rafmacalaba/datause-encoder-data huggingface dataset:
# **Data Appendix**

## **Variable definitions**

**_Employment_** is the total sum of all unique IRP5/IT3(a) forms with positive and non-missing employment income per unique tax reference number. Temporary workers are weighted by the duration of their contract (see Pieterse et al., 2018).

**_Age_** is determined in reference to the birth year. Only when birth year is missing, age is considered to be entry (age 1) when firm is first observed in the data.<sup>40</sup> .

**_Unit of observation_** is the firm. In the SARS-NT Panel, a firm is defined as a CIT-registered entity that has completed an IT14 and/or ITR14 form (Pieterse et al., 2018). In other words, each unique CIT reference number is associated with a firm.

**_Firm size_** is determined as the number of workers employed by a firm. The employment variable is used to construct the categories of firm’s size. A firm-level labor indicator for the SARS-NT Panel is derived from the associated IRP5 forms (i.e. IRP5 and IT3a forms) submitted by PAYE-registered firms (Pieterse, 2018).

|**Firm size c**|**ategories**||**Age cate**|**gories**||
|---|---|---|---|---|---|
|1-19|1-9|1-9|1-5|1|1-5|
|20-99|10-19|10-19|6-9|2-5|6-9|
|100+|20-99|20-49|10+|6-9|10-19|
||100+|50-249||10+|20-29|
|||250-499|||30+|
|||500+||||

**_Employment Growth_** is calculated following Davis and Haltiwanger (1992, 1999) by taking the difference in two consecutive years and divided by the average of employment in those two years. The measure is monotonically related to the conventional growth rate and a second order approximation of the logarithmic first difference. It has the convenient property of being symmetric around zero and bounded by values -2, and +2 avoiding any arbitrary treatment of outliers and mitigating the mean-reversion effect. Entry is set to maximum growth of +2 (previous unobserved year has employment zero), and exit has a growth rate of -2 year (with employment zero next year to last seeing in the data. This measure allows computing

> 40 Because all firms are observed first the first year of the period, this is inferred only for years after the first year period.

32
described in Kauffman et al. (2010).

There are five WGI variables. _Voice and Accountability_ quantifies the ability of a country’s citizens to participate in the selection of the government, as well as related freedoms such as freedom of speech. _Political Stability and Absence of Violence/Terrorism_ measures the degree to which political violence threatens the stability of the government. _Government Effectiveness_ is an indicator of the perceived quality of public services, the civil service, and related matters. _Regulatory Quality_ measures the quality of regulations that permit and promote private sector development. _Control of Corruption_ is the perceived influence of corruption on governance, including capture of the state by elites and private interests. These variables are all increasing with the quality of governance, and we expect them to reduce clearance times.

## **3.3.3 Trade infrastructure variables**

The final set of variables we include as potential controls are measures of trade infrastructure taken from Portugal-Perez and Wilson (2012). These authors construct four variables that are intended to measure aspects of the quality of trade infrastructure. The fourth of these, _Border and Transport Efficiency_ is constructed with inputs that are entirely from the TAB database, and are also closely related to policies contained in the TFA (documents required for trading, and days required for trading). We therefore exclude that variable but consider the other three trade infrastructure variables in our estimation procedure.

_Physical Infrastructure_ is an indicator of the quality of the relevant transport infrastructure, including ports. _Information and Communications Technology_ (ICT) summarizes information on the absorption and use of technology in a country’s private sector and in government. The _Business Environment_ variable summarizes indicators of government transparency, public trust, and corruption.<sup>23</sup> Portugal-Perez and Wilson (2012) construct these variables for only 120 of the countries/territories that we include in our sample so we use multiple imputation for these variables as well. Panel B of Table 1 reports summary statistics for the trade infrastructure variables.

# **4 Estimating framework**

Our estimating framework takes into account two features of the data that are relevant to our exercise. First, our primary outcome data are reported in discrete units (days in customs), even

> 23The Business Environment variable may well be correlated with the WGI data described above.

13
and meetings, the administration of a village fund takes time; collectively, VF committee members spent 4.6 hours on fund-related work in the week prior to the survey (or 2 hours, if the median value is used). Grossed up, this represents an annual effort of 239 hours per VF fund. Much of this is compensated by the honoraria that Village Fund committees pay themselves; these amount to 1.1% of the total value of lending, but in some cases the honoraria fall short of compensating committee members for the time they spend; we estimate the value of true volunteer time to be worth a further 0.4% of the value of lending.<sup>2</sup>

# **4. Modeling Village Fund Behavior**

With these basic institutional details in mind, we now need to address our central questions, which are how we might expect Village Funds to (i) behave, and (ii) perform. We first discuss the relevant theory, then define and present our measures of performance, and subsequently try to measure the determinants of that performance.

# _Standardized Loans_

The first important “stylized fact” is that any given Village Fund offers a very standard loan contract. Of the VF loans extended in 2010, 96.8% were for 12-month terms, and just 2% were for shorter periods (mainly 1, 2, 3, or 6 months). Although interest rates vary somewhat from village to village, the rate charged by a given village is the same for almost all of its loans; the exception is the handful of “emergency” loans – constituting 5% of all lending – that are for smaller amounts and shorter terms, and often carry higher rates. Almost half (48%) of loans extended in 2009 charged an annual rate of 6.0%, and a further third charged rates of 3, 5, 7, or 8 percent, as Table 7 shows; we model these differences more formally in section 7 below.

|**Table 7. Breakdown o**|**f VF loan**|**s by interest rate**|**charged**||||||
|---|---|---|---|---|---|---|---|---|
|Interest rate<br><3|3|4<br>5|6|7|8|9-11|12|>12|
|% breakdown<br>6.1|6.2|3.6<br>13.5|47.6|7.0|7.4|4.1|4.1|0.4|
|Note: Mean interest rate: 6.0%<br>Source: Thailand Socio-Econom|p.a.; standa<br>ic Survey 20|rd deviation of interest ra<br>09|te, 2.3%.||||||

The amounts lent vary somewhat from one loan to the next; formally, the borrower requests an amount, and although there are informal norms, there is no set amount. The mean amount borrowed in 2009 was 15,790 baht, equivalent to about 8% of income, and 78% of VF loans in 2009 were between 10,000 and 20,000 baht, as Table 8 shows. In 71% of all village funds, the maximum loan is 20,000 baht; in 10% of cases the

> 2 The average rural wage is about 300 baht per person per day.  We define volunteer time as any time spent by VF committees that is not compensated, in the form of honoraria, at this rate, and then multiply the quantity of volunteer time by the average rural wage to get the value of volunteer time.  The data come from the 2010 Village Fund survey.

10
|**Table2.ReturnstoInves**<br>**c**|**tmentinE **<br>**apitaInco**<br>Meanper<br>capita|**ducatio**<br>**meGro**|**nbyLevel,**<br>**up(Percen**<br>Social|**Latest **<br>**tage)**|**Year,A **|**veragesb **<br>Private|**yper**|
|---|---|---|---|---|---|---|---|
|PerCapitaIncomeGroup|(IJS$)|Primary|Secondary|Higher|Primary|Secondary|Higher|
|HighIncome($9,266ormore)|22,530|13.4|10.3|9.5|25.6|12.2|12.4|
|LowIncome($755orless)|363|21.3|15.7|11.2|25.8|19.9|26.0|
|MiddleIncome(to$9,265)|2,996|18.8|12.9|11.3|27.4|18.0|19.3|
|World|7,669|18.9|13.1|10.8|26.6|17.0|19.0|

Source: Table Al.

# **Table 3. The Coefficient on Years of Schooling: Mean Rate of Return (based on Mincer-Becker-Chiswick)**

|PerCapitaIncomeGroup|Meanpercapita<br>(US$)|Yearsof<br>schooling|Coefficient<br>(percent)|
|---|---|---|---|
|HighIncome($9,266ormore)|23,463|9.4|7.4|
|Low Income($755orless)|375|7.6|10.9|
|MiddleIncome(to$9,265)|3,025|8.2|10.7|
|World|9,160|8.3|9.7|

Source: Table A2.

**Table 4. The Coefficient on Years of Schooling: Rate of Return (based on MincerBecker-Chiswick), Regional Averages**

||Meanper<br>capita|Yearsof<br>schooling|Coefficient<br>(percent)|
|---|---|---|---|
|Region|(US$)|||
|Asia|5182|8.4|9.9|
|Europe/Middle East/NorthAfrica|6299|8.8|7.1|
|LatinAmerica/Caribbean|3125|8.2|12.0|
|OECD|24582|9.0|7.5|
|Sub-SaharanAfrica|974|7.3|11.7|
|World|9160|8.3|9.7|
|Source:TableA2.||||
|*Non-OECD.||||

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<!-- Start of picture text --> 1<br>0.98 Naïve signal receiver<br>Accurate Communication<br>0.96 Overconfidence (shift in precision)<br>0.94<br>0.92<br>= 0.9<br>0.88<br>ξξ<br>0.86<br>-4 -3 -2 -1 0 1 2 3 4<br>ee<br>ss −cc<br><!-- End of picture text -->

# **Figure 2**

# **Ratio of skilled entrants: Accurate, selective communication, and overconfidence**

The graph plots the expected rate of skilled entrants for different values of the peer signal (equation 2 in the text). The figure compares the predicted rates from three different models: (i) a model where information is accurately shared among individuals (solid line), (ii) when only positive peer outcomes are shared (dashed line), and (iii) when potential entrants overestimate the precision of their private signal (dashed dotted line). The parameters are , = 0.5 , = 2, = 4, = 1, and . The overconfidence parameter is . ee vv SS UU vv̅ = cc ττ ττ ττ ττ ξξ= 0.9 θθ= 1.2 51
Moss, J., Wolff, G., Gladden, G., & Gutierrez, E. (2003). _Valuing Water for Better Governance. How_ _to promote dialogue to balance social, environmental, and economic values?_ OECD. (2013). _Green Growth in Kitakyushu, Japan._ Paris: OECD Publishing. Obtenido de http://dx.doi.org/10.1787/9789264195134‐en Oklahoma Deparment of Commerce. (2016). _The Economic Impact of Oklahoma's Brownfields_ _Program._ http://okcommerce.gov/wp‐content/uploads/2016/05/Brownfields‐Program‐ Economic‐Impact‐Report.pdf Ozturk, I., & Al‐Mulali, U. (2015). Investigating the validity of the environmental Kuznets curve hypothesis in Cambodia. _Ecological Indicators, 57_ , 324‐330.

Palfreman, J. (2015). _Waste Management and Recycling in Dar es Salaam, Tanzania._ Panayotou, T. (1993). Empirical tests and policy analysis of environmental degradation at different stages of economic development. _ILO Working Papers_ (WP 238).

Rees, W. (2003). Understanding urban ecosystems: an ecological economics perspective. . En B.

A. al, _Understanding urban ecosystems._ New York: Springer.

Rosen, Sherwin. "Markets and diversity." American Economic Review 92, no. 1 (2002): 1‐ 15.Rwanda Environment Management Authority, “Inventory of Air Pollution Sources in Rwanda: Determination of Future Trends ad Development of National Air Quality Control Strategy”. January, 2018.

Shafik, N., & Bandyopadhya, S. (1992). Economic Growth and Environmental Quality: Time Series and Cross‐Country Evidence. _Background Paper for the World Development Report._ , 1‐50. Sharing Copenhagen . (2014). _Copenhagen European Green Capital. A Review._ SIWI (Stockholm International Water Institute). (s.f.). _Making Water a Part of Economic Development. The economic benefits of improved water management and services._ Stern, D. I. (2004). The Rise and Fall of the Environmental Kuznets Curve. _World Development, 32_ (8), 1419–1439.

Stern, D. I., & van Dijk, J. (2016). Economic growth and global particulate pollution concentrations. _Centre for Climate Economics & Policy, Working Paper 1604_ .

- The Business Year. (2013). _Keep it Clean_ . https://www.thebusinessyear.com/azerbaijan‐ 2013/keep‐it‐clean/interview

34
# **3.2. Evidence on the Costs of RoO**

It is difficult to estimate the costs associated with RoO because these costs are not observable. Estimating the costs of RoO is further complicated by the fact that, with the exception of the estimates presented in section 4 below, all econometric estimates so far have been obliged to rely on dummy variables to capture the presence of a RoO requirement (i.e. one can only note the presence or the absence of, say, an exception or a technical requirement). Finally, data on utilization rates are only available for NAFTA (and more recently for the EU preferential schemes). As a result, statistical evidence relates to the EU and US schemes, and furthermore it is partial. Below are the highlights of the econometric evidence.

# <u>Distortionary Costs</u>

Most of the evidence rests on isolating the effects of various PSRO criteria on utilization after controlling for the influence of preferential margins. Carrère and de Melo (2006) assume that the utilization of preferences for product line _i_ is a positive function of the difference between the tariff preference margin , τ _i_ , and (unobserved) total compliance costs,<sup>_c_</sup> _i_<sup>(expressed as a percentage of unit price) associated with</sup> ' applying the RoO criteria. That is they suppose that. _ui_ = _f_ ( τ _i_ − _ci_ ); _f_ (.) > 0 , and ' that _ci_ = _g_ ( _RoOi_ ) _g_ (.) > 0 . These assumptions lead to an estimable relation of the form:

> where _Rooik_ is a vector of dummy variables capturing the presence of PSRO (technical requirements, change of tariff classifications, exceptions). Results from estimating (0.6) on data from NAFTA by the authors (and by Cadot et al. (2006a) for the EU) indicates that utilization rates are positively related to preferential margins, and negatively to the presence of PSRO.  Carrère and de Melo combined their estimates with R-index values to compute an estimated ad-valorem equivalent of total RoO compliance costs (administrative and due to higher input costs). Their estimates range from 3.5% for a change of chapter to over 15% for combinations of RoOs involving technical requirements.

Even if the estimates point in the same direction, it is difficult to gather a sense of robustness from estimates derived from a relation like `(0.6)` . The reason is that there is so much heterogeneity and unobservables influencing preference uptake that estimates are quite sensitive to the inclusion of dummy variables controls (this is especially the case for estimates based on GSP preferences extended by the EU to many trading partners).

This said, all estimates point in the same direction. When multiple criteria are used to determine origin, utilization rates are, ceteris paribus lower. Moreover, results based on estimates regressing utilization rates on dummy variables capturing the presence of different types of PSRO, suggest that technical requirements tend to be associated with high compliance costs, whereas changes of tariff classification, even at high ``` 10 ```
WPS4039

# **_A Practitioner’s Guide to Intergovernmental Fiscal Transfers_**

## **Anwar Shah**

## **ABSTRACT**

Intergovernmental fiscal transfers are a dominant feature of subnational finance in most countries. They are used to ensure that revenues roughly match the expenditure needs of various orders (levels) of subnational governments. They are also used to advance national, regional and local area objectives such as fairness and equity and creating a common economic union. The structure of these transfers creates incentives for national, regional and local governments that have a bearing on fiscal management, macroeconomic stability, distributional equity, allocative efficiency and public services delivery. This paper reviews the conceptual, empirical and practice literature to distill lessons of policy interest in designing the fiscal transfers to create the right incentives for prudent fiscal management and competitive and innovative service delivery. The paper provides practical guidance on the design of performance-oriented transfers that emphasize bottom-up, client-focused and results-based government accountability. It cites examples of simple but innovative grant designs that can satisfy grantors’ objectives while preserving local autonomy and creating an enabling environment for responsive, responsible, equitable and accountable public governance. The paper further provides guidance on the design and practice of equalization transfers for regional fiscal equity as well as the institutional arrangements for implementation of such transfer mechanisms. The paper concludes with negative (practices to avoid) and positive (practices to emulate) lessons from international practices.

## World Bank Policy Research Working Paper 4039, October 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._
# **2.2 COVID-19 Pandemic Response**

Less than a week after the World Health Organization declared a global pandemic on March 11, 2020, President Duterte declared a State of Calamity in the Philippines. This involved the implementation of a set of severe mobility restrictions, labeled Enhanced Community Quarantine (ECQ). ECQ was first implemented in Metro Manila and eventually expanded to most provinces in the country. Under ECQ, all non-essential businesses were required to close and individuals were required to remain at home, except to acquire basic necessities and (for essential workers only) to go to work. By May 2020, the ECQ was replaced with a more lenient General Community Quarantine (GCQ) in most provinces, though restrictions continued to change – both tightening and loosening – for the next two years.

In order to support vulnerable households during a period of almost complete economic shutdown, the government rolled out a large-scale emergency social protection program, known as the Social Amelioration Program (SAP), announced at the end of March. The SAP aimed to provide unconditional cash transfers to 18 million households, or 70% of the population. It succeeded in reaching 68% of the population and was the sixth largest COVID-19 cash transfer program in the world in terms of total beneficiary counts (Gentilini et al., 2022).

SAP transfer amounts ranged from PhP 5,000 to PhP 8,000 (100 to 160 in 2020 USD), depending on the province, which roughly amounts to the monthly wage of a minimum wage worker. The majority of targeted beneficiaries received these transfers in April and May of 2020. There was a second SAP transfer targeted to a smaller subset of beneficiaries (“waitlisted” households unable to receive the first SAP payment and recipients of the first SAP transfer living in regions still under ECQ in May 2020), distributed throughout the months of June to November.

Of the 18 million households targeted for the first SAP transfer, 4 million were already part of the 4Ps program. Because the majority of these households already had an ATM card to receive their 4Ps funds, these households received a SAP payment top-up (the difference between the SAP total amount and their regular 4Ps payment) electronically around April 3-5, less than two weeks after the SAP had been announced. New beneficiaries, on the other hand, had to manually register for the program using paper application forms at their local government units (LGUs). Recipients also had to pick up their transfers in person. Because of this, transfer receipt was substantially delayed. According to administrative records, only one-fourth of the targeted new beneficiaries had received a SAP payment by April 25. The median SAP receipt date for new beneficiaries in our dataset was April 28.<sup>4</sup> There were also differences in the timeliness of the second SAP payment. Most of the of 4Ps households

> 4We display the entire distribution of SAP receipt dates in Figure 4.

6
|Capital intensity (K/L)|24.868***|0.140***|
|---|---|---|
||(2.485)|(0.052)|
|Firm size (log employment)|-15.956**|0.318***|
||(6.507)|(0.052)|
|Constant|85.101***|2.270***|
||(28.095)|(0.678)|
|Firm fixed effects|Y|Y|
|VSIC*Year fixed effects|Y|Y|
|Observations|124,347|62,884|
|R-squared|0.065|0.120|
|Adjusted R-squared|0.0637|0.118|
|Number of firms|116,106|59,151|
|N-cluster|63|63|
|Robust standard errors in parentheses<br>*** p<0.01, ** p<0.05, * p<0.1|||

# _5.2 What drives changes in local business environment?_

Our results show that corruption consistently matters for firm productivity. This, however, raises concerns about a potential reverse causation problem if the provincial government changes corruption behavior in response to firm outcomes. In fact, Bai et al. (2016) find that provincial corruption in Vietnam decreases with firm growth. Schmitz et al. (2012) also show that the private sector played a large role in improving the level of efficiency and governance of the provincial bureaucracy in Vietnam. Hence our result on the positive impact of (less) informal charges on firm productivity could be driven by this relationship.

To investigate and control for this endogeneity problem, we utilize an institutional arrangement for revenue sharing between the Vietnamese central government and its provinces to construct an instrumental variable. More specifically, provinces in Vietnam are allowed to keep all or part of their corporate income tax revenues after the central government evaluates and redistributes revenues considering budget demands from all provinces together. The percentage of revenue that provinces are allowed to keep is fixed for every 3-5 year period – known as the Stability period - to maintain some degree of budget predictability (World Bank, 2015). Our 2009-2012 sample spans two Stability periods 2007-2010 and 2011-2015, allowing us to exploit the changes in revenue allocation to instrument for the PCI indices. Changes in the sharing rates are reported in Table 9. In total, 13 out of 63 provinces, covering more than 65% of our firm-level observations, experienced a change in the tax sharing rate.

**Table 9: Tax sharing rates for provinces experienced a change between 2007-10 and 11-15**

|Province|Sharing rate<br>2004-06|Sharing rate<br>2007-10|Sharing rate<br>2011-15|
|---|---|---|---|
|Hanoi|32|45|42|
|Quang Ninh|98|76|70|
|Hai Phong|95|90|88|
|Vinh Phuc|86|67|60|
|Bac Ninh|100|100|93|
|Da Nang|95|90|85|
|Khanh Hao|52|53|77|
|Quang Ngai|100|100|61|

17