rafmacalaba/datause-encoder-data huggingface dataset:
## CLEMENS & TIONGSON

Table 10 explores how the households of test-failers in the survey sample differ from the same outcomes in a nationally-representative survey conducted by the Philippine government.<sup>25</sup> We leave out test-passers so as to remove the effects of EPS-Korea migration.

Households in the survey sample are much more likely to already have a member abroad than typical households in the Philippines. Sample households have somewhat more income (about 35% more) than typical households, a difference entirely accounted for by the fact that they have more remittance income. Sample households are less likely to have monthly savings (and when they save, save less), are much less likely to have businesses, and live in somewhat better-quality houses. They are more likely to be in Luzon. Their heads of household are younger and have 3.5 years more education, and their children are 12 percentage points more likely to be in school.

In short, relative to the country as a whole, the survey sample captures households that have similar incomes in the absence of remittances, have more experience with migration and thus somewhat higher incomes due to remittances, are more likely to invest in human capital and work for wages than to run a business, and save less. The broad pattern is that households in the survey sample emphasize investments in human capital (education, migration) over physical capital (entrepreneurship, savings).

# 9. Conclusion

We find that migration from the Philippines to temporary jobs in Korea has important effects on migrants’ households. These are theoretically and empirically different from the effects of remittances, as remittances are just one portion of the bundled treatment that is migration. For example, Yang (2008) finds that remittances to the Philippines encourage some types of entrepreneurial activity, conditional on the household already

> 25We use a household-matched nationally representative sample from the 2006 Family Income and Expenditure Survey (FIES) and Labor Force Survey (LFS). 2006 is the most recent matched FIES-LFS microdata publicly-available from the National Statistical Office at the time of writing. We inflate all peso figures from 2006 to 2010 using the Consumer Price Index.

24
that it was the threat of competition from Chinese imports that put the impetus on family-run firms to try to improve their management practices through participating in the government program.

Supporting export- and innovation-oriented firms that are doing something different also alleviates the concern that helping some firms may just simply lead them to stealing the business of firms not supported, with no growth in the overall market. This is what happened with a subsidized loan program in China (Cai and Szeidl, 2022), with firms that got loans using them to take market share from those in the same industries that did not. In contrast, business training in Kenya helped firms to figure out new products to sell, and sales in the market as a whole expanded, with untrained women not being hurt by having others in their market receive training (McKenzie and Puerto, 2021). Supporting many firms so that they have to compete, rather than just protecting one firm, can help ensure these benefits arise. In a study of private schools in Pakistan, when only one school in a village received a grant, it increased capacity (taking students from other schools), but not quality, whereas when all schools got grants, they competed on quality and children’s test scores improved (Andrabi et al, 2020). Supporting groups of firms can also generate additional benefits if they can learn from one another and form new network connections. In Colombia, providing consulting to small groups of firms that were not directly competing with one another delivered lasting improvements in management at one-third the cost of providing individual consulting (Iacovone et al, 2022).

Finally, another concern of policymakers is that subsidies for firms to use training or business consulting can end up undercutting the market for business service provision. However, empirically this often may not be the case, due to the presence of substantial information frictions in the market for business service provision. In a study in Nigeria, we found that most small and medium enterprises did not know of the existence of most service providers, found it hard to tell good from bad providers, and did not trust them (Anderson and McKenzie, 2022). A program which subsidized firms to try using HR consultants, accounting firms, and marketing firms not only improved management practices and firm growth, but also led to firms being more likely to go back to the market and use their own funds to buy more of these services. That is, government support, rather than undermining competition in business service provision, can be used to build market demand.

6
Noida for Delhi; Thane for Mumbai); 67 districts located within 0 – 10 kilometers away from the GQ are the “treated” ones; 32 districts are 10–50 kilometers away; and 175 “control” districts located over 50 kilometers away are assumed to be minimally affected by the GQ, but track broad movements in the economy. Long differences in outcomes resulting from nearby GQ completion can thus be compared. To study adjustment patterns across plant ages, the data are further divided into age cohorts: 0-5 years (young), 6-24 years (mature) and plants with 25 and above years of age (old). Our results are not sensitive to moderate changes in category definition.

The data also permit creating a new geo-coded plant-level panel from two available versions of the ASI data: (i) a repeated cross-section of plant-level data with district identifiers, but no plant identifiers (“common factory ID”) and (ii) with plant identifiers, but without district identifiers. Using characteristics of firms such as industry code, ownership, establishment year, months of operation, capital assets, employment, wages, income from services etc., the two data sets are combined following Martin et al. (2017) with both district and plant identifiers with a match rate greater than 98%. This allows measuring year-to-year plant-level changes in revenue-based total factor productivity (TFPR), total revenues, output, labor and skill demand, and plant exit. Annex Table A.1 tabulates the summary statistics, splitting the outcomes by age groups and distance bands for pre-GQ and post-GQ years.

# **III. Estimation**

## (i) **Long difference in difference estimation:**

As a first approach to the data, we compare district activity in 2000, the year prior to the start of the GQ upgrades, with district-level activity in 2009, the year when GQ was nearly and totally complete. Indexing districts with _i_ , the specification takes the form: where , is the change in relative economic outcome observed for a district _i_ during the period 2000-09 and captures seven outcome variables: three aggregate measures at the district level- the natural log of ii ΔYY establishment counts, employment, and output; two capturing average plant size measured as employment and output; and changes in average labor productivity, defined as output per employee and Total Factor Productivity (TFP), estimated using LP-Sivadasan methodology. The set _D_ contains the three distance bands from the GQ network: nodal district, 0–10 kilometers, and 10–50 kilometers. The coefficients, measure for each distance band the change in outcome, over the 2000–09 period relative to the dd ββ ii YY 4 4
# **Box 3 Key Indicators from the Doing Business Report**

|<br>Starting a business: all procedures that are officially required for an entrepreneur to start up and<br>formally operate an industrial or commercial business.|
|---|
|`o`<br>Procedure is defined as any interaction of the company founders with external parties (for<br>example, government agencies, lawyers, auditors or notaries).|
|`o`<br>Time is recorded in calendar days. The measure captures the median duration that<br>incorporation lawyers indicate is necessary to complete a procedure with minimum follow-<br>up with government agencies and no extra payments.|
|`o`<br>Cost is recorded as a percentage of the economy’s income per capita. It includes all official<br>fees and fees for legal or professional services if such services are required by law.|
|`o`<br>The paid-in minimum capital requirement reflects the amount that the entrepreneur needs to<br>deposit in a bank or with a notary before registration and up to 3 months following<br>incorporation and is recorded as a percentage of the economy’s income per capita.|
|<br>Getting Credit: the legal rights of borrowers and lenders with respect to secured transactions<br>through one set of indicators and the sharing of credit information through another.|
|`o`<br>The strength of legal rights index measures the degree to which collateral and bankruptcy<br>laws protect the rights of borrowers and lenders and thus facilitate lending.|
|`o`<br>The depth of credit information index measures rules and practices affecting the coverage,<br>scope and accessibility of credit information available through either a public credit registry<br>or a private credit bureau. The public credit registry coverage indicator reports the number<br>of individuals and firms listed in a public credit registry with information on their<br>borrowing history from the past 5 years.|
|`o`<br>The private credit bureau coverage indicator reports the number of individuals and firms<br>listed by a private credit bureau with information on their borrowing history from the past 5<br>years.|
|<br>Enforcing contracts: the efficiency of the judicial system in resolving a commercial dispute.<br>`o`<br>The list of procedural steps compiled for each economy traces the chronology of a<br>commercial dispute before the relevant court.|
|`o`<br>Time is recorded in calendar days, counted from the moment the plaintiff decides to file the<br>lawsuit in court until payment.|
|`o`<br>Cost is recorded as a percentage of the claim, assumed to be equivalent to 200% of income<br>per capita.|
|Source: The World Bank, DoingBusiness|

32
ECA High Income * T1C/TC

|T1C/TC<br>ECA Emerging* T1C/TC||-1.094***<br>(0.096)||-0.524***<br>(0.103)|
|---|---|---|---|---|
|Constant|90.862***|28.654***|93.608***|24.845***|
||(4.502)|(6.810)|(4.727)|(7.292)|
|Observations|78,022|81,661|67,671|69,672|
|R-squared|0.105|0.104|0.094|0.090|
|Year FE|Yes|Yes|Yes|Yes|

Note: *, **, and *** represent statistical significance at 10%, 5%, and 1% two-tailed level, respectively. Robust standard errors in parentheses clustered at the bank level. All independent variables are lagged one year.

The figure below shows the estimates from the regression analyses. A 10% increase in RWA/TA results in a significant increase in risk (-3.5 decline in the z-score) in countries excluding ECA, which is represented by the first bar in the figure. However, the impact of a similar increase in the ECA highincome and developing regions is negligible, which are represented by the other two bars in the figure.

**Figure 13: Relationship Between Risk-weights and Bank Risk**

<!-- Start of picture text --> Z-Score<br>1.00<br>0.50<br>0.00<br>-0.50 ROW ECA High Income ECA Emerging<br>-1.00<br>-1.50<br>-2.00<br>-2.50<br>-3.00<br>-3.50<br>-4.00<br><!-- End of picture text --> Source: Own calculation using archived data from Bureau van Dijk’s Bankscope and BankFocus. Note: We report the effect on the Z-Score of increasing risk weighted assets (RWA/TA) by 10% for EU ECA countries and the other ECA countries (Emerging ECA). The estimates are obtained after controlling for bank size (log(TA)), bank liquidity (LiquidA/TA), bank profitability (ROA), reliance on short-term funding (ShortFund/TA), and loan ratio (Loans/TA).

Examining the impact of the proportion of Tier 1 in regulatory capital, we find that the quality of capital matters in reducing bank risk. Consistent with the notion that capital that is not Tier 1 can be severely undervalued during times of distress and not able to absorb shocks, we find that capital is less effective in reducing bank risk if a higher proportion of it is in the form of Tier 2 capital. However, as mentioned earlier, what constitutes Tier 1 capital varies across countries and Tier 1 stringency is lower in the ECA region. If supervisors can limit what can be included as Tier 1, we would expect a greater 17
villages, although some social mobilization and behavior changes were ongoing more recently than anticipated.

In what follows we present the effects of the different exposure to TSC between treatment and control areas on outputs and intermediate outcomes along the value chain. We present some analysis by subgroups of interest and we finalize with a discussion of main findings.

# **4.3  Toilet Construction, Sanitation Behavior, and Health Outcomes**

## **Secular Changes from Baseline to Follow-up (Table 6)**

Table 6 includes means of key variables between follow-up and baseline in the intervention and control groups. Between baseline and follow-up surveys, the household demographic and economic variables have remained stable. Household access to JMP improved water sources was nearly universal at follow-up in intervention (97%) and control (95%) villages, and water quality, measured by _E. coli_ indicator bacteria in stored drinking water, also improved over time in both groups. Diarrhea prevalence was lower at follow-up in both groups in part because of the aging cohort and possibly because the follow-up survey took place in a different season than baseline.

## **Toilet Coverage and Sanitation-related Behavior (Table 7, Panel 2)**

The TSC program increased JMP improved toilet coverage by 19 percentage points in intervention villages compared to control villages (41% v. 22%). At follow-up, JMP improved toilet coverage in the intervention villages ranged from 5% to 79% (median 42%, IQR 27%, 57%), and in control villages it ranged from 0% to 74% (median 26%, IQR 5%, 36%). Although fewer households in intervention villages were likely to report daily open defecation compared to control villages for adult men (73% v. 83%), adult women (74% v. 84%), and children < 5 y (84% v. 89%), these reductions in reported OD behavior were smaller than the gains in improved toilet coverage. For example, although treatment groups reports lower OD levels, overall levels remain high (73% for adult men). Among the 630 households in intervention villages that had a JMP improved toilet at follow-up, 41% reported that adult men still practiced daily OD and 64% reported that children < 5 years practiced daily OD. Among 339 control village households with a JMP improved toilet at follow-up, 32% reported that adult men practiced daily OD and 54% reported that children < 5 years practiced daily OD. Although OD behavior remained prevalent among young children in both groups, households in intervention villages were more likely to report proper disposal of child feces based on the JMP definition (27% v. 18%). We observed no difference between groups in the proportion of households with no human or animal feces observed in their living area around the structure (40% in both groups).

## **Drinking Water Quality (Table 7, Panel 3)**

Drinking water quality was poor in the study population. In control villages, 82% (331/403) of household drinking water samples tested positive for _E. coli_ ; water quality was slightly improved in intervention households where 77% (310/404) of samples tested positive for _E. coli_ .

15 / 41
# **Appendix 1.**

**Table A1. Determinants of feeding posts placement. Dependent variable: presence of Partage / feeding post in the village**

|**Explanatory variables**|**coefficient**|
|---|---|
|Mean of (log) per capita consumption in the village|0.101<br>(1.48)|
|Daily Market in Community|0.121<br>(2.61)***|
|Crop disaster in past 10 years|0.273<br>(2.76)***|
|Drought disaster in past 10 years|-0.054<br>(1.11)|
|AIDS main adults’ death cause|0.045<br>(0.97)|
|Mean male height of adults aged 25-32|-0.003<br>(0.60)|
|Mean female height of adults aged 25-32|<br>0.002<br>(0.31)|
|Urban|-0.017|
||(0.27)|
|Health worker in village|-0.120<br>(2.08)**|
|Distance to the closest health center|-0.013<br>(1.96)*|
|Is Motorable Road Near|-0.041|
||(0.35)|
|Road impassable|0.115<br>(2.46)**|
|Ratio of children aged 0-3 that are vaccinated|<br>-0.198<br>(1.75)*|
|Constant|<br>-0.612|
||(0.37)|
|Observations|<br>197|
|R-squared|0.21|

**Note:** Regression based on observations at village level and not on the entire sample of children’s observations. Absolute value of t statistics in parentheses. * indicates significance at 10% level; ** at 5% and *** significant at 1% level of confidence.

26
|that are women|34.00|23.50|0|30|100|2,021|
|---|---|---|---|---|---|---|
|Faculty are evaluated more than once per year|0.85|0.360|0|1|1|2,103|
|Almost all or all faculty participated in professional training last year|0.55|0.50|0|1|1|2,103|
|_Other Practices_|||||||
|Update or review admin data more than once per year|0.66|0.47|0|1|1|2,103|
|Admission requirements|||||||
|General or specific knowledge test|0.59|0.49|0|1|1|2,103|
|Interview|0.50|0.50|0|1|1|2,103|
|Min. score in HS GPA or national entry test|0.66|0.47|0|1|1|2,103|
|Percent of governing body that belongs to:|||||||
|Private sector|18.90|21.20|0|12|100|2,103|
|Government|11.90|16.40|0|1|100|2,103|
|Faculty|39.00|27.80|0|33|100|2,103|
|**Panel B. Student body, program, and institution characteristics**|||||||
|_Student body characteristics_|||||||
|Academic deficiencies|||||||
|Mathematics is lacking in incoming students|0.82|0.39|0|1|1|2,103|
|Reading is lacking in incoming students|0.70|0.46|0|1|1|2,103|
|Writing is lacking in incoming student|0.68|0.47|0|1|1|2,103|
|Percent of students that are|||||||
|25+ years old|28.94|29.30|0|20|100|2,103|
|full-time|43.89|39.00|0|33|100|2,103|
|Women|38.19|29.20|0|40|100|2,103|
|Student body characteristics (PCA score)|-0.02|1.40|-2.78|-0.31|4.88|2,103|
|_Program characteristics_|||||||
|Program duration (semesters)|5.20|0.97|2|6|8|2,101|
|Program has high quality accreditation|0.19|0.39|0|0|1|2,103|
|Total number of students in the program last year|221.60|332.80|1|125|4,321|2,030|
|Program age (years)|11.50|9.46|0|10|70|2,103|
|Program characteristics (PCA score)|0.02|1.25|-7.59|0.20|2.43|2,082|
|_Institution characteristics_|||||||
|HEI is public|0.30|0.46|0|0|1|2,103|
|HEI is a university|0.21|0.41|0|0|1|2,103|
|HEI is for profit|0.20|0.40|0|0|1|2,103|
|HEI age|37.84|30.80|1|32|481|2,094|
|Number of programs in the HEI|21.59|36.30|1|10|268|2,103|
|<br>HEI characteristics (PCA score)|0.08|1.16|-1.84|-0.21|9.57|2,094|
|**Panel C. Noise controls**|||||||
|Survey conducted during COVID|0.48|0.50|0|0|1|2,103|
|Number of attempts to complete the survey|8.36|3.16|1|9|17|2,103|
|Survey completed by phone|0.33|0.47|0|0|1|2,103|

_Sources_ : Own calculations using WBSCPS and administrative data.

_Notes_ : This table shows the descriptive statistics of the main variables used in the analysis. An observation corresponds to a program. Dummy variables included in the list are those with means between 0.1 and 0.9. Statistics are weighted by WBSCPS sampling weights. Panel A refers to quality determinants, presented by category. Panel B refers to characteristics of the student body, program, and higher education institution (HEI); Panel C refers to survey noise controls. Total number of surveys completed is 2,103. Values in the “Obs.” column vary depending on the number of valid responses. Tuition is presented in dollars but transformed in logs for estimation. Mean of PCA Scores are different than zero due to the use of sampling weights. All variables in panel B are included in the corresponding indexes (PCA Scores), except for “HEI is public,” which is included separately in the corresponding regressions as a “fixed” control.

42
26 Eisner, Robert (1989). “Budget Deficits: Rhetoric and Reality.” <u>Journal of Economic Perspectives. Vol. 3, No. 2, Spring, pp. 73-93.</u> Elmendorf, Douglas W. and N. Gregory Mankiw (1998). "Government Debt." NBER Working Paper No. 6470, March.

Griliches, Zvi (1986). “Economic Data Issues.” in Handbook of Econometrics. (Volume 3) ed by Zvi Griliches and Michael D. Intriligator. North-Holland, Netherlands.

Hoover, Kevin D. and Mark Siegler (2000). “Taxing and Spending in the Long View: The Causal Structure of U.S. Fiscal Policy, 1791-1913.” Oxford Economic Papers. (forthcoming) Johnson, D. Gale (1998). "Population and Economic Development." Seminar Paper presented at University of California at Davis, November 13, 1998.

Kelly, Allen C. (1988). "Population Pressures, Saving, and Investment in the Third World : Some Puzzles." Economic Development and Cultural Change. Vol. 36, No. 3, pp 449-464.

Kristov, Lorenzo, Peter Lindert and Robert McClelland (1992). "Pressure Groups and Redistribution." Journal of Public Economics. Vol. 48, pp. 135-163.

Lindert, Peter H. (1996). "What Limits Social Spending?" Explorations in Economic History. Vol. 33, pp. 1-34.

Mankiw, G. (1987) “The Optimal Collection of Seigniorge: Theory and Evidence.” Journal of Monetary Economics. Vol. 20, pp. 327-342.

Meltzer, Allan H. (1992). “Measurement, Economic, and Political Issues of Debt and Deficits.” In Marvin H. Kosters (ed), <u>Fiscal Politics and the Budget Enforcement Act. The American</u> Enterprise Institute (AEI) Press.

Meltzer, Allan H. and Scott F. Richard (1981). “A Rational Theory of the Size of Government.” <u>Journal of Political Economy. Vol. 89, pp 914-927.</u> Meltzer, Allan H. and Scott F. Richard (1981). “Tests of a Rational Theory of the Size of Government.” In Allan H. Meltzer, Alex Cukierman and Scott F. Richard. <u>Political Economy.</u> Oxford University Press.

Ross, Levine and David Renelt (1992). “A sensitivity Analysis of Cross-Country Growth Regressions.” American Economic Review. September, Vol. 82, No. 4, pp. 942-963.

Roubini, Nouriel and Jeffrey Sachs (1989a). “Government Spending and Budget Deficit in the Industrial Countries.” Economic Policy. April, Vol 8 pp 100-132.

Roubini, Nouriel and Jeffrey Sachs (1989b). “Political and Economic Determinants of Budget Deficits in the Industrial Democracies.” European Economic Review. Vol. 33, pp 903-938.

Roubini, Nouriel (1991). "Economic and Political Determinants of Budget Deficits in Developing Countries." Journal of International Money and Finance. Vol. 10 (Supplement), pp S49-S72.

Tait, Alan A., Wilfrid L.M.Gratz and Barry J. Eichengreen (1979). "International Comparisons of Taxation for Selected Developing Countries, 1972-76." IMF Staff Papers. Vol. 26, pp 123-156, March.
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