rafmacalaba/datause-encoder-data huggingface dataset:
i

# **Contents**

|**ACKNOWLEDGEMENTS......................................................................................................III**|
|---|
|**EXECUTIVE SUMMARY ....................................................................................................... V**|
|**1.**<br>**THE PEDAGOGY OF POVERTY ...............................................................................1**|
|**2.**<br>**THE EFFECTS OF INSTRUCTIONAL TIME USE: A LITERATURE REVIEW..........4**|
|**Time Loss at the School Level...............................................................................................8**|
|**Teacher Absenteeism .............................................................................................................9**|
|**Use of Classroom Time (Time on Task)..............................................................................13**|
|**Time Spent Learning Prescribed Curricula (Academic Learning Time) ..........................16**|
|**Relationships between Instructional Time, Poverty, and Achievement ..........................16**|
|**3.**<br>**INSTRUCTIONAL TIME DATA COLLECTION AND FINDINGS .............................21**|
|**How Much of the Intended Time Was Spent on Learning Activities?..............................22**|
|**How Often Were Teachers Absent or Late? .......................................................................23**|
|**Was There Recuperation of Canceled Classes?................................................................25**|
|**What Policies Would Increase Teacher Attendance and Punctuality?............................26**|
|**How Often Were Students Absent?.....................................................................................26**|
|**How Much of the Classroom Time Was Used for Learning Activities?...........................27**|
|**How Much Homework Did Students Complete? ................................................................30**|
|**Trends in Linkages between Learning Outcomes and Poverty Status............................31**|
|**Methodological Lessons and Data Limitations..................................................................31**|
|**4.**<br>**IMPROVING INSTRUCTIONAL TIME USE: POLICY IMPLICATIONS ...................33**|
|**Economic Implications of Instructional Time Use .............................................................34**|
|**Teacher Attendance and Accountability.............................................................................36**|
|**Effective Supervision - Strengthening the Supervisory Chain.........................................37**|
|**Improving Local Governance and Stakeholder Involvement............................................39**|
|**Training and Consciousness Raising on the Consequences of Time Loss ...................39**|
|**Options for Increasing Student Achievement Given Limited Time Use..........................41**|
|**Instructional Time Measurement for Evaluation and Research .......................................42**|
|**REFERENCES......................................................................................................................73**|
Policy Research Working Paper 8915

# **Abstract**

At 50.9 percent, female labor force participation in Indonesia is far below the regional average of 60.8 percent. Is it being hindered by a lack of affordable childcare services in the country? This paper exploits the joint variations in preschool age eligibility and access to preschool across regions and over years in a difference-in-difference-in-differences framework. With a longitudinal survey that tracks individuals for an average of 22 years, a panel of mothers was constructed to estimate the elasticity of maternal employment to preschool access. The analysis finds that an additional public preschool per 1,000 children increases the work participation of mothers of preschool age eligible children by 11–16 percent from the baseline mean. Private preschools do not increase work participation at the extensive margin, but they increase the likelihood of holding a second job. The availability of preschools induces mothers to informal sector occupations that do not require full-time commitments.

This paper is a product of the Gender Innovation Lab, East Asia and the Pacific Region and the Gender Global Theme. 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://www.worldbank.org/prwp. The authors may be contacted at dhalim@worldbank.org, hjohnson1@worldbank.org, and eperova@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
R&D which constitute an investment that pays o↵in the long run. Currently, expenditures on R&D are not included as investment in GDP in the official accounts but instead they are treated as current period expenditures. Treating R&D as investment rather than as intermediate expenditures results in important changes to the calculation of GDP. In BEA’s National Income and Product Account (NIPA), _business_ R&D expenditures are included as intermediate rather than final expenditures which means that they are not added up in deriving GDP. Other expenditures in R&D which are included in the calculation of the GDP cannot be separately identified from other components reported in the NIPA tables.<sup>19</sup> Although those expenditures are included in GDP, they are not treated as investment which means that they are not subject to depreciation.

In 2006, the Bureau of Economic Analysis (BEA) jointly with NSF launched an R&D satellite account to explore investment in R&D and its larger economic e↵ects. The BEANSF R&D satellite account provides a measure of the value of R&D output and adjusted GDP by transforming R&D expenditures into measures of real investment.<sup>20</sup> The nominal value of R&D is the sum of the costs of the R&D activity of both private and government organizations. Private organizations consist of businesses such as private universities and colleges, private hospitals, charitable foundations, other nonprofit institutions serving households and most Federally Funded Research and Development Centers (FFRDC). Government organizations consist of the Federal Government, state and local governments (excluding uni-

> 19Expenditures on R&D by government and nonprofit institutions are included in consumption expenditures; Federal purchases of R&D, expenditures on in-house R&D performed by the federal government and state and local purchases of R&D are included in government consumption; Spending on R&D by foundations and non-profit institutions serving households are included in personal consumption expenditures; R&D services are also included in exports and imports while the cost of patents for the use of R&D are included in royalties and licencing fees. For more information refer to Mataloni and Moylan (2007).

> 20BEA plans to formally incorporate R&D spending as investment into its core accounts around 2013.

28
# **<mark>4. Estimation method</mark>**

To measure the impact of childcare attendance on maternal employment, we use a regression discontinuity method. Development and discussion of regression discontinuity design methods can be found in a large number of studies (e.g., Van der Klaauw, 2002; Imbens and Lemieux, 2008; and Lee and Lemieux, 2010). In regression discontinuity designs (RDD), a treatment group is selected for treatment if the value of at least one observed variable crosses a cut-off threshold. In particular, there exists a conditioning variable _Z,_ such that the treatment variable, denoted by _D,_ is equal to 1 if and only if _Z_ is larger than a specific value _Z=c_ . We can identify the treatment effect by comparing outcomes for individuals (denoted by _Y_ ) just above and below the threshold _c_ :  _RD_  _Y_   _Y_  .

The proportion of children attending childcare increases by age, and as discussed earlier, children’s current age is used to determine their enrollment eligibility. We thus use birth months as the conditioning variable that determines childcare attendance. Specifically, we compare the employment of women whose children were born in December and January in two contiguous years. Since the school year starts in September, a child born in January of a given year is more likely to start attending childcare 1 year later than a child born in December of the previous year, even though the two children differ in age by only 1 month.<sup>11</sup> Figure 3 shows the proportion of children age 1-5 attending childcare by birth month in two consecutive years. Older children were born from July to December, while younger children were born from January to June of the following year. Children born in January are one month younger than those born in December. Panel A of Figure 3 presents a graph using data from the pooled sample of children. Other panels of the figure present graphs for different age groups. For example, for children age 1-2, panel B of the figure shows the percentage of childcare attendance of children age 2, born in July to December, and of children of the age of one and born from January to June. Older children have a higher rate of childcare attendance. However, there is an obvious, large gap in the incidence of childcare attendance between children born in December and those born in January.

> 11 The VHLSS contain data on age (year and month), but not an individual’s full date of birth. Thus, we cannot use the date of birth as the conditioning variable. But we also offer robustness checks in Section 5 where we vary the width of the months around the cut-off threshold.

10
the system of National Accounts, but is almost as good as a census for our purpose as it covers all business establishments in the country employing 10 or more persons. The 2002 and 2009 waves covered about 4, 700 establishments spread out across scores of industries on three‐digit or four‐digit ISIC and collected information on data on enterprise assets, employment, and purchase and sales based on which labor productivity and other indicators of productivity are measured at the level of the enterprise.

Moreover, because both waves of the UBI provide information on the activity of each enterprise in the form of a four‐digit ISIC code, we have been able to link data from each wave to data from UBR 2001 and UBR 2010 at that level in order to measure the effect on aggregate productivity of the shift in employment between industries as observed over the period spanning 2001 and 2010. This is important because, as we have indicated already, our assessment of the scale and type of structural transformation that the Ugandan economy might have undergone over that decade has to be based on the measurement of the changes in employment and the count of enterprises between UBR 2001 and UBR 2010 in major three digit industries (on aggregate and by business size groups and business age groups). However, in trying to gauge the effect on aggregate labor productivity of that change we have to rely on the measurement of productivity gaps between those industries in UBI 2002 and UBI 2009 since the UBR dataset has no information on output or productivity.

It is equally important in that context that sample of UBI 2002 is a multi‐stage stratified sample drawn from the same listing of enterprises as the census listing of UBR 2001 and that, likewise, the sample of UBI 2009 is also a multi‐stage stratified sample drawn from the same listing of enterprise as UBR 2010. This provides us with appropriate sampling weights with the help of which we can obtain consistent estimates of the population values of variables interest for the years 2002 and 2009 from the observations of UBI 2002 and UBI 2009, respectively.

# **_2.2. Broad sectors and comparative advantage industries_**

UBR 2001 and UBR 2010 covered all three digit ISIC Rev. 3 industries (with the exception of government institutions and embassies). So did the samples for UBI 2002 and UBI 2009. Unlike other sectors where informal businesses were also covered, only formal businesses activities were covered for the agricultural sector in UBR2001. This was changed in UBR 2010, which covered both formal and informal agricultural businesses.

As already noted each of the two datasets –that is, UBR 2001 and UBR 2010 on one hand, and UBI2002 and UBI2009 on the other‐ have been aggregated spatially to the sub‐county level as the unit of observation and functionally to the four‐digit –ISIC industry as the alternative unit of observation.

In describing the pattern of structural change as observed between UBR2001 and UBR2010 we have focused on four major divides , namely, 11
additional environmental challenges in comparison with what occurs in the more advanced economies.

Starting with capital markets, Rajan and Zingales (1998) and Beck _et al._ (2008) clearly show that firms in financially dependent industries grow much faster in financially developed countries; in contrast, new small firms in DCs are credit and equity rationed in the vast majority of cases because their financial markets are underdeveloped (see Nugent and Nabli, 1992; Banerjee and Newman, 1993; Ayyagari, Demirgüç-Kunt, and Maksimovic, 2008; Lian, Sepehri and Foley, 2011 and Section 4.2.2 below). In fact, capital markets in DCs are characterized by: 1) a lower depth (measured, for instance, by a low ratio of bank deposits to GDP; see Paravisini, 2008, for the case of Argentina; Banerjee and Duflo, 2004, for the case of India); 2) by a lower level of competition between financial intermediaries generating misallocation of funds (see Banerjee, Duflo and Munshi, 2003, studying misallocation of capital in India; Cole, 2009, discussing agricultural credit in India); 3) by higher information asymmetries due to institutional and infrastructural underdevelopment (see Klapper and Love, 2011, for a general discussion, while Canales and Nanda (2008) discuss lending to small businesses in Mexico)<sup>17</sup> .

By the same token, a non-transparent regulatory environment with regard to labor market rules, taxation, red tape procedures, property rights and bankruptcy laws, is particularly harmful to firms’ growth in DCs and may be fatal for young entrepreneurial activities (see Goedhuys and Sleuwaegen 1999; Sleuwaegen and Goedhuys, 2002; Beck, Demirgüç-Kunt and Maksimovic, 2005; Chen and Puttitanun, 2005; Lee _et al_ ., 2011). For instance, in a recent study, Ardagna and Lusardi (2010), dealing with GEM microdata from 37 countries including 8 DCs, showed that stringent entry regulation, soft contract enforcement rules and labor market rigidities play an important role in hindering entrepreneurship and in strengthening the adverse impact of risk aversion.

Moreover, apart from the legal and institutional drawbacks characterising a developing country, a prominent role is also played by the wide diffusion of bribing, which may abort any chance of growth of a fragile new entrepreneurial activity<sup>18</sup> . For instance, Fisman and

> 17 A possible solution for credit-rationed potential entrepreneurs is to rely on networks and “social capital”, in order to identify financing subjects such as venture capitalists (see Batjarga and Liu, 2004). 18 Aterido, Hallward-Driemeier and Pagés (2009, p.10), using evidence from the World Bank Enterprise Surveys, show that 42% of firms declare they have paid bribes, with an average amount paid of 1.5% of sales.

17
9 Table 2: Indon:e **Motor** Vehide Tax **Sture (1978)**

|||General<br>|Specific Use<br>_,.<br> <br>|r Charge<br>|
|---|---|---|---|---|
|||Revenue<br>|<br>Fxed<br>|Variable<br>|
||TIIofax|Ta|Admin. Revenue|Admin, Revenue|
|A. Mot|or Vehicies and Parts||||
|1.|Customs Duty|x|x||
|2.|Importers corporate tax|x|||
|<br>3.<br>|<br>Import sales tax<br>|x|x||
|4.|Manuftcturing sales tax|x|x||
|_5._|Dealers corporate tax|x|||
|6.|Ownership transfer tax||x||
|<br>7.|<br>vehicle tax||x<br>x||
|B|. Ve|hidle Oprations||||
|1.|Duty on imported lubricants|x|||
|2.|Importers corporate tax on lubs.|x|||
|3.|<br>Import sales tax on lubs.|x|||
|<br>4.|<br>Dealers corporate tax on lubs.|x|||
|S.|Sales tax on lubricants|x|||
|6.|Sales tax on spare parts|x|||
|7.|Sales tax on tyres and tubes|x|||
|8.|Drivers license fees||x||
|9.|Tax on fuel|||x|
|10.|Rural road levies|||x|
|11.|Tol road levies|||x|
|C.|||||
|1.|Weigh bridge fees||x||
|<br>2.|<br>Vehicle inspection fees||x||
|3.|<br>Paddng fees|||x|
|4.|Commercial route license fees||x||
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Table 8: Probability of birth: Heterogeneity by socioeconomic status

||**Mothe**|**r’s Education**||**Wea**|**lth**||**Mother’s E**|**mployment**||
|---|---|---|---|---|---|---|---|---|---|
||Illiterate|Literate||Bottom 40%|Top 20%||Paid employment = 0|Paid employment =|1|
||(1)|(2)|(1)-(2)|(3)|(4)|(3)-(4)|(5)|(6)|(5)-(6)|
|_Firstborn girl_|0.0159***|0.0511***|-0.0352***|0.0215***|0.0533***|-0.0318***|0.0333***|0.0278***|0.00549|
||(0.00418)|(0.00431)|(0.00319)|(0.00642)|(0.00410)|(0.00603)|(0.00606)|(0.00460)|(0.00352)|
|_Firstborn girl_ * _Post1_|-0.0150***|-0.0277***|0.0128***|-0.0201***|-0.0223***|0.00216|-0.0262***|-0.0157***|-0.0105***|
||(0.00182)|(0.00263)|(0.00303)|(0.00114)|(0.00345)|(0.00359)|(0.00180)|(0.00248)|(0.00325)|
|_Firstborn girl_ * _Post2_|-0.0268***|-0.0506***|0.0237***|-0.0332***|-0.0451***|0.0119**|-0.0454***|-0.0326***|-0.0128***|
||(0.00251)|(0.00295)|(0.00382)|(0.00302)|(0.00351)|(0.00541)|(0.00233)|(0.00287)|(0.00351)|
|N|1,351,795|1,103,838||972,072|600,212||1,494,683|959,110||
|Baseline mean|0.289|0.279||0.288|0.263||0.287|0.283||
|||**Caste**||**Rura**|**lity**|||||
||SC|General/OBC/ST||Rural|Urban|||||
||(7)|(8)|(7)-(8)|(9)|(10)|(9)-(10)||||
|_Firstborn girl_|0.0204**|0.0324***|-0.0119***|0.0216***|0.0491***|-0.0275***||||
||(0.00762)|(0.00475)|(0.00375)|(0.00571)|(0.00450)|(0.00456)||||
|_Firstborn girl_ * _Post1_|-0.0136***|-0.0232***|0.00962**|-0.0198***|-0.0244***|0.00464||||
||(0.00417)|(0.00153)|(0.00402)|(0.00174)|(0.00266)|(0.00303)||||
|_Firstborn girl_ * _Post2_|-0.0336***|-0.0415***|0.00784|-0.0356***|-0.0472***|0.0116***||||
||(0.00632)|(0.00185)|(0.00603)|(0.00273)|(0.00277)|(0.00397)||||
|N|374,747|2,080,886||1,598,757|856,876|||||
|Baseline mean|0.295|0.284||0.289|0.278|||||

NOTES: Estimates of specification (2) in the text. The dependent variable is an indicator for birth to a given mother in a given year. Each column within a panel is a separate OLS regression. We use a mother-year data set for all mothers who have ever given birth, for each year from their year of marriage to the year of interview. Standard errors in parentheses are clustered by state. Baseline mean is the mean probability of birth in a given year during the pre-ultrasound period, by mothers with the specific socioeconomic status. The third column in every panel shows a test of the significance of the difference between the coefficients for the sub-groups shown in the first two columns. Data: NFHS. *** 1%, ** 5%, * 10%.
Beck, T. (2008). The econometrics of finance and growth. World Bank Working Paper 4608.

Beck, T., A. Demirguc-Kunt and M. Peria. (2008). Banking services for everyone? Barriers to bank access and use around the world. World Bank Economic Review, 22(3): 397-430.

Beck, T., A. Demirguc-Kunt and R. Levine. (2007). Finance, inequality and poverty: Crosscountry evidence. Journal of Economic Growth, 12(1): 27-49.

Beck, T., A. Demirgüç-Kunt and V. Maksimovic. (2006a). The influence of financial and legal institutions and firm size. Journal of Banking and Finance, 30: 2995-3015.

Beck, T., A. Demirguc-Kunt, L. Laeven and V. Maksimovic. (2006b). The determinants of financing obstacles. Journal of International Money and Finance, 25(6): 932-952 Beck, T., A. Demirguc-Kunt and V. Maksimovic. (2005). Financial and legal constraints to firm growth: Does firm size matter? Journal of Finance, 60(1): 137-177.

Beck, T., R. Levine and N. Loayza. (2000). Finance and the sources of growth. Journal of Financial Economics, 58(1-2): 261-300 Benito, A. (2005). Financial pressure, monetary policy effects and inventories: Firm-level evidence from a market-based and a bank-based financial system. Economica, 72(286): 201-224.

Bennett, M. and R. Donnelly. (1993). The determinants of capital structure: Some UK evidence. British Accounting Review, 25(1): 43-59.

Berger, A.N. and Udell, G.F. (1998). The economics of small business finance: The roles of private equity and debt markets in the financial growth cycle. Journal of Banking and Finance, 22(6-8): 613-73 Berger, A.N. and G.F. Udell. (1995). Relationship lending and lines of credit in small firm finance. Journal of Business, 68(3): 351-81.

Berman, N. and J. Hericourt. (2010). Financial factors and the margins of trade: Evidence from cross-country firm-level data. Journal of Development Economics, 93(2): 206-217.

Besley T., R. Burgess and B. Esteve-Volart (2007). The policy origins of poverty and growth in India. Chapter 3 in Delivering on the Promise of Pro-Poor Growth: Insights and Lessons from Country Experiences, edited with T. Besley and L.J. Cord, Palgrave MacMillan for the World Bank.

Bhaduri, S. (2002). Determinants of corporate borrowing: Some evidence from the Indian corporate structure. Journal of Economic and Finance, 26(2): 200-215 Bigsten A., P. Collier, S. Dercon, M. Fafchamps, B. Guthier, W. Gunning, M. Soderbom, A. Oduro, Oostendorp, C. Patillo, F. Teal and A. Zeufack. (2000). Credit constraints in 45