percent higher than the male rate (World Bank, 2019). Women in this age group are also less likely than men to work for a wage: while 20 percent of men ages 15-35 are employed in the wage sector, only 11 percent of women in this age group are. Because work in the wage sector is more lucrative than that in other sectors, this gap is an important driver of the lower earnings of women relative to men. These employment challenges stem in part from human capital constraints. Filmer and Fox (2014) argue that human capital is a key facilitator for youth in their efforts toward obtaining productive work. Although Ghana has made significant progress in improving access to education, less than a third of young people ages 15-24 have any senior secondary schooling (29 percent), only slightly higher than the rate for ages 25-34 (25 percent) though much higher than that for the oldest cohort (13 percent for ages 35-65). Skills are rarely reported as the most important obstacle for businesses in Ghana; nonetheless, they are cited as a major obstacle by nearly 20 percent of firms (Honorati & Johansson de Silva, 2016).

The skills deficit in Ghana is also driven in part by the education system, where large numbers of students fail to critical such as the end of progress beyond junctures junior high school. Compulsory education in Ghana consists of six years of primary school and three years of junior high school. Upon completing junior high school, young people can choose to continue their studies by attending a senior high school, a secondary technical school, or a technical institute (Gondwe & Walenkamp, 2011). Access to these institutions 6
more sensitive to seasonality; in this case, the MFI may be forced to manage the portfolio in such a manner that is not profitable. That is, the test of seasonality of financial intermediation for the MFIs ultimately rests on whether _β2_ is statistically different from zero.

Estimation of equation (2) involves empirical issues such as the joint distribution of lending and other indicators of MFI portfolio investment. That is, measurement errors in loan disbursement, for example, are likely to be correlated with measurement errors in loan outstanding, inducing an attenuation factor that biases the coefficients toward zero. We are using a lagged dependent variable (LDV) method to reduce such bias. We also use some exogenous agroclimate data such as monthly rainfall and temperature as additional controls to reduce attenuation bias (Table 3). As this is an aggregate-level (MFI-level) data analysis, it is difficult to control such bias fully. Yet, as the estimates are subject to downward bias, we would observe some seasonality in financial intermediation if we find that _β2_ is statistically significant even at the 10 percent level.

Table 5 presents the summary statistics of all essential variables for our econometric analysis across the projects. The average monthly disbursement is the highest for microenterprise loans followed by seasonal loans. This means that the monthly volumes of loans outstanding, loans recovered and savings mobilized are the highest for microenterprise than other activities. The loan recovery rate is about 99 percent for all categories of lending except for rural loans (98 percent). It seems that the MFI performance is slightly better in the urban areas than in rural areas. It is no wonder that in urban areas loan volume is higher, so is recovery, outstanding, and savings mobilized compared to those in rural loans. Women members in MFIs are six times as numerous as male members. Note that not all members are necessarily borrowers at a given time.

Tables 6-9 present the regression results of equation (2) showing the impacts of seasonality of disbursement on loans outstanding, loans recovered, savings mobilized, and the percentage of MFIs that default. The Breusch-Godfrey p value for all individual regressions is greater than 0.01, indicating that 20
- Leipziger, D., Fay, M., Wodon, Q., & Yepes, T. (2003). Achieving the Millennium Development Goals: The Role of Infrastructure (Policy Research Working Paper 3163). Washington, DC: World Bank.

- Lokshin, M., & Yemstov, R. (2005). Has Rural Infrastructure Rehabilitation in Georgia Helped the Poor? _World Bank Economic Review, 19_ (2), 311-333.

- National Institute of Population Studies (NIPS) [Pakistan], and Macro International Inc. 2008. _Pakistan Demographic and Health Survey (DHS) 2006–07._ Islamabad, Pakistan: National Institute of Population Studies and Macro International Inc.

- Njoh, A. (2009). The Development Theory of Transportation Infrastructure Examined in the Context of Central and West Africa. _The Review of Black Political Economy, 36_ (3), 227-243.

- Organization for Economic Co-operation and Development (OECD). (2002). _Handbook of Biodiversity Valuation: A Guide for Policy Makers._ France: OECD.

- Public-Private Infrastructure Advisory Facility (PPIAF). (2013). Retrieved from World Bank Private Participation in Infrastructure Database website: http://ppi.worldbank.org/index.aspx

- Sanchez-Triana, E., Afzal, J., Biller, D., & Malik, S. (2013). _Greening Growth in Pakistan through Transport Sector Reforms: A Strategic Environmental, Poverty, and Social Assessment._ Washington, DC: World Bank.

- Seetanah, B., Ramessur, S., & Rojid, S. (2009). Does Infrastructure Alleviate Poverty in Developing Countries? _International Journal of Applied Econometrics and Quantitative Studies, 6_ (2), 31-36.

- Straub, S. (2008). Infrastructure and Growth in Developing Countries: Recent Advances and Research Challenges (Policy Research Working Paper 4460). Washington, DC: World Bank.

- World Development Indicators (WDI). (2013). Retrieved from World Bank World Development Indicators Database website: http://data.worldbank.org/country

- World Health Organization (WHO), & United Nations Children’s Fund (UNICEF). (2013). Retrieved from WHO/UNICEF Joint Monitoring Programme (JMP) for Water and Sanitation website: - =

- <u>http://www.wssinfo.org/documents links/documents/?tx_displaycontroller[type] country_files</u>

- World Bank. (2003). _World Development Report 2004: Making Services Work for Poor People._ Washington, DC: World Bank

World Bank. (2012). _More and Better Jobs in South Asia_ . Washington, DC: World Bank.

- Yescombe, E. R. (2007). _Public-Private Partnerships: Principles of Policy and Finance._ Oxford: Butterworth-Heinemann.

Yoshino, Y. (2008). Domestic Constraints, Firm Characteristics, and Geographical Diversification of FirmLevel Manufacturing Exporting in Africa (Policy Research Working Paper 4575). Washington, DC: World Bank.

37
showing that democratization becomes a less important factor in reducing the size of countries when there are major conflicts. We also examine how the impact of democratization changes when external conflict is introduced into the empirical analysis. Data on _External Conflict_ also comes from the ICRG dataset. We adjusted this variable, however, to have higher points representing worsening of conflicts.<sup>14</sup> Finally, we use _GCC_ as a dummy variable for the Gulf Cooperation Council (GCC) countries to control for the possible impact of heavy dependence on oil on the government structure of these countries.<sup>15</sup>

# **4.3. Empirical Results**

We provide the summary statistics on the variables described above in Table 7. We start with the first set of regression results in Table 8. In addition to the explanatory variables explained above, we also include country fixed effects in our Table 8 regressions. Hence, country characteristics such as land area, being an oil producer, and all time invariant institutional factors are captured by the fixed effects estimation. We also see that serial correlation of errors could be a serious problem in our data. We run AR(1) fixed effects regressions to correct for the serial correlation problem.

We find that the coefficients of the size variables, _GDP per capita_ and _Population_ have the expected negative sign but not statistically significant in all four regressions. The coefficient of the _Share of Urban Population_ is positive, significant and robust across all regressions. This finding is counter to the argument by Wallis and Oates (1988). We see in columns (1) and (2) that both institutional variables have the expected

> 14 We have also tried with a variable on internal conflicts but that didn’t turn out be a significant factor in any of the regressions.

> 15 We removed _GCC_ and _Land Area_ from the first set of regressions as these cause perfect multicollinearity in the fixed effects regressions.

17
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wages in the village labor market, and result in lower household per capita consumption. A 1 percentage point increase in land under restrictions reduces per capita household consumption by 0.084 percent. We provide a rich array of evidence that rejects collateral value and insecurity of property rights as relevant mechanisms through which land restrictions affect employment choices and household consumption. The evidence is strong that the effects are driven primarily by higher migration costs due to restrictions on sales and rental of land. The theoretical and empirical analysis in this paper thus has two important contributions: (i) it shows that land market restrictions can result in a reverse structural change in the rural economy, and lower per capita household consumption, and (ii) it allows us to discriminate among three possible causal mechanisms, and provides clear evidence in favor of the migration costs channel which has been relatively neglected in the recent development literature.

# References

Acemoglu, D and Simon Johnson and James A. Robinson, 2001. “The Colonial Origins of Comparative Development: An Empirical Investigation, _American Economic Review_ , American Economic Association, vol. 91(5), pages 1369-1401, December.

Besley, T (1995), “Property Rights and Investment Incentives: Theory and Evidence from Ghana”, Journal of Political Economy, Oct, 1995, pp. 903-937.

Blundell, Richard and James Powell, 2001. Endogeneity in nonparametric and semiparametric regression models, CeMMAP working papers CWP09/01, Centre for Micro-data Methods and Practice, Institute for Fiscal Studies Briet, O. J. T, P. Vounatsou, D. M. Gunawardena, G. Galappaththy, P. Amerasinghe (2008), Temporal Correlation Between Malaria and Rainfall in Sri Lanka, Malaria Journal, May 2008.

Carter, M.R. and P. Olinto. (2003). Getting Institutions Right for Whom? Credit Constraints and the Impact of Property Rights on the Quantity and Composition of Investment. American Journal of Agricultural Economics, 85(1), 17386.

Clark, Colin, The Conditions of Economic Progress, 3 ed., London: Macmillan, 1957.

Clemesha, W (1934), “Brief Account of the Natural History of Malaria in Ceylon”, _Ceylon Journal of Science_ , 1934, 3: 157-172.

26
likely to be unemployed. Thus, a reason for leaving school at an early age for this group may be a greater availability of jobs. However, the result may stem from the fact that because Baptists are more likely to stay in school, dropout Baptists are a group with lower mean characteristics than their peer group of dropouts.<sup>29</sup> Marriage appears to force youth out of school and into employment. Tables 4.4 and 4.8 show that marriage is statistically significantly positively related to the probability of being out of school and is negatively related to being unemployed. Because the quality of jobs is not accounted for in the model, the positive correlation may be that married youth are indirectly forced to accept any job available to provide for the spouse, etc. On the other hand, economics of the family generally suggest that people who are married have better traits than their peers and married individuals would therefore be more likely to find (good) jobs.

Table 4.8: Probability of Youth being Unemployed or Inactive, 2001<sup>30</sup>

|Unemployed/inactive|Coefficient|Std.Err.|t|
|---|---|---|---|
|Age|-0.377|0.168|-2.24|
|Age squared|0.007|0.004|1.70|
|Female*|0.576|0.061|9.39|
|Metropolitan*|0.280|0.108|2.60|
|Rural*|-0.023|0.071|-0.33|
|Primary education*|0.112|0.066|1.69|
|Secondary education*|0.441|0.086|5.13|
|Tertiary education*|-0.113|0.380|-0.30|
|Migrated*|-0.276|0.088|-3.12|
|Married*|-0.313|0.081|-3.86|
|Head–primary education*|-0.134|0.070|-1.92|
|Head–secondary education*|-0.090|0.102|-0.88|
|Head–tertiary education*|-0.017|0.285|-0.06|
|Catholic*|-0.204|0.084|-2.41|
|Voodoo*|-0.372|0.195|-1.91|
|Other religions*|-0.310|0.097|-3.18|
|Constant|5.124|1.659|3.09|

_Note:_ No. observations: 2859. * is a discrete dummy variable; t is the test of the underlying coefficient being equal to 0. Household heads excluded. Variables left out: Urban, no education, head no education, Baptist.

_Source:_ Own calculations based on ECVH 2001.

Finally, although migration was not found to have an impact on the probability of dropping out of school, it does have a significantly negative impact on the probability of being unemployed. That is, migrants are more likely to be employed. This supports the theory that Haitian migrants are positively self-selected, as described below.

> 29 Unobserved heterogeneity (Justesen 2004, Heckman and Singer 1984).

> 30 Family size and family size squared came out statistically and insignificantly different from zero.

20
products.<sup>15</sup> In blue (red) are the products for which Tanzania was estimated to be a net exporter (importer) in 2005.

**Figure 1. Changes in Commodities World Prices** (TNZ shillings, January 2006-June 2008)

<!-- Start of picture text --> SORGHUM, 83%<br>PETROLEUM, 48%<br>FERTILIZERS, 192%<br>SUGAR, -31%<br>TEA, -19%<br>TOBACCO, -3%<br>COFFEE, 6%<br>COTTON, 12%<br>OIL SEEDS, 167%<br>BEANS, 114%<br>WHEAT, 97%<br>PADDY, 74%<br>MAIZE, 92%<br>-80% 0% 80% 160% 240%<br><!-- End of picture text --> Applying these changes in commodities prices to net trade positions by end- 2005 results into a negative terms-of-trade shock of 3.1 percent of GDP, in line with the estimated average for Sub Saharan Africa over the same period, 4.2 percent.<sup>16</sup> Excluding petroleum oil though, the overall impact of the shock declines to 0.5 percent of GDP, reflecting Tanzania’s balanced trade position vis-à-vis agricultural products.<sup>17</sup> Simulations suggest that the impact of global commodity inflation on domestic prices (including indirectly through their impact on costs of production) could remain moderate for most items in the short run, but for wheat, fertilizers and petroleum oil. In

> 15 One exception is cashew nuts (which represented 1-2 percent of total export receipts in 2005), for which world price data until mid-2008 were not found at the time of writing this paper. FOB prices of cashew nuts into the US did not significantly evolve over the period 2005-7 (Red River Foods, 2008) and we thus assume unchanged terms of trade for cashew nuts for the period January 2006-June 2008. 16 See World Bank (2008b)

> 17 In 2005/6, Tanzania recorded a food self-sufficiency rate of 103 percent. This ratio went up to 106 percent in 2007/8.

8
Policy Research Working Paper 7834

# **Abstract**

A significant body of research demonstrates that teachers and the quality of their teaching are crucial components of student learning. Many teachers in resource-poor environments have limited knowledge, skills, or motivation. Some impact evaluations have shown promising results from interventions to improve the quality of teaching. This paper reviews the existing body of evidence on what kinds of in-service teacher training interventions are most effective, and highlights the knowledge gaps. It reveals the dearth of detail on the nature of teacher training interventions and proposes a standard set of indicators—the In-Service Teacher Training Survey Instrument—for reporting on such programs as a prerequisite for understanding which interventions lead to improved student learning. Across a set of 26 programs with impact evaluations and student learning results, programs that provide complementary materials, focus on a specific subject, and include follow-up visits tend to show higher gains. Programs that use non-education professionals as trainers tend to have worse outcomes. Statistical power to identify these effects is limited, and use of these standard indicators in future impact evaluations will facilitate more precise inference.

This paper—prepared as a background paper to the World Bank’s _World Development Report 2018: Realizing the Promise of Education for Development_ —is a product of Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at apopova@stanford.edu and devans2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
_Table 2: Sample TVIs, Type, and Sample Size_

|Region|Name of TVI|Type|Size of TVI|Sampled<br>|former trainees<br>|
|---|---|---|---|---|---|
|||||Original|After trimming|
|III|Gonzalo Puyat School of Arts and Trade|Private|Small|29|28|
|III|Jocson College|Private|Small|49|43|
|III|Provincial TrainingCenter - Orion|Public|Large|160|150|
|III|Provincial TrainingCenter - Tarlac|Public|Large|112|106|
|III|Regional TrainingCenter - Mariveles|Public|Large|194|140|
|IV-A|Jacobo Z. Gonzales Memorial School|Public|Large|150|134|
|IV-A|Provincial TrainingCenter Rosario|Public|Medium|85|80|
|IV-A|Quenas National Agricultural School|Public|Medium|18|10|
|NCR|MFI|Private|Large|145|142|
|Total||||942|833|

Source: _DTS Tracking Survey Data, 2015, World Bank_ 27
- 28 -

as do sensitivity analyses exploring the impact of poverty on infrastructure expenditures (Table B8 in Appendix B).

The opposite conclusion is reached when inflation is used as an indicator of the degree of internal balance, as Table B4 in Appendix B indicate. A significantly negative, but nonlinear effect is found bottoming out only when the inflation rate reaches about 230 percent. It may be that what is more important than the actual size of the budget deficit is the extent to which the deficit is financed in a sustainable manner; the inflation rate is a more accurate indicator of internal imbalance in this respect. Significance levels remain low (never reaching the 5 percent level), however. Further, the estimated impact is not as large as might be expected. At the sample mean inflation rate and per capita central budget infrastructure expenditure level, the elasticity is only -0.12.

# _8. External Balance_

There is some evidence that the external balance modestly influences per capita central budget expenditures on infrastructure. As can be seen in Tables 6 and 7 and Appendix B, although significance levels are low (generally only 10 percent level), a better external balance exerts a positive influence on infrastructure expenditures. Significance levels are generally highest in regressions accounting for the importance of commercial bank flows in total foreign savings flows and lowest in regressions accounting for the importance of direct foreign investment in total foreign savings flows.

The magnitude of the estimated impact varies somewhat across specifications, although it is never large. In the FCINFPA base regressions including total foreign savings alone, the estimated coefficient implies an increase in per capita central government expenditures ranging from (1980) US $ 0.23 (Table 6) to $ 0.30 (Table 7) for every percentage point increase in TBWTGDPL. The estimated impact increases to (1980) US $ 0.33 and $ 0.38 in Tables 6 and 7, respectively, when the magnitude of commercial bank flows are included, but decreases to (1980) US $ 0.17 and $ 0.25 in Tables 6 and 7, respectively, when the relative importance of direct foreign investment is included. The estimated impact is about half the above amounts in the base regressions using FCINFPP and FCINFGDP. It is uniformly somewhat higher when level of development is measured in purchasing power parity terms (GDPCAP2).

One's conclusion concerning the importance of the external balance for infrastructure expenditures is also sensitive to how the external balance is measured, as can be seen from Table B5 in Appendix B. When the trade balance is measured as the black market foreign exchange premium, significance levels are somewhat greater. Still, the estimated impact is small with a 1 percentage point increase in the black market foreign exchange premium leading to only about a (1980) US $ 0.02 decrease in per capita central government infrastructure expenditures. When the external balance is measured as DOLLAR4, no significant impact is found.