rafmacalaba/datause-encoder-data huggingface dataset:
Table 14 Financial Assets and Liabilities of Personal Sector (billion yen)

||1955/12|1960/12|1971/3|1981/3|1991/3|
|---|---|---|---|---|---|
|Total assets|na|12741|70525|333598|954509|
|Cash|530|907|3982|13425|29205|
|Demand deposits|1116|2169|8035|26728|54603|
|Time/savings<br>dep|1891|5163|32802|181577|414933|
|Trust accounts|144|426|4062|21016|64942|
|Insurance<br>res|388|1282|9552|47108|199484|
|Securities|75D|2308|9982|40762|178149|
|Liabilities||||||
|PFI loans|631|1902|15694|79599|221759|
|GPI loans|214|591|2696|21151|51411|
|Trade credit|593|1677|11185|31024|58374|

Source: Bank of Japan, Economic _Statlatico_ Annual, various issues.

45
few countries in the region conduct national household surveys with consumption/expenditure-based questionnaires, and where they exist, they are conducted infrequently. It is expected that SEDLAC will incorporate consumption data in the future as LAC countries start collecting information on household spending more frequently.

SEDLAC’s US$ 2-a-day poverty line in local currency units for each country is computed using the 2005 international poverty line adjusted by PPP and the national price index of the country for the month in which the respective country survey was conducted.

# _Old Age Poverty Rates under Current Policies_

Figure 1a presents the poverty ratio for the persons older than 60 and for the whole population in Latin America. The poverty ratio is based on a poverty line equal to half the median income of the household. Figure 1b present the poverty ratio using a different definition of the poverty line, namely a poverty line equal to $2.5 a day. The equivalence scale we use is the OECD scale that is equal to 0.5 + 0.5 x number of adults + 0.3 x number of children (up to age 16).<sup>11</sup> With the poverty rate calculated with the OECD scale and a poverty line equal to half the median income, Brazil, Chili, Uruguay, Argentina (and to a lesser extent Nicaragua) have low poverty rates comparable to most OECD countries (below 11%).<sup>12</sup> These four

> 11 Poverty rates for the population aged 65+ (instead of 60+) and for per capita income (instead of equivalized income) are presented in the Appendix.

> 12 These figures for Latin America can be compared with the figures for Africa where the percentage of households with elderly living alone is small. Elderly with children are also a small percentage (about 1% in Uganda, Burundi, Ghana, Guinea, Kenya, Malawi).  However, households headed by the elderly are more frequent, ranging between 12 to 27 percent.  The incidence of poverty among elderly persons is generally higher than on average, and higher than among the non-elderly in 11 of the 15 countries for

8
the year 2010 with almost six million customers. Of these, 5.4 million are in Kenya, 474,000 are in Uganda and 28,000 are in South Sudan. As a consequence of this expansion, Equity Bank became the fifth largest bank in Kenya in terms of the share of gross assets and deposits of the whole system and is by far the bank with the largest number of deposit and loan accounts. The number of deposit and loan accounts of Equity Bank represents around the 50% and 30% of the total number of deposit and loan accounts in Kenya, respectively (see Table 11). These deposit and loan market shares suggest that the emergence of Equity Bank has played an important role in expanding the bank branch network to under-served territories and thus expanding the use of banking services to population segments ignored by other large traditional commercial banks. Moreover, these stylized facts suggest that Equity Bank’s model has not only expanded the use of financial services to broader segments of the population but it has also created profits in the process. As shown in Figure 8, Equity Bank’s profits before taxes and shareholder funds have experienced a strong positive trend.

There is preliminary evidence that the bank branch expansion and emergence of Equity Bank have paid off in terms of access to banking services in Kenya. In fact, as reported in Table 12, the proportion of individuals having a bank account in Kenya increased considerably going from 14 percent in 2006 to 23 percent in 2009. The proportion of individuals having a loan from a bank showed a more modest increase from 2.9 percent to 4.3 percent. This increase in access to banking services has coincided with a stronger presence of Equity Bank. While Equity Bank had branches in 48% of the districts in Kenya in 2006, it had branches in 87% of them in 2009.

In light of the preliminary evidence presented above, in an ongoing work Allen et al. (2011) explore the impact of Equity Bank, as well as other banks, on the use of banking services in Kenya by combining a new dataset on bank presence with household-level survey data in 2006 and 2009. The dataset on bank presence is based on branch-level information that is aggregated to construct a 36
The _relative_ rankings between States are broadly similar to those in the Prayas Report, although some jurisdictions to which the Ministry of Power gave good marks for regulation were heavily criticized in the Prayas Report – notably Haryana and Delhi State. However, the _absolute_ scores in the Ministry of Power’s report suggest a much more optimistic picture than in the Prayas Report. For most of the major reformers in the Ministry of Power report, SERC factors were given marks of 15 or more out of 20 (ie the glass was three-quarters full or more). The states so categorized were: Andhra Pradesh, Karnataka, Haryana, Rajasthan, Delhi State and Uttar Pradesh. This list – and the judgments on the adequacy of the SRC’s in some of these States - is not only very different from that in the Prayas Report but also from that of other observers.

# **3.4 Conclusions from Surveys of Regulatory Governance Practice**

The key conclusions from this section are:

- 1) _It is possible to operationalize the measurement of informal as well as formal regulatory governance by systematic, structured case studies using a common framework._

Note that this typically requires probing under a framework of headings by informed observers. It cannot, in practice, be carried out by fully specified questionnaire-driven interviews and particularly not by web-based/e-mailed or postal questionnaire. The latter are only suitable for collecting basic information on formal governance characteristics, as in the EBRD/WB survey of transition country regulators and the Domah survey used in Domah, Pollitt and Stern.

Establishing a strong, common coding framework is crucial for the success of such studies.

- **2)** _For DTEs, studies that concentrate on formal governance characteristics alone typically over-state the quality of regulatory governance, usually by substantial amounts_ .

In addition, studies of regulatory practice can only reliably be made after the regulatory agency has been in place for a reasonable period (eg 2-3 years or more).

- **_3)_** _As a consequence of 2), econometric studies that use indicators of regulatory governance based only on formal characteristics may provide poor and biased estimates of regulatory effectiveness._

We will discuss this last point in more detail in Section 5 below.

22
portfolio after the stock market liberalization event occurs.

The entry of foreign investors thus triggers an efficiency-enhancing structural change, pushing the recipient countries’ export portfolios towards the products that make optimal use of the available resources. While we find strong evidence for this disciplining effect following the opening of stock markets to foreign investors, we find no such effect when looking at stock market development in general. This uniqueness of foreign investors seems to be in accordance with the existing literature that makes the case for superior monitoring abilities of foreign investors (Gillan and Starks 2003; Ferreira and Matos 2008; Aggarwal et al. 2011; Bena et al. 2017; Luong et al. 2017).

Future research could further explore the possible transmission channels behind these results, providing new insights into the specific role of foreign investors. For instance, existing research focusing on US investors and firms has examined the “voice” (board memberships, voting or behind-the-scenes interventions) and “exit” (selling of equity stakes) as two alternative mechanisms through which investors shape the actions of firms (see, e.g. the survey by Edmans and Holderness 2017). Foreign investors might make different choices between these two strategies, depending among other things on the different legal and regulatory frameworks they are facing. The entry of foreign investors could also increase the liquidity of a hitherto closed domestic stock market. This would improve the effectiveness of the exit strategy for the existing domestic shareholders as well, along the lines of Edmans and Manso (2011). By contrast, the impact of increased liquidity on the voice strategy of existing shareholders could be either positive (e.g., Maug 1998) or negative (e.g., Bhide 1993; McCahery, Sautner, and Starks 2016).

# **References**

> Aggarwal, R., I. Erel, M. Ferreira, and P. Matos, 2011, “Does Governance Travel around the World? Evidence from Institutional Investors,” Journal of Financial Economics 100, 154-181.

> Albuquerque, R., L. Brandao-Marques, M. Ferreira, and P. Matos, 2019, “International Corporate Governance Spillovers: Evidence from Cross-Border Mergers and Acquisitions,” Review of Financial Studies 32(2), 738-770. Alessandria, G., and J. Qian, 2005, “Endogenous Financial Intermediation and Real Effects of Capital Account Liberalization,” Journal of International Economics 67, 97-128.

> Amiti, M., and D.E. Weinstein, 2011, “Exports and Financial Shocks,” Quarterly Journal of Economics 126(4), 1841-1877.

> Aslan, H., and P. Kumar, 2016,“The Product Market Effects of Hedge Fund Activism,”Journal

30
(EMDEs) were entering their own “lost decade.” By contrast, the “Global” Financial Crisis (GFC) of 2007-2009 primarily affected the advanced economies. As the United States and Europe slipped into recession, China grew vigorously and lifted global commodity prices, boosting growth in EMDEs. China’s surging overseas lending to many EMDEs further supported economic activity and facilitated V-shaped economic recoveries following the global turbulence of late 2008-early 2009.<sup>2</sup> To find peacetime parallels to the cross-country synchronicity and global economic effects of COVID-19, one has to extend the search back to the 1930s.

While the coronavirus crisis did not start as a financial crisis, it may well be morphing into one. The headlines of the year have been dominated by news of the pandemic’s spread, output collapses, and surges in poverty and the newly unemployed.<sup>3</sup> Behind these disturbing trends, a quieter financial balance sheet crisis is gathering momentum across a broad swath of countries. The financial fallout from the pandemic, in principle, does not respect differences by region or income status, but middle-to-low-income countries are proving especially vulnerable. Scores of financial institutions worldwide are facing and will continue to face for some time a rise in nonperforming loans (NPLs).

Historically, banking crises emerge after a lengthy expansion in economic activity. Growth is often fueled by a credit surge and rising leverage. Under the motto of _this time is different_ , asset price bubbles emerge during the boom (whether in real estate, commodities, equity, or bonds varies by episode). As the economic expansion slows and turns into recession, the loans made during prosperous circumstances are looked upon with regret. Sometimes the balance sheet problems undermine confidence and runs on banks and financial institutions turn the crisis into a full-fledged panic.<sup>4</sup>

> 2 Horn, Reinhart, and Trebesch (2019) document the surge in China’s lending to EMDEs during this period. They also draw parallels to the boom in lending by US commercial banks in the late 1970s that ushered in the debt crises of the 1980s.

> 3 World Bank (2020a) documents the rise in global poverty rates during the pandemic; it is the first increase in this measure of poverty since the late 1990s.

> 4 Cross-country studies that discuss or examine the antecedents of financial crises include Conant (1919), Kindelberger  (1978), Bernanke and James (1991), Eichengreen (1992), Caprio and Klingbiel (1996), Kaminsky and

4
Table 3: Main economic activity of ICT firms interviewed

||ISIC 4|interviews|
|---|---|---|
|Computer programming, consultancy and related activities|62|29|
|Telecommunications|61|15|
|Information service activities|63|6|
|Architectural and engineering activities; technical testing and analysis|71|6|
|Civil engineering|42|2|
|Publishing activities|58|1|
|Motion picture, video and television programme production, sound<br>recording and music publishing activities|59|1|
|Total, excluding maintenance and trade||60|
|Repair of computers and personal and household goods|95|56|
|Retail trade, except of motor vehicles and motorcycles|47|9|
|Wholesale trade, except of motor vehicles and motorcycles|46|1|
|Total||126|

_Sources:_ Authors’ survey.

**Travel times.** A key ingredient of the analysis is the estimate of travel times. We borrow this part of the analysis from Fratto (2019). Travel times are computed using the shortest route between the centroid of two locations using ArcGIS. Table 4 summarizes the assumptions on the travel times and delays at each obstacle. Vehicles cannot pass through roadblocks, earthmounds, barrier gates, and across the Separation Wall. Due to an absence of information on road gates, they are assumed to be open.

For the checkpoints, there are three independent sources. First, Eklund and Martensson (2012) conducted an extensive survey in 2009 in which they asked questions on the shortest delays at a checkpoint (ranging from 0 to 90 minutes, with responses concentrated between 0 and 30 minutes) and the longest delays (between 0 and 1,080 minutes, with responses concentrated between 0 and 360 minutes). They find an average shortest delay of 15 minutes. Van Der Weide et al. (2018) provide a similar average delay using information from the UN. The PCBS also conducted a survey on the topic in 2006 and found that the average shortest delay was 12 minutes and 35 seconds. In this paper, it is assumed that crossing a checkpoint entails a delay of 15 minutes. The road network is held constant. This is consistent with the evaluations from IEG (2010) that argue there has been little progress between 2000 and 2009 on the development of the Palestinian road network.

14
_40 Political Economy of Policy Reform in Turkey_ Table 4. Average Tariff Rates _(all averages unweighted)_

||1987|1988|November 1989|
|---|---|---|---|
|All Categories|30|24|11|
|Agriculture|22|18|11|
|Mining (& petroleum)|17|18|6|
|Manufacoiring|31|25|11|
|Consumer goods|45|35|17|
|Intermed. goods|21|17|6|
|Capital goods|32|30|10|

_Source: Bateman and Arslan, 1989, tables 2, 4._ In 1989 and 1990 the direction of change became clearer, with major liberalization of import licensing and tariffs -- the main instruments of protectionism in the past. In August and September 1989, tariffs (and surcharges) were lowered on over 300 items, mostly consumer goods. (During 1989 the import regime was modified at least seven times.) In January 1990 the list of items subject to import licensing was abolished, all import deposit guarantees were eliminated, custom duties were reduced for _7,545_ items and eliminated for 333 items, and surcharges were reduced for _1,255_ items and eliminated for _2,357_ items. Some barriers and distortions remained, as discussed below, but the main instruments of protection had been eliminated or reduced to such a level that it would involve a major policy decision to restore widespread import protection.

The vacillation of trade policy in 1986-88 is consistent with a story of the government having a general objective of trade liberalization, but frequently compromising in order to limit trade deficits and to reward politically favored groups. Major steps were taken in the direction of trade liberalization and establishing full currency convertability in August 1989. What explains the more consistent liberalizing trend after mid 1989? The government had planned after the election in fall 1987 to liberalize imports further. Liberalization had to come sometime in the next few years anyway, in order to meet Turkey's obligations to GATT and to get back on track with a process of unifying tariffs with the European Community, a process that the Turks had halted during their troubles of the late 1970s. (Bateman and Arslan 1989, p. _5;_ M. Nishimizu, interview, May 1991). From the view point of a traditional electoral cycle viewpoint, early 1988 would have been a good time, in order to get short-run costs out of the way and to have time to realize the efficiency gains from liberalization. In early 1988, however, the government faced a recession resulting from its anti-inflation stabilization efforts and therefore decided to defer the import liberalization. Also, there were still immediate concerns about external credit and foreign exchange constraints. By late 1988 the situation had changed. The economy was booming, led by export growth that was strong enough to generate a current account surplus. In December
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sector j<sup>9</sup> . Therefore, _Backwardjt_ is a measure for foreign participation in the downstream industries of sector j. It is defined as The value of  _jk_ is taken from the 2002 input-output table<sup>10</sup> representing the proportion of sector j‘s production supplied to sector k. Finally, _Forwardjt_ is defined as the weighted share of output in upstream industries of sector j produced by firms with foreign capital participation. As Javorcik points out, since only intermediates sold in the domestic market are relevant to the study, goods produced by foreign affiliates for exports ( _Xit_ ) should be excluded. Thus, the following formula is applied: The value of  _jm_ is also taken from 2002 input-output table. Since _Horizontaljt_ already captures linkages between firms within a sector, inputs purchased within sector j are excluded from both _Backwardjt_ and _Forwardjt_ .

# **B. Data and Broad Trends**

The dataset employed in this paper was collected by the Chinese National Bureau of Statistics. The Statistical Bureau conducts an annual survey of industrial plants, which includes manufacturing firms as well as firms that produce and supply electricity, gas, and water. It is firm-level based, including all state-owned enterprises (SOEs), regardless of size, and non-state-owned firms (non-SOEs) with annual sales of more than 5 million yuan. We use a ten-year unbalanced panel dataset, from 1998 to 2007. The number of firms per year varies from a low of 162,033 in 1999 to a high of 336,768 in 2007. The sampling strategy is the same throughout the sample

> 9 For instance, both the furniture and apparel industries use leather to produce leather sofas and leather jackets. Suppose the leather processing industry sells 1/3 of its output to furniture producers and 2/3 of its output to jacket producers. If no multinationals produce furniture but half of all jacket production comes from foreign affiliates, the _Backward_ variable will be calculated as follows: 1/3*0+2/3*1/2=1/3.

> 10 Input-ouput tables of China (2002) Table 4.2, which divides manufacturing industry into 71 sectors.

8
this anecdotal explanation - since the baseline matters - e.g. if pollution is persistent.

Figure 4 presents the results of extending the Google Mobility Index (which always lags by one week) with air quality-related information: temperature and fine particulate matter (PM2.5), for countries for which data are available. The information is gathered from the Air Quality Open Data Platform from January 1, 2019 to April 30, 2020 for Argentina, Brazil, Chile, Colombia, Mexico, Peru and El Salvador. The shaded areas represent the information estimated backward and forward using Equation 6. The extended information suggests that the period prior to the COVID-19 crisis was signaling a recovery in Argentina, and a significant decline in Chile and Mexico, although stronger in the former. Meanwhile, the index points to stability in Brazil, Peru and El Salvador. Appending air quality data to the mobility index is warranted on both statistical and economic grounds, with the latter being the main motivation for this analysis. The predicted value of the Google Mobility Index using more up-to-date information confirms that economies may have bottomed-out in April, with Mexico being the exception.

The final set of results, which nowcasts industrial production using the appended Google Mobility Index, is summarized in Table 1 for Brazil, Chile, Colombia and Mexico.<sup>1</sup> The monthly growth rates are gathered from the Economic Indicators database of the OECD. The _R − sq._ achieves a maximum of 25 percent in Brazil and a minimum of 18 percent in Colombia. In most cases, the results point to a deterioration of March growth rates compared to February, and an even stronger decline in April. Specifically, Mexico is expected to decline by 5 and 6 percent for March and April, respectively, from a 0.7 percent decline in February. Similarly, Brazil is expected to decline by nearly 7 and 3 percent, while Chile is expected to decline by 1 and 2 percent; and Colombia 0.4 and 2 percent, respectively. In all regressions the optimal number of lags is 3, while the polynomial degree varies from 3

> 1In Table 3 in the Appendix section, various consistency checks with different combinations of the Google Index and the air quality data are compared. The differences between the various explanatory variables are insignificant.

11