rafmacalaba/datause-encoder-data huggingface dataset:
exceed 5 percent of the amount of the credit (iff/ZEW 2010). In 2008, the _Slovak Republic_ set its interest rate cap at twice the average APR for the type of consumer credit extended. It cannot be four times higher than the value of the weighted average APR cost and the average interest rates for all types of consumer credits (iff/ZEW 2010). In 2013, the _Kyrgyz Republic’s_ parliament passed a usury bill imposing an interest rate cap on microloans to be set by its central bank and calculated as the weighted interest rate for bank loans plus 15 percent.<sup>14</sup> In 2006, _Turkey’s_ central bank established a monthly maximum conventional interest for credit cards announced every three months. Then, in 2012, a new debt law imposed a ceiling on interest rates for consumer and credit card loans. Annual interest on consumer and credit card loans may be as much as 13.5 percent, or 50 percent more than the official interest rate set at 9 percent by the cabinet in 2006.

# **_3.4. Latin America and the Caribbean_**

Fourteen countries in Latin America and the Caribbean cap interest rates on loans. Like the ECA region, in most of LAC countries the maximum interest rates that financial institutions can charge are encoded in usury laws (Porteous, Collins, and Abrams 2010). In _Chile_ , the law that set the maximum interest rates on loans dates from 1981. That rate cannot exceed by more than 50 percent the current interest rate set at the time of the convention. The current interest rate and the maximum interest rate are published by the Superintendence of Banks and Financial Institutions, which determines nine categories of credit operations based on currency, indexation, size, and term (Flores, Morales, and Yáñez 2005). Since 1995, _Paraguay_ has also imposed interest rate controls on several segments. That country defines _usury_ as an effective interest rate that exceeds by 30 percent the average of the maximum annual interest rates that banks and financial institutions charge on consumer credits, distinguishing by amount and term of the obligation. In _Argentina_ , the usury law implemented in 1998 establishes that the maximum interest rate that banks can charge over credit cards may not exceed by more than 25 percent the rate that the lender charges for personal lending operations. In the case of NBFIs, the interest rate cannot exceed by more than 25 percent the average interest rate for personal lending operations published by Argentina’s central bank (Capera, Murcia, and Estrada 2011).

From 2000 onward, more countries in LAC have imposed interest rate caps. In 2000, for example, _Colombia_ defined a usury interest rate for credit operations at 1.5 times the weighted average of interest rates for the specific segments of commercial credit, consumption credit, and microcredit. By law, financial institutions that offer microcredit are able to charge fees and commissions in addition to the

> 14 http://www.globalenvision.org/2013/10/15/microfinance-crosshairs-will-kyrgyzstans-usury-bill-backfire.

16
failed biddings, a negotiated deal may be entered with the sole bidder. However, the decision was to re-bid.

Finally, the fourth attempt was successful when more than one qualified bidder emerged. The National Grid Corporation of the Philippines (NGCP) formally took over and has been operating the facilities and assets of the TRANSCO since January 15, 2009. The NGCP is the corporate vehicle of the consortium of Monte Oro Grid Resources Corporation, Calaca High Power Corporation, and the State Grid Corporation of China which offered the highest financial bid of US$ 3.950 billion that exceeded the Reserve Price and won the TRANSCO concession through competitive bidding on December 12, 2007. Subsequently, NGCP obtained a congressional franchise in late 2008, remitted US$987.5 million to PSALM, equivalent to 25 percent of concession price which was the required commencement fee, fulfilled all pre-conditions and signed the Deed of Transfer.

The Concession Agreement provided that the balance of seventy five percent of the Concession Fee amounting to US$2,962 billion would be converted to Philippine Pesos at a fixed exchange rate equal to 42 pesos and 75 centavos (PnP42.75) for every U.S. dollar (US$1.00) with interest in 40 semi-annual installments. The interest rate was set at the Philippine Dealing System (PDS) Treasury Fixing for 10-year tenors, or PDST-F 10-year benchmark published by the Philippine Dealing and Exchange Corporation as at November 12, 2007 for the period until December 2010 and reset every five years (consistent with the Regulatory Period of the Transmission Business with ERC).

Using the dollar to Philippine peso exchange rate in the Concession Agreement for the 75 percent of the Concession amounted to PhP169 billion, which is slightly greater than the ERC determined Regulatory Asset Base for the regulatory year ending 2007 which was at PhP163 billion in the published ERC Final Determination for the Regulatory Reset for the National Transmission Corporation for 2006 to 2010.

By allowing the deferred payment of the Concession Fee, PSALM also awarded to NGCP an additional upside equivalent to the difference between the Vanilla Weighted Average Cost of Capital of 15.87 percent granted by ERC for Return on Capital in the said Final Determination and the interest rate on the deferred payment which is estimated at 9.4 percent for 75 percent of the value of the transmission assets. This would mean that NGCP would recover its investment costs in less than 6 years.

But the true benefit of the TRANSCO privatization was the transfer of the responsibility for capital investments from government to the private sector. NGCP had to complete whatever projects were on-going beginning 2009. For the years 2011 to 2016, the ERC approved NGCP capital expenditures amounted to PhP49 billion (approximately US$1 billion).

# _2.2.3.3 Bidding and the privatization process_

Having even just one outcome of the privatization effort challenged would have derailed and endangered the whole privatization process, and it is notable that not a single privatization 20
value. The elasticities are consistently negative and increasing in absolute size when evaluated at a good level of govemance. There is a clear pattem that in countries with low levels of governance (i.e., countries which are rated as very corrupt or are rated to have very ineffective bureaucracy), public health spending at the margin will be inefficacious. Moreover, these findings are consistent along all dimensions, i.e., method of estimation (OLS or 2SLS), govemance indicator (corruption index or quality of bureaucracy), and health status (under-5 mortality or infant mortality).

# **_5._ Measuring the Efficacy of Public Education Spending**

As discussed in Section 2, past research findings on the link between public education spending and measurable outcomes provide mixed evidence. In studying this relationship, our aim is to examine how is it affected when we move from a state of low quality to high quality govemance.

## **_5.1 Choosing a measure of education outcome_**

One important difference between the health and education sectors is that it is difficult to find educational outcome measures that are consistent across countries. In education there is no equivalent to the child and infant mortality indicators used in the health sector. Data on enrolment rates are widely available, but they do not reflect quality differences across countries. Moreover, enrolment numbers, especially at the primary level, include repeaters as well as students that subsequently drop out of school. For example, Filmer and Pritchett (1999) report that in almost all countries in South America, enrolment in the first grade is nearly 100 percent, but due to high dropouts rates, a large proportion of those enrolled do not complete primary school education.

Educational _attainment_ has been used by several researchers to measure outcomes (Barro and Lee 1996, 2000).8 Attainment can be defined as the number or proportion of school-age children that enter _and complete_ primary or secondary school, or a particular grade. This is a

> 8 Literacy rates are also measures that can be used for educational attainment. However, data on these are generally available only as stock rather than flow measures; for example, figures on the proportion of _all_ adults that are literate are widely available (e.g., World Development Indicators 2000). But it is difficult to link such a measure with the available data on annual education spending, which is a flow measure. Standardized test scores are available for very few countries, mostly developed.

_16_
generating a simple and transparent index that focuses on the variation in the quantity and quality of education across countries, the HCI uses a single benchmark value of φ�0.08 , or 8 percent per year of school, for all countries and all levels of school. To put this value in perspective, consider the following sets of estimates from the existing literature:

- Montenegro and Patrinos (2014) estimate returns to education using household survey data from 139 countries.  They find an overall average return to an additional year of school of 10.1 percent, and disaggregated returns of 10.6 (7.2) (15.2) for primary (secondary) (tertiary). Averaging the primary and secondary returns which are most relevant for the HCI gives a value of 8.9 percent.  Jedwab and Islam (2018) update these estimates in background analysis for the 2019 World Development Report, and find an average return to school of around 8 percent as well.

- In a highly‐influential review of the development accounting literature, Caselli (2005) summarizes the empirical consensus on returns to school as 13 percent (for less than four years), 10 percent (for four to eight years) and 7 percent (for more than eight years).  This parameterization was also adopted in the Penn World Tables 9.0 estimates of human capital (Feenstra, Inklaar and Timmer (2015)).  Assuming that primary and secondary school each last �

- six years, the baseline parameterization implies a return to primary school of �0.13 � �

- � � � �0.10 �0.12 and a return to secondary school of �0.10 � �0.07 �0.08 .

- � � �

The value of φ�0.08 is deliberately chosen to be at the low end of this range. This is because the vast majority of estimates of the return to school do not also control for health, while the human capital measure in Equation (1) is intended to reflect the partial effects of education and health on worker productivity. To the extent that empirical studies of the return to education are unable to control of health (or the factors determining health), the resulting estimates may overstate the partial effect of education on productivity. For more discussion of this point see Caselli (2014), who advocates using a conservative estimate of the return to school in a similar setting.

A final issue concerns the fact that the vast majority of empirical estimates of the returns to school use simply the number of years of education, and do not adjust for the quality of education. This may be a concern if the returns to learning‐adjusted years of school differ from returns to years of school. One study that addresses this point is Hanushek and Zhang (2009), who generate quality‐ 35
# **2.2 Sample and data**

## **2.2.1 Sample**

The intervention was delivered in ELA clubs managed and operated by BRAC Uganda. BRAC has established ELA club branch offices throughout the country, each of which oversees several clubs. Eight ELA branch offices in and around the Kampala area were selected for this study, of which six were located in urban areas and two in peri-urban areas. The eight branches can be considered as corresponding to eight distinct geographical areas in and around Kampala. A total of 115 ELA clubs fell under their purview, with each branch managing 8 to 15 clubs. Out of the 115 clubs, 9 were excluded as they were found to no longer exist or function regularly, and the remaining 106 were randomized into one of the three treatment arms.

The sample of adolescents enrolled into the study were selected from the villages and residential areas surrounding the ELA clubs. A radius of 500-meters around each club was drawn and enumeration teams listed all adolescent females aged 13-19 who resided within these catchment areas.<sup>12</sup> During the listing, consenting (and/or assenting, as appropriate) individuals were screened using the eight-item Patient Health Questionnaire depression scale (PHQ-8). Individuals who scored 10 or above, which indicates a risk of moderate or severe depression (Kroenke et al., 2009), were recruited into the study. Overall, 1,919 adolescent females across 106 clusters were successfully recruited into the study.<sup>13</sup>

## **2.2.2 Data**

There were four rounds of data collection during the study. A baseline survey was conducted – between June-August 2019, prior to the IPT-G intervention. The IPT-G intervention i.e., – the 14 weekly group therapy sessions then ran between September and December 2019 at all the clubs assigned to the two treatment arms. A second round of data collection, the rapid resurvey, took place immediately following completion of the intervention, between November 2019 and February 2020. The cash transfers were announced and distributed to the individuals in the combined (IPT-G+) treatment arm after the rapid resurvey was completed, in March 2020.

– There were two additional rounds of data collection: a midline survey approximately

> 12In a small number of clusters, due to high population density, the teams were not able to complete the screening of all 13-19-year-old females before having to move on to the next community. In those clusters, it is possible that the population that was screened was not a random subsample of all households with at least one 13-19-year-old female member.

> 13The PHQ-8 threshold of 10 threshold was met by 45% of screened individuals. Within the screened-in individuals, the consent rate was high, with only 3% refusing to participate.

9
# Figure 15: SAR: Investment growth and correlates

# A. Investment growth

<!-- Start of picture text --> Percent 2000-21 average<br>15<br>10<br>5<br>0<br>-5<br>-10<br>South Asia EMDEs<br>C. Investment<br>Index, 2019 = 100<br>130<br>Current<br>125<br>120 Pre-pandemic<br>115<br>110<br>105<br>100<br>95<br>90<br>85<br>2019 2020 2021 2022 2023<br>E. Terms of trade<br>Index, 2010 = 100<br>115<br>110<br>105<br>100<br>95<br>90<br>2010 2012 2014 2016 2018 2020 2022 2014 2016 2018 2020 2022.<br>2010 2011 2013 2014 2015 2016 2017 2018 2019 2020<br>2010 2012<br>2012<br><!-- End of picture text --> <!-- Start of picture text --> B. Share of SAR countries with weak<br>investment<br><!-- End of picture text --> <!-- Start of picture text --> growth<br>Percent of countries<br>100 Below long-term average<br>Contracting<br>75<br>50<br>25<br>0<br>2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> <!-- Start of picture text --> D. Contribution to investment growth<br>Percentage point<br><!-- End of picture text --> <!-- Start of picture text --> 14<br>10<br>6<br>2<br>-2<br>-6<br>-10<br>Private Public<br>-14<br>-18<br>SAR ex India India<br>F. Political stability<br>Index, 0-100, 100 = best<br>66<br>64<br>62<br>60<br>58<br>56<br>54<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>2012 2014 2016 2018 2020 2012 2014 2016 2018 2020<br><!-- End of picture text --> Sources: Haver Analytics; PRS Group; Ministry of Finance of Sri Lanka; Reserve Bank of India; World Bank. Note: EMDEs = emerging market and developing economies; SAR = South Asia. A. Weighted averages. Sample includes 98 EMDEs and 5 from SAR.

B. Share of SAR economies with investment growth below its long-term average or negative. Long-term averages are country specific and refer to available data over 2000-21.

C. Based on projections in the January 2020 and January 2023 Global Economic Prospects reports. 2023 indicates forecast.

D. “SAR ex India” is weighted average for Bangladesh, Nepal, and Pakistan.

E. Investment-weighted averages.

F. Investment-weighted average of ICRG index of Political Risk. An increase denotes greater political stability.
widely across different parts of the country - notably, the remote and fertile Southern zone is the major food surplus area.<sup>3</sup> Third, trade routes (potentially) involve coastal and inland water transport as well as road transport, both within the country and the region. Fourth, the eight countries that border Tanzania vary in terms of their net food import needs. Fifth, while the government influences prices through export bans, prices within the country are determined by market forces. Consequently, the 18 Tanzanian markets we study cover several different types of food staple markets that exist in Sub-Saharan Africa, rendering our analysis relevant to many types of local food markets across the continent.

The paper proceeds as follows. Section 2 presents an overview of the Tanzanian maize market, including its physical and policy aspects. Section 3 describes the framework relevant to the estimation of long-term trends and short-term dynamics. Sections 4 and 5 discuss results on the price and non-price factors affecting domestic maize prices. Section 6 examines the mechanisms through which the export ban influences domestic prices. Section 7 focuses on the 2011 export ban by estimating the path that domestic prices would have followed had Tanzania not imposed the ban. Section 8 concludes.

# **2 The Tanzanian Maize Market**

Tanzania, Sub-Saharan Africa’s sixth most populous nation, borders eight countries, three lakes, and the Indian Ocean. It has two major ports, Dar es Salaam and Tanga, and two smaller ones, Lindi and Mtwara (Figure 1). Nairobi, East Africa’s largest city and major commercial center, is a key destination of Tanzania’s maize exports. Other major destinations include Malawi and Mozambique, and occasionally other countries in the region.

## **Physical Characteristics of the Tanzanian Maize Market**

Maize has been cultivated in East Africa since the 17th century as a garden crop. The early 20th century saw a major shift in the types of maize cultivated, reflecting the introduction of better-performing varieties from the United States, initially in South Africa and later in Southern and Eastern Africa including Tanzania (McCann (2005)). Two policy changes in the first half of the 20th century incentivized the expansion and commercialization of maize: The 1911 introduction by the London Corn Exchange of grading standards for African imports, and the 1925 Rhodesian Maize Act, which codified the primary cultivation of white dent corn into law.

Today, maize is Tanzania’s most important food staple, contributing almost half of the country’s total calorie intake. It accounts for 40 percent of the country’s cropped area, with

> 3USAID’s Famine Early Warning System Network (FEWS NET) provides a clear quantitative description of Tanzanian food markets : http://www.fews.net/east-africa/tanzania.

2
Our findings also point to the mechanisms through which this effect operates. We find that contractual institutions have effects through both extensive and intensive margins. In particular, the effects of institutional quality on the probability of exporting (extensive) and the volume of export conditional on exporting (intensive) matter most for products for which contracting is relatively difficult. These results are robust to using alternative definitions of the extensive and intensive margins. We corroborate this finding using more granular export data based on firm-level aggregates. We also find that, regardless of the definitions of margins that are used, the effect of institutional quality on the intensive margin is greater (in magnitude) than that on the extensive margin. We also find that institutional quality enhances the chance of exporting countries to survive in the market, and particularly for more contract-intensive products.

Moreover, we have examined if the influence of institutional quality on trade differs between country groups. The return to better institutions is higher in developing countries than in their developed (OECD) counterparts. This holds true for the intensive margin (value exported conditional on exporting or value of export per variety). In terms of the probability of exporting in more contract-intensive products or the number of product varieties exported, OECD countries benefit more than non-OECD countries. This is in contrast to our expectation that the extensive margin is likely to be more important in developing than in developed countries. However, we have analyzed the effect of institutions on trade over and above the effect on domestic production, which is an important channel behind the hypothesis.

The effects of institutions on the trade pattern established for the full set of countries are also affirmed for within-region and across-region trade patterns. The quality of contracting institutions has a statistically significant differential (positive) effect on the export of contract intensive products. This conclusion generally holds for both extensive and intensive margins and for within OECD, within non-OECD, within region as well as across region trade.

In a nutshell, institutional quality is a robust source of comparative advantage. Its effect on the pattern of trade materializes through both the intensive and extensive margins – the intensive margin being quantitatively more important than the extensive.

25
WPS3758

# **Japan’s Changing Industrial Landscape**<sup>*****</sup>

## **Shahid Yusuf**<sup>******</sup> **And Kaoru Nabeshima**

## **Development Economics Research Group World Bank**

### **Abstract**

This paper explores various strategic options available to Japanese firms faced with the increasing market pressures in the global economy. Whether Japanese firms are able to retain the competitive edge will depend on the continuing gains in their manufacturing capability, their capacity to exploit and adapt to changing market circumstances, and innovativeness at many levels. We discuss six main developments that are changing the industrial landscape in Japan and in this context, we explore the nature of the responses required of firms and examine some implications of the measures adopted for the Japanese economy as a whole.

### **JEL Code:** F14; J14; O14; O31; O33; L22; L23

### **Key Words:** Japan; Firm Strategies; production networking; innovation;

### World Bank Policy Research Working Paper 3758, November 2005

_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._

* A shorter and earlier version of this paper was presented at the Nihon University 100th Anniversary

Symposium on November 27, 2004.

** Contact author: Shahid Yusuf, World Bank, 1818 H St. NW, Washington, DC 20433.  Phone: (202) 478-

2339. FAX: (202) 522-1150. Email: syusuf@worldbank.org 1