rafmacalaba/datause-encoder-data huggingface dataset:
# The Information Content of Capital Controls<sup>_∗_</sup>

Owen Nie<sup>_†_</sup> _Keywords_ : Capital controls, Macroprudential policy, Nowcasting, Financial crisis, Capital flows _JEL Classification Numbers_ : E61,E65,F41,F38

> _∗_ I am grateful to my doctoral advisors Kathryn Dominguez, Chris House, John Leahy, Linda Tesar and Pablo Ottonello from the University of Michigan for their guidance, and to Davide Mare(DECFP) and Tatsiana Kliatskova(EFNFS) at the World Bank for providing internal peer review. For helpful comments and suggestions I thank Olivier Jeanne, Enrique Mendoza, Stephanie Schmitt-Grohe and participants at the international economics seminar at the University of Michigan, the Rimini conference in economics and finance, the Annual Meetings of the European Economic Association and the Midwest Macroeconomics Meetings. I am also grateful to Javier Bianchi for sharing a set of codes to compute a small open economy model with a collateral constraint. All remaining errors are my own. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.

- Email correspondence: `onie@worldbank.org`
ting, Section 3 describes the data, and Section 4 explains the methodology used. Section 5 presents the findings, and Section 6 discusses the implications of the results. The Appendix provides more details about the study’s design and the analysis that was conducted.

# **2 Setting**

Domestic revenue mobilization efforts in Indonesia lag many comparable countries. Between 2009 and 2019, Indonesia’s revenue collection as a share of GDP was less than two-thirds of the average for countries in Emerging East Asia and less than half of the average for Emerging Market economies more generally. Even though reasonably similar tax policies are in place in some neighboring countries (e.g., the Philippines, Cambodia, and Malaysia), Indonesia collects several percentage points of GDP less in tax revenue. Moreover, Indonesia’s revenue as a share of GDP exhibited a downward trend from a peak of around 20 percent in 2009 and falling to 15 percent of GDP by 2019 (World Bank 2024a; IMF, 2024). Weak tax compliance is often blamed for the country’s relatively low and worsening revenue collection.

Taxes paid by firms constitute the primary source of government revenue and thus are essential for examining why relatively low tax revenue exists. Over the past three decades, taxes collected from firms (e.g., Corporate Income Tax (CIT) and Value Added Tax (VAT), among others) have gained increased significance in Indonesia’s tax revenue composition. These taxes increased from 60 percent of total tax revenue in the 1990s to around 67 percent in the 2010s.<sup>5</sup> This has occurred concomitantly with a decline (around six percentage points) in the share of tax revenue collected in personal income taxes. Indonesia has a statutory CIT rate of 22 percent on business profits. Firms with gross annual revenue less than around US 3.2 million receive a 50 percent tax rate discount on the profits attributable to their first US 310,000 in gross revenue. When a firm is newly registered, it may be temporarily eligible for a simplified alternative scheme with a tax rate of 0.5 percent on gross revenues (rather than the CIT on profits). Firms are eligible so long as gross annual revenue is less than around US

> 5IMF data and World Bank staff calculations.

5
other changes in the economic environment. Two approaches can be taken to this problem. One is to compare trends in the years before and after reform; to the extent that pre-reform data can be found. The other is to compare trends geographically between areas with and without reform. In fact, this latter approach is only possible for water and sewerage, since electricity and telecommunications sector reform were nationwide. Finally, by comparing outcomes across sectors, it is possible to see to what extent the different approaches taken fed through into different consequences for service expansion.

**Rate of new connections.** A first key question is whether the rate of new connections to basic services increased noticeably as a result of the reform? Across all sectors there is a noticeable spike in the rate of new connections in the immediate aftermath of the reform (Figure 3.3). It seems likely that this spike relates to a catch-up effect as service providers cleared the backlog of unsatisfied service requests. However, in the case of electricity and fixed line telephony, this spike is short-lived and within about a year the rate of new connections reverts to its pre-reform levels. While for water and sewerage, the increase is sustained through to the present.

**Figure 3.3: Rate of new connections**

<!-- Start of picture text --> 25000<br>20000<br>Electricity<br>15000<br>Water<br>Sewerage<br>10000<br>Fixed telephones<br>5000<br>0<br>19931994199519961997199819992000<br>New connections per year<br><!-- End of picture text --> _Sources:_ Electropaz, AISA and COTEL (2000).

Indeed, the annual rate at which new connections to both water and sewerage services increased by about 66% following the reform. Thus, the annual rate of new water connections rose from 10,469 during the period 1993-96 to 17,187 during the period 1997-99. While the annual rate of new sewerage connections rose from 5,906 during the period 1993-96 to 9,906 during the period 1997-99. Consequently, as of end 2000, AISA looks set to meet its connection targets for 2001. The growth in private household connections has been matched by a gradual closure of public standpipes, the number of which has declined by around a third in La Paz and over 80% in El Alto.

However, the rate of expansion of traditional household utility services of around 10,00020,000 connections per year, is dwarfed by comparison with what has been achieved in the mobile telephone market, which grew at an average annual rate of 66,583 new connections over the same period. Indeed, the total number of mobile telephone subscribers increased by a factor of 10 between 1996 and 1999, so that by the end of the 12
# Coping or Hoping? Livelihood Diversification and Food Insecurity in the COVID-19 Pandemic<sup>∗</sup>

Ann M. Furbush<sup>1</sup> , Anna Josephson<sup>2</sup> , Talip Kilic<sup>3</sup> , and Jeffrey D. Michler<sup>2</sup>

> 1 _Cambridge Econometrics_

> 2 _Department of Agricultural and Resource Economics, University of Arizona_

> 3 _Development Data Group (DECDG), World Bank_

## _JEL Classification_ : F6, I38, O2, Q18

_Keywords_ : COVID-19 pandemic, coping strategies, food security, pre-analysis plan, Sub-Saharan Africa ∗ Corresponding author email: jdmichler@arizona.edu. Authors are listed alphabetically. A pre-analysis plan for this study was filed prior to completion of post-outbreak data collection at https://osf.io/nu593. Funding for data collection and analysis comes from the World Bank Multi-Donor Trust Fund for Integrated Household and Agricultural Surveys in Low and Middle-Income Countries (TF072496). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We acknowledge the research assistance provided by Lorin Rudin-Rush and Joshua Brubaker. We appreciate comments and feedback from participants at the 2021 NIFA Agriculture Policy Conference, the 31<sup>_st_</sup> International Conference of Agricultural Economists, the 2021 World Bank Development Data Group Learning Series, the Learning from Longitudinal Studies in LMICS: Before, During, and After COVID-19 workshop in 2021, the IFAD Conference 2022, and the Agricultural and Applied Economics Association Annual Meeting in Anaheim. We thank the individuals involved in the design, implementation and dissemination of high-frequency phone surveys on COVID-19, specifically the World Bank LSMS team, and the phone survey managers and interviewers at the Malawi National Statistical Office, the Nigeria Bureau of Statistics, the Uganda Bureau of Statistics and Laterite Ethiopia.
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE and water and sanitation—and various policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Burkina Faso, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Two sets of African benchmarks will be used to evaluate Burkina Faso’s situation: nonfragile LICs and MICs. Detailed comparisons will also be made with immediate regional neighbors in the West African Community.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, to make comparisons across countries, we had to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

In the years 1997–2007, Burkina Faso’s economy grew at an average annual rate of 6 percent, which (taking population growth into account) translates to roughly 2.5 percent growth per capita during the period. About half of this improved performance can be traced to improvements in the country’s infrastructure platform (Calderón, 2009), much of it in the ICT sector, which contributed 1.14 percent points alone. Meanwhile, deficient roads held growth back by at least 0.1 percent points (figure 1a).

**Figure 1. Infrastructure has contributed much to economic growth—but could contribute much more**

a. Infrastructure’s contribution to annual per capita economic growth in West African countries, in percentage points, 2001-05 <!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>Roads Electricity Telecom<br>Percentage Points<br>n a go r e<br>o<br>Beni Cote d'Ivoire Mauritius Guinea Burkina Faso Senegal Ghan Mauritania T Nigeria South Africa Gambia Nig Sierra Leone Guinea-Bissau<br><!-- End of picture text --> <!-- Start of picture text --> b. Potential contributions of infrastructure to annual per capita<br>economic growth in West African countries, in percentage points<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>Roads Electricity Telecom<br>Percentage Points<br>i us<br>Niger Togo Nigeria Benin Gambia Senegal Ghana Guinea Maurit<br>Burkina Faso Guinea-Bissau Sierra Leone Mauritania Cote d'Ivoire South Africa<br><!-- End of picture text --> _Source_ : Calderón 2009.

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# **Table 10:  Determinants of Indoor Air Pollution Six Rural Areas of Bangladesh**

||Sam<br>|ple Incidenc<br>|e of Pollution F<br>|actors<br>|
|---|---|---|---|---|
|Rural Area<br>of:|Mud<br>Walls<br>%|Thatch<br>Roof<br>%|Detached<br>Kitchen<br>%|Open-Air<br>Kitchen<br>%|
|Cox's Bazar|71|51|14|0|
|Faridpur|0|8|60|36|
|Jessore|64|16|66|12|
|Rajshahi|89|0|70|7|
|Rangpur|0|20|72|20|
|Sylhet|20|18|12|35|

Our survey results indicate the potential importance of regional customs and differential availability of building materials and fuels. Mud walls, the most important ventilation characteristic, are extremely common in Cox’s Bazar, Jessore and Rajshahi, but extremely scarce in Faridpur and Rangpur. Significant variation is also apparent for thatch roofs. Detached or open-air kitchens are present in 14% of households in Cox’s Bazar, 47% in Syhlet, and 96% in Faridpur.

To capture the simultaneous effect of ventilation factors and fuel use in each surveyed household, we use the PM10 model in Table 7 to estimate PM10 concentrations in kitchens and living areas. We drop ventilation after midday meals because this was not recorded in the country-wide survey, and use of jute fuel because its incidence in our sample is negligible (3 users in 599 households). We tabulate the results by geographic area in Table 11, which highlights the effect of local variations. For kitchens in rural areas, mean estimated PM10 concentrations range from 410 in Cox’s Bazar to 202 in Faridpur. Similar variation characterizes living spaces, although the range is somewhat more limited. For Cox’s Bazar and Sylhet, there is a clear pattern of declining PM10 concentration in the transition from rural to urban areas. As Table 12 shows, a significant part of this pattern is attributable to increased use of clean fuels.

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**Table 5: Decomposing the Transition matrix into the propensity to move and the conditional probability**

||**% w**|**ho mov**|**e**|**% moving i**<br>**unemploym**<br>**total mover**|**nto**<br>**ent as s**<br>**s**|**hare of**|**% moving o**<br>**unemploym**<br>**total mover**|**ut of**<br>**ent as s**<br>**s**|**hare of**|
|---|---|---|---|---|---|---|---|---|---|
|Male 15-18|**Argentina**<br>25.4|**Brazil**<br>17.8|**Mexico**<br>29.8|**Argentina**<br>20.6|**Brazil**<br>7.5|**Mexico**<br>5.9|**Argentina**<br>15.9|**Brazil**<br>7.7|**Mexico**<br>5.9|
|Male 19-24|41.5|21.7|32.1|20.6|12.0|5.7|20.2|15.1|6.3|
|Male 25-44|28.3|14.9|24.0|19.7|11.3|4.2|19.2|12.4|4.3|
|Female 15-18|18.5|12.8|24.0|19.0|8.1|6.7|13.6|9.0|7.3|
|Female 19-24|34.0|18.1|26.8|19.1|12.7|6.4|19.1|14.8|7.7|
|Female 25-44|27.5|15.2|21.8|16.7|8.2|4.0|17.0|9.7|4.6|
|1<sup>st</sup>income quintile 15-18|27.0|17.6|28.2|22.1|8.8|6.7|20.6|10.7|6.9|
|1<sup>st</sup>income quintile 19-24|43.7|23.6|32.8|20.8|13.8|6.4|31.4|17.9|8.2|
|1<sup>st</sup>incomequintile 25-44|40.4|19.6|25.7|18.5|10.3|4.2|33.7|14.5|5.0|
|5<sup>th</sup>income quintile 15-18|9.2|12.3|20.9|10.0|6.6|5.2|5.8|6.6|4.7|
|5<sup>th</sup>income quintile 19-24|28.8|19.6|28.3|13.3|10.6|4.6|10.1|12.4|5.1|
|5<sup>th</sup>incomequintile 25-44|17.0|13.9|20.0|13.3|8.1|3.1|8.0|8.9|3.5|

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For each formality regression, we include the other formality variable as an explanatory variable in its regression specification. In each regression, we also include a proxy for the enforcement of regulations. In addition, we include covariates related to the firm’s capacity to comply with regulations, such as the product per worker and total employment, as wells as firm characteristics.

Ordinary least squares (OLS) estimation does not take into account the simultaneity in the decisions to comply with business and labor regulations; this makes each formality variable an endogenous explanatory variable in the regression of the other formality variable. To address this problem, we apply two-stage least squares (2SLS) using instrumental variables to eliminate the endogeneity. We use the enforcement proxy for each type of formality as its instrumental variable in the regression of the other formality variable.

Specifically, for business formality (�������), we use the proximity to SUNAT as our instrumental variable. Given that firms in Peru regard SUNAT as an institution with effective and high sanctioning capacity, the rationale behind the instrument is that the farther a firm is from a SUNAT office, the lower are the chances of detection. For labor formality (�������), we use the percentage of firms in the Electronic Payroll Registry of the total number firms in the Tax Registry. The rationale behind this instrumental variable is that the larger is the fraction of firms already in the Electronic Payroll Registry, the higher is the likelihood that the Ministry of Labor may target the firm for labor inspection in the region.

# **5. Pathways to Formality: Results**

Before delving into the regressions, it is worth exploring trends and raw transition probabilities, as these provide a rich picture of the complexity of the pathways to formality.

Compliance with business and labor formality regulations is high in our sample, and compliance with business regulations is higher than compliance with labor regulations. When we analyze both dimensions, however, we find that compliance _decreased_ between 2011 and 2012 along the business and labor dimensions: compliance with municipal regulations declined from 81 to 79 percent, and compliance with labor regulations declined from 68 to 63 percent in our data. Compliance with tax regulations remains unchanged for all the SMEs in our data between 2011 and 2012. However, using complementary data provided by INEI for 2016, we find that compliance with business formality from the standpoint of tax regulations declined from 100 percent in 2012 to 85 percent in 2016. The decrease in formality rates in this sample 14
Evidence for the relative costs of corruption impacts suggests that a sole focus on bribe payments as the indicator of the costs of corruption in infrastructure is misplaced. Bribes that are paid in order to win contracts for well-selected projects that are subsequently well-constructed are less damaging than corruption which skews spending priorities or lowers construction standards. In Indonesia, one estimate is that each dollar stolen from road construction reduces economic benefits from the road by $3.41. Ten percent additional project costs due to corrupt payments cannot account for the negligible macroeconomic returns to public investment in Africa, but expenditure diversion and poor construction related in part to corruption might.

Such findings suggest priorities for infrastructure anti-corruption research and activities in projects. Evidence on the extent of perceived bribery is only to some degree related to actual levels of bribe payments, and the level of bribe payments are a weak proxy for the development impact of corruption. A focus on inputs and outputs might provide better evidence of weak governance and high corruption in infrastructure, and in particular on the development impact of these problems. Such a focus would involve an evaluation of budgeting priorities and project selection procedures as well as the quality of construction of selected projects at the sectoral level. There remains an important place for focused, well-designed, large surveys to evaluate modes and levels of corruption in a particular environments, and potential policy responses. There also remains a place for improved procurement and detection procedures at the project level. But perhaps the most efficient tools for measuring and reducing the development impact of corruption at the sectoral level will have a broad governance focus.

3
in-difference estimation to observe changes in the average over time of people living in those regions affected by the disasters relative to those living in unaffected regions.

Data for India are taken from the 1998 (International Institute for Population Sciences (IIPS) and ORC Macro 2000) and 2005 (International Institute for Population Sciences (IIPS) and Macro International 2007) surveys, and the earthquake occurred in Gujarat 2001. Data for Pakistan are taken from the 1990 and 2006 surveys, and the earthquake occurred the North West Frontier in 2005. Data for Turkey are taken from the 1998 and 2003 (Hacettepe University Institute of Population Studies 2003) surveys, and the earthquake occurred in Izmit in 1999.

As the data for each of the countries are not a panel, we cannot observe changes in behavior at the household level and rely on changes in the regional average over time. There are a number of issues that come about because of this, each of which ameliorates the effect of the earthquake on the observed behavioral response. The response to the earthquake may not be limited to those who were directly affected by it, and thus there are spillovers into the control groups (regions other than those affected by the earthquake). If there is a response in the control group, then in the difference-in-difference estimation the change in fertility over time in the treatment group compared to the control group may appear to be smaller than what it actually is in response to the earthquake. The second issue that arises from not having a household panel survey is that outmigration following the earthquake may induce a selection bias: those household with preferences for large families before the earthquake remain in the treatment area and those with smaller families have the capability to move away from the affected region. This then means that when the post earthquake data are observed the regional average family size for the affected area is larger as the smaller families moved away from the treatment area. To control for this in a robustness check, the sample is limited to respondents who have never moved. The third issue to recognize is the dampening effects emergency foreign aid may have on a household’s behavioral response. While this is a welcome response for those people affected by the disaster, it distorts household decisions that may include a fertility response.

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temporarily in 2004; however, in 2006 it reached the 2003 level and continued to accelerate in 2007–2008 (Chart 9).

**Chart 9. Net change of loans outstanding to households by currency breakdown**

<!-- Start of picture text --> HUF Bn HUF Bn<br>500 500<br>450 450<br>400 400<br>350 350<br>300 300<br>250 250<br>200 200<br>150 150<br>100 100<br>50 50<br>0 0<br>-50 -50<br>-100 -100<br>Loans from banks - HUF Loans from banks - FX<br>Loans from financial enterprises - HUF Loans from financial enterprises - FX<br>Total lending<br>2003Q1 Q2 Q3 Q4 2004Q1 Q2 Q3 Q4 2005Q1 Q2 Q3 Q4 2006Q1 Q2 Q3 Q4 2007Q1 Q2 Q3 Q4 2008Q1 Q2 Q3 Q4<br><!-- End of picture text --> Source: MNB. Note: ―Banks‖ includes banks, branches, and cooperative credit institutions.

A specificity of Hungarian foreign currency lending was the fact that loans to households were almost exclusively denominated in CHF (Chart 10). In an attempt to secure the largest possible market share, foreign-owned banks initially entered into cost-based competition, which was subsequently replaced by risk-based competition. CHF-denominated loans gained ground as a result of risk-based competition (Banai et al., 2010a). Looking at the data recorded before October 2008 (the escalation of the crisis), the HUF/EUR exchange rate was only slightly less volatile than that of the Swiss franc. In contrast, Swiss interest rate levels were invariably lower than those prevailing in the euro area. Based on the former data, the lower initial installment amount associated with the lower interest rate appeared attractive even amid slightly higher exchange rate volatility. However, this ignores the fact that the Swiss franc had appreciated continuously since the 1970s, albeit at a different pace and with slight interruptions: initially against the Deutsche mark, and later also against the euro. Moreover, those having recourse to this product did not consider that, being perceived by investors as a safe haven, demand for Swiss-franc-denominated assets would surge during a potential crisis (MNB, 2010). Accordingly, in certain periods the volatility of the HUF/CHF exchange rate may significantly surpass that of the HUF/EUR exchange rate. Finally, debtors ignored the risk factor implied by the fact that the domestic monetary policy has little effect on the HUF/CHF exchange rate.

As a last chapter in foreign currency lending in Hungary, already overshadowed by the looming crisis, some banks began to issue Japanese yen (JPY)-based mortgage loans in late 2007. Even before the crisis, the Japanese interest rate level had been close to zero for a protracted period, resulting in even lower initial installment amounts than those of Swiss franc loans. However, the fundamentals of the European and the Japanese economy are not closely related. Consequently, the volatility of the JPY/HUF exchange rate was considerably higher than that of the EUR/HUF or the CHF/HUF exchange rates. Although the MNB and the Hungarian Financial Supervisory Authority (HFSA) warned the banks of the risks as early as the beginning of 2008 (MNB, 2008), 11