rafmacalaba/data-use-mentions-extended huggingface dataset:
**CONFOUNDINC** FINANCIAL **REPRESSION** WITH **RICID URBAN REGULATIONS:**

# **LESSONS OF THE** KORRV. HOUSING KARKET

# **I.** INTRODUCTION

1. The objective of this paper is to explore the interactions between financial and urban policies, and their joint impact on the performance of the housing sector during the course of economic development. The central hypothesis is that extended periods of financial repression and the scarcity of mortgage lending have generated significant distortions in the output of the Korean housing sector. In addition, combined with very restrictive urban planning and land use regulations, this financial situation may have led to under-investment in the urban sector of Korea during much of the past two decades. A broader question which is left unaddressed is whether such distortions and under-investment have been an integral component of the rapid growth policies ol other East Asian market economies such as Japan and Taiwan, or what differences there might be.

2. The original impetus for thinking that financial policies have as much to do with urban outcomes as physical conditions or even income levels came from comparing housing markets operating under different policies and institutions. For instance, why would France which enjoys the largest land area in Europe have higher housing costs and smaller units than Holland which reclaims lar,d from the sea? In Asia, what could be the role of financial policy regimes in two of the most primate cities of Asia: Seoul and Bangkok. During the 1970s and early 1980s both cities were growing rapidly. However, their housing markets were operating in contrasting ways. Seoul had repressive financial policies and skyrocketing real estate values while Bangkok grew rapidly with moderate rises in housing prices under much less interventionist financial policies.

3. Data collected during the preparation of housing projects in Thailand and Korea were showing thit Thai rates of real estate appreciation were quite moderate in spite the rapid demographic growth of Bangkok and significant per capita income gains. As recently reported for Bangkok: "'.asidential land prices as a whole
family and about 24 percent reporting obtaining credit from a financial institution or using a credit card. The share of formal borrowing increased slightly (by 2 percentage points between 2014 and 2017), while the share of informal borrowing declined by 3 percentage points, though these changes are not statistically significant. Informal borrowing is common in the developing economies of ECA, except in Armenia, Croatia, and Turkey, where most borrowing is from a financial institution or credit card (figure 12).

**Figure 12. Informal borrowing is prevalent in most developing economies in the region**

<!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Formal (bank or credit card) Semi‐formal Family/friends Other<br>Percent of adults who reported borrowing<br><!-- End of picture text -->

## _Source_ : Global Findex database.

_Note_ : The height of the bars represents the percentage of adults aged 15 and above who indicated borrowing in any way in the previous 12 months.

One common purpose of borrowing is to buy land or a home, the largest financial investment many people make. In 2017, 27 percent of adults in high‐income economies reported having an outstanding housing loan from a bank or other type of financial institution. The share was less than 10 percent in developing economies. In the developing economies of ECA, the proportions were 13 percent in 2014 and 12 percent in 2017. In the euro area, the figures were 23 and 25 percent, respectively.

# _Financial Resilience_

The 2017 Global Findex survey provides information on financial resilience by asking respondents whether they would be able to come up with an amount equal to 1/20th of gross national income per capita in local currency within the next month. It also asks what their main source of funding
Table 2.1: Major government spending items, 1991-2009 (Percent of spending)

||1991|1994|1997|2000|2003|2006|2009|
|---|---|---|---|---|---|---|---|
|Defence|23.3|39.0|18.2|16.8|18.2|15.0|16.4|
|Health|3.7|4.1|3.3|4.1|4.0|3.9|3.5|
|Education|15.0|21.6|15.2|18.0|17.0|13.8|16.3|

Source: Authors' calculations based on Bulletin of Government Finance Statistics.

# _Evolution of MDG indicators 1990-2009_

Yemen is off track with respect to meeting the MDGs (MOPIC, 2003; MOPIC, 2010). As shown in Table 2.2, progress has been made for primary net enrolment (MDG 2), underfive mortality (MDG 4), and water and sanitation access (MDGs 7w and 7s, respectively). However, no target has been reached and Yemen is off track for all MDGs. On the malnutrition front, due to a combination of stagnating per capita income levels and worsening nutritional patterns (including rising consumption of junk food and qat), the situation has deteriorated over time, with the proportion of children under five who are underweight rising from 30 percent in 1992 to 46 percent by 1997/98 and remaining at that level through 2003.

Table 2.2: Key MDG indicators and target by 2015 for Yemen

|MDG and associated indicator|1990(1)|2004|Most recent(2)|2015 target|
|---|---|---|---|---|
|MDG 1:
riskier firms with access to the program.<sup>1</sup> At the macro level, risks appear to be small, in part because risky firms weigh little in the aggregate credit allocation. Although risky firms increase their indebtedness in percentage points the most, the overall amount of credit going to less risky firms is much larger (even when it represents a smaller increase in relative terms for them). Some of the features driving the micro and macro results are due to the program design, while others are due to how the demand and supply sides respond to incentives and to general market conditions.

The transaction-level data we gather are unique in their comprehensive nature and level of detail. We employ the universe of formal bank credit to all firms collected by Chile’s financial supervisory agency and high-frequency-matched administrative data for the universe of formal firms compiled by the tax authority and the central bank. The former include credit flows and balances, default history, firm loan applications, bank approvals and rejections, and terms of the individual transactions. The latter contain time-varying financial statements, input use, and sales collected monthly, plus industry and municipality of where firms are located. These micro data, especially loan applications and approvals, allow us to measure selection and to disentangle supply and demand factors determining the countrywide equilibrium allocation of credit. The micro data also enable us to estimate the macro risk by calculating risk at the firm level and then aggregating across types of firms.

In addition to offering novel data, the large and sudden quasi-natural experiment in Chile contains attractive features to study the drivers and effects of crisis credit. The size requirements of the credit program, dynamic lockdowns, and an alternative employment program, among other things, provide sources of exogenous variation and useful information to analyze the effects of the credit program implemented when the pandemic erupts. In exchange for banks granting loans at a predetermined low interest rate to most firms, the government offers to take most of the tail credit risk. The flat low interest rates
increase of _recent foreign migrants_ when compared to the level of _recent foreign migrants_ present in Turkey in 2009 (baseline year omitted). The dashed line shows the trends in the border regions and the solid line shows trends in the rest of the country.

While there is an uptick in recent migrants near the Syrian border regions, it is important to remember that the sample size in the LFS is small, especially among recent migrants (Table 3). Notice, that the sample of recent migrants is relatively constant from 2009 to 2012, but in 2013 and 2014 there is a clear acceleration in the growth of the number of recent migrants being included in the survey. Since the number of recent migrant households begins to increase substantially in the 2013 LFS, there is evidence pointing to the representation of SUTPs in the survey. The analysis in this paper will be as transparent and rigorous as possible under the data conditions and limitations. This will translate into high standard errors in our statistical estimates, but estimates and especially trends of poverty rates can still be informative.

8
# **6. Student Retention**

As mentioned above, about 2.5 years following the intervention a student tracking survey was undertaken to determine which students were still in school. Assuming no repetition, these students would have been in Senior Secondary 1 grade (Grade 10). The methodology for tracking is laid out in Section 2.2. Note that this does not rely on speaking to students directly. Instead we rely on teachers and head teachers at the students’ Junior Secondary Schools.

We were unable to trace 11 percent of the original sample (55 of 497 students). Of the students we were able to trace, 365 were still engaged in some kind of educational activity (school, vocational training) while 78 were not. Impacts of eReader provision and informational provision on student retention are presented in **Tables 10** and **11** , respectively. To explore the potential influence of attrition (missing students) on our findings, we calculate Lee (2009) bounds on the potential treatment effect given extreme assumptions on attrition bias and report the results in **Table 12** .

Consistent with some of the predictions of the reciprocity literature, we find that, compared to the control group, assignment to receive an eReader with non-curriculum content (T1) has a positive impact on student retention of 13 percentage points (significant at the 5 percent level). This positive impact is mostly concentrated among the more disadvantaged students - those with lower than median baseline reading ability, low maternal education, and low wealth. On the other hand, compared to the eReader with just the digital library, receiving additional curriculum content _reduces_ retention in school ( **Table 10** ).

Finally, we document positive impacts of the information treatment on student retention ( **Table 11** ). Those who received the scripted message on returns to secondary education in Lagos were 11 percentage points more likely to continue their education (significant at the 5 percent level).
where _i_ denotes banks and _t_ denotes years. _C_ is total operating plus financial costs, _Q_ is total assets, _W1_ , _W2_ , and _W3_ are the same input prices used in equations (1) and (2) and defined above. _D_ and _α0i_ denote time effects and bank-level fixed effects, respectively. As in most papers, our estimations impose the restrictions of symmetry and degree one homogeneity in the price of inputs.<sup>**13**</sup> We compute the H-statistic and the Lerner index using bank-level balance sheet and income statement data from Bankscope for the period 2002-2008. We compare competition in Russia to that in Brazil, China, and India using similar Bankscope data for these countries. Appendix Table A1 reports the number of banks and observations that go into the calculations of the H-statistic and the Lerner index for each country in each year. Also, because Russia has a fragmented banking sector, we compute and compare the H-statistic and Lerner index across different groups of banks, namely: top 20 banks (in terms of assets), non-top 20, governmentowned, privately-owned, foreign-owned, domestic-owned, business-oriented, and individualoriented.

# **6. Results for the H-statistic and the Lerner index**

Panel A of Table 6 shows that the Russian banking sector can be best characterized as operating under monopolistic competition, given an H-statistic equal to 0.74 and considering that we can reject the nulls that the H-statistic equals zero and, also, 1. The same can be said about the banking sectors in Brazil, China, and India. Brazil appears to be more competitive than Russia, since the value of the H-statistic for the former is significantly higher than that for the latter.

> **13** However, the results do not change if we drop these constraints.

16
|46.6|31.5|103.8|117|-<br>11|
|Hudid Cantral|0|0|0|3.0|80.2|292.7|375.9|42|+ 334|
|Sub-Huid<br>and<br>bfantalm East|0.1|3.1-|3.2|4.4|21.2|15.5|47.5|81|-<br>33|
|Sub-Hudd<br>and<br>Smd Arid South|0.2|4.2|8.9|21.8|91.9|6.9|133.9|62|*72|
|Totals|2.6|24.0|22.5|51.5|263.4|370.2|734.2|U?|+ 328|

Sourcet Caleated from tAO data. Quoted ia -hw Capacity of the African tUnvroment to fuatain ropulatio Grwtbh by H. Charles etreira, p. 37 (smmo).
consumption, investment and net exports: _GDP_ = _C_ + _I_ + ( _E − M_ ). The figures for _C_ and _I_ incorporate government consumption and investment. This definition of GDP corresponds to the format usually given in IMF country reports.

In our model the goods that are consumed (ignoring leisure), can be divided between _imports_ and non-imports ( _N_ ), where non-imports comprise _domestic_ and _untaxed_ . Total consumption in the model is thus given by _C_ = _N_ + _M_ . Using the definition of GDP we calculate the amount of non-imports consumed as _N_ = _GDP − I − E_ . Consumption of non-imports is equal to production of non-imports. Production of _domestic_ , the taxed non-imported good in the model, is calculated by dividing VAT revenue by the tax rate, giving the tax base: _D_ =<sup>_<u>R</u>_</sup> _tD_<sup>_<u>D</u>_.</sup> Consumer expenditure on the _untaxed_ good (equal to the value of production) is thus the residual of non-imports: _U_ = _N −_ (1 + _tD_ ) _× D_ .

Funds received by firms are spent on factors. Allocation of firm revenue between capital and labor is achieved using assumed labor-output ratios. The amount of formal factor usage is determined by dividing factor tax revenues by their tax rates. Informal factor usage is then the residual of total factor expenditure less formal factor expenditure.

The calibration process can be illustrated with data from Guinea-Bissau and the SAM of Table 2. The value of _investment_ , as a percentage of GDP at market prices, is available directly from the national accounts: _I_ = 18 _._ 9. The producer value of exports is derived by subtracting export tax revenue from the market value of exports: _E_ = 27 _._ 21 _−_ 2 _._ 61 = 24 _._ 6. The producer value of _domestic_ is determined by dividing tax revenue for domestic goods
Policy Research Working Paper 9534

# **Abstract**

A forward-looking measure of “vulnerability to poverty” is estimated and a concerted effort is made to understand the sources of vulnerability in the drought-prone lowlands of Ethiopia. Using the Household Consumption Expenditure Survey and the Welfare Monitoring Survey of 2016, the analysis reveals that vulnerability is remarkably higher in the drought-prone lowlands than in the other ecological zones, although differences in poverty rates are modest; the vulnerability rate is more than two times larger than the poverty rate in the lowlands, while the ratio is only 1.6 for the whole country. The analysis also reveals important distinctions in the sources of vulnerability. In the drought-prone lowlands: (i) vulnerability due to aggregate shocks such as droughts is lower than vulnerability due to idiosyncratic shocks in absolute terms, but its relative importance is higher compared with other ecological zones; and (ii) poverty-induced vulnerability is relatively more important than risk-induced vulnerability in contrast to other regions where risk-induced vulnerability is higher than poverty-induced vulnerability. These findings attest to the unique nature of the droughtprone lowlands compared with the other agroecological zones and points in favor of policies and programs tailored specifically to the areas.

This paper is a product of the Poverty and Equity 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 eskoufias@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
2001 Multiple Indicator Survey and 2007 Malaria Indicator Survey.

# **Challenges**

Angola still has the highest rate of diarrheal disease in the world, with 114 years of life lost to diarrheal diseases for every 1,000 Angolans. In 2006 a cholera epidemic hit Luanda, affecting 23,000 people and causing almost 300 deaths (LUPP 2007). Contaminated water, inadequate storm-water drainage, deficient operation of the limited sewer system, and high reliance on open defecation have resulted in high—and steadily increasing—rates of water- and excreta-related diseases (USAID 2009). The situation is particularly bad in periurban informal settings and in refugee camps, where more than a million internally displaced persons still reside.

Angola’s urban population is growing at an increasingly fast rate (4.7 percent per year), and the current piped and stand-pipe water supply and management system cannot keep up with the demand for domestic water consumption. This gap is increasingly being filled by private water vendors selling largely untreated water. The supply of water from tankers rose from 10 percent of the population in 2001 to 37 percent in 2007 (figure 17). Indeed, in periurban areas of Luanda, 70 percent of residents purchased their water from water vendors. This water is extremely expensive, with prices varying from $4/m<sup>3</sup> close to the distributional area of the water tank to $20 in more distant areas (Cain, Daly, and Robson 2002), leading to a significant increase in the number of small-scale operators (figure 17). Most of the water comes from the ANGOMENHA filling station and is untreated, posing serious health risks. Tanker drivers who buy 29
than 200 employees). All BE variables and indicators of competition are interacted with these four size indicators. The results are shown in table 9.

_Government Protection_ . Containing corruption is more positively associated with firm employment growth for smaller firms. Using both the Enterprise Survey Corruption Obstacle and Global Competitiveness Corporate Ethics measures, small firms, but especially micro firms, benefit the most from a relatively noncorrupt business environment. Reducing the Enterprise Survey Corruption Obstacle by one standard deviation, for instance, is associated with an increase in the firm employment growth rate for micro firms of 0.9 percentage point and for small firms of 0.4 percentage point, but a decrease for large firms of 0.4 percentage point. Reducing corruption thus enables the performance of both micro and small firms.

The positive association between indicators of physical safety and firm productivity tends to be stronger for large firms. Physical safety is associated with higher productivity for medium and large firms, using both the Enterprise Survey’s Security Costs and the Global Competitiveness Police System measures. On the other hand, the relationship between physical safety and firm growth is not robust depending on the source of data: a higher score on the Global Competitiveness Police System mesure is positive (and marginally significant) for firm growth only for large firms, but a lower score on the Enterprise Survey’s Security Costs measure (and therefore better physical safety) is associated with higher employment growth only for small firms. Given that the developing countries are dominated by micro firms (Hsieh and Olken 2014), we conclude that physical safety is relatively important for firm performance for non-micro firms.

_Infrastructure_ . Availability of modern infrastructure is clearly enabling to improved business performance of smaller firms. The Enterprise Survey Web measure is positively and significantly associated with firm growth only for micro firms. Moreover, both the Enterprise Survey Web and Global Competitiveness Broadband mesures are associated with higher productivity, with the association being stronger and increasing from medium to small and then to micro firms. Increasing the Enterprise Survey Web measure
with probability one third and J$10,000 or J$15,000 with probability two thirds. In this case, JBDC was better prepared to pre-announce the dates of the training and to commit to those dates. This was reflected in overall higher attendance levels as we show below.

In order to implement the randomization, we asked each participant to randomly pick a card where a code was written. As they picked the card, they exited the room and went to another room where they were told the treatment condition that corresponded to that card. In this case, all treatments were framed as an scholarship for the course.<sup>13</sup> Half of the participants assigned to a positive price were also randomly allocated to a credit option, as in the BDM design, where they were allowed to pay in 3 installments (before the first, sixth and eight class, respectively).

Column 4 of Table 1 shows that random allocation to offered prices worked well. Table A3 shows that balance was also achieved across treatments (credit vs no credit).

# **3.5 Training Implementation**

Entrepreneurs who purchased the course were invited to attend training. For the BDM sample, the training started in December 2017 and lasted for 10 weeks. Seven groups were formed in 6 different locations. For the TIOLI sample, there were two training periods, one from March to May 2018 (8 groups in 4 locations), and a second one from May to July 2018 (3 groups in Kingston).

# **3.6 Data**

We rely on three sources of data: baseline data, administrative data on payment and attendance, and follow-up data. The baseline survey was conducted either on the phone or online on a rolling basis between June 2017 and May 2018. Attendance and payment data were recorded by JBDC.

We conducted a phone follow-up survey with all entrepreneurs who attended demonstration sessions from late September 2018 to February 2019. We hired a private local survey
|Change in local average Tier 1 ratio (% points)|0.002<br>(0.001)|-0.001<br>(0.003)|
|---|---|---|
|Change in local average Tier 1 ratio (% points) x MENA|0.031*|0.090|
||(0.014)|(0.055)|
|Local green management|-0.009|0.233***|
||(0.006)|(0.041)|
|Local green management x MENA|-0.057**|0.304***|
||(0.020)|(0.075)|
|MENA|0.951|0.153|
||(1.053)|(2.036)|
|Observations|17,902|17,902|
|Clusters|3,022|3,022|

Note: This table shows the first stage regressions corresponding to Tables 5 and 6. Regression controls for localitylevel credit market controls (log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius); an indicator for no firms in other sectors in a 15km radius with data on credit constraints; an indicator for no firms in a 15km radius in higher deciles with data on green management; population size class; and region and sector fixed effects. Locality-clustered bootstrapped standard errors in parentheses. ***, **, * - statistically significant at 1%, 5%, and 10%, respectively.

Source: Enterprise Surveys and authors’ calculations.

# _Type of green investment_

The second stage average marginal effect estimates in Table 5, Panel A show that once we account for potential endogeneity of credit constraints and green management, the only significant estimates are found for both capital- and non-capital intensive green investment