rafmacalaba/datause-encoder-data huggingface dataset:
gaps in coverage between men and women or by _hukou_ status are associated with the industry and ownership sectors of firms where workers are employed. If rural migrants work almost exclusively for private sector employers, for example, and very few private sector employers provide social insurance benefits, then the coefficient on the rural migrant indicator should fall to zero once we control for ownership.

As social insurance coverage is supposed to be mandatory for workers who sign contracts under the Labor Contract Law which went into effect in January 2008, we estimate a model including an indicator for whether a worker has a contract. As social insurance contributions are mandated, we expect a high correlation between the incidence of a contract and participation in employer-based social insurance. As some contracts are of a temporary nature and monitoring is imperfect, it is unlikely that participation is perfectly collinear with existence of an employment contract. Next we introduce interactions that aim to pick up gender differences across migrant status (gender-migrant indicator interactions), differences in coverage of migrants over time (migrant-y2010 interactions), and finally gender differences in changes in migrant coverage (gender-migrant-y2010 interactions).

_<u>Does the Tax Wedge Create Higher Disincentives at Low Incomes</u>_ ? Given that the employer-based social insurance programs have a higher labor tax wedge at low incomes, we next estimate models in which we include the estimated labor tax wedge faced by the worker and employer. It is important to recognize that the estimated labor tax wedge faced by a particular worker will still be associated with both observed and unobserved factors affecting the wage or salary of the worker. As unobservables may also be related to factors affecting contract terms, including social insurance coverage, we cannot make a causal interpretation of the coefficient on the estimated labor tax wedge. At best we document a descriptive relationship between participation and the labor tax wedge.

With an eye toward distinguishing the effects of the tax wedge on more and less mobile workers, we estimate these effects separately on local residents and migrants. Local _hukou_ residents, who are more likely to remain in the city until retirement, may show greater responsiveness to the tax wedge than migrants. While some migrants have lower hourly wages than local residents, they also tend to be more mobile and have less certainty about where they will retire.<sup>35</sup> For migrants, who may never expect to be fully vested in the social pooling portion of the pension program, the discounted benefits of participating will be distinctly lower at any level of the tax wedge.

> 35While national administrative data suggests that hourly earnings of migrants are significantly lower than local residents, the CULS, however, finds that income of migrants and locals does not differ by much, and that this is explained by the fact that migrants work many more hours per month than local residents.

17
e¤ect on rice yield only if the upazila is predominantly rural in its economic activity. We thus exclude upazilas located in two main metropolitan areas from our sample. In addition, we control for the share of "urban" households in total households in the upazila.

# (4) Data

For the empirical analysis, we combine di¤erent data sources to de...ne a upazila (subdistrict) level panel data set covering the period 2000 to 2009/2010. Our main data source for the outcome variables (wage, employment in di¤erent activities and household consumption expenditure) is three rounds of the Household Income and Expenditure Surveys (HIESs), available for 2000, 2005, and 2010. The HIESs are based on a nationally representative sample of households.<sup>12</sup> These surveys are conducted primarily for the estimation of poverty incidence and thus provide reliable information on household economic activities, per capita household expenditure and regional price de‡ators. Upazila level data on outcome and explanatory variables are generated from the HIESs using appropriate population weights.

As noted earlier, productivity growth in agriculture is measured by growth in crop yield. The predominant crop in Bangladesh is rice/paddy, of which three di¤erent types (Boro, Aman and Aus) are grown.<sup>13</sup> The o¢ cial source of agricultural statistics provides yield data at district level, and unfortunately there are no estimates at the upazila level.<sup>14</sup> The source of the yield data used in this paper is the community part of the HIES. We

> 12The sample sizes for HIES are 7,440 in 2000 10,080 in 2005 and 12,240 in 2010.

> 13High yielding variety of Boro rice now accounts for more than half of rice production (56%). Aman is the next important crop accounting for 44% of rice production. Yields of both of these varieties are much higher than Aus.

> 14These data are actually reported at old (and much larger) district level – there are about 20 old districts. With newly created districts, there are now 64 districts in Bangladesh. The source of these data are Statistical Yearbooks published by the Bangladesh Bureau of Statistics.

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I/M program in 1984. It required gasoline-powered automobiles to pass inspections every two years (Faiz et al., 1996). In other states, depending upon the state’s performance standards, motorists have to satisfy I/M requirements (Harrington et al., 2000).

Within the European Union, the member states have implemented the requirements of the Roadworthiness Framework Directive. It requires vehicle owners to go for a compulsory vehicle inspection and is enforced to ensure the necessary maintenance and upkeep of vehicles (CCAP, 2004). EU Directive 96/96/EC regulates I/M programs and safety inspections. The directive also provides some leeway to the member states in terms of: (i) setting a higher frequency of tests; (ii) making the testing of optional equipment compulsory; (iii) expanding test requirements to other classes of vehicles; and, (iv) prescribing additional or more stringent tests (USAID, 2004).

In Australia, a pilot I/M scheme was introduced in July 1998 in the greater Sydney area. Its main aim was to include all light duty vehicle by the year 2000. The main goal of the National In-service Emissions (NISE2) study was to establish a primary phase and a main phase testing that would aid in the establishment of the current emissions performance of light duty petrol vehicles (CONCAWE, 2006). The primary phase was designed to develop and validate reliable emission tests for light duty gasoline vehicles that are based on “real world” driving patterns. It was intended to provide the basic tools for use in the main phase for generating a more accurate and representative measure of the actual amount of pollutants emitted from the light duty gasoline fleet (CONCAWE, 2006).

China’s I/M programs require regular inspections, which include yearly inspections, firstclass maintenance, second class maintenance, and vehicle overhaul. In big cities such as Beijing, Shanghai, and Guangzhou, I/M programs have been effective, to a large degree, in lowering vehicle emissions (Kebin and Chang, 1999).

Despite their emission reduction potential, I/M programs have certain limitations, which are primarily on two fronts: (i) inefficient use of resources and inconvenience to motorists; and, (ii) infectiveness in identifying gross polluting vehicles (Calvert et al., 35
11

**Table 2: Share of Central and Subnational Taxes, Selected Countries and Years** **_(percent)_**

|**Country and year**|**Total**|**tax reve**|**nues**|**Tax**|**es on inco**|**me**|**Taxe**|**s on prop**|**erty**|**Dom**<br>**good**|**estic taxe**<br>**s and ser**|**s on**<br>**vices**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||%Central|%State|%Local|%Central|%State|%Local|%Central|%State|%Local|%Central|%State|%Local|
|***Germany 1998**|70.7|22.0|7.3|43.4|36.6|20.0|0.8|48.6|50.6|62.8|37.0|0.2|
|***Spain 1997**|83.0|7.5|9.4|85.7|8.7|5.7|2.8|52.4|44.7|78.5|5.4|16.0|
|**_Ukraine 2001_**|_74.3_|_0.0_|_25.7_|_35.6_|_0.0_|_64.4_|_0.0_|_0.0_|_0.0_|_80.5_|_0.0_|_19.5_|
|***Canada 1999**|52.5|38.5|9.0|63.5|36.5|0.0|0.0|21.1|78.9|41.0|59.0|0.1|
|**_*Russia 2001_**|_69.7_|_0.0_|_30.3_|_27.6_|_0.0_|_72.4_|_5.2_|_0.0_|_94.8_|_82.7_|_0.0_|_17.3_|
|**_*South Africa 1998_**|_92.8_|_0.5_|_6.7_|_100.0_|_0.0_|_0.0_|_21.7_|_0.0_|_78.3_|_98.6_|_1.4_|_0.0_|
|***Switzerland 2000**|66.0|20.0|14.0|30.3|39.1|30.7|30.9|42.8|26.3|92.2|7.6|0.2|
|***Australia 1999**|77.4|19.3|3.3|100.0|0.0|0.0|0.0|63.6|36.3|66.2|33.8|0.0|
|***United States 2001**|69.3|19.1|11.6|83.0|15.5|1.5|10.0|8.0|82.0|15.7|67.6|16.8|
|**_*Argentina 2001_**|_59.7_|_40.3_|_0.0_|_50.5_|_49.5_|_0.0_|_54.4_|_45.6_|_0.0_|_94.6_|_5.4_|_0.0_|
|**_*India 1999_**|_62.6_|_37.4_|_0.0_|_100.0_|_0.0_|_0.0_|_14.9_|_85.1_|_0.0_|_41.5_|_58.5_|_0.0_|
|**_China 1999_**|_45.0_|_55.0_|_0.0_|_24.4_|_75.6_|_0.0_|_0.0_|_100.0_|_0.0_|_55.7_|_44.3_|_0.0_|
|**_Indonesia 1999_**|_97.1_|_2.9_|_0.0_|_100.0_|_0.0_|_0.0_|_74.9_|_25.1_|_0.0_|_95.9_|_4.1_|_0.0_|

_Note:_ *indicates a federal country. Data for the emerging country group are shown in italics. _Source:_ As calculated from International Monetary Fund (2002) by Martinez-Vazquez, McLure, and Vaillancourt (2006). Countries are listed in order of tax-to-GDP ratio (as based on a variety of sources and in some instances for a later year than the detailed tax data included in this table).
respectively time, NACE 4-digit sector, and county fixed effects. Equation (13) is estimated separately for each 1-digit sector (that is, manufacturing, services, and mining) and for the whole economy, while the equation parameters are estimated by OLS, always using heteroscedasticity-robust standard errors.

**Figure 14. Productivity (TFPR) growth dividends from a 10 percent reduction of average markup, by sector**

<!-- Start of picture text -->
1.6<br>1.47<br>1.4<br>1.2<br>1<br>0.82<br>0.8<br>0.6<br>0.48<br>0.4<br>0.2<br>0<br>Manufacturing Services All<br>Percentage points per year<br><!-- End of picture text -->

_Source:_ World Bank staff calculations based on SBS data set. _Note:_ Results from year, county, and 4-digit sector fixed effects. Coefficient for mining was not statistically significant.

**Results shows that on average, reducing markups would have a positive impact on aggregate productivity expansion.** Results displayed in table A1.8 in annex 1 suggest that lower (past) markups are associated with higher (current) productivity growth: all coefficients for lagged markups are negative and statistically significant at conventional values for all sectors except mining. Overall, this negative correlation between past markup level and productivity growth is an indication of a positive association between competition and productivity growth, which would be driven by the escape competition effect described by Aghion et al. (2008).<sup>34</sup>

**The magnitude of the economic effect of markup reduction on productivity growth is sizable** . Figure 14 shows that a 10 percent decrease in the average markup of 6.1 percent for the overall economy in the 2008–17 period would imply an increase in productivity (TFPR) growth of 0.82 percentage point per year. The economic effect for the manufacturing sector would be substantially higher: reducing the average markup of 4.03 percent for manufacturing in the same period would imply an increase in TFPR growth of 1.47 percentage points per year.

**Further, productivity dividends stemming from markup reduction vary substantially across NACE 4-digit sectors.** The same specification expressed in equation (9) is estimated for each NACE 4-digit sector separately, controlling for year and county fixed effects. The TFPR growth dividends from markup reduction are computed for each NACE 4-digit sector drawing from the estimated coefficients (see table A18 in annex 1). Results vary substantially

> 34 An extended specification—where the squared value of lagged markup is included as an additional dependent variable—was also estimated. The estimated coefficient of the squared term was not statistically significant (see results in table A1.7 in annex 1).

28
**Table 4 continued from previous page**

||Cho|se the ICT|or the en|ergy voca|tional trai|ning|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||||||(0.025)|(0.028)|
|Female role model|||||0.054**|0.052**|
||||||(0.021)|(0.018)|
|Can seek professional advice from individuals outside<br>the family|||||0.002|0.003|
||||||(0.041)|(0.043)|
|**Gender attitudes and agency**|||||||
|Gender attitudes (z-score)||||||-0.030|
|||||||(0.027)|
|Agency: input in productive decisions (z-score)||||||0.021**|
|||||||(0.011)|
|Observations|1128|1128|1128|1128|1128|1128|
|Cohort FE|YES|YES|YES|YES|YES|YES|
|City FE|YES|YES|YES|YES|YES|YES|

_N_ otes: Robust standard errors in parentheses.

*** ** * p _<_ 0.01, p _<_ 0.05, p _<_ 0.1 .

Standard errors are clustered by zone. Due to a low number of clusters (6), we rely on wild bootstrap to compute p-values. For each column, we include cohort and city fixed-effects. The acronym EICT stands for Energy or Information Communication and Technology.
**Table 9 – Welfare and Budget Effects of the 2014 Subsidies Reforms (Direct effects)**

||**Pre-Ref**|**Post-Ref**|**Change**|
|---|---|---|---|
|Welfare (per capita)|17,420|17,244|-177|
|Poverty (%)|4.155|4.208|0.053|
|Inequality (%)|42.433|42.389|-0.044|
|Subsidies (in millions)|10,371|4,871|-5,500|

Source: Authors’ estimations using SUBSIM and household budget survey data.

In order to better understand the trade-offs between the gain in terms of government revenues and the losses in terms of poverty increases of subsidies reforms, Figure 2 shows the increase in poverty (leftpanel) and the impact on government revenues (right-panel) of price increases between 1% and 100% for all products considered. From the standpoint of poverty (left-panel), the only products that would have a real impact on the poverty headcount are electricity and water for price increases above 30-40%. However, the reforms implemented in 2014 did not reach such price increases and did not affect the lower tariff block which concerns the poor the most.

From the standpoint of government revenues, the most promising product is electricity. This sector results in government savings higher than those resulting from increasing prices on other products. Water is the second sector in terms of promising government savings while gasoline follows. Hence, the government has taken the right choice in terms of increasing electricity and water tariffs for upper blocks and also increasing the number of blocks. In fact, by increasing the number of blocks, it is possible to achieve savings while respecting the household capacity to pay for electricity (consumer’s demand). Moreover, prices were increased particularly for those products that are poverty neutral (in terms of direct effects) such as gasoline and diesel.

**Figure 2 – Sensitivity of changes in Poverty and Government Revenues to Changes in Prices**

<!-- Start of picture text --> 2 1.00e+10<br>Electricity Electricity<br>Water Water<br>Gasoline Gasoline<br>Diesel 8.00e+09 Diesel<br>1.5<br>6.00e+09<br>1<br>4.00e+09<br>.5<br>2.00e+09<br>0 0<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Increase in price in % The increase in prices (in %)<br><!-- End of picture text --> Source: Authors’ estimations using SUBSIM.

# **5.1.2.Indirect effects**

We now turn to the simulation of direct and indirect effects using I/O and HBS data combined. The baseline data for the price shocks are in Table 10 below. With I/O tables it is not possible to simulate price increases by product or by tariff block given that the I/O tables are aggregated by sector. Therefore, we use averages across products belonging to the same sector or across tariffs blocks. As shown in Table 15
<!-- Start of picture text --> Landed households, maize<br>° | |<br>§e '| !3<br>F48 |\\ |\\<br>—_ 4<br>2<br>so |<br>Bo: a |<br>i}<br>I!<br>-2 0 2<br>Soil moisture anomaly<br>— Linear -——— Quadratic ——— Cubic<br><!-- End of picture text -->

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<!-- Start of picture text --> Landed households, highland<br>82 ;<br>| |<br>8 | '<br>E> |<br>Zo<br>8 | 1<br>e© \ \;<br>$=)= | '<br>S., | \<br>se<br>§ 1 !<br>2 | |<br>e<br>= | |<br>:<br>-2 Novi anomaly0 2<br>——— Linear ———— Quadratic ——— Cubic<br><!-- End of picture text -->

<!-- Start of picture text --> Landed households, roots & lowland<br>+ | '<br>c 1 1<br>2 ' '<br>a f i}<br>E | \<br>Bn | |<br>88 }\ |'<br>£ | |<br>2 |<br>©ad i} i}<br>Bo<br>=<br>8 | |<br>a \ 1\<br>x \ \<br>'<br>-2 0 2<br>NDVI anomaly<br>——— Linear ———— Quadratic ———— Cubic<br><!-- End of picture text -->
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demand-supply relations in the newbuilding market can be expected to deteriorate, if no corrective measures are taken.

Table 7: THE INTERNATIONAL COMMERCIALLY OPERATED FLEET Changes in Fleet Composition

|Vessel Category|Status<br>Number|In the mi<br>dwt<br>(mill.)|d**80s**<br>Share<br>in Total<br>dwt(%)|Status<br>Number|in the ea<br>dwt<br>(mill.)|rly 90s<br>Share<br>in Total<br>dwt(%)|Change<br>(in dwt)|
|---|---|---|---|---|---|---|---|
|Oil Tankers|5,723|240.7|39.3|5,962|256.1|39.8|6.4 %|
|Chemical Tankers|874|6.2|1.0|968|6.2|1.0|0|
|LNG/LPTolkers|755|10.3|1.7|802|11.0|1.7|6.8 %|
|Dry Bulk Carriers|4,595|183.9|30.0|4,660|199.7|31.1|8.6 %|
|**O**<br>QCriits|372|31.3|6.4|352|36.8|5.7|- 6.3 %|
|Container Vessels|1,027|21.1|3.4|1,189|27.0|4.2|27.9 %|
|Girenercatl o Ships<br>t(Sigleck|7,166|25.3|4.1|6,757|24.5|3.8|3.2 %|
|General Cargo Ships<br>(Multi Deck)|7,266|56.9|9.3|6,274|48.2|7.5|- 15.3 %|
|Reefer Ships|1,273|6.7|1.1|1,386|7.6|1.2|13.4 %|
|RoRo Vessels|960|7.7|1.3|1,022|8.2|i.3|6.5 %|
|Combined<br>_go-_<br>Pnesen,<br>- -|-<br>tii37--0|4.8|0.1|311|0.6|0.1|25%|

Source: Insttute of Shipping Economics and Logistics. _'Statistical Yearbook"._ Various issues. Bremen 66. As can be inferred from Table 9 on page 35, the bulk of shipbuilding activities in recent years took place in a limited number of countries. The twelve leading shipbuilding nations presently control over 90 percent of the market. For many years now, the Far Eastern yards had the lion's share in these activities, with Japan and South Korea in the lead. The composition of the leading shipbuilding nations has remained essentially unchanged since the early 1980s. To maintain the market shares of their shipyards in newbuilding activities, the different countries pursued a variety of strategies. In addition to subsidy schemes these strategies focused on yard industry restructuring. Such restructuring led to improved productivity. The most notable result of the restructuring measures was the reduction in labor involved in shipbuilding. Since 1975 the related work forces were reduced by 62 percent in WestK European shipyards, and a staggering 73 percent in Japanese yards. Increased automation of building, activities, reinforced by computer-aided techniques, allowed the yards to cut cost and thus to maintain their market shares. But during the intervening years of slackening demand for newbuildings, the cost pressures continued, particularly in the Far Eastern and West European yards.
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