rafmacalaba/datause-encoder-data huggingface dataset:
the United States, it started from a much lower baseline share of affected exports in 2007 relative to most of these other economies.

# **5. Countervailing Duties and Subsidies Before and After the Crisis**

The rules governing the use of countervailing duties allow the imposition of new trade barriers to offset the allocation of foreign government subsidies to firms that export products that subsequently injure import‐competing industries in another market. Like the antidumping law, economists have questioned the implementation of CVD provisions into trade agreements like the WTO, as well as trade agreement rules limiting the national imposition of subsidies more broadly (Bagwell and Staiger, 2006; Ruta, Brou, and Campanella, 2009). Regardless of whether use of or rules governing CVDs are economically sensible, understanding the extent of CVD use is economically important.

Furthermore, a number of recent political‐economic events coincided to increase the likelihood that CVD use is on an upward trend. First, the 2007 U.S. reversal of its mid‐1980s _Georgetown Steel_ decision has resulted in a policy shift so that the U.S. now accepts domestic petitions to apply CVDs against imports from China. After more than twenty years of refusing to consider imposition of CVDs against imports from non‐market economies, the change resulted in the United States applying duties on Chinese imports after 17 separate investigations between 2007 and 2009. Second, a number of other WTO member economies have also either recently implemented new CVD legislation thus expanding their TTB policy arsenal (India, China, Turkey), or they have joined the United States and also started to use CVDs against China after having previously declined to do so (Australia, Canada, European Union). Third, given that many economies have now decided that they should no longer tie their hands regarding CVD use against China, the longstanding concern with the relative valuation of China’s currency and allegations that it acts as an implicit export subsidy may also fuel increased CVD use.<sup>27</sup> Fourth, the 2013‐2014 expiration of the China‐specific transitional safeguard, as well as China’s non‐ market economy (NME) status under antidumping may restrict how much discretion countries have to use _other_ TTB policies against China’s exports and thus push policymakers to use alternative TTB instruments like CVDs. Fifth, the 2008‐2009 crisis resulted in a number of major WTO members bailing out sizable domestic industries, and the Global Trade Alert (Evenett, 2009) documents dozens of examples of such subsidies with the potential for negatively impacting foreign commerce since

> 27 Staiger and Sykes (forthcoming) provide a notable critique of the hypothesized link between China’s exchange rate undervaluation and export subsidization, and whether any such link could be addressed through CVDs.

29
# **Insurers: Too Many, Too Few, or “Just Right”?**

## **Initial Observations on a Cross-Country Dataset of Concentration and Competition Measures**

Craig Thorburn<sup>1</sup> Key Words: Insurance Markets, Competition, Concentration, Herfindahl Index

> 1 Craig Thorburn is a Senior Financial Sector Specialist in the Finance and Private Sector Development Vice Presidency at the World Bank. His email is <u>cthorburn@worldbank.org</u> .

1
complexity, such agency problems can be more severe in large banks. There are two types of information that banks can collect and rely on to make loan decisions. One type is more standard and hard information, such as firms’ financial statements, which is easily communicated and verified among people. The other type is less standard and soft information, such as the ability and characters of firm managers, local market conditions, etc. In order to collect soft information, the lender often needs to keep closer contact or form a long-term relationship with the borrower. Furthermore, communication of such soft information between information collectors and others is often a tough task (Petersen, 2004). In large banks which are more organizationally complex, the authority to make lending decisions is often granted to bank officers at higher levels. Due to difficulty in the communication and verification of nonstandard information, these loan officers rely mainly on standard information. Expecting little reward to such efforts, local branch officers are discouraged from collecting soft information. Instead local bank officers in large banks have more incentive to collect hard information which is easily communicated to their supervisors. On the contrary, in small banks, where information collectors are often lending decision makers or close to lending officers, soft information can be more effectively transmitted and utilized. Thus small banks can provide more incentives for their local officers to collect soft information about borrowers<sup>1</sup> (Stein, 2002). The distinction between large and small banks in producing information can affect their choice of borrowers in a systematic way. Small businesses usually lack complete and audited financial statements, have a short credit history, and own less physical assets that can be pledged as collateral. The ability and characters of the owner or chief managers can exert more significant influence on the operation of small businesses. Therefore, banks lending to small businesses have to rely on soft information and small banks have advantages in this regard. In addition, it should be much easier for local small banks to observe and forge long-term relationships with small firms in the region<sup>2</sup> . On the contrary, large banks prefer to do business with large firms which are able to provide more

> 1 Consistent with these discussions, empirical studies find that big banks make lending decisions in a way systemically different from small banks. Big banks rely on standard information such as firms’ formal financial statements to make loan decisions. Small banks depend more on qualitative information about borrowers acquired by the bank staff in their personal interactions with borrowers or by keeping exclusive relationships with borrowers (Cole et al, 2004; Berger et al, 2005).

> 2 Please refer to Petersen and Rajan(1994) for the importance of lender-borrower relationship in small business lending.

12
**-** " *1 -

|__._._._:_.._._._:_._Xter|;<br>..||.<br>;<br>.<br>i<br>Tah<br>*<br>**Eu**<br>**ron**<br>EarapeanCo|le 3:.r.<br>*<br>*<br>-b-<br>untriesand Albaiza|.|||
|---|---|---|---|---|---|---|---|
|||S|ize<br>of Local**Go**|**vernment**|||**Urbanii**|
|Country|Typo of Loc¢al<br>Government|<br>Name|Number|Average<br>Pbpulation|Ra<br>minumum|nge<br>maximum|Percent of<br>Population Living<br>in Urban Areas<br>(Towns ><br>10,000) 21|
|Albania|Districts<br>Municipalitia/<br>Communes|_rethe_<br>_baXshki/comuna_|37<br>352|96,118<br>6,936|23,800<br>1,300|414,350<br>270,000|35|
|Bulgaria|Countica<br>Municipalities|_oblast_<br>_obshitna_|_9_<br>_255_|941,413<br>33,226|630,500<br>1,650|1,218,600<br>340,800|68|
|Czech Republio<br>1/|Districts<br>Municipalities|_okres_<br>_mesadkyurat_|75<br>5,768|133,300<br>1,735|50,000<br>S0|230,000<br>1,200,000|78|
|Hungary<br>.___<br>_|Counties<br>_<br>Municipalities|_megye_<br>_onko,rmanyzas_|19<br>3,070|SS7,447<br>3,450|212,000<br>200<|1,956,800<br>243,000|61|
|Poland|Municipalities|_gmynza_|2,383|_16,050_|_858_|848,514|62|
|Romania|Counties<br>Municipalities|_judea_<br>_munfc(pl/orasl_<br>_commune_|41<br>2,948|564,672<br>7,853|237,900<br>500 <|2,318,900<br>2,036,9W|53|
|The Slovak<br>Republic|Distriis<br>Municilities|_okres_<br>_okresni urat_|37<br>2,834|140,540<br>1,834|44,000<br>50<|441,000|78|

Excluding guope which is di utncth and municipality. 2a Data are for Czechoslovakia before it scparation into tho Czech and Slovak Republics Source: Depa_ment of Statists, Government of Albania: data for other countries supplied by Felix Jakob; data on population in urba amas from _World Dcvelopment Report,_ 1992 (Washington, D. C.: World DLnk, IM), Tablo 31, p. 278.

**lmm15-kaSw4**
Table 13: Deep trade agreements and trade in parts and components interval estimations, PPML

||(1)|(2)|(3)|
|---|---|---|---|
|No. of provisions|0.00439***<br>(0.00158)|||
|Core provisions||0.00777**<br>(0.00355)||
|PCA depth|||0.0305**<br>(0.0138)|
|BIT|0.146***|0.144***|0.145***|
||(0.0349)|(0.0350)|(0.0350)|
|PTA (in force or inactive)|‐0.0520|‐0.0771|‐0.0581|
||(0.0560)|(0.0555)|(0.0555)|
|Observations|116,400|116,400|116,400|
|R‐squared|0.988|0.989|0.988|
|Country‐pair FE|Yes|Yes|Yes|
|Country‐time FE|Yes|Yes|Yes|
|Period|3‐year|3‐year|3‐year|

Clustered standard errors at the country‐pair level in parenthesis. *** p<0.01, ** p<0.05, * p<0.1 31
the quarterly ATEs estimation. The empirical result also suggests a clear declining trend in negative average treatment effects; while _Formosa_ is estimated to have caused as much as a 65 percent reduction in fishery income in the second quarter of 2016, the effects decreased to approximately a half in the last quarter, and are statistically indistinguishable from 0 in the “pooled” specifications. While it is encouraging to observe a rapid recovery, the fact that fishery earnings still declined by over 30 percent half-a-year after _Formosa_ took place, and especially when fishing activities have almost resumed to the normal rate (recall from Table 6), shows how devastating the disaster was.

Last but not least, we study the potential spillover effects of _Formosa_ on other industries. Recall that we have shown in Table A3 (Panel B) that _Formosa_ did not seem to have any effect on the highly-unrelated industries such as manufacturing, construction and retail, which facilitates our adoption of workers in these industries as counterfactuals to saltwater fishery workers. We now pay attention to the potential spillovers to individuals employed in other industries that have a higher level of linkages to fishery (Table 8). We examine four industries, including freshwater fishery, husbandry, restaurants and lodging. On the one hand, freshwater fishery and husbandry are industries that produce direct substitute products to saltwater seafoods; hence it is reasonable to expect certain spillover effects of _Formosa_ , most likely through a supply-determinant channel such as substitute-product pricing. On the other hand, restaurant and lodging are selected because of the potential damage to the coastal tourism industry. In fact, together with saltwater fishery, restaurant and lodging are among the sub-industries eligible for several _Formosa_ compensation schemes, as documented in the government’s official reports (VOA, 2016a).<sup>30</sup> As Table 8 indicates, there seems to be a positive spillover to the earnings in freshwater fishery in provinces where _Formosa_ had the strongest impact (i.e. Quang Tri and Hue). This spillover effect is robust to the adoption of both geographic (columns 1-4) and industry control groups (columns 5-8), as well as different levels of added fixed effects. The estimated ATEs range between 20 and 25 percent increase in monthly income for the freshwater fishery workers, and are likely due to the positive demand shock for freshwater seafoods after – the breakout of _Formosa_ prices of the saltwater-substituted products soared when the demand for them elevated. However, unlike the case of freshwater fishery, we do not find any robust and consistent evidence suggesting spillover on the earnings of workers employed in husbandry, restaurant, or lodging.

> 30 Together with saltwater fishery, restaurant, and lodging, the other compensation-eligible sub-industries include salt manufacturing and fishery services. However, we do not observe sufficient sample of workers in these two sub-industries in the labor force surveys.

22
# **2.2. Developing an Expenditure Transaction Profile**

The transactions profile is a measure that gauges how all government expenditure transactions are distributed by size. This was first presented in the 2018 Policy Research Paper [10], which shows that the actual pattern of financial transactions can have significant implications for budgetary control strategies and how to improve the effectiveness of the FMIS for budget management. This study follows suit in the estimation strategy, and calculates the number of transactions, percent of transactions, the cumulative share of the number of transactions, as well as the cumulative share of the amount processed through the FMIS for specific sets of transaction ranges. Table 3 provides a sample template with illustrative figures that was populated for both Pakistan and Cambodia.

_Table 3 Sample Template for Developing the Transaction Profile_

|**Ranges In**<br>**US$**<br>**Equivalent**|**Number of**<br>**Transactions**|**Share of**<br>**Transactions**|**Cumulative**<br>**Share**|**Total**<br>**Amount**<br>**(US$) of**<br>**Transactions**|**Share of**<br>**Amount**<br>**Processed**<br>**Through**<br>**the FMIS**|**Cumulative**<br>**Share of**<br>**Amount**<br>**Processed**<br>**through**<br>**FMIS**|
|---|---|---|---|---|---|---|
|< 100|3126|34.38%|3.80%|17,748|0.00%|0.00%|
|100‐200|2519|27.71%|9.30%|70,160|0.01%|0.01%|
|200‐500|1467|16.14%|18.00%|150,081|0.02%|0.05%|
|500‐1k|962|10.58%|24.50%|443,526|0.05%|0.11%|
|1k‐5k|489|5.38%|52.30%|7,019,724|0.74%|1.08%|
|5k‐10k|249|2.74%|63.50%|7,532,601|0.80%|2.13%|
|10k‐25k|99|1.09%|75.30%|18,427,032|1.95%|4.68%|
|25k‐100k|78|0.86%|88.60%|65,989,063|6.98%|13.82%|
|100k‐500k|52|0.57%|97.00%|131,150,367|13.88%|37.52%|
|500k‐1,000k|27|0.30%|98.50%|151,599,040|16.04%|52.98%|
|1,000k‐50,000k|17|0.19%|100.00%|231,045,308|24.45%|100%|
|> 50,000k|7|0.08%|100.00%|331,450,281|35.08%|100%|
|**Totals**|**9,092**|**100%**|**100%**|**944,894,931**|**100%**|**100%**|

_Source:_ Authors.

As shown in the hypothetical example above, a larger percentage of the transactions typically falls in the lower amount ranges. At the same time the total amounts of these transactions tend to be low. On the other hand, the numbers of transactions in the higher ranges is low but their share of the total amount processed through the system is 9
household level analysis is an increase in aggregate agricultural production, despite a decrease in the agricultural price index. Farmers respond to the changes in relative prices induced by the WTO by shifting to more competitive activities (livestock, fish, vegetables and rice), resulting in a net increase in agricultural production.

These two studies show that a disaggregated analysis of the impact of trade reform yields insights that diverge from insights gained at the national level. Our objective in this paper is to contribute a village-level perspective on trade reform in China. Adjustment responses of different household types and within-village interactions are central to this analysis. This study complements earlier studies by zooming in on the differential impact of trade liberalization on households within a village. A new methodology that accounts for family-farm production specifics in a village-economy setting gives us a unique perspective on the impact of trade reform. Going beyond the household level study of Huang _et al_ . (2003) we account in addition for the impact of farm income on rural household consumption. We furthermore account for the impact of rural-urban migration, thus not limiting the analysis to developments in the agricultural sector.

In this study we combine a macro level analysis of trade reform, with a village level general equilibrium model of a rice-producing village in Jiangxi Province. To be able to combine the macro and village level analysis of trade reform, we need to ascertain how trade reform affects households. Prices and labor demand form the key transmission mechanisms through which macro level trade reform affects rural households (Winters, 2002). We therefore focus on changes in prices for consumed goods, agricultural inputs and outputs, and on the increased demand for labor by labor-intensive sectors in which China has a comparative advantage.

Our analysis of further trade reform builds on a baseline that encompasses China’s recent WTO accession, phasing out the export quota for textiles under the Agreement on 4
illness, which is any current illness that has lasted for more than six months; days of restricted activity due to illness or injury in the week prior to survey; and days of no activity due to illness or injury in the week prior to survey. We also examine food consumption per adult equivalent in the household.<sup>7</sup>

# **2.5. Controls**

We control for the following set of baseline characteristics at the individual and household level: gender, highest grade completed, a quadratic in age, season of interview, assets, household size, the number of children living outside the household, and an indicator for sample enumeration classification (which equals one if the elderly individual’s initial household had experienced an illness shock prior to 1991). We also control for the histories of crop and illness shocks in each year over the time span of the panel.

# **2.6. Summary statistics**

Table 2 reports means for dependent and independent variables for four groups: the pooled sample of elderly individuals at baseline, the subsample re-interviewed, and, among those re-interviewed, elderly individuals who did or did not experience a primeage death in the household.

Comparing the first two columns in Table 2, we find some statistically significant differences between the baseline characteristics of older adults re-interviewed in 2004 and the entire sample (1,060) including those who died. Those re-interviewed were more likely to be female, were younger, worked more, and were healthier in 1991. We address this selective attrition bias using a re-weighting procedure which is described below.

Second, among the 613 older adults we study, we observe some statistically significant differences in baseline characteristics between those who do and those who do not experience a death before 2004 (top half of Table 1, columns 3 and 4). Those who will experience a death are more likely to be women, are doing less farm work and chores

> 7 The scale for calculating adult equivalent household size is the same scale used by the Tanzania National Bureau of Statistics for the analysis of poverty using the Household Budget Survey data. It is an adjustment based on the age and gender of household members, where non adult males have weights below 1. It does not account for economies of scale in household consumption.

11
- **_Opportunity structure_** is defined as the formal and informal institutional context in which actors operate. These formal and informal rules and norms largely determine the actor’s ability to transform agency into effective action.

As Figure 1 illustrates, working together, agency and opportunity structure give rise to three different degrees of empowerment, which are measured by: (i) the presence of opportunity to make a choice; (ii) whether or not an individual or group actually uses the opportunity to choose either directly –through participation– or indirectly –through representation; and (iii) if they do, whether the choice is transformed into desired outcomes (Alsop and Heinsohn, 2005).

**Figure 1.  Empowerment: institutions and actors**

<!-- Start of picture text --> Agency<br>Degree of Development<br>Empowerment Outcomes<br>Opportunity<br>Structure<br><!-- End of picture text --> Source: Alsop and Heinsohn, 2005.

The degree of empowerment that different local actors (i.e. elected representatives, administrative bodies and citizens) achieve also depends on the type of decentralization being pursued. Decentralization is not a uniform process, but rather one that involves devolution of distinct decision-making powers and responsibilities to subnational units. Three types of decentralization are recognized (Shah and Thompson 2004):

- **Political decentralization** implies directly elected local governments who are accountable to citizens. It supports democratization by giving citizens, or their representatives, more influence in the formulation and implementation of policies.

- **Administrative decentralization** empowers elected local governments to hire and fire local staff (thereby making local officials accountable to elected officials) without any need for consultation with higher-level governments.

- **Fiscal decentralization** entails the responsibilities of local revenue raising and allocation.  It ensures that elected officials are careful in their spending decisions as they face the possibility of being voted out.<sup>5</sup>

Decentralization affects the degree of empowerment at the local level, both by giving citizens the opportunity to engage with and hold their local governments accountable, and 5 More technically, fiscal decentralization seeks to optimize social welfare by allocating resources across tiers of government according to three criteria: economic stability, allocative efficiency, and distributive equity.

3
31 Cost of Production and crop prices, using the state of Hariana in India as an example, were taken from GOI (1993) and are presented in Table 4. Prices are in constant 1993 $US (31.5 Rs = 1 $US).

**Table 4: Production costs for Cotton and Wheat**

|._________________________|______<br>Cotton|Wheat|
|---|---|---|
|Output price|$.8/Lb ($1750/ton)|$.15/Lb ($300/ton)|
|Nitrogen price|$.089/Lb ($.199/Kg)|$.089/Lb ($.199/Kg)|
|Costs unrelated to water or nitrogen|$78.5/acre ($196.3/ha)|$45. 1/acre ($112.7/ha)|

The marginal cost of water supply can be approximated from water charges to industry; these range between Rs6O and Rs500 per thousand cubic meter (equivalent to $0.195-1.633 per acre-inch or $0.0019-0.158/cubic meter). A water fee of $.653 per acre-inch is widely used (World Bank, 1994) and is adopted here as the marginal cost of water supply.

The farmer chooses the cropping pattern and allocates inputs to each crop subject to the land and water constraints, taking all prices and water charges parametrically (see Section 3). The water agency chooses the pricing scheme and the water rates. We shall consider the volumetric (marginal cost) pricing, per unit area pricing, tax on input, and tax on output. Some of these pricing schemes differentiate between crops.

Results are presented in Table 5. In some cases, the water charges are determined such that the proceeds of the water supplier are equal to those collected under volumetric (marginal cost) pricing. This was achieved by solving: NMin.r {7t(zw)_7C(z<sup>T</sup> )} subject to profit maximizing behavior of farmers, where 7c(zw) is the water agency revenue under the marginal cost pricing rule and 7r(zT), T=g,x,a, is the agency proceeds under the other schemes.

Following Howitt and Vaux (1994), the marginal cost of water supply is represented by MC(Q) = 115 + 0.000671.Q,