rafmacalaba/datause-encoder-data huggingface dataset:
# **b. Banking supervisor can exert this power against external auditors? (% of respondents answering yes)**

Source: Waves 4 and 5, BRSS. Note: Off-shore financial centers are excluded from the calculations. DEV are developing countries. The number of countries for which we have a response for the specific item/question for both rounds appears within parentheses. * next to the number of countries represents a statistically significant difference in the mean values of a regional group in the two reference years at the 10% level using a two-tailed test.

Supervisory capacity did not keep pace with growing regulations, increasing bank size, and growing complexity of bank operations. There has been a substantial increase in the number and complexity of regulations in the aftermath of the global financial crisis. Under Basel III, there has been greater emphasis placed on systemic stability and macroprudential regulation, which requires a focus not on the risk of individual financial institutions, but on an individual bank’s contribution to the risk of the financial system as a whole. Following the Financial Stability Board (FSB) guidelines, new rules have been proposed and implemented for the resolution of systemically important financial institutions. The new resolution schemes in many countries provide a significant amount of discretion and flexibility to supervisors. At the same time, new macroprudential rules require continuous monitoring and stress-testing of large financial institutions.<sup>14</sup> These new capital and macroprudential regulations require a sizable investment in supervisory infrastructure and personnel. Figure 12, Panel A shows the log change in the number of supervisory personnel from BRSS rounds 4 to round 5 plotted against the log change in total bank assets

> 14 See Chapter 2 of _The Global Financial Development Report 2019/2020: Bank Regulation and Supervision_ for more detail _._

21
we use the log minimum capital recruitment in the analysis. Summary statistics for this variable are in panel B of table 1.

Our measure of bank concentration is the asset share of the three largest banks in a country in the year 2000 from the GFDR 2013 database. We chose this variable as opposed to other measures of bank concentration that include a larger number of players in the banking market (asset share of the five largest banks or Herfindahl index) since our hypothesis 2 in section 2 relates specifically to the existence of a few very large banks. That is, the disincentives to sharing credit information are particularly strong for banks that capture a very large share of the market. Panel B of table 1 shows that the average level of bank concentration in our sample is 0.73, with a minimum of 0.21 and a maximum of 1.

Data on the minimum capital requirement and on bank concentration is only available for about 135 countries, thus reducing the number of countries included in our empirical analysis.

# **3.3 Control variables**

The last panel of table 1 shows the descriptive statistics for the control variables used in this paper. The first two variables are (i) a measure of economic development (log of GDP per capita in the year 2000 from the World Development Indicators) and (ii) a proxy for the depth of the financial sector (ratio of private credit to GDP in 2000 from the Financial Structure database).

We include two measures of a country’s institutional environment. The first one is an indicator of the quality of contract enforcement: the log number of days it takes to enforce a contract from the Doing Business database. We use the first available year for this variable (2003), covering 144 countries. Our second institutional variable is a proxy of creditor rights quality: the credit rights index from Djankov, McLiesh, and Shleifer (2007) for the year 2000. This index measures the strength of legal rights of creditors against defaulting debtors and ranges from 0 (weak creditor rights) to 4 (strong creditor rights). Also from Djankov, McLiesh, and Shleifer, we use dummy variables for the legal origin of a country (French, Scandinavian, German or socialist/transition origin, with British legal origin being the omitted category). We add these variables based on the findings of Djankov, McLiesh, and Shleifer (2007), who find that legal 11
mechanisms as indiscriminate protection tools, since their application has affected less than 1% of the country’s total imports (see Table 12 below).

**Table 12 – Estimation of Mexican Total Trade Affected by Antidumping or Safeguard Measures, 2002**

|**Countries**|**Total imports per**<br>**country of origin**<br>**(USD million)**|**Imports of items**<br>**subject to**<br>**antidumping duties**<br>**(USD million)**|**Percentage of imports**<br>**from the country of**<br>**origin**|
|---|---|---|---|
|1. China|6,274|600.44|9.5697%|
|2. Ukraine|83|1.68|2.0078%|
|3. HongKong|509|7.74|1.5201%|
|4. Russia|236|2.18|0.9239%|
|5. United States|106,557|890.25|0.8355%|
|6. Denmark|177|0.74|0.4155%|
|7. Taiwan|4,250|8.07|0.1899%|
|8. Korea|3,910|0.97|0.0248%|
|9. Brazil|2,565|0.05|0.0018%|
|10. Japan|9,349|0.12|0.0013%|
|+ 17 investigated countries||No duties in en 2002|-|
|Mexico’s total imports|168,679|1.512|0.8965%|

Source: World Trade Atlas and 2002 Antidumping Agreement.

One concern that usually arises out of the Mexican context is the adverse effect that antidumping duties and safeguard measures may have on the country’s manufacturing structure. Trade liberalization had been too difficult a process to risk losing its benefits as a result of an excessive use of antidumping duties. Consequently, Mexican authorities decided to resort to several mechanisms to mitigate the negative effects of antidumping duties, particularly in the 1991-1996 period. Several cases presented in this chapter illustrate how Mexican authorities were able to apply contingent trade protection laws with a flexible criterion, so as to protect manufacturing sectors deserving protection measures while taking other national interests into account.

The discretionary application of laws on contingent trade protection is a double-edged sword. It can be beneficial when it protects certain sectors at critical moments of the trade liberalization process, but it can also degenerate into an indiscriminate protection instrument. The China Package case is a typical example of how antidumping duties can be applied to support the opening of trade, while it also unveils its potential for discretionary use. In the 43
# **1. Introduction**

Food and fuel commodity prices, which had been rising since 2001 (see Figures 1 and 2), reached record levels by mid-2008 [21, 52, 65]. According to the International Monetary Fund (IMF) primary commodity price database, world food commodity prices increased 100% or more from 2001 to 2008 (in 2005 US $), with prices increasing by almost 300% for rice (see table inset in Figure 2).

The period between 2001 and 2008 was also the period during which production of biofuels such as ethanol and biodiesel produced from food crops grew several fold. During this time, global ethanol production from maize and sugarcane more than doubled from 30 billion liters to 65 billion liters, while biodiesel production from edible oil seeds such as soybean, oil palm, and rapeseed expanded six fold from 2 billion liters to 12 billion liters [46]. The increase in biofuel demand, which was concentrated in the United States and the European Union (EU), was primarily a response to government mandates and subsidies.<sup>1</sup> This has led to the popular opinion that biofuel policies in the high-income countries are one of the principal causes for the inflation in food commodity prices.

Biofuels reduce demand for oil and increase demand for agricultural goods.<sup>2</sup> With crops comprising a small share of the final cost of food in high-income countries, the impact of biofuels on food consumers is small. To low-income countries, where expenditure on raw grains and vegetable oils comprises a much larger share of the household food budget, a given increase in crop prices will have a much larger impact on food consumers.

This paper aims to identify the main factors affecting food commodity prices, and to also quantify the contributions of these factors. A distinguishing feature of our analysis is taking into account adjustments in inventories of agricultural goods in response to these various factors. Although conceptually an important component of food commodity markets, to the best of our knowledge, it is not explicitly incorporated into existing empirical/computational models.

> _1Growth in domestic biofuel demand in Brazil, a large biofuel producer, also increased, but was less significant relative to growth in demand in the U.S. and EU countries._

> _2Biofuels reduce the Organization of Petroleum Exporting Countries’ (OPEC) market power, and therefore reduce energy prices [32,33]._

2
with boundary condition _ulm VF(k)e-P_ = O **f-r** The soludon for _V_ is given by where, _BF_ **=** 1 **_(aF-_ p** From the F.O.C., it is clear that consumption is given by _c, pk,,_ a fixed fraction of wealth. Therefore, the individual's capital stock evolves according to Let the distribution of capital in the economy at time t be given by the density function _f,(k)._ That _is, fAc(kddk_ is the "amount" of people with _k,_ = ko at time _t._ Also, let **I,** be the fraction of people that belongs to the formal secmor at time _t;_ then where the integral is taken over the set _F_ of formal agents. Total capitl! in the formal sector (kF) at time t is given by _kF_ **=** _f ,(k)& = p,E(kJiebF)_ When no capital flows to or from the formal sector, aggregation over formal individual _-9-_
# Figure 11. LAC: Output growth and drivers of potential growth

A. GDP growth

# B. Potential GDP growth

<!-- Start of picture text --> Percent Percent<br>10 LAC Median Range for EMDE regions 8 LAC Median Range for EMDE regions<br>8<br>6<br>6<br>4 4<br>2<br>2<br>0<br>-2 0<br>2000-21 2000-10 2011-21 2022-23 2000-21 2000-10 2011-21<br>C. Potential growth by different measures D. Potential TFP growth<br>Percent Percent<br>4 4<br>2000-10 2011-21 LAC Median Range for EMDE regions<br>3 3<br>2 2<br>1 1<br>0 0<br>PF MVF UVF Exp. 2000-2021 2000-2010 2011-2021<br>E. Investment growth and changes in F. Investment growth<br>terms of trade<br>Percent Percent<br>Investment Terms of trade<br>15 14 LAC Median<br>10 12<br>5 10<br>0 8<br>-5 6<br>4<br>-10<br>2<br>-15<br>0<br>2000-21 2000-10 2011-21<br>Sources: Haver Analytics; National statistical agencies; Penn World Tables; UN Population Prospects; World<br>Bank.<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> Note: EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates). A.B.D.F. Bars show period averages of annual GDP-weighted averages. Markers show the median of GDPweighted averages of the six EMDE regions; orange whiskers show min-max EMDE range (of which LAC is the minimum).

B. Estimates based on production function approach.

C. MVF = multivariate filter; PF = production function approach; UVF = univariate filter (specifically, the Hodrick-Prescott filter). Expectations-based estimates (“Exp.”) are potential growth proxied by five-year-ahead IMF World Economic Outlook growth forecasts. Sample is a consistent set of 10 economies.

D.F. Sample includes 53 EMDEs, of which 16 are LAC economies.

E. Investment-weighted average growth rates and GDP-weighted terms of trade changes. Sample includes 20 LAC economies.
consumption of maize, rice or cassava in urban areas are more likely to be doing so because they have no production of their own and depend on market purchases. It is harder to make this assumption for the rural areas since most rural households are engaged in farm activities to varying degrees. Zero auto-consumption here is likely to be a result of the household running out of their own stocks during the period when the consumption data were collected.

The sample of matched IOF-2014/15 and AIS-2015 households is used to calculate consumption, production and net-benefit ratios. Table 7 presents the results of this analysis. The main differences are observed in the mean production ratios, which are in general lower compared to earlier as a result of setting production value to zeros for those households showing no auto-consumption in the urban areas and those with no neighbors producing maize, rice or cassava.<sup>11</sup> Overall, we find that the welfare results for maize and cassava in the rural areas are qualitatively similar to the province level estimates. Yet, there are some differences in direction of the effects of a rise in the price of rice and the net effects in urban areas. An increase in the price of rice is associated with a reduction in household welfare whereas increases in the prices of the three staples lead to net welfare losses in urban areas.

The largest negative impacts continue to be driven by maize in the rural areas, where a 10% price increase results in a 1.1% reduction in welfare. The largest welfare reductions (over 2%) are experienced in the provinces of Niassa, Tete, Manica and Sofala. The direction of the maize effects in rural areas is consistent with the finding using province-level values, but the magnitude of the effects is higher. Compared to the results in the earlier section, the effects for maize diverge a lot more in the urban areas. This is likely the result of correcting for the oversampling of farmoperating households in the urban AIS-2015 sample. The household-level analysis consistently shows negative welfare impacts across urban areas over the country owing to lower PR values. There is a net welfare reduction of about 0.2% for a 10% increase in the price of maize, much smaller than the estimated effect for rural areas.

The welfare implications for cassava are in line with the patterns observed in the estimates in Table 5. We continue to see a negative welfare incidence of cassava price increase in the provinces of

> 11 The consumption ratios differ slightly from those in Table 5 because of differences in about 1.4% IOF sample due to missing or incorrect geographic coordinate variables.

21
minute in 1998. More recently, charges have fallen to around US$0.15 per minute as a result of the introduction of the possibility of teleselection of the long distance operator<sup>9</sup> .

In addition, four licenses have been issued for mobile telephony services. Calls are charged at around $0.14 per minute, with some calling plans costing less than $10 per month.

The reform has had a major impact on the performance of the telecommunications sector in Guatemala. The efficiency of the sector improved markedly, with the number of mainlines per 1,000 employees rising from 60 in 1996 to 130 in 1999. While, at the same time there have been massive gains in coverage.

According to SIT data, the total number of fixed plus cellular telephone lines rose almost fivefold from around 350,000 to over 1,600,000 between 1996-01, raising the teledensity index from 4.2 to 19.7. Much of this growth came from new cellular lines, which now represent more than half of the total (57%). The development of the cellular network has been less concentrated in the capital city. Whereas in 2001, 70% of the country’s fixed lines were located in the Department of Guatemala, only 43% of the cells of the mobile telephony network were located in that Department.

# **_Water and sanitation_**

Although sector reforms have been under discussion for some years in the water sector, it has not been possible to reach a political consensus on this issue.

At present, the provision of water and sanitation services in the metropolitan region remains the responsibility of the state-owned enterprise EMPAGUA. EMPAGUA serves about 70% of the market in the central city area (falling to 50% if the surrounding municipalities are taken into account). More than 200 private companies meet the shortfall in demand, of which the largest are Aguas de Mariscal and San Cristóbal, but the majority are small-scale operations serving a specific neighborhood or housing estate. A recent study (Solo, 1999) found that whereas charges by the main utility fell in the range $0.09-$0.42 per cubic meter depending on the consumption group, charges applied by alternative suppliers were substantially higher at between $0.25-$2.70 per cubic meter.

# **<u>Table 2.3: Summary of the structure of the Guatemala water and sanitation sector</u>**

Metropolitan area 50% EMPAGUA, 50% small scale private operators Non-metropolitan urban areas Municipal utilities Rural areas <u>Community Based Organization with support from central government UNEPAR</u> Outside of the metropolitan region, the country’s 240 municipalities are responsible for providing water and sanitation services, at least in urban areas. However, they do not tend to reach isolated rural areas, where community-based organizations typically take charge of services, often with some financial support from central government via the Unidad Ejecutora del Programa de Acueducto Rural (UNEPAR) or from the various social investment funds.

> 9 The lower cost of international calls is an important consideration for the approximately 10% of households in Guatemala who obtain about 10% of their income from international remittances.

9
**Table 1: Stationarity Properties**

||_——Levels wit_|_hout trend——_|_——Levels wi_|_th trend——_|_——First dif_|_ferences——_|
|---|---|---|---|---|---|---|
||_ADF_|_PP_|_ADF_|_PP_|_ADF_|_PP_|
|_Prices_|||||||
|Maize|-1.06|-0.90|-2.12|-2.17|-6.41***|-6.61***|
|Wheat|-1.40|-1.21|-2.45|-2.43|-5.70***|-6.20***|
|Rice|-2.00|-1.87|-2.60|-2.54|-5.64***|-6.29***|
|Soybeans|-1.14|-1.28|-1.83|-2.29|-6.75***|-6.87***|
|Palm oil|-0.78|-1.65|-1.52|-2.64|-6.24***|-8.21***|
|Crude oil|-0.70|-0.76|-1.59|-1.79|-6.59***|-6.58***|
|_Macroeconomic vari_<br>|_ables_||||||
|MUV|-1.55|-1.36|-1.45|-1.08|-3.90***|-3.83***|
|Exchange rate|-1.47|-0.88|-4.05**|-2.92|-5.35***|-5.48***|
|Interest rate|-1.36|-1.45|-1.46|-1.11|-6.66***|-4.61***|
|GDP|-0.65|-0.22|-2.72|-1.48|-3.54**|-3.53**|
|_S/U ratios_|||||||
|Maize|-1.88|-1.91|-1.22|-1.89|-7.30***|-7.37***|
|Wheat|-2.54|-3.92***|-2.53|-3.89***|-5.88***|-10.65***|
|Rice|-1.66|-2.35|-1.01|-1.26|-3.85***|-5.97***|
|Soybeans|-3.41**|-3.34**|-4.22***|-3.71**|-3.07**|-10.25***|
|Palm oil|-2.98**|-2.83*|-2.82|-2.58|-9.80***|-10.66***|

**_Source_ :** Authors’ estimates.

**_Note_ :** All variables are expressed in logarithms. ADF and PP denote the Augmented Dickey-Fuller and Phillips-Perron statistic for unit roots, respectively (Dickey and Fuller 1979; Phillips and Perron 1988). The lag length of the ADF statistic was based on the Akaike information criterion (up to 10 lags were allowed), while the spectral estimation for the PP statistics was based on the Bartlett kernel method. GDP = gross domestic product; MUV = manufacture unit value; S/U = stock-to-use. Significance level of stationarity: * = 10 percent, ** = 5 percent, *** = 1 percent.

— 20 —
never married (men more frequently than women, of which one third are living in India), those who are divorced or separated (women representing the majority of the poor in that group, a quarter of them being in Latin America and the Caribbean), and those who are widowed (women more frequently than men).

_Figure 5. Distribution of the poor by sex and marital status (ages 15+)_ <!-- Start of picture text --> 100 2.3<br>10.2<br>1.0<br>90<br>3.3<br>80 28.5<br>16.9<br>70<br>2.7 2.4<br>60<br>50<br>40<br>66.8 65.7<br>30<br>20<br>10<br>0<br>Women Men<br>Married Living together Never married Divorced/ separated Widowed<br>Percent of poor<br><!-- End of picture text --> Note: Total sample 89 countries Source: WB Staff’s calculations based on GMD.

A focus on gender differences in poverty through the lifecycle, as captured by age transitions (childhood to adolescence, to adulthood, to elder years) and the related changes in own household formation (marital status and living arrangements, and presence of children), reveals meaningful gender differences in poverty rates.

Early marriage is correlated with higher poverty for girls and boys, and marital shocks at that age make girls extremely vulnerable to poverty. Girls who are married or widowed in the 15‐17 age group (only one percent of all poor girls) are 11.2 and 30.6 percentage points more likely to live in poor households compared to the average young women of that same age, among whom the most frequent marital status is single or never married<sup>24</sup> . Married young males have poverty rates 17 percentage points higher than the average men of that same age group. But, contrary to what we observe with girls, poverty rates are lower for the less than one percent of boys (0.13 percent) who report being divorced or widowers.

Figure 6 shows the impact of changes in marital status on other age groups, and how certain life‐ events at specific age groups correlate with higher poverty rates. Marriage is the most common marital status for women and men aged 18‐64 (as noted by the size of the marker), and for both men and women, widowhood when experienced in that age group shows higher poverty rates. For women, divorce or separation also increases their poverty level (marginally) when compared with those who are married, whereas poverty rates are lower for men who are divorced or separated than for their married counterparts. Divorce and separation affect women more negatively than men, but do not necessarily translate into higher poverty rates for women. Yet, divorced/separated women aged 18‐49 are more than

> 24 Marital status is only available for individuals 15 years old and above in the GMD. The data refers to marital status at the time of the survey.

14
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