rafmacalaba/datause-encoder-data huggingface dataset:
- Establishment of enquiry points for information on import and export regulations, customs clearance rules, and other border measures.  This could include provisions for advance notice and comment on new border measures prior to adoption.

- Requirement to provide advance rulings on tariff classification and valuation for traders.

- Requirement to publish and make available on the internet trade rules and procedures for imports and exports.

- Creation of formal border agency “cooperation mechanism” for the exchange of information among members.

- Limits on inspection of goods in transit and elimination proscribed transit routes.

These are useful steps towards ensuring greater transparency in the trading system. An agreement may also play a catalytic role by creating additional incentives to improve border management systems by countries through technical support and capacity building funds from sources such as the World Bank, regional Banks, and bilateral donors. Among the major issues remaining are what manner of technical assistance will be provided and the link to commitments undertaken in the WTO with special and differential treatment for developing countries The much larger agenda and benefits to transparency in the trading system go beyond these steps at the WTO, however. Sustained progress in a broader context requires reform at the national level in ensuring the rule of law, increasing the transparency of trade, investing in regulatory reform, and upgrading infrastructure to lower trade costs. New Bank research suggests that action in this type of broad and dynamic longer-term agenda could have a significant positive impact on world trade.<sup>7</sup>

> 6 This list is illustrative and does not include all items under discussion.  See “Needs, Priorities, and Costs Associated with Technical Assistance of a WTO Trade Facilitation Agreement: A Comparative Study Based on Six Developing Countries, World Bank Trade Department, November 2006 and WTO Negotiations on Trade Facilitation: Compilation of Members Textual Proposals,” WTO, TN/TF/W/43/Rev.14, March 12, 2008.

> 7 “Governance, Corruption, and Trade in the Asia Pacific,” Abe and Wilson (2008, mimeo).

15
# Contents

|1|Introduction<br>3|
|---|---|
|2|Some Basic Facts<br>5|
|3|A Formal Framework<br>8|
||3.1<br>Production<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>8|
||3.2<br>The Labor Market<br>. . . . . . . . . . . . . . . . . . . . . . . .<br>9|
||3.3<br>The Pay-as-you-go Pension System . . . . . . . . . . . . . . .<br>17|
||3.4<br>Other Model Features<br>. . . . . . . . . . . . . . . . . . . . . .<br>19|
|4|Calibration and Parameter Values<br>22|
||4.1<br>The Social Accounting Matrix . . . . . . . . . . . . . . . . . .<br>23|
||4.2<br>Behavioral Parameters . . . . . . . . . . . . . . . . . . . . . .<br>24|
|5|Simulation Experiments<br>25|
||5.1<br>Reduction in Payroll Taxes on Unskilled Labor . . . . . . . . .<br>25|
||5.2<br>Cut in Public Sector Wages<br>. . . . . . . . . . . . . . . . . . .<br>28|
||5.3<br>Public Sector Layofs . . . . . . . . . . . . . . . . . . . . . . .<br>30|
||5.4<br>Subsidies to Private Employment . . . . . . . . . . . . . . . .<br>32|
||5.5<br>Reduction in Unions’ Bargaining Strength<br>. . . . . . . . . . .<br>34|
||5.6<br>A Composite Reform Program . . . . . . . . . . . . . . . . . .<br>35|
|6|Summary and Policy Lessons<br>36|
|Ap|pendix A: Equations of the Model<br>40|
|Ap|pendix B: Variable Names and Defnitions<br>46|
|Re|ferences<br>53|

2
# **5 Coping Mechanisms, Fishing Recovery and Industry Spillover Effects**

In this section, we turn to focus on the different coping mechanisms of the victimized fishery workers. We corroborate evidence from both the satellite and labor force data. First, we discover a large distributional impact to fishery incomes by location. We also show that, dependent upon where fishers were located, these individuals likely responded differently to the shock. Those who could feasibly travel to safe grounds likely did so to sustain fishing activities. In contrast, fishers who were likely “trapped” inside the contaminated zone chose to substitute fishery work-hours with having secondary jobs. Next, we look at disaster recovery, examining the _Formosa_ impacts on both fishing intensity and fishery income by quarters. We find a gradual reduction in disaster damage to both outcomes, even though the impact on earnings still remained sizable in the last quarter of 2016. Lastly, we study potential spillover effects of _Formosa_ on related industries, and find evidence suggesting an increase in freshwater fishery earnings.

## **5.1 How did the victims cope with the shock?**

To motivate our discussion on the coping mechanisms of fishery workers following _Formosa_ , we first provide evidence that the impact of the crisis is highly heterogeneous. In Table 4, we split the treatment provinces into two separate groups by geographic location: Ha Tinh and Quang Binh (i.e. the “upstream” group), and Quang Tri and Hue (i.e. the “downstream” group). Immediately, we discover a stark difference on how _Formosa_ affected fisheries. In terms of the impact on incomes, the estimated ATE size (i.e. the size of average income reduction) for the downstream fishers is found to be between 10 and 15 percentage points larger than that for the upstream counterparts, suggesting a more severe impact of _Formosa_ on the economic well-being of fishers living downstream.

In order to investigate the reason underlying such discrepancy in the geographic distribution of earning impacts, we turn to satellite information. Recall from the initial observation in Figure 2, there seems to be a transition in fishing grounds (i.e. the bright pixel clusters) from within the contaminated zone to the “safe” region located north of Ha Tinh. The anecdotal explanation for this altered fishing pattern pertains to the southward flow of the ocean; toxic substances discharged by _Formosa_ in Ha Tinh were likely spread south along the current (hence affecting also Quang Binh, Quang Tri, and Hue), leaving the marine region 18
|_Western Balkans_|55|65|63|
|---|---|---|---|
|Albania|28|38|40|
|Bosnia and Herzegovina|56|53|59|
|Kosovo|44|48|52|
|Republic of North Macedonia|74|72|77|
|Montenegro|50|60|68|
|Serbia|62|83|71|
|_Western Europe_|97|98|97|
|Austria|97|97|98|
|Belgium|96|98|99|
|France|97|97|94|
|Germany|98|99|99|
|Ireland|94|95|95|
|Luxembourg|95|96|99|
|Netherlands|99|99|100|
|United Kingdom|97|99|96|
|Total|69|77|81|

_Source_ : Global Findex database.

_Note_ : Table shows percentage of adults 15 and older who own an account at a financial institution or through a mobile money provider.

# Inequality in Account Ownership Is High in Parts of the Region

Globally, inequality in account ownership along gender, income, and other dimensions persists, despite continued growth in account ownership. Gender inequality is especially persistent. While 72 percent of men around the world have an account, just 65 percent of women do. The gender gap of 7 percentage points was also present in 2014 and 2011. In developing economies, the gender gap remained constant at 9 percentage points (figure 2).

The gap between richer and poorer households also failed to narrow. Among adults in the richest 60 percent of households within economies, 74 percent have an account. Among the poorest 40 percent, only 61 percent have an account. The global average gap between richer and poorer households stood at 13 percentage points in 2014 and 2017. Account ownership is also lower among young adults, the less educated, and people who are out of the labor force.

6
participation in society, then the evidence identified earlier suggests that substantial economic and social benefits can be generated. In this paper, we aim to empirically test this widely held view in the context of the Chinese experience.

# **3. Data and Methodology**

In this section we present a brief discussion of the data used in the analysis. This is then followed by an explanation of the methodology used to evaluate the social and economic impact of the program.

# _3.1_ **_Data_**

Although the survey and the community program were primarily designed to assess the impact of a woman's participation in community life on her fertility preferences, the information available from the survey is sufficiently extensive to allow us to address a somewhat broader set of issues related to women's economic and social participation. The survey collected detailed information in three areas: (1) household socio-economic data; (2) women's community participation activities; and (3) women's attitudes to gender and fertility issues. This information was collected for both control and treatment households in 1996; the absence of baseline data for 1993 obviously restricts the nature of our analysis (e.g. we are unable to control for unobserved individual fixed effects). Table l a provides a statistical summary of the survey data for both sets of villages and Table lb describes some of the variables in more detail and how they enter into our regression analysis.

A central objective of our analysis is to determine if the program has some features of capital (i.e. both economic and social), which increase income. To the extent that the control and treatment localities were randomly selected, a simple comparison of mean income across both groups can provide an unbiased estimate of the program impact on income. However, from 13
The resulting indexes for labor earnings in the informal sectors of Cote d'Ivoire and Senegal show there was no deflationary trend over the 1980s and early 1990s. These indexes are represented in Figures 2 and 3 respectively.6 In C6te d'Ivoire, labor earnings in the informal sector increased roughly at the same pace as labor costs in the formal sector, at least until the end of the 1980s. In Senegal, they increased at a much faster pace. Figures 2 and 3 also show that both labor earnings in the informal sector and labor costs in the formal sector increased far more than the SMIG.

The plots in Figures 2 and 3 do not imply that formal sector wages are flexible downwards though. They only suggest that downward rigidity, if it does exist, did not play a role in the series of events leading to the 1994 devaluation of the CFA Franc. In particular, they reject the hypothesis that labor market policies and institutions were the obstacle preventing wages in the formal sector to adjust to a more unfavorable international context. But the downward rigidity of wages, if there is such a thing in CFA countries, could still represent a serious problem when facing truly deflationary pressures. The fact that these pressures did not materialize in the 1980s and early 1990s does not rule them out for the future.

There is, however, some evidence that formal sector wages are flexible downwards, at least in M6te d'Ivoire. The reason why this flexibility may not be apparent is because analyses based on averages tend to neglect composition effects. In a study using data collected at the plant level, Levy and Newman 6 The represented series are unweighted averages of the indexes for hair-cuts and laundries. Each of these two, in turn, is an unweighted average of the indexes for all of the items available in the European consumption bundle of the two countries. In Cote d'Ivoire, the price index for hair cuts is based on items 41011 and 41012, whereas the price index for laundries is based on items 31018, 31019 and 31020. In Senegal, items 118 and 119 were used for hair-cuts, and item 148 for laundries.

26
coastal area.

Table 4 Fiscal disparity among provinces (RMB/person)

|Year<br>Province|1995|1996|1997|1998|1999|2000|2001|2002|2003|
|---|---|---|---|---|---|---|---|---|---|
|guangdong|525.90|659.50|748.20|881.40|1053.90|1252.50|1491.09|1528.97|1653.90|
|shanghai|1489.60|1902.80|2255.50|2582.70|2849.10|3292.90|3776.16|4362.78|5179.59|
|jiangxi|148.40|182.20|209.00|229.60|248.50|263.90|315.29|332.89|395.32|
|hunan|165.60|199.60|210.00|240.00|254.90|271.00|311.42|348.69|403.19|
|guizhou|104.60|133.30|150.60|176.10|200.20|229.70|262.57|282.20|321.85|
|yunnan|234.60|310.10|358.80|401.30|411.90|431.20|446.19|477.18|523.31|
|disparity<br>coefficient|1.069|1.104|1.133|1.134|1.131|1.153|1.16|1.21|1.24|

Source: China Statistical Yearbook from 1995 to 2004. The disparity coefficient is the standard deviation divided by the arithmetic mean.

# The mismatch of governmental function and fiscal capacity among

different levels Another type of fiscal disparity is that between higher levels of government and lower tiers.

The reform of 1994 and its aftermath shifted fiscal revenues upward to the center. At 21
Table 3.2 reports the five Brazilian occupations with the strongest requirements for each broad activity bundle and the five Brazilian occupations with the weakest requirements for each broad activity bundle. Car washers and window washers are among the most routine occupations, while orchestral musicians and missionaries are among the least routine occupations in Brazil. By contrast, psychiatrists are highly non-routine task intensive and hand knitters are among the least non-routine task intensive occupations in Brazil.

Our main goal is to assess how intensively such activity bundles are used by Brazilian establishments, firms, and industries, and whether the intensity changes over time in response to the adoption of digital technologies. The final step in calculating the dependent variables in our analysis aggregates over all workers (and their respective occupations) in an industry-municipality in a given year. Since the occupation information from O*NET is specific to the year 2000, the time variation in these dependent variables results as employers shift the workforce composition over time. In the first column of Table 3.3, we report average values across all establishments for the main dependent variable—the ratio of non-routine to routine tasks. We note that the relative value of non-routine to routine tasks is always less than one, signaling Brazil’s comparative advantage in lower-skilled tasks due to an abundance of lower-skilled labor. However, over the 10-year time horizon, the relative importance of non-routine tasks in the workforce has steadily increased, by about two percentage points.

# **3.3. Digital technologies data**

**Rollout of internet services**

**Rollout of internet services** Beginning in 1999, the Brazilian Census Bureau (IBGE) has conducted an annual survey of all Brazilian municipalities, the _Pesquisa de Informações Básicas Municipais_ (known as MUNIC), on the structure and functions of municipal institutions. In the years 1999, 2001, 2005, and 2006, the survey specifically asks questions about the existence of various cultural activities. Among these is a question regarding whether the city has a company that provides internet services. We assume that no Brazilian city had access to internet services in 1996.

15
These changes in duration correspond, of course, to swings in separation and transition rates and here again we find great similarities in behavior across the sectors. The probabilities of transiting between formality and the two sectors of informality (Figure 5) suggest pro-cyclical patterns of job allocation across all sectors of employment and the movements are highly correlated within pairs of bilateral flows, especially in the case of self-employment and formality: the de-trended series of S-F and F-S transition rates, and I-F and F-I transition rates show correlations of 0.84 and 0.44 respectively. These patterns correspond closely to the pro-cyclical patterns in job-to-job flows observed in U.S. literature on job-to-job flows (Shimer 2005c) that are generally attributed to workers finding better jobs in tighter job markets, or when workers are involuntarily separated in the normal churning process but find another before entering the unemployment pool. They are less consistent with the informal sector being the disadvantaged sector in a segmented labor market which would imply negative correlation between these flows across the business cycle. That said, again, the raw series suggest a structural change occurring, in the early 1990s. Flows between SE to F diverge with F to SE staying high and the reverse falling. The comovements among F and I broadly reverse sign across the same period.

Figures 6a show the flows from each of the employment sectors into unemployment and inactivity. For all sectors, as found for the US by Blanchard and Diamond (1991) and Hall (2005), flows into inactivity are pro-cyclical whereas flows into unemployment are clearly countercyclical and dramatically so during the 1983 and 1999 crisis. That said, consistent with Mexico, formal separations are relatively invariant while the informal show the largest volatility in separations, perhaps reflecting the risk attending informal micro enterprises and the necessary adjustments via quantities to cope with economic fluctuations. But it is not the case that the informal sectors are playing the role of disguised unemployment. Table 5 shows that the correlation of the HP de-trended flows from formality into unemployment, informal salaried and self employed work with respect to unemployment rate are 0.10 and -0.19 and -0.56 respectively suggesting very different motivations for entry.

12
Figure 4: % Change in Average Real Income per Percentile (1994=100) <!-- Start of picture text --> 1990 1995 2000<br>Year<br>Q1 Q2<br>Q3 Q4<br>Q5<br>1990 1995 2000<br>Year<br>P20 P90<br>P95 P99<br>Data source: ENIGH<br>110<br>100<br>90<br>80<br>% Change in Real Incomes (1994=100) 70<br>110<br>100<br>90<br>80<br>% Change in Incomes (1994=100)<br>70<br><!-- End of picture text --> The persistent high income inequality and the changes registered after the peso crisis might be explained by sectoral redistributions caused by the 1994-95 macro shocks (see Figure 3). For instance, real incomes of heads of household working in the agricultural sector could have been stagnant during the general recovery period of the economy (1996-2000) and this might explain the increasing gap between the household incomes of the poorest and richest cohorts during those years. To explore this possibility, let us define 4 sectors in the economy: the urban manufacturing sector (tradable sector), urban service sector (non-tradable sector), agricultural sector (rural tradable) and the informal sector. In Figure 5 we show the average real personal income and the average years of formal education of heads of household in each sector of the economy.

9
The results, reported in Table 4, suggest little systematic variation across individual households but substantial variation across space, even though vanilla farmers are relatively concentrated. The story is probably that the bad shape of roads has the effect of segmenting the area into smaller sub-areas even though it is small to start with. However the data does not allow us to tell whether the price variation generated by the segmentation is in terms of transport costs or market power of local intermediaries. Thus, the best that can be said of the regression results is that they suggest that there is substantial noise in the producer-price variable. This should of course be kept in mind for the rest of the analysis.

Table 4 Estimation results, vanilla price regressions

||1993|1997|1999|2001|
|---|---|---|---|---|
|Ln HH size|0.118|0.226|0.188|0.215|
||(0.42)|(1.45)|(1.53)|(1.42)|
|HH head age|1.094|-1.635|-2.135|-1.639|
||(0.25)|(0.43)|(0.60)|(0.43)|
|HH head age 2|-0.037|0.214|0.276|0.218|
||(0.07)|(0.42)|(0.56)|(0.43)|
|HH head schooling|0.044|0.010|0.004|0.009|
||(0.99)|(0.27)|(0.13)|(0.24)|
|Members age|-0.075|-0.313|0.087|-0.331|
||(0.33)|(2.14)*|(0.64)|(2.30)*|
|Cropland size|-0.018|0.073|-0.309|0.079|
||(0.32)|(0.70)|(1.57)|(0.76)|
|Inputs|0.101|0.926|-0.388|0.922|
||(0.40)|(1.29)|(1.36)|(1.27)|
|fivondroanana303|-1.115|-0.504|-1.285|-1.235|
||(8.72)**|(1.28)|(5.22)**|(5.51)**|
|fivondroanana304|-1.074||-0.988|-0.649|
||(13.82)**||(5.83)**|(1.26)|
|fivondroanana710|0.219|0.086||-0.654|
||(1.35)|(0.16)||(3.15)**|
|fivondroanana711||0.319|0.390|-0.420|
|||(0.65)|(1.70)|(1.90)|
|fivondroanana712|-0.625|0.214|-0.388|-0.527|

the OLS regression, so using a Heckman procedure on very small samples was unlikely to give better results). Remoteness and transport-cost variables were not included because they are collinear with fivondroanana dummies.

22
Table 1: Baseline Characteristics of Families (2000-2001).

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Variable|N|All<br>Mean|S.D.|N|Ever eligible<br>Mean|<br>S.D.|
|Any subsidy|1788715|0.35|0.48|390382|0.51|0.50|
|SAP|1788243|0.16|0.37|390184|0.09|0.29|
|SUF|1094825|0.24|0.43|258880|0.47|0.50|
|Housing<br>|||||||
|Legal occupation of house|1788706|0.60|0.49|390380|0.39|0.49|
|Owner of house (condition on legal occupation of house)|1072670|0.82|0.39|152437|0.77|0.42|
|<br>Adequate walls|1788717|0.45|0.50|390382|0.24|0.43|
|Adequate roof|1788717|0.64|0.48|390382|0.33|0.47|
|Overcrowding|1783921|1.24|0.78|389537|1.59|1.03|
|Water from public network|1788717|0.90|0.31|390382|0.73|0.44|
|Fridge|1788706|0.59|0.49|390380|0.27|0.44|
|Sewage connected|1788717|0.65|0.48|390382|0.30|0.46|
|Heating|1788706|0.26|0.44|390380|0.02|0.15|
|CS and CAS|||||||
|CAS|1788706|542.15|55.22|390380|478.06|34.28|
|Ever in CS|1788717|0.22|0.41|390382|0.47|0.50|
|Labor Market and Income|||||||
|Employed (head)|1788599|0.71|0.45|390349|0.69|0.46|
|Self-employed (head)|1788599|0.41|0.49|390349|0.57|0.50|
|Dependent worker (head)|1788599|0.31|0.46|390349|0.12|0.33|
|<br>Employed (spouse)|1152166|0.21|0.41|239648|0.13|0.34|
|Self-employed (spouse)|1152166|0.11|0.31|239648|0.11|0.31|
|Dependent worker (spouse)|1152166|0.10|0.30|239648|0.03|0.17|
|Imputed income|1639269|0.57|0.50|323166|0.79|0.40|
|Monthly Income per capita|1788717|30317.15|27087.26|390382|16453.52|14854.09|
|Demographics|||||||
|Age of head|1788717|45.82|14.41|390382|45.73|15.06|
|Single headed|1788717|0.36|0.48|390382|0.39|0.49|
|Male head|1788717|0.70|0.46|390382|0.70|0.46|
|Years of Schooling of Head|1788714|7.36|3.82|390380|4.85|3.26|
|<br>Years of Schooling of Spouse|732779|7.44|3.74|150185|7.43|3.75|
|Presence of children|1788717|0.61|0.49|390382|0.66|0.47|
|Family Size|1788717|3.64|1.71|390382|3.83|1.87|
|<br>Minutes family takes to nearest health center|966954|24.05|23.19|220796|31.48|32.28|
|Family belongs to dominant ethnicity in neighborhood|513308|0.89|0.31|120593|0.89|0.31|
|Rural|1788717|0.17|0.38|390382|0.34|0.47|

Note: The table includes the mean and standard deviation for selected variables for the whole sample of families in the data (columns 1-3) and for the set of families that were eligible for CS at least once according to the official cutoff between 2002 and 2006 (columns 4-6). There is one observation per family in the table which is measured prior to the introduction of CS in 2002 (in particular, we include the characteristics of families when they were first surveyed in 2000 or 2001). The only variables which were measured after 2002 are ”Minutes family takes to nearest health center” and the indicator for whether ”Family belongs to dominant ethnicity in neighborhood”, which are measured in FPS (2007-2009). The time it takes from family’s residence to the nearest health center is the average for the observations a family has in the FPS data, whereas ethnicity of family is the ethnicity of the head the first time a family has FPS data (which can be 2007, 2008 or 2009).

19