rafmacalaba/datause-encoder-data huggingface dataset:
Pipeline comparisons might also be combined with discontinuity designs. Although I have not seen it used in practice, a possible identification strategy for projects that expand along a well defined route is to measure outcomes on either side of the project’s current frontier. Examples might include projects that progressively connect houses to an existing water, sanitation, transport or communications network, as well as projects that expand that network in discrete increments. New facilities (such as electrification or telecommunications) often expand along pre-existing infrastructure networks (such as roads, to lay cables along their right-of-way). Clearly one would also want to allow for observable heterogeneity and time effects. There may also be concerns about spillover effects; the behavior of non-participants may change, in anticipation of being hooked up to the expanding network.

# **7. Higher-order differences**

So far the discussion has focused on single-difference estimators that only require a single survey. More can be learnt if we track outcomes for both participants and non-participants over time. A pre-intervention “baseline survey” in which one knows who eventually participates and who does not, can reveal specification problems in a single-difference estimator. If the outcome regression (such as equations 4 or 5) is correctly specified then running that regression on the baseline data should indicate an estimate of mean impact that is not significantly different from zero (Heckman and Hotz, 1989).

With baseline data one can go a step further and allow some of the latent determinants of outcomes to be correlated with program placement given the observables. This section begins with the double-difference ( _DD_ ) method, which relaxes the conditional exogeneity assumption of single-difference NX estimators by exploiting a baseline and at least one follow-up survey postintervention. The discussion then turns to situations — common for safety-net programs set-up to address a crisis — in which a baseline survey is impossible, but we can track ex-participants; this illustrates the triple-difference estimator.

**_The double-difference estimator:_** The essential idea is to compare samples of participants and non-participants before and after the intervention. After the initial baseline survey of both non-participants and (subsequent) participants, one does a follow-up survey of both groups after the intervention.  Finally one calculates the difference between the “after” and “before” values of the mean outcomes for each of the treatment and comparison groups.  The

31
||Inv|Gov exp|Cons|TB/GDP|Int rate|Employment|
|---|---|---|---|---|---|---|
|OECS data|0.61|0.39|0.36|-0.27|-0.36|n/a|
|**Independent fiscal policy**|||||||
|**1. Independent country ris**|**k**||||||
|(a) all shocks|0.69|0.00|0.93|-0.28|-0.42|0.90|
|(b) no z shocks|0.62|0.01|0.94|-0.40|-0.84|0.95|
|(c) no G shocks|0.69|0.00|0.93|-0.35|-0.42|0.90|
|(d) no R*, D shocks|0.84|0.00|0.99|-0.17|0.00|1.00|
|**2. Induced country risk**|0.46|0.01|0.94|-0.22|-0.58|0.93|
|(d) no R*, D shocks|0.84|-0.00|0.99|-0.11|-0.02|1.00|
|**Pro-cyclical fiscal policy**|||||||
|**3. Independent country ris**|**k**||||||
|(a) all shocks|0.69|0.20|0.91|-0.38|-0.42|0.90|
|(b) no z shocks|0.62|0.01|0.94|-0.39|-0.84|0.95|
|(c) no G shocks|0.69|0.48|0.91|-0.46|-0.42|0.90|
|(d) no R*, D shocks|0.84|0.23|1.00|-0.34|0.00|1.00|
|**4. Induced country risk**|0.46|0.17|0.93|-0.28|-0.58|0.93|
|(d) no R*, D shocks|0.84|0.14|0.99|-0.21|-0.00|1.00|

_Source_ : Authors’ calculations.

Next we consider a scenario in which country risk-premium is endogenous to productivity changes (Panel 2 “Induced country risk”). Here, we calibrated the standard deviation of innovations to all shocks such that we replicate the volatility of GDP, interest rate, and government spending. All other parameters at set to their baseline values given in Table 5. We find that with induced risk-premium, the volatility of all non-targeted variables increases relative to the benchmark scenario. For instance, the percentage standard deviation of investment goes up from 4.57 in the benchmark model with independent risk premium to 7.74 under induced risk premium, i.e. rises by 70%. Similarly, employment volatility goes up by 5.2%, while the volatility of net exports rises by 37%. This is because the effects of productivity shocks on the economy are amplified in the presence of endogenous risk-premium due to the presence of working capital constraint.<sup>10</sup> Table 7 summarizes the co-movement patterns of various variables with GDP. Here, again, the benchmark model (panel 1.a) performs quite well. It easily reproduces the positive comovement of consumption and investment with GDP found in the data. More importantly, the

> 10 The increase in volatility due to endogenous risk-premium is similar under the pro-cyclical fiscal policy, with the exception that consumption volatility is affected more.

23
Previous studies using Indonesian manufacturing census data (Harrison and Scorse, 2005; Alatas and Cameron, 2008) have documented a large amount of residual wage dispersion remains after workers’ observable characteristics are accounted for. The impact of minimum wages is heterogeneous across firms, with large firms being more likely to comply with minimum wage legislation than small firms. These findings underscore the importance of controlling for employer characteristics. Overall, the minimum wage increases that occurred from the early to mid-nineties led to substantial increases in pay, but had rather limited employment impacts, at least in the short run.<sup>iv</sup> However, these studies have not assessed the extent to which minimum wage increases affect gender differences in earnings and employment prospects by level of education.

Nonetheless, there is some evidence that the impact of minimum wages varies by gender. Rama (2001), for example, focuses on the early 1990s and finds that women’s average wages in manufacturing were more responsive to minimum wage changes than men’s average wages using data from the national wages survey (Survey Upah) (see also World Bank, 2010 and Suryahadi et al., 2003).

# **3 Data and Descriptive Statistics**

The Indonesian Annual Manufacturing Survey, the Statistiki Industri (SI) plant-level dataset contains information on all manufacturing plants with more than 20 employees<sup>v</sup> and has rich information on wages and employment for production and non-production workers, as well as on plant’ productivity, ownership structure, export status, labor force composition, material inputs usage, and value-added. We primarily focus on the years 1995, 1996, 1997 and 2006 since in these years information on the educational attainment of the production workforce is 9
# **References**

- Adamopoulos, Tasso. 2011. Transportation Costs, Agricultural Productivity and Cross-Country Income Differences. _International Economic Review_ , **52** (2), 489–521.

- Adamopoulos, Tasso, & Restuccia, Diego. 2014. The Size Distribution of Farms and International Productivity Differences. _American Economic Review_ , **104** (6), 1667–1697.

- Blanchard, Olivier J, Nordhaus, William D, & Phelps, Edmund S. 1997. The Medium Run. _Brookings Papers on Economic Activity_ , 89–158.

- Bryan, Gharad, Chowdhury, Shyamal, & Mobarak, Ahmed Mushfiq. 2014. Underinvestment in a Profitable Technology: The Case of Seasonal Migration in Bangladesh. _Econometrica_ , **82** (5), 1671–1748.

- Buera, Francisco J, Kaboski, Joseph P, & Shin, Yongseok. 2011. Finance and Development: A Tale of Two Sectors. _American Economic Review_ , **101** (5), 1964.

- Caballero, Ricardo J, Hoshi, Takeo, & Kashyap, Anil K. 2008. Zombie Lending and Depressed Restructuring in Japan. _American Economic Review_ , **98** (5), 1943–1977.

- Cai, Wenbiao. 2015. Structural Change Accounting With Labor Market Distortions. _Journal of Economic Dynamics and Control_ , **57** , 54–64.

- Caselli, Francesco. 2005. Accounting for Cross-Country Income Differences. _Handbook of Economic Growth_ , **1** , 679–741.

- Duarte, Margarida, & Restuccia, Diego. 2010. The Role of the Structural Transformation in Aggregate Productivity. _Quarterly Journal of Economics_ , **125** (1), 129–173.

- Elsby, Michael WL, Hobijn, Bart, & Şahin, Ayşegül. 2013. The Decline of the US Labor Share. _Brookings Papers on Economic Activity_ , 1–63.

- Gollin, Douglas, & Rogerson, Richard. 2014. Productivity, Transport Costs and Subsistence Agriculture. _Journal of Development Economics_ , **107** , 38–48.

- Herrendorf, Berthold, & Schoellman, Todd. 2015. Wages, Human Capital, and Structural Transformation. _Arizona State University_ .

- Hsieh, Chang Tai, & Klenow, Peter J. 2009. Misallocation and Manufacturing TFP in China and India. _Quarterly Journal of Economics_ , **124** (4), 1403–1448.

- Karabarbounis, Loukas, & Neiman, Brent. 2014. The Global Decline of the Labor Share. _Quarterly Journal of Economics_ , **129** (1), 61–103.

23
**Table 1. Ordinary least squares estimates for the effect of public spending on education on inequality of opportunity a. Mathematics**

|**a. Mathematics**<br>Estimated effect of public spending on educa<br>**Variable**|tion on inequalit<br>|y of opportunity:<br>|Dc= α+ βspe|**Dependen**<br>ndingc+ Xcθ+|**t variable: Inequ**<br>εc|**ality index in mat**|**hematics**||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Public expenditure per pupil in primary as|-0.8432***|-0.6705***|-0.4740***|-0.3909**|||||||||
|a % of GDP per capita (circa 2004)|(0.1717)|(0.1437)|(0.1191)|(0.1447)|||||||||
|Public expenditure per pupil in secondary|||||-0.6192***|-0.4713***|-0.3132***|-0.2371**|||||
|as a % of GDP per capita (circa 2010)|||||(0.1375)|(0.1065)|(0.0909)|(0.1080)|||||
|Public expenditure per pupil in tertiary ed.|||||||||-0.0186|-0.0513|0.0171|0.0440*|
|as a%of GDPper capita(circa 2002)|||||||||(0.0646)|(0.0538)|(0.0306)|(0.0245)|
|Constant|24.6519***|38.6425***|20.8994***|20.0128***|24.4104***|36.9827***|17.4341***|17.2508***|<sup>9.9473**</sup>|<sup>*</sup>29.7394**|* 0.4341|1.6762|
||(3.4942)|(4.0257)|(5.5058)|(5.7559)|(3.7515)|(4.3523)|(5.5692)|(5.7443)|(2.4799)|(5.5725)|(5.7542)|(5.2406)|
|**Average years of schooling (year 2000)**|No|Yes|Yes|Yes|No|Yes|Yes|Yes|No|Yes|Yes|Yes|
|**Gini index for income (circa 2002)**|No|No|yes|Yes|No|No|yes|Yes|No|No|yes|Yes|
|**GDPper-capita(US$, PPP, 2002)**|No|No|No|Yes|No|No|No|Yes|No|No|No|Yes|
|**_R2_**|0.3817|0.6734|0.7539|0.7667|0.3676|0.6051|0.6666|0.7221|0.0025|0.3633|0.6395|0.6969|
|**Observations**|46|45|45|45|48|47|46|46|44|44|44|44|

# **b. Reading**

|**b. Reading**<br>Estimated effect of public spending on educa<br>**Variable**|tion on inequalit<br>|y of opportunity:<br>|Dc= α+ βspe|**Depen**<br>ndingc+ Xcθ+|**dent variable: Ine**<br>εc|**quality index in r**|**eading**||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Public expenditure per pupil in primary as|-0.6263***|-0.5325***|-0.4624***|-0.3654**|||||||||
|a % of GDP per capita (circa 2004)|(0.1362)|(0.1300)|(0.1246)|(0.1370)|||||||||
|Public expenditure per pupil in secondary|||||-0.4136***|-0.3237***|-0.2381***|-0.1616*|||||
|as a % of GDP per capita (circa 2010)|||||(0.0966)|(0.0859)|(0.0763)|(0.0854)|||||
|Public expenditure per pupil in tertiary ed.|||||||||-0.0265|-0.0471|-0.0070|0.0302|
|as a%of GDPper capita(circa 2002)|||||||||(0.0579)|(0.0539)|(0.0428)|(0.0238)|
|Constant|19.1909***|27.5892***|21.2582***|20.2228***|17.9500***|25.8624***|16.4369***|16.2523***|9.1167***|21.5879**|* 4.3771|6.0928|
||(2.6691)|(2.9027)|(3.7469)|(3.8526)|(2.6359)|(3.0998)|(3.6031)|(3.6088)|(2.1759)|(4.0119)|(5.5615)|(4.4515)|
|**Average years of schooling (year 2000)**|No|Yes|Yes|Yes|No|Yes|Yes|Yes|No|Yes|Yes|Yes|
|**Gini index for income (circa 2002)**|No|No|yes|Yes|No|No|yes|Yes|No|No|yes|Yes|
|**GDPper-capita(US$, PPP, 2002)**|No|No|No|Yes|No|No|No|Yes|No|No|No|Yes|
|**_R2_**|0.4078|0.6198|0.6396|0.6733|0.3220|0.5087|0.5016|0.6111|0.0084|0.2411|0.3959|0.5739|
|**Observations**|46|45|45|45|48|47|46|46|44|44|44|44|

13
|**Study**|**Method**|**Sample**|**Intervention(s)**|**Impact**|
|---|---|---|---|---|
|Mano et al.<br>(2009)|Non-<br>randomized<br>experiment|115<br>entrepreneurs<br>in metalwork<br>cluster in Kenya|Similar training on<br>entrepreneurship,<br>production and quality<br>management, and record-<br>keepingand costing|+ Improved business practices<br>+ Increased value-added and producer surplus|
|World Bank<br>(2011)|RCT|Four clusters of<br>100-180 small<br>firms in<br>Ethiopia,<br>Tanzania, and<br>Vietnam (two<br>sites)|Tailored training on KAIZEN<br>production management<br>techniques, and standard<br>classroom management<br>training, including a module<br>on KAIZEN|+ Positive impact on business practices in all four sites,<br>though the specific aspects affected varied across the sites.<br>Analysis of impact on business outcomes is forthcoming|
|Bali Swain<br>and<br>Varghese<br>(2011)|Propensity<br>score<br>matching and<br>pipelining|841 micro-<br>finance clients<br>belonging to<br>self-help groups<br>(SHGs)in India|Skill-formation training for<br>members of SHGs, provided<br>by NGOs or government<br>officials, provided over an<br>average of 2-3 weeks|+ Positive impact on assets, particularly for clients in villages<br>with better infrastructure, and for training led by NGOs<br>rather than government officials<br><> No impact on income<br><> Duration of training (number of weeks)has no impact|
|Klinger and<br>Schündeln<br>(2007)|Regression<br>discontinuity,<br>based on<br>application<br>scores in a<br>business plan<br>competition|655 male and<br>female<br>entrepreneurs<br>with small firms<br>in Guatemala,<br>Nicaragua, and<br>El Salvador|Two rounds:<br>1) Training program to<br>refine business plan, for<br>accepted applicants<br>2) Cash prize of $6,000-<br>$15,000 to most successful<br>participants in training|+ Training program has significant effect on starting a new<br>business or expanding an existing business<br>+ Winning cash prize increases probability of starting a<br>business much more for females than for males|
|Giné and<br>Mansuri<br>(forth-<br>coming)|RCT|~4,000 male and<br>female<br>microfinance<br>clients in rural<br>Pakistan|2x2 comparison between:<br>1) Eight-day business<br>training course<br>2) Loan lottery|+ Training led to improved business and household<br>outcomes, particularly for males<br>+Training increased demand for larger loans without<br>affecting repayment<br>+ Weaker impact of loan lottery<br><> Insignificant or even negative treatment effect for<br>females. Time allocation analysis suggests this may be<br>because women spend significantly more time on<br>household/familyversus business activities than do men.|

34
- 35 -

Table 8 Unit costs<sup>of operation of bank branches, by age of branch, 1984/5</sup>

|Age of branch|Salary and<br>Allowances|Other<br>expenses|Total<br>expenses|Amount of<br>loans out-<br>standing w/<br>borrowers|Expenses as<br>share of<br>outstanding<br>loans|
|---|---|---|---|---|---|
|||(Tk thousand)||||
|Up to 6 months|30.8|9.6<br>|40.4<br>|165<br>|24.5<br>|
|6 months to 1.0 year|46.0|13.0<br>|59.0<br>|365<br>|16.2<br>|
|1.0-1.5 years|72.8<br>|15.8<br>|88.6<br>|1,041<br>|8.5<br>67|
|1.5-2.0 years|87.8|17.8<br>|105.6<br>|1,576<br>|.<br>|
|2.0-2.5 years|94.0|19.6<br>|113.6<br>|2,015<br>|5.6<br>4|
|2.5-3.0 years|100.8|18.8<br>|119.6<br>|2,198<br>|5.<br>57|
|More than 3.0 years|107.6|20.8|128.4|2,259|.|

Source: M. Hossain (1988)
financing sources through capital markets have a higher share of household credit. While overall institutional development is positively correlated with the share of household credit in total credit, effective contract enforcement is negatively associated with the household credit share. Once we control for legal origin and the religious composition of countries, however, the correlation with institutional development and legal system efficiency turns insignificant.

This paper is related to a large body of literature on finance and growth, as surveyed by Levine (2005). Aghion, Howitt, and Mayer-Foulkes (2005), however, provide theoretical and empirical evidence that financial sector development helps countries catch up to the productivity and thus income level of high-income countries, while it is not related to growth in high-income countries. Along similar lines, Rioja and Valev (2004a,b) show that financial sector development has a strong growth effect for middle-income, but a smaller effect in high-income countries. Neither of these papers, however, has considered the composition of credit.<sup>5</sup> This paper is the first to assess the independent impact of firm and household credit on economic growth across a broad sample of countries.

This paper is also related to the literature on the determinants of financial sector development. Researchers have focused on macroeconomic stability, legal system efficiency and the information framework as explaining cross-country variation in financial sector development (Boyd, Levine and Smith, 2001; Djankov, McLiesh and Shleifer, 2007; Beck, 2006). Market and ownership structure have also been related to cross-country variation in financial sector development (La Porta, Lopez-de-Silanes and Shleifer, 2002; Demirguc-Kunt, Laeven, Levine, 2004; Claessens, Demirguc-Kunt and Huizinga, 2001). Finally, bank regulatory and supervisory approaches have been shown to be critically linked to differences in financial sector development (Barth, Caprio and Levine, 2006, La Porta, Lopez-de-Silanes and Shleifer, 2006; Beck, Demirguc-Kunt and Levine, 2006). This paper assesses whether these different policies have a differential impact on enterprise vs. household credit.

This paper is a first attempt at understanding the composition of bank lending across countries and is therefore subject to several caveats. First, the definition of household vs. firm credit is not homogenous across countries and our variable is therefore subject to measurement error. Further, a strict separation into firm and household credit might not be possible in the case

> 5 Buyukkarabacak and Krause (2005) show that a higher share of credit to households in total credit is associated with higher trade deficits, while Buyukkarabacak and Valev (2006) show that firm and household credit growth enter individually and separately into banking crisis prediction models.

3
The village and teacher surveys included questions about the ECD services available in the sample village. This allowed us to monitor implementation of program interventions and precisely know when each preschool service was available. Parts of the teacher survey and the classroom observation tool administered at endline are based on the Measuring Early Learning Environments (MELE) module of MELQO. MELE includes key domains for quality in early learning environments and the sample items used to measure them. During pretests preceding the endline data collection, constructs to be measured were selected and specific items were adapted to take into account culture-specific views on what defines a high-quality learning environment. We divided the final module into five domains: teacher characteristics, equipment, classroom setting, curriculum content and pedagogy, and teacher-child interactions.

# **3.2.2 Balance and Attrition**

The main baseline characteristics of villages, households, children, and their caregivers are summarized in Table 3. To test for statistically significant differences, we regress each variable on binary indicators for the treatment groups and a set of province dummies to account for stratified randomization. Overall, the sample is balanced in child, household, and caregiver characteristics.

Table 3 also highlights some significant baseline differences in preschool enrollment and take-up of demand-side interventions. These differences are due to the aforementioned early roll-out of some preschool interventions. Children in program villages are about 8 pp more likely to have enrolled in preschool at baseline (driven by community preschools which, by definition, were newly constructed). Similarly, the HBP intervention started in some treatment villages before baseline. These baseline imbalances at the individual level are confirmed by imbalance in the availability of preschools and HBP at the village level, shown in the baseline panel of Table 5. At baseline, some villages in the treatment group had access to CPS and many treatment groups’ villages had access to HBP. There are reasons to believe that these early interventions are unlikely to affect our estimation. baseline results do not pick up any significant cognitive differences between the treatment groups and the control, indicating that early enrollment in preschool intervention had not given a significant benefit to the treatment group. Relatedly, expressed in number of days at school, early enrollment in preschool programs is only marginally higher in the treatment groups. At baseline, treatment children had approximately only 10 extra days of preschool compared with the control. It further means that the roll-out of our baseline survey was on average 10 days too late compared to the roll-out of the the CPS construction. Given the size of the program, we consider executive function) were then summed into a domain score and standardized again. The individual tests and scoring methods are summarized in Appendix 6.

14
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- Van den Berg, Henk, and Janice Jiggins. 2007. “Investing in Farmers - The Impacts of Farmer Field Schools in Relation to Integrated Pest Management.” _World Development_ 35 (4): 663– 86. https://doi.org/10.1016/j.worlddev.2006.05.004.

- Vanlauwe, B., K. Descheemaeker, K. E. Giller, J. Huising, R. Merckx, G. Nziguheba, J. Wendt, and S. Zingore. 2015. “Integrated Soil Fertility Management in Sub-Saharan Africa: Unravelling Local Adaptation.” _SOIL_ 1 (1): 491–508. https://doi.org/10.5194/soil-1-491-2015.

- Ward, Patrick S., Andrew R. Bell, Klaus Droppelmann, and Tim G. Benton. 2018. “Early Adoption of Conservation Agriculture Practices: Understanding Partial Compliance in Programs

- with Multiple Adoption Decisions.” _Land Use Policy_ 70 (January): 27–37. https://doi.org/10.1016/j.landusepol.2017.10.001.

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