rafmacalaba/datause-encoder-data huggingface dataset:
From a policy perspective, the comprehensive environmental and tax structure of the IRENCGE-DF model makes it well suited for analyzing a wide range of policy scenarios. In particular, the “Fit for 55” package is analyzed in the model by limiting the level of emissions and letting the model calculate the endogenous price consistent with this cap. Thanks to the extensive focus on tax modeling, the IRENCGE-DF model is able to properly fit scenarios with new structures of tax rates based on the energy content and environmental performance of the fuels and electricity and also to simulate broader taxable bases by including more products in the scope and by removing some of the current exemptions and reductions. Several simulation scenarios will be discussed in the following sections.

The remainder of the paper is organized as follows. In Section 2, we present the data and methodology, reviewing the main aspects of the IRENCGE-DF model. In the Section 3, we present the results of our policy simulations. The final section concludes with some policy implications.

# **2 Data and Methodology**

# **2.1 SAM Construction**

The model is calibrated on the 2017 SAM. We update the benchmark data to 2020 by using macroeconomic variations resulting from the latest public economic and financial documents (i.e. Italian annual budget law, etc.).

The information contained in the SAM is obtained by combining national account data, such as the national account matrix for 2014, supply-use tables for 2017, and Eurostat data. In addition, we integrate missing information with data from tax returns available at the Department of Finance.

National accounts data provide detailed information on the final and intermediate consumption at activity and commodity levels, though they do not contain detailed information on taxation. Hence, we use the tax return data to distribute taxes and subsidies per commodity. In Table 1, we provide a description of the sets and structure used in the analysis. For the purpose of this study, the SAM distinguishes between 77 activities, 68 commodities, 10 household groups and 10 tax categories.

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“First and foremost it will help alleviate poverty directly by providing jobs for rural and outer island workers who often lack income-generating work. The earnings they send home will support families, help pay for education and health, and sometimes provide capital for those wanting to start a small business.” Winston Peters, New Zealand’s Foreign Affairs Minister, 25 October 2006<sup>1</sup>

# **1. Introduction**

New Zealand’s new Recognized Seasonal Employer (RSE) program, which allows workers from the Pacific to work in seasonal employment in the horticulture and viticulture industries in New Zealand, is expected to have positive development benefits for the participating Pacific nations. However, the development impact of the program will depend in large part on whether rural unskilled workers really do participate in the program, or whether in practice employers end up recruiting more educated, wealthier, urban workers with better English skills who still stand to benefit from the higher wages offered in New Zealand.

This paper examines the process of selection into the RSE program in Tonga, using a large specialized survey intended as a baseline for assessing the development impact of the RSE. We find that the process of village-level nomination of workers and government-orchestrated recruitment has resulted in the RSE workers being largely agricultural workers with lower than average incomes and schooling. The RSE workers are also seen to be significantly more rural and less educated than individuals applying to

> 1 Quoted in _Islands Business_ ,

> <u>http://www.islandsbusiness.com/news/index_dynamic/containerNameToReplace=MiddleMiddle/focusMod uleID=130/focusContentID=6691/tableName=mediaRelease/overideSkinName=newsArticle-full.tpl</u> [accessed March 15, 2008].

- 2 -
# <u>China</u>

The China study focuses on Pingbian County in Yunnan Province near China’s southern border with Vietnam.<sup>13</sup> The area is considered a biodiversity hot spot, and the World Wildlife Fund has placed Yunnan Province on its Global 200 list of eco-regions that are conservation priorities. The region is remote and mountainous and its ecosystems are relatively intact with clean water systems. Roughly a quarter of the county’s 471,000 acres is protected as part of the Da Wei Shan Nature Reserve. The reserve’s forests contain more than 3,000 plant species and more than 500 animal species.

About 62 percent of Pingbian’s population of 147,000 belong to ethnic minorities, and the county is given a measure of autonomy for this reason. The Miao people, who are tied ethnically to the Hmong of northern Laos and Vietnam, comprise the largest group. The county is overwhelmingly agricultural and about 90 percent of the households earn their living by farming small plots of land. Despite recent gains, the county remains overwhelmingly poor; 61 percent of the population makes do with incomes less than US$105 annually per capita and about half earn less than US$76 per capita per year. The study focuses on the shifting composition of farm output, related changes in farming practices and the out-migration of farm labor following trade reforms taken prior to the start of China’s WTO membership.

# <u>Vietnam</u>

The Vietnam study centers on Ca Mau Province, which is situated on a low-lying peninsula between the South China Sea and the Gulf of Thailand.<sup>14</sup> Ca Mau Province makes up about 13 percent of the Mekong River Delta. Its climate is sub-tropical climate with seasonal monsoons. The region once supported extensive mangrove forests but much of the land has been cleared for agriculture and is protected from flooding by a system of dykes. The delta has been modified to facilitate transport and a system of canals connecting natural rivers and estuaries now allows for passage from the South Sea of China to the Gulf of Thailand. Though reduced in size, Ca Mau’s mangrove forest remains the largest in Vietnam and

> 13 This section draws on Daming, Jiang and Cochran (forthcoming).

> 14 This section draws on Thong, Mai et al. (forthcoming). For a good review of the spatial relationships between natural resources and poverty in Vietnam, see World Bank (forthcoming.)

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# **Appendix 1** (continued)

|**Variable**|**Description**|**Source**|
|---|---|---|
|Lending rate|Lending interest rate (percent) 2007|IFS|
|Market capitalization/GDP|Market capitalization of listed companies as percent of GDP, 2007|World Development Indicators|
|Net interest margin|Net Interest Margin, 5‐year average (2002‐2007)|Financial Structure Database|
|Number of outstanding loans|Regulatory agency provided the latest estimate for the total number of|Financial Access Database|
|to SMEs|outstanding loans to SMEs|(2010)|
|Number of SMEs with<br>outstanding loans|Regulatory agency provided the latest estimate for the total number<br>of SMEs with outstanding loans|Financial Access Database<br>(2010)|
|Offshore financial center|Equals one if the country was defined by the IMF as an offshore center in<br>2008|IMF|
|Overhead costs/total assets|Bank Overhead Costs divided by Total Assets, 5‐year average (2002‐2007)|Financial Structure Database|
|Periodic reporting|If regulatory agency monitors the level of lending to SMEs through<br>periodic surveys of financial institutions|Financial Access Database<br>(2010)|
|Regular reporting|If regulatory agency monitors the level of lending to SMEs through regular<br>reporting|Financial Access Database<br>(2010)|
|Reporting from credit registry|If regulatory agency monitors the level of lending to SMEs using estimates<br>from credit registry|Financial Access Database<br>(2010)|
|Self‐employed|Self‐employed as percent of total employed, 2007|World Development Indicators|
|Share of rural population|Rural population (percent of total population) 2007|World Development Indicators|
|SME definition ‐ loan size|Regulatory agency uses loan size as criteria to define SMEs|Financial Access Database<br>(2010)|
|SME definition ‐ max number<br>of employees|Regulatory agency uses member of employees as criteria to define SMEs|Financial Access Database<br>(2010)|
|SME definition ‐ sales|Regulatory agency uses sales as criteria to define SMEs|Financial Access Database<br>(2010)|
|Starting a business ‐ time|The total number of days required to register a firm, 2007|World Bank Doing Business<br>Indicators (2008)|
|Value of outstanding loans to|Regulatory agency provided the latest estimate for the total value|Financial Access Database|
|SMEs|of outstandingloans to SMEs|(2010)|

30
infrastructure stock in Bangladesh is about 2 percent of GDP<sup>7</sup> [Guasch and Kogan, 2001] while this figure is above 12 percent in the US<sup>8</sup> . One year worth of savings in inventory holding costs would be enough to double Bangladesh’s infrastructure stock; infrastructure improvement could pay for itself. At the firm’s level, the impact of these high levels of inventories is also enormous. Given the high cost of capital in many developing countries, cutting inventory levels in half could reduce unit costs by over 20 percent with a significant impact on competitiveness, aggregate demand, and employment.

These calculations are merely a lower bound on the cost of additional inventories. First, there are certain transactions that would have been worthwhile were it not for the high level of inventory holdings necessary to complete them effectively. It is difficult to estimate the size of these lost transactions. Second, firms in developing countries will take costly steps to mitigate the institutional or structural factors creating a need for high inventories. Suppose that for a particular firm, 30 days of inventory are sufficient when transportation networks are well developed but 90 days of inventory are required when transportation networks are poor. The firm might choose to reduce these 90 days to 60 days by requiring suppliers to locate nearby. Additional costs due to poor infrastructure as measured by increased inventory levels would be 30 days while the actual costs are higher. Third, high inventories can obscure efficiency problems. Current thinking in the manufacturing and operations research fields suggests that low inventories make it easier to trace problems in the production processes.

The direct impact of inventory costs is quite large as Table 5 shows. Given the high levels of cost of capital, on average they can reach about 19 percent of product value. If countries could rely in near just-in-time strategies, those costs could be cut in half, with significant impact on competitiveness and export growth.

**TABLE 5: INVENTORY CARRYING COSTS AS A PERCENTAGE OF PRODUCT VALUE**

|**Element**|**Average (%)**|**Ranges (%)**|
|---|---|---|
|**Capital Cost**|15.00|8– 40|
|**Taxes**|1.00|0.35– 1.52|
|**Insurance**|0.05|0.01 –0.25|
|**Obsolescence**|1.20|0.5–3|
|**Storage**|2.00|0– 4|
|**TOTALS**|19.25|9–50|

_Source: Guasch and Kogan, 2006. From various studies._ The analysis carried out by Guasch and Kogan [2001] explains the magnitude and the determinants of the inventory holdings and the potential cost to the economy and the benefits on

> 7 Rough calculation, based on graphs of infrastructure stock per capita and composition of Infrastructure. See World Bank, _World Development Report_ [1994], figures 1 and 2.

> 8 Non-military, non-residential net public stocks of highways and streets.

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The corresponding index for producer prices is: Moreover, one can easily show that the following is true as an approximation: and As a result the aggregate Solow residual can be written as the weighted sum (with value-added weights) of the ...rm-level Solow residuals. More speci...cally: where �log prit is de...ned as: We can use this result to examine the sectoral sources of productivity growth, which is the key to welfare change, within a country. We can ask a variant of the same question for ...rms, as we explain in the results sub-section. Finally, we can compare cross-sectional summary statistics. For example, we can ask whether small or large ...rms contribute more to national welfare improvement.

# 4 Data and Measurement

Our main source of information is Amadeus, a comprehensive ...rm-level pan-European database developed by Bureau Van Dijk. For every ...rm it provides data on the industry where it operates (at the 4-digit NACE level), its location, the year of incorporation, the ownership structure and the number of employees, in addition to the complete balance sheets and the pro...t and loss accounts. The data set includes both publicly traded and non traded companies. We limit our analysis to a subset of countries: Belgium, France, Great Britain, Italy, and Spain. We focus on manufacturing companies with operating revenues greater than or equal to 2 million Euros and continuous observations within the period of analysis. (We restrict ourselves to the balanced panel because Amadeus does not supply census data; there is no way to distinguish between entry into the sample and actual entry into the economy.) We also use industry-level yearly data from the EU-KLEMS project, which provides output, input and price data for industries at roughly the 2-digit level of aggregation across a large number 15
terminated by the employer.

Improvements in job finding rates and retention translate into sizeable gains in work experience. After two years, people in the treatment group who graduated from vocational schools have spent 1.03 more months (14.8%) in formal employment, driven by a 1.23 month (25.7%) increase in experience with permanent contracts. Importantly, when people switch jobs, even involuntarily, they do not experience a drop in wages, suggesting that the skills workers learn on the job are not firm-specific (Becker, 1964). Especially in LMICs where youth turnover rates are high (Donovan et al., 2020), obtaining general skills is important for helping workers maintain positive wage growth, even as they transition between firms.

Fourth, we provide evidence for the underlying mechanisms of these treatment effects and for why not a larger share of secondary school graduates pursue formal employment. We show that high reservation wages are binding and affect graduates’ career choices. The shift from informal to formal employment and transition from temporary to permanent contracts among the formally employed is driven by job seekers with reservation wages just above formal sector starting salary levels. This raises the question of why reservation wages are so high given the rapid wage growth for youth with entry-level formal jobs.

To guide our analysis of underlying mechanisms, we adapt a dynamic job search model with segmented labor markets and job seeker beliefs (Meghir et al., 2015; Mueller and Spinnewijn, 2021). We find that youths in our sample have very high discount rates, which reduces the benefit of future wage growth and dampens its negative effect on reservation wages.<sup>3</sup> In addition, we collect incentive-compatible data on beliefs and find that job seekers have fairly accurate beliefs about job separation rates but underestimate formal wage growth. Even two years after graduating, the median youth believes that formal wages increase by less than 10% over the first six months compared to the actual growth rate of about 25%. Mueller and Spinnewijn (2021) show that this type of misconception reduces welfare as it leads to sub-optimally high reservation wages and low search effort for formal jobs. Our wage incentive may counter the adverse effects of false beliefs by lowering reservation wages and thus nudging youths towards accepting initially lower-paying formal jobs with higher wage

> 3One explanation for high discount rates is that people are liquidity constrained (Cohen et al., 2020) and are thus forced to accept a certain type of employment (Oreopoulos et al., 2012). We do not find evidence for this explanation in our sample as discount rates are not correlated with beneficiaries’ socio-economic status or other measures of being liquidity constrained.

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# **_4.  Empirical Considerations_**

To illustrate how the above framework might help an analyst assess the propoorness of economic growth in a particular country, we use data for Indonesia presented in table 4.1 for the period 1993-2002.

Table 4.1. Distribution of Household Expenditure in Indonesia (1993-2002)

|Year|Mean|Lowest<br>Decile|2nd|3rd|4<sup>th</sup>|5th|6th|7h|8th|9th|10<sup>th</sup>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1993|68.54|3.88|4.80|5.68|6.59|7.59|8.70|10.09|11.97|15.13|25.57|
|1996|86.62|3.57|4.39|5.20|6.05|6.99|8.08|9.44|11.34|14.63|30.31|
|1999|66.17|4.01|4.95|5.83|6.69|7.62|8.63|9.87|11.55|14.40|26.45|
|2002|81.84|3.64|4.77|5.57|6.35|7.20|8.20|9.53|11.45|14.78|28.51|

Source: World Bank Global Poverty Monitoring Database.

Each row of table 4.1 shows the mean of monthly household expenditures in 1993 Purchasing Power Parity<sup>17</sup> (PPP) dollars, along with expenditure share of each decile. The year 1993 (our base year) is also the year when Indonesia became a middle-income country according to the World Bank’s classification.

Indonesia has a long reputation of high achievements in growth and poverty reduction, even in the face of adverse conditions. In the 1980s, for instance, the country suffered a serious deterioration in its terms of trade with the rest of the world. The government responded with an aggressive adjustment program. The rate of economic growth slowed down during the adjustment period, but remained positive and above the rate of growth of the population.<sup>18</sup> Poverty continued to decline over the period.

It is commonly believed that the poverty reduction experienced by the country between 1993 and 1996 (see table 4.2) is part of the trend that started in the mid 1980s. Based on an absolute poverty line of about 2 dollars a day, poverty incidence fell from 61.5 percent in 1993 to 50.5 percent in 1996. Over that period of time, the country also successfully adjusted to the oil price shock, the overheating of the economy early in the

> 17 The World Bank 1993 PPP conversion factor is equal to 635.655 rupiahs to a dollar.  This is what one would use to convert between dollars and local currency in 1993.  The local Consumer Price Index (CPI) can then be used to translate these values into values for a year other than 1993.

> 18  Population growth _per se_ does not affect our analytical structure: note that the population size _N_ , which was introduced in equation (2.1), disappears from all ensuing analysis (see also footnote 1).

18
in Europe, where the tiered vehicle tax is often based on carbon emissions and key vehicle attributes such as weight, engine rating, and cylinder capacity.

Norway has the most aggressive tax incentives for EVs in the world - all purchase and import taxes are waived for EVs. In fact, the net acquisition cost of EVs is comparable to gasoline vehicles in Norway. Although the central government has been gradually scaling back incentives from 2014 to 2018, Norway still remains as the country with the most generous EV incentive at about $8,800 per vehicle on average in the world. Figure 7a shows average incentive from 2013 to 2020 by country offered by central (or federal) governments. The average EV incentive is around $6,000 in the United states while that in China is around $3000. Switzerland and Canada only have regional incentives, thus the population weighted regional incentive is used in the study. Overall, sizable financial incentives are available in almost all major EV markets.

Figure S5a plots the relationship between EV market shares and average financial incentives at the country-year level. It shows that countries with higher financial incentives have a larger EV share in general. In addition to financial incentives, there are a variety of non-financial incentives for EV purchases. Many countries designated distinctive-looking green plates for EVs, allowing EVs to stand out on the road as well as facilitating the implementation of free parking or HOV lane privileges. These non-financial policies reduce the ownership cost of EVs and bring non-monetary benefits that could encourage EV adoption.

Lastly, we look at the role of changing country demographics in EV penetration. Figure S5c shows the correlation between the real income per capita and the EV market shares. Richer countries tend to have higher EV shares: A $10,000 increase in a country’s real income growth is associated with a 2.2% increase in EV market shares. Other than income, other demographic variables, such as the rate of urbanization (shown in Figure S5d), do not exhibit a strong correlation with EV market shares. These correlation plots suggest that substantial variation in EV diffusion cannot be accounted for by the demographic variables. In the following, we use panel regression models to examine the role of various market and policy factors in the spatial and temporal patterns of EV diffusion.

# **Methods**

**Data** Our analysis is based on seven major data sets for 13 countries from 2013 to 2020: (1) model-level EV sales by country by year, (2) model-level vehicle attributes by country by year, (3) charging ports by charging speed by country by year, (4) model-level financial incentives for

4