rafmacalaba/datause-encoder-data huggingface dataset:
# **Education and Wage Differentials in the Philippines**<sup>*****</sup>

Xubei Luo (The World Bank)<sup>**</sup> Takanobu Terada (University of Maryland)<sup>***</sup> Key Words: Wage differentials, returns to education, inequality

> * The authors would like to thank Jehan Arulpragasam, Jean-Jacques Dethier, Swati Ghosh, Ulrich Lächler, Silvia Redaelli, Erwin Tiongson, and Nong Zhu for useful comments.

> ** Email: <u>xluo@worldbank.org</u>

> *** Email: <u>tnterada@umd.edu</u>
# **1. Introduction**

In this paper, we document the extent to which emerging market economies were resilient to the 2008–2009 global financial crisis. We interpret resilience from both an ex ante and ex post perspective. From an ex ante point of view, resilience means that economies have a lower likelihood of being affected by an external shock. From an ex post perspective, it means a better recovery when there are contagion or spillover effects from external crises. We relate the behavior of emerging economies during the global crisis to the crisis transmission mechanisms (trade and finance) and a structural break in the way they conducted their policies. We describe some of the factors that might have facilitated (or hindered) the global transmission of the crisis and analyze the policies emerging economies adopted to become more resilient to crises.

We question the usual claim that, on impact, emerging market economies suffered less than advanced economies in terms of economic activity.<sup>1</sup> Even though emerging economies had a better performance when focusing on plain growth rates, the conclusions change when considering changes in growth rates relative to the pre-crisis growth rates. Emerging economies endured declines in real gross domestic product (GDP) growth comparable to those in advanced economies. Nonetheless, emerging economies were able to avoid the amplification of external shocks that were typical of past episodes that tended to end up in banking, currency, and/or debt crises. During the recovery phase, emerging economies were more resilient. At first sight, GDP data suggest that emerging economies recovered similarly to developed economies, with most economies rebounding during the crisis aftermath according to how much they collapsed. Yet,

> 1 Our data are comprised of 183 economies, classified as ―advanced‖ (or ―developed‖) if they fall under the World Bank’s July 2010 classification of ―high-income‖ economies. All others are ―developing economies.‖ Within the latter, economies are classified as ―emerging‖ if they have access to loans from the World Bank (IBRD) and as ―low-income‖ if they only have access to funds from IDA (the World Bank’s branch that focuses on the poorest nations). Economies not falling under the ―advanced economies‖ group are assigned to regions according to the World Bank’s July 2010 classification. The region ―Middle East & Africa‖ includes economies in the Middle East and North and Sub-Saharan Africa. The region ―Asia‖ includes economies in East Asia, South Asia, and the Pacific.

2
# **4 What lies behind the incidence of corruption?**

Having estimated measures of the incidence of corruption, in this section we explore some of its correlates. We do this by running regressions of the Graft Index of Firm Transactions on a number of different regressors, motivated by the existing literature. Specifically, we consider whether firms are more likely to fall prey to corrupt officials in overly-regulated economies and in less democratic countries. Admittedly, cross-country data make it difficult to identify the causal links between graft and potential explanatory variables. With this caveat in mind, our aim is to shed light on some of the factors that are believed to be drivers of corruption.

10 The existence of burdensome business regulations stands out as a potential driver of graft. While some degree of regulation could be justified under the argument that it is required to safeguard the public interest, a competing explanation, the _tollbooth_ view (Shleifer and Vishny 1993), is that regulations are put in place in order to extract rents in favor of specific business interests or government officials. Djankov et al. (2002) explore such alternative explanations and conclude that, rather than protecting the public interest, regulation —in their study, business entry rules— is associated with greater levels of corruption. In the same vein, Svensson (2005) presents econometric evidence showing a positive link between greater regulation and more corruption. More recently, Olken and Barron (2007) look at bribe payments by truck drivers at checkpoints along Indonesian roads and find support for Shleifer and Vishny’s (1993) tollbooth hypothesis. Both the study by Djankov et al. (2002) and that of Svensson (2005) rely mainly on corruption perception measures which, as we have argued, are only an imperfect approximation to actual corrupt practices. Thus, it is worth asking whether regulation might be behind corruption when we use our index of the actual incidence of graft.

Figure 2 shows that there is a clear positive correlation between the incidence of graft and the extent and nature of the regulatory and legal obligations that firms face. The latter is obtained from the “Ease of Doing Business” indicator in the _Doing Business_ dataset, with a higher measure indicating a less benign business environment. The GIFT allows us to delve into the subject. For example, in Figure 3 we show that the probability of being the victim of graft when requesting an operating license or a construction permit are positively correlated, respectively, with _Doing Business_ measures of restrictions on starting a business and problems in dealing with licenses in construction projects.

Econometric results in Table 6 confirm what we see graphically: excessive regulation is associated with more graft even after taking into consideration other factors that may explain the level of corruption. The results in column 2 suggest that a one-standard deviation decline in obstacles to doing 10 See Bardhan (1997) for a discussion.
**Table 7 Productivity regression results: Quality of the legal framework**

|Dependent Variable: Log Output|_O_<br>(1)|_LS_<br>(2)|_I_<br>(3)|_V_<br>(4)|
|---|---|---|---|---|
|Bribe tax|-0.432||-19.284||
||[0.432]||[10.369]*||
|Bribe tax * LF|0.126||6.435||
||[0.148]||[3.566]*||
|Time tax||-0.001||-0.087|
|||[0.008]||[0.190]|
|Time tax * LF||0.001||0.030|
|||[0.003]||[0.064]|
|Legal framework|0.346|0.346|-1.517|-0.836|
||[0.105]***|[0.117]***|[1.894]|[1.185]|
|L|0.300|0.289|0.320|0.269|
||[0.049]***|[0.050]***|[0.059]***|[0.061]***|
|M|0.423|0.421|0.401|0.450|
||[0.033]***|[0.035]***|[0.044]***|[0.040]***|
|K<sup>Equipment</sup>|0.069|0.075|0.089|0.078|
||[0.017]***|[0.018]***|[0.027]***|[0.024]***|
|K<sup>Land</sup>|0.002|0.010|-0.002|0.004|
||[0.014]|[0.015]|[0.017]|[0.017]|
|Exporter|0.230|0.215|0.135|0.223|
||[0.053]***|[0.057]***|[0.086]|[0.075]***|
|Age|-0.000|-0.000|0.001|-0.001|
||[0.001]|[0.001]|[0.002]|[0.001]|
|SizeM|-0.487|-0.506|-0.397|-0.425|
||[0.106]***|[0.110]***|[0.146]***|[0.159]***|
|SizeL|-0.270|-0.276|-0.240|-0.224|
||[0.061]***|[0.065]***|[0.098]**|[0.093]**|
|Foreign Owned|0.209|0.212|0.373|0.327|
||[0.071]***|[0.078]***|[0.137]***|[0.087]***|
|Innovator|0.069|0.073|0.080|0.082|
||[0.055]|[0.058]|[0.072]|[0.075]|
|Competition|-0.024|-0.020|-0.001|-0.033|
||[0.015]|[0.015]|[0.026]|[0.019]*|
|Courts|-0.018|-0.047|0.045|-0.001|
||[0.050]|[0.052]|[0.083]|[0.066]|
|Political Stability|0.063|0.038|0.097|0.032|
||[0.049]|[0.051]|[0.069]|[0.062]|
|Constant|YES|YES|YES|YES|
|Country Dummies|YES|YES|YES|YES|
|Sector Dummies|YES|YES|YES|YES|
|R squared|0.87|0.87|0.59|0.81|
|<br>Wu-Hausman [p-value]|||0.00|0.63|
|Hansen J [p-value]|||0.91|0.79|
|F-Stat|||2.51|2.75|
|Observations<br>_Note_<br>_: Standard errors clustered by countr_<br>_*** significant at 1%. Instruments: XYZ; c_|1504<br>_y-industry in brack_<br>_ountry-industry ti_|1363<br>_ets. * signific_<br>_me tax; country-in_|1470<br>_ant at 10%; ** si_<br>_dustry bribe tax._|981<br>_gnificant at 5%;_<br>|

31
Policy Research Working Paper 9179

# **Abstract**

This paper defines three new indicators that capture the remoteness of local communities within a country context and the overall scale of population dispersion and settlement sparsity across a country. The paper also exploits the World Bank’s subnational geography database to estimate the geographical scale of population dispersion and dispersion-adjusted population sparsity for 100 countries around the world. The new indicators are evaluated and explored for several Pacific Island countries, which are often characterized as being remote (in a global context) and highly dispersed. However, within each Pacific Island country, there is enormous variation in the remoteness of individual communities and the extent to which communities are clustered or dispersed from one another, and these conditions can be related to communities’ socioeconomic characteristics. The results reflect this. The paper empirically contextualizes the settlement patterns evident in the Pacific Island countries within a broader global context, highlighting the extreme degree of population sparsity in the Pacific, relative to all the other countries that are assessed.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 rutz1@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
registered one child, and very few registered more than two (158). Focusing on the 1,329 families who registered two children, however, allows us to conduct three comparisons. First, we can directly test whether the receipt of a subsidy changes the allocation of opportunities in the household by comparing children who received subsidies to those who did not. If families reallocate resources to equalize (unbalance) the allocation of opportunities, then the resulting treatment effect will be smaller (larger) than the overall treatment effect. Second, we can compare treated children in families that receive one treatment to treated children in families that receive two treatments. If resources are shared between children, then treated children in families that only receive one subsidy may experience smaller educational gains than children in families in which both children are treated. Finally, we can also compare the untreated children in a family that receives one treatment to the registered children of families that receive no treatment. If families shared resources between treated children, then we would expect untreated children in households receiving one treatment to be more engaged in school than registered children in households with no treatment. However, if the receipt of a treatment causes families to allocate additional resources to a treated child, then untreated children in households that receive one treatment may in fact be less engaged in school.

The results from these comparisons are contained in Table 9. Because of the smaller sample size, we pool the treatments and then only break out the estimates by locality and then gender. For each comparison, we estimate differences along three variables. First, we estimate the effects on all children using our verified attendance measure. Second, for children who were not in grade 11 in 2005, we estimate the effects using the administrative enrollment data. Finally, we also estimate differences using our measure of labor market hours.

Panel A, for reference, contains a simple estimate of the overall treatment effects by comparing students who received a treatment to those who did not within all families who registered two children. While the sample size is substantially smaller than in Tables 5 and 6, the estimates in families with two registered children are slightly stronger but consistent. Overall, the receipt of a treatment increases attendance by 1.6 percentage points, reenrollment by 2.1 percentage points, and reduces hours worked by 0.9 hours. The results are also distributed as we would expect from the previous results. The increase in attendance is consistent across all groups. The increase in enrollment and reduction in working hours is concentrated in Suba, but evenly distributed by gender.

- 31 -
cause very few farmers report any challenges related to operations and maintenance over the four years of survey data collection. Second, the government planned to charge farmers in the command area land taxes, which were unconditional on cultivation decisions, to fund O&M in the schemes. To test whether these taxes limit farmers adoption of irrigation, we randomized tax subsidies for farmers. We find no evidence this experiment changed cultivation practices. This result is likely because compliance with the fees was extremely low (4%), so collected fees were too low to plausibly constrain farmers. We discuss these experiments further in Appendix I.2, and conclude here that these issues were not relevant in this context.

Third, we test whether financial and informational constraints limit adoption of irrigation. We assigned horticultural minikits to randomly selected farmers from water user group member lists. Each minikit included horticultural seeds, chemical fertilizer, and insecticide, in sufficient quantities to cultivate 0.02 ha. In principle, these minikits should resolve constraints related to input access, including credit constraints. In addition, they should reduce basis risk which may resolve insurance constraints and facilitate experimentation if information is a constraint. In other contexts, minikits of similar size relative to median landholdings have been shown to increase adoption of new crop varieties or varieties with low levels of adoption (Emerick et al., 2016; Jones et al., 2018). To test for spillovers, water user groups were randomly assigned to 20%, 60%, or 100% minikit saturation, with rerandomization for balance on Zone and O&M treatment status. Minikits were offered to assigned individuals prior to 2017 Rainy 1 and 2017 Dry.<sup>46</sup>

# **6.1 Empirical strategy and results**

We estimate the impact of minikits using the specification

> 46Each of these three interventions exist only in the command area. As such, the effects of irrigation estimated throughout this paper are averages across the experimental treatments. Overall, this concern is mitigated by the fact that all three experimental treatments had very limited impacts on cultivation practices. In addition, the first two of these treatments (fee subsidies and monitoring systems) vary characteristics which would be heterogeneous across different irrigation systems; we are therefore comfortable with the interpretation that estimates above exist for the average of these treatments. Readers may be most concerned about interpretations of treatment effects in the presence of the minikit treatment; in addition to the modest effects on cultivation described below, we have also conducted analysis excluding minikit winners and conclusions are qualitatively unaffected.

42
2004 to 2013 (Figure 2c). These monitoring boreholes belong to a network of some 1,250 monitoring wells across the entire country that have been managed by the Bangladesh Water Development Board (BWDB) since the early 1960s. We estimated depth to mean dry-season groundwater levels (i.e., maximum depth below ground level) using the ground surface as a reference level.

# **2.4 Demography, access to water supply, and social vulnerabilities**

Demographic data sets on population, poverty, tubewells, and access to pipe water supplies in Bangladesh at the upazila level are collated from a GIS database ( _The Bangladesh Interactive Poverty Maps_ ) published by the World Bank (2016). The country-level demographic database allows one to explore and visualize socioeconomic data at both Zila (district) and Upazila (subdistrict) level. The online GIS-based mapping tool enables an easy access to different types of indicators including poverty, demographics of the population, children’s health and nutrition, education, employment, and household access to energy, water, and sanitation services (World Bank, 2016). These maps (see maps in supplementary Figure S1) were constructed by combining three different data sources all of which are publicly available: (i) 2010 Bangladesh Poverty Maps, (ii) 2011 Bangladesh Census of Population and Housing, and (iii) 2012 Undernutrition Maps of Bangladesh (BBS/WFP/IFAD, 2012). Children’s health and nutrition data sets were produced by the World Food Programme (WFP) and are constructed based on data from the Child and Mother Nutrition Survey of Bangladesh 2012 (MICS) and the Health and Morbidity Status Survey 2011 (HMSS).

Upazila-level total population and percentage of poor population (i.e. percentage of the population that lives below the official national upper poverty line, which is based on household's poverty status assessed using per capita consumption) are shown in Figure S1. According to the 2011 National Population Census, conducted by the Bangladesh Bureau of Statistics, the total population of the country was 144 million with 76% of the total population living in rural areas in Bangladesh (see Supplementary Texts for further details).

Upazila-level, percentage of households with access to tubewell and tapwater supplies in Bangladesh are shown in Figure S2. The data on access to water supply come from the 2011 Census of Population and Housing. The national average of households with an access to tubewell and tapwater supply (town or municipal water supply via piped network) is 82% and 6
Table B4: Full variance decomposition for KE

|||Absolute<br>|contributi<br>|ons(in s.d.<br>|units)<br>|||R<br>|elative sha<br>|res(in %)<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(a)<br>π = 0|(b)<br>π = 0.5|(c)<br>π = 1|(d)<br>UBH|(e)<br>UBS|(f)<br>MLM|(a)<br>π = 0|(b)<br>π = 0.5|(c)<br>π = 1|(d)<br>UBH|(e)<br>UBS|(f)<br>MLM|
|σ��<br>�|0.21|0.21|0.20|0.21|0.24|0.19|4.39|4.27|4.16|4.38|5.73|3.63|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.06)|(0.06)|(0.05)|(0.06)|(0.06)|(0.05)|
|σ�<br>�|0.52|0.38|0.34|0.53|0.29|0.37|26.63|14.58|11.26|28.22|8.54|13.79|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.19)|(0.14)|(0.13)|(0.20)|(0.11)|(0.14)|
|σ�<sup>�</sup>|0.29|0.38|0.53|0.14|0.51|0.42|8.44|14.14|28.45|2.00|25.75|17.81|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.18)|(0.23)|(0.33)|(0.09)|(0.31)|(0.26)|
|2Σ��|0.04|0.24|-0.06|0.12|0.16|0.25|0.18|5.62|-0.31|1.39|2.48|6.41|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.02)|(0.11)|(0.03)|(0.06)|(0.07)|(0.12)|
|2Σ�<sup>�</sup>�|0.19|0.15|0.12|0.21|0.11|0.12|3.44|2.28|1.46|4.52|1.19|1.48|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.06)|(0.05)|(0.04)|(0.07)|(0.03)|(0.04)|
|2Σ���|0.06|0.12|0.16|0.07|0.17|0.14|0.31|1.36|2.47|0.50|2.90|2.02|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.02)|(0.04)|(0.06)|(0.03)|(0.07)|(0.05)|
|σ�<sup>�</sup>|0.75|0.76|0.72|0.77|0.73|0.74|56.61|57.75|52.51|58.98|53.40|54.86|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.20)|(0.20)|(0.19)|(0.21)|(0.20)|(0.20)|
|σ�<br>�|1.00|1.00|1.00|1.00|1.00|1.00|100.00|100.00|100.00|100.00|100.00|100.00|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|.|.|.|.|.|.|
|ρ��|0.01|0.20|-0.01|0.09|0.08|0.20|.|.|.|.|.|.|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|.|.|.|.|.|.|

Note: following equations (3b) and (3c), the table sets out the absolute and relative variance shares attributable to each component of the test score; absolute shares are in standard deviation units; different models/estimators are indicated in the columns – (a) to (c) refer to results from a partitioned iterative algorithm for different choices of initialization scalar _π_ , (d) is the household upper bound, (e) is the school upper bound, and (f) is a mixed linear model; _ρhs_ is the estimated correlation coefficient between household and school effects; standard errors are reported in parentheses, calculated using the asymptotic approximation due to Ahn and Fessler (2003).

Source: own calculations.

49
# **1. Introduction**

The professional business services market of providers offering accounting, marketing, human resources, consulting, and legal services to other businesses is estimated to exceed US$5 trillion in annual revenue.<sup>1</sup> However, few small firms in developing countries use these services. In a recent experiment in Nigeria, we provided subsidies to firms with 2 to 15 workers, which paid for them to use a human resources firm to insource an accounting or marketing worker, or paid for them to outsource business functions to a professional accounting or marketing firm (Anderson and McKenzie, 2020). We found that this approach of getting firms to use the market for business services led to a much larger improvement in business practices than the traditional approach of trying to train the entrepreneur in these skills, and resulted in these firms using higher quality digital marketing practices, innovating with new products, and achieving greater sales and profits growth over a two-year horizon.

A natural follow-up question is then to ask why more small firms do not use the market for professional business services, and whether there is scope for interventions other than direct subsidies to encourage use. We hypothesized that information frictions may be a key constraint preventing more firms from using these services. When firms in our previous experiment were directly asked why they thought more firms did not use the market for business services, approximately half said a reason related to information or search frictions, such as firms not knowing of any business service providers that could help them, finding it difficult to judge the skills and quality of outside providers, believing it will take a lot of time to find someone, and not being confident that they can trust an outside firm. In more developed economies, these frictions are reduced through information sources such as business directories and industry organizations; reputation mechanisms such as the Better Business Bureau and Angie’s List; and functioning legal institutions that can provide a remedy for breaches in trust. The lack of these supporting institutions in many developing countries may lead to underutilization of professional business services by small firms.

> 1 Source: https://www.thebusinessresearchcompany.com/report/professional-services-market [accessed 26 February, 2021].

2