rafmacalaba/datause-encoder-data huggingface dataset:
- 16 -

overall decline in productivity despite an increase in the proportion of containerized cargo during the period analyzed. These are data from the mid-i980's and certainly in Bombay and in Venezuelan ports, major efforts are now underway to raise productivity to more acceptable levels. The general tendency is clear: increased containerized traffic results in a very substantial increase in productivity, substantially more than is likely to be taken up by traffic increases. In quantitative terms, an increase of between 1OZ and 20Z in the level of containerization may result in a threefold or fourfold increase in overall productivity. These general results are borne out by the specific results for the UK reported in an earlier report (Galenson, 1989, page 87).

42. Given that the increase in the proportion of containerized cargo and of bulk cargoes, is a world wide and inescapable phenomenon, the reduction of restrictive practices is now largely synonymous with the adaptation of labor practices to these new methods of cargo presentation. The port industry world wide is changing from a labor intensive industry relying on limited mechanization and moderate specialization to a highly mechanized industry with a high degree of specialization and a - - substantially and dramatically reduced labor force. These changes are largely inevitable because of the relative economics of bulk/container compared with conventional ships, which strongly favor the former. The international nature of shipping operations means that high labor costs at one end of the voyage, usually in a developed port, tend to drive change in the port at the developing country end of the route. For example, an analysis of the Papua New Guinea to Australia route showed clearly that overall route economics favored containers, on account of the costly practices in Australian ports, despite the cost pattern of the ports in Papua New Guinea and the inland transport restrictions there. In addition the advantages of multimodal (door to door) transport, which are in practical terms realizable only with containerized or some similar form of transport and handling, favor the new methods. Within the different options open to the developing port for container handling, there will be differing degrees of labor intensity and this should be a factor in their selection. It remains true however that there is no truly labor intensive method of handling a container that is 20 feet long and may weigh up to 25 tons (and still less a 40 foot container).

# Approaches to the Elimination of Restrictive Practices

43. Three basically different approaches may be identified in the effort to eliminate restrictive practices:

- a) gradualist: in this approach, the existing labor agreements are modified by negotiation, in the attempt to achieve the progressive elimination of restrictive practices;

- b) reformist: in this approach the existing labor agreements are replaced by a new agreement, which represents a major departure from previous practices; and
# **Table S16. Prevalence of food insecurity by** **<u>gender of household head</u>**

||Moderate or<br>Severe Food<br>Insecurity|Severe Food<br>Insecurity|
|---|---|---|
|Female Head|0.111***|0.039***|
||(0.023)|(0.015)|
|Round 2|-0.075***|-0.042***|
||(0.017)|(0.011)|
|Round 3|-0.087***|-0.070***|
||(0.029)|(0.019)|
|Female×Round 2|-0.027|-0.028|
||(0.029)|(0.023)|
|Female × Round 3|-0.040|0.011|
||(0.045)|(0.036)|
|Ethiopia|-0.172***|-0.098***|
||(0.021)|(0.016)|
|Nigeria|0.108***|0.089***|
||(0.020)|(0.017)|
|Uganda|-0.323***|-0.205***|
||(0.015)|(0.011)|
|Observations|15,687|15,687|
|R-squared<br>_Note_: Each column report|0.130<br>s results from a singl|0.114<br>e regression of the|

_<mark>Note</mark>_ <mark>: Each column reports results from a single regression of the</mark> prevalence of either moderate or severe food insecurity or just severe food insecurity. Food insecurity is measured using the FIES. The FIES variable is regressed on indicators for each country, with Malawi as the base case, and round, along with an indicator equal to 1 if the household head is female, as opposed to male. Data come from all rounds of phone survey data in which FIES questions exist. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

43
<!-- Start of picture text --> 50 - _ _ __ _ _ _ _ _ _ _<br>40-<br>2 --<br>y<br>6 1 3 2 4 7 7 4 4 8 .4 8 8 6 5 8 7 9 9<br>° - , , I I I I* I I I I I I I I I I X<br>1966 1967 1968 1969 1970 1971 1972 1973 1974 197S 1976 1977 1978 1979 1980 1961 1982 1983 1984<br>Yeca of Obserotion<br>Watd8ar* - 40842<br><!-- End of picture text --> Figure **7:** Trend of Self-fnancing Ratio.
Although several factors contributed to the surge,<sup>13</sup> firm entry into exporting and the discovery of new products and markets was an important part of the story. Thus, the study of this sector allows us to examine the transition to equilibrium, and in particular the export decisions by firms as was explored in the model in the previous section.

We start by describing the data and then we proceed to explore the predictions from the theoretical framework.

# i.  Description of the Data

We use transaction data on Peruvian export flows included within Chapter 7 (Edible vegetables and certain roots and tubers), Chapter 8 (Edible fruit and nuts; peel of citrus fruit or melons), Chapter 9 (but only the lines related to the exports of paprika) and Chapter 20 (Preparations of vegetables, fruit, nuts or other parts of plants) of the HS Code. Although we have daily information on all shipments between years 1994 and 2007, for much of the analysis, we report annual results. The dataset allows the identification of the exporter (information on firms’ names and corresponding Tax ID number), the destination market for each trade flow, the custom port from which the merchandise is shipped, the description of the item exported (at 10-digit) and the FOB value of each shipment.

The values exported by year/date of the different products under analysis in this study (i.e. asparagus, prepared/preserved artichokes, avocados, mangoes, paprika, grapes,

> 13  Two important factors that led to large scale investment were land privatization in 1993, which removed constraints on the size of plots, and the capture of the head of the Shining Path in 1992, which greatly improved investor confidence in rural areas in Peru.  Some additional conditions accelerated this investment.  The introduction of the drip irrigation system (imported from Israel) in the late 1980s was completed in the late 1990s.  The Andean Trade Preferences Act (then extended under the ATPDEA) eliminated the tariffs for the Peruvian exports of asparagus to the United States from 1993. Currently, these preferences have been included in the FTA signed between Peru and the United States.

21
|No schooling|-0.07|Non-agricultural work|0.29|
|---|---|---|---|
|<br>Up to grade 5|-0.14|<br>Non-livestock work|0.25|
|Grade 5-10|-0.31|Non-fishing work|0.01|
|O or A level|0.27|No disability|0.07|
|College Degree or higher|0.16|Cash transfer beneficiary|0.13|
|Household Assets||||
|House|0.04|||
|Computer|0.27|||
|Landline phone|0.23|||
|TV|0.28|||
|Housing characteristics||||
|Roof|0.14|||
|Private Toilet|0.18|||
|Wall|0.19|||
|Waste disposal|0.15|||
|Safe water|0.13|||
|Main cooking fuel is wood|-0.23|||
|Electricity for light|0.27|||
|Head Education||||
|No schooling|-0.11|||
|Up to grade 5|-0.17|||
|Grade 5-10|-0.13|||
|O or A level|0.29|||
|College Degree or higher|0.13|||
|Age of head in years|0.04|||
|Head employment status||||
|Unemployed|-0.0005|||
|Public sector|0.15|||
|Private sector|-0.6|||
|Out of labor force|-0.04|||
|Male head|0.11|||
|Head marital status||||
|Unmarried|-0.04|||
|Married|0.12|||
|Widowed|-0.10|||
|Divorced|-0.06|||
|Number of variables|35||11|

_Notes: Table contains factor loadings from the first principal component, estimated in the census weighting by household size. Dependency ratio is equal to the ratio of non-prime age adults (0 to 14 and 65+) to household size._ 6
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lending were the primary activities for many years. Moreover, as deposit growth did not keep pace with loan growth, the already high loan to deposit ratio for finance companies and commercial banks increased further: Commercial banks' loan to deposit ratio increased from 96 percent in 1988 to 112 percent in 1995 and from 116 percent to 138 for finance companies respectively.<sup>4 1</sup> The increase in this ratio also reflected the fact that the financial system (especially commercial banks) substituted higher yielding loans assets with low margin cash or government bonds which was reflected in rising loan to deposit ratios. Loan to deposit ration well above 100 percent made banks and finance companies susceptible to a sudden change in investor confidence and depositor withdrawals.

**Figure 8. Composition of Commercial Banks Assets (1988-1996)**

<!-- Start of picture text --> 12% 90%<br>10% . _ u * 80% 0<br>A * _ Claims on commercial banks 70% i6<br>Claims on other financial institutions<br>8% . Claims on government 60% 3<br>0<br>Claims on business and household 50% w<br>6% . sector<br>40% o<br>rs 4% 4%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(L . , ~~~~~~~~~~30% Zn<br>20% °<br>2% CD<br>10%<br>0% _ 0%<br>1988 1989 1990 1991 1992 1993 1994 1995 1996 O<br>Year<br><!-- End of picture text --> _Source:_ Bank of Thailand, Quarterly Bulletin.

41 World Bank, Shadow Financial Sector Review, 1997.
## **Table 1:** Optimal Final Consumption Levels as Market Liquidity Changes

|q,=.2,<br>q2 B=l-qj-qA<br>q2A (RateOf<br>participation in<br>secondary<br>market)|cll|C22A|C22B|C22BRC1|
|---|---|---|---|---|
|0.00|1.306019|2.612038<br>(not relevant)|1.84699|0.54097|
|0.05|1.236068|2.472136|1.84262|0.60655|
|0.10|1.187342|2.374684|1.83942|0.65208|
|0.15|1.151383|2.302766|1.83697|0.68559|
|0.20|1.123724|2.247448|1.83503|0.71131|
|0.25|1.101773|2.203546|1.83346|0.73169|
|0.30|1.08392|2.16784|1.83216|0.74824|
|0.35|1.069111|2.138222|1.83106|0.76195|
|0.40|1.056624|2.113248|1.83013|0.77350|
|0.45|1.045953|2.091906|1.82932|0.78337|
|0.50|1.036726|2.073452|1.82861|0.79189|
|0.55|1.028668|2.057336|1.82799|0.79932|
|0.60|1.02157|2.04314|1.82744|0.80587|
|0.65|1.015269|2.030538|1.82695|0.81168|
|0.70|1.009639|2.019278|1.82650|0.81687|
|0.75|1.004576|2.009152|1.82611|0.82153|
|0.79|1.00088|2.00176|1.82576|0.82488|

also that banks cannot cross-subsidize investors. When all investors participate in the market, the amount of liquidity is independent of the activities of the banking system.

# **5.2 Illiquid Secondary Markets: Limited Participation**

22
that a negative import demand shock affects imports from all sources, placing the preceding relative supply shock in a different light. This interpretation is underscored by a comparison with closely-related reference commodities in which no petition is filed. Among these commodities, the relative supply shock is also evident, and of similar magnitude and duration. However, the reference commodities do not experience the negative demand shock, which suggests that a narrow negative demand shock is a key event in the run-up to a petition. Overall, it appears that an export supply shock is a necessary but not sufficient condition for a petition to be filed, and that it is a particular combination of shocks that triggers the filing of an AD petition.

It is the narrow import demand shock that is the most novel finding of the paper, and the shock is a sizable one. In the subject commodities total imports fall by approximately eight percent in the six months prior to the petition. At the same time, imports of the reference commodities that will not be subject to a subsequent petition continue to grow. Overall imports in the subject commodities fall by eight percent over the two year window, while imports of reference commodities grow by seven percent, producing a 15 percentage point gap in the relative import growth rates between subject and non-subject commodities over the two years leading up to the petition.

Our work complements the existing literature, which can be divided into two broad types of papers: those that study the determinants of a petition and/or the applied duties and those that consider the consequences of protection once applied. This paper falls into the former group but takes a different approach; one distinguished by the level of detail embodied in the trade data we employ. Our detailed data allow us to match exactly the definition of a product under investigation and the exact timing of initiation of the process.

Most papers addressing the determinants of filing utilize data on domestic industry performance. However, because domestic data are aggregated over products and time, these studies are constrained to deal with industry level information on an annual basis. In contrast, we utilize product-level import data that is reported on a monthly basis. The additional detail in both these dimensions turns out to be critically important since one of the shocks is common to both subject and nonsubject products and long lived, but the other is narrow and occurs _within_ the year of filing. This implies that the previous more aggregate approach puts all the emphasis on the common export supply shock (since it is also evident in the year before filing at the industry level) but misses the pronounced import demand shock that occurs immediately before filing.

2
# **I. Introduction**

**The objective of this paper is to address the main considerations for China of including financial services**<sup>**1**</sup> **in its preferential trade agreements (PTAs)** . China has made significant progress in liberalizing its domestic financial system since its WTO accession in 2001, and it is currently engaged in multiple bilateral (Australia, Singapore, Peru, Iceland) and plurilateral (GCC, SACU) PTA negotiations in addition to undertaking feasibility studies for more such agreements. The question arises as to whether – and how – financial services should be included in such PTAs, as well as the implications in terms of the negotiations preparation process. The paper addresses these issues by reviewing international experience and placing it in the context of China’s financial system circumstances and policy priorities. Particular emphasis is placed on lessons from Latin American PTAs given their more frequent and extensive coverage of financial services compared to trade agreements in other regions.

**The paper is structured as follows** :

- Section II briefly describes China’s financial liberalization process (including in financial services trade) and state of the domestic financial system;

- Section III discusses the main considerations (both positive and negative) of including financial services in China’s PTAs;

- Section IV compares and contrasts the different ‘architectural’ approaches used by countries to include financial services in their PTAs;

- Section V reviews good practices in preparing for financial services negotiations to facilitate the overall process and achieve the desired outcomes

Finally, Appendix I summarizes the experience of including financial services in FTAs for selected countries – Chile, Colombia and Costa Rica – in the Latin America and the Caribbean (LCR) region<sup>2</sup> .

> 1 The definition of financial services in this paper covers banking, insurance and insurance-related services, as well as other financial services.

> 2 The Appendix is based on Goncalves and Stephanou (April 2007).

3
which increases to 82 percent at the end of the season. Only these farmers are considered employed by the 19<sup>th</sup> ICLS standards, while the remaining farmers are considered as own-use production workers. The corresponding figures for Malawi and Nigeria are much lower – 22 percent (Malawi) and 29 percent (Nigeria) at the start of the season, increasing to 48 percent (Malawi) and 41 percent (Nigeria) at the end of the season.<sup>22</sup> In all three countries, fewer farmers are considered employed under the revised standards at the start than at the end of the season, though the increase over time is much larger in Malawi and Nigeria than in Ghana. Moreover, whenever we find a significant difference in the intended use of output between male and female farmers, women appear to be less likely than men to report producing only or mainly for sale and are therefore disproportionately classified as own-use production workers. However, gender gaps are only significant in Ghana at the beginning of the season, and at the end of the season in Malawi and Nigeria.

These gender patterns are consistent with the notion that female farmers in Africa are disproportionately engaged in subsistence foodstuff production (Darity 1995; Warner and Campbell 2000; Doss 2002; Carr 2008; Arora and Rada 2017). They could, however, also highlight the differences in how men and women respond to questions about the intended use of their output; a distinction that will be revisited later in this section.

**Table 1: Share of farmers producing only or mainly for sale – activity level**

||Gh<br>|ana (4 distric<br>|ts)<br>|Mal<br>|awi (2 distr<br>|icts)<br>|Ni<br>|geria (nation<br>|al)<br>|
|---|---|---|---|---|---|---|---|---|---|
||Male|Female|Total|Male|Female|Total|Male|Female|Total|
|Start of||||||||||
|season|0.79|0.72***|0.76|0.21|0.22|0.22|0.29|0.29|0.29|
||(0.02)|(0.02)|(0.01)|(0.03)|(0.03)|(0.021)|(0.0)|(0.01)|(0.01)|
|N|649|605|1254|172|197|369|3752|2509|6261|
|End of||||||||||
|season|0.82|0.82|0.82|0.58|0.41***|0.48|0.42|0.39*|0.41|
||(0.01)|(0.02)|(0.01)|(0.04)|(0.04)|(0.03)|(0.01)|(0.01)|(0.01)|
|N|674|645|1319|144|200|344|2583|1754|4337|

_Note:_ Standard errors in parentheses; * denotes the male-female difference is significant at 10%, ** at 5%, *** at 1% level. The estimates for Ghana refer to individuals whose main occupation is in agriculture, while the estimates for Malawi and Nigeria refer to individuals who worked at least one hour in agriculture during the reference week (see footnote 14). Based on the Ghana and Malawi Agricultural Labor Surveys and Nigeria GHS Panel Wave 3.

The increase in farmers’ employment between the start and the end of the season (reported in Table 1) may reflect changes in the self-reported intended use of output. However, since not all individuals were

> 22 It should be noted, however, that the estimates for Ghana and Malawi are not nationally representative – so the differences across countries are partly driven by the purposive selection of the districts (which were more commercialized and connected to markets in Ghana than in Malawi).

14
-9-

fiows and stocks implied by the debt data, the<sup>balance of payments, the fiscal accoun and the</sup> monetary accounts. Similar calculations were<sup>done after balance of payments data in the national</sup> accounts were compared with the figures in<sup>the balance of paymen. The rule of thvmb was to</sup> identify those adjustments which made the data<sup>in each input worksheet internally consistent,</sup> reconciled stocks with flows and achieved<sup>intersectoral consistency in relation to the national</sup> accounts.

2.17 Historical net foreign assets of the central bank<sup>and monetary authority were also adjusted</sup> for consistency with their capital sourcm. This<sup>adjuement became the balancing item in the</sup> capital sources of the balance of payments.<sup>An adjustment was also needed to make the capital</sup> uses of government revenue equal to the capital<sup>sources. This adjustment balanced the private</sup> sector's sources and uses of funds. Finally,<sup>the total historical sources of the capital funds of</sup> the central government were changed by an amount<sup>which became the balancing item for the</sup> historical uses of capital funds by the private<sup>sector.</sup> After reconciliation for flow-of-funds consistency, the historical data are used by<sup>the model as the basis for projections and policy</sup> simulations.
<!-- Start of picture text --> 0.50%fo) maize<br>0.45%<br>0.40% wheat<br>= 0.35%<br>Bw<br>OS 0.30%<br>oa<br>aS 0.25%<br><!-- End of picture text --> <!-- Start of picture text --> x<br>3<br>2 0.20%<br>‘o<br>D> 0.15%<br>s | barley<br>OS 0.10%<br>0.05% | rice<br>0.00%<br>South Africa Kenya Algeria China Philippines Saudi Arabia<br><!-- End of picture text -->
Furthermore, self-control is another factor that predicts fraud victimization (Schreck, 1999; Schreck et al., 2006; Reisig and Holtfreter, 2013). Individuals with lower levels of selfcontrol are more likely to become fraud victims because they are more likely to be shortsighted, and they find it hard to resist the temptation of promised immediate return with little inputs (Holtfreter et al., 2008; Van Wilsem, 2013).

However, most of the aforementioned results are correlational evidence, and are based on selected samples consisting of either limited number of victims or telephoneinterviewed older population. Alves and Wilson (2008) study the vulnerability of the elderly by using a sample consisting of 28 fraud victims. Titus and Gover (2001) and Mears et al. (2016) employ a telephone-survey data set to estimate the incidence and prevalence of fraud victimization. About 2,000 telephone respondents aged 60 and above form the sample used in Mears et al. (2016). Sample selection and insufficient sample size issues make the findings less convincing.

Several papers have examined the impact of credit accessibility on _crime_ from the perspective of criminal offenders. Garmaise and Moskowitz (2006) find that local bank merger induces poorer credit access and results in a large increase in crime. Cortés et al. (2016) find that cash grabbing crimes increase disproportionally after the crashing down of Ponzi schemes in 2008 in Colombia, and the effect is driven by limited access to credit. Aneja and Avenancio-León (2019) document that when there is a lack of access to credit, the deterring effect of incarceration on crime is smaller, and thus indirectly show credit constraints induce criminal behaviors. Palmer et al. (2019) find that financial assistance, on the other hand, can reduce criminal behavior. The total arrests in Chicago fell 1-2 years after temporary financial assistance to eligible individuals and households. This is likely due to greater housing stability associated with financial assistance. While this literature links credit constraints to crime, it does not address the relationship between credit constraints and financial fraud in particular.

The impact of credit constraints on households is well studied in the literature. Failure to borrow desired amounts of money may affect household behaviors. Zeldes (1989) shows that borrowing constraints lead to a violation of the Euler equation, which explains why household consumption would not imply Keynesian behavior empirically as predicted by report their fraud experiences than others.

9