rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3**

# **Budget Resources for Primary Expenditure: Domestic Revenues, Grants, Borrowing and Interest Payments; 2006 and Projections for 2007-11 and 2012-16**

Percentage of GDP

||2006|2007-11<br>average|2012-16<br>average|
|---|---|---|---|
|Domestic Revenue|18.8%|19.8%|21.8%|
|Grants|1.2%|2.0%|2.2%|
|Borrowing|1.1%|8.5%|3.0%|
|Net External Borrowing|1.6%|8.8%|3.2%|
|Disbursements|2.9%|10.0%|4.9%|
|Amortisation|-1.3%|-1.3%|-1.7%|
|Net Domestic Borrowing|-0.5%|-0.2%|-0.2%|
|Privatisation Receipts|0.5%|||
|Interest Payments|0.5%|0.9%|1.0%|
|Budget Resources for Primary Expenditure|21.1%|29.5%|25.9%|
|NPV External PPG debt/GDP|23%|41%|40%|
|NPV External PPG debt/exports|99%|174%|163%|

Notes, grants and domestic borrowing exclude the MDRI debt relief in 2006 Source: 2006, IMF, 2007-11 and 2012-16 are author’s projections It is very difficult to make any quantitative projections of the volume of budget grants over the long term, because the range of possible outcomes is so large. At the low end of the range, Tajikistan might receive no increase in grants, because donors remain unconvinced that fiduciary standards are improving sufficiently and/or because Tajikistan contracts more loan finance from non traditional lenders, thereby reducing the perceived need for more grant aid. At the other end of the range, grants could increase several fold, possibly to around 5% of GDP, as donors both increase grant aid to Tajikistan as part of a more general expansion of development assistance to low-income countries and channel a much larger share of their grant aid to the government budget. The latter will clearly require improvements in PFM to strengthen and make more transparent the budget process and to improve the allocation of expenditures, especially in those sectors which can contribute to meeting 21
16 Even after stripping out the effects of inter-country heterogeneity, the variable remains statistically significant although reduced in size. This is consistent with Denizer _et al._ (2013). On the other hand, the _number_ of past operations taken to the board by the task team leader had limited to no impact on program outcomes. There is more to explore in this area, but initially it appears that the quality of the work done by the task team leader – as reflected in the rating of the recent operations – is the determining factor. The robustness of the task team leader track record needs to be examined with caution, however, on account of the possibility that task team leaders in particular countries may benefit or lose from the level commitment of the countries involved to reform implementation.

The variables on programmatic engagements and loan size did not show any statistical significance. Based on our qualitative review of program documents we observe that the more opportunistic stand-alone loans typically have shorter term results that tend to be less ambitious and easier to achieve than those of programmatic engagements (that have two loans or more). At the same time, reforms in programmatic engagements can be followed up from gestation to full implementation. Perhaps this result shows how these two factors counter-balance each other when performing regression analysis. Other variables tested, such as the regional dummies (accounting for the 6 regional departments of the World Bank) showed statistical significance only in the t-test on difference in means (see Table 1). However, the statistical significance did not hold in the OLS and ordered probit regressions, presumably as other variables absorbed their effect.

The lack of statistical significance of the CPIA variables— viz. the components for macroeconomic management and governance— may be surprising at first sight and not aligned with the findings of studies on the correlates of investment lending (e.g., Denizer _et al._ 2013). However, two important factors may explain this situation. First, unlike investment loans or the old style structural adjustment loans— which are used as data in the cited literature— the new policy-based loans (post 2004) require a country to have an adequate macroeconomic policy framework. This requirement excludes countries that do not fulfill that requirement at a particular time. Second, while these loans do not have a minimum threshold for “governance” for the recipient country, analysis undertaken under the World Bank’s DPL Retrospective (2012) suggests that the larger share of policy-based loans between 2005 and 2011 went to countries
_2_ l TABLE 2

# Sample Characteristics

S.1 ::iplh Characteristics KMtans and Standard Errors of the Spreads)

|'.ariable|nI_|1|Variable|<br>0|1|
|---|---|---|---|---|---|
|D19|1 13<br>(0.44)|1.28<br>(0.49)|OR|1.12<br>(0.44)|1.39<br>(0.50|
|DI)<br>**20**|1.12<br>(0.38)|1.21<br>(0.48)|B<br>60|1.11<br>(0.45)|1.28<br>(0.48)|
|D30|1.08<br>(0.44)|1.33<br>(0.47)|**AL6 0**|**1.09**<br>(0.44)|**1.27**<br>(0.48)|
|D31<br>D3019|1.06<br>(0.43)|1.28<br>(0.50)|LD 302<br>3020|**1.25**<br>(0.49)|**1.22**<br>(0.47)|
|D3020|1.03<br>(0.42)|1.22<br>(0.47)||||

# Notes:

Spreads are the spreads on commercial bank<sup>loans</sup><sup>**from the Eurocurrency**</sup> credit markets during 1968-1981.<sup>Only U.S.$denominated loans with Libor</sup> base rate are included in the sample.

D19, D30, OR, and B60 are defined in<sup>Table 1.</sup><sup>**The remaining dummy**</sup> variabies are: D is one if D or D is one, and zero otherwise. 3019 19 30 D302 is one if DO or D30 is one, and<sup>zero otherwise.</sup> AL60 is one if OR or B 6 **is** one and **zero otherwise.** LD 3020: is zero if a country has not defaulted<sup>**anytime during 1820-1929 or**</sup> during the 1930s; in addition it is<sup>zero for countries that were</sup> not sovereign during the period. It<sup>is one otherwise.</sup>
# **1. Introduction**

The Chinese economy has witnessed impressive development since the economic reforms of the late 1970s. Over the past three decades, there have been important reforms and transformations in the enterprise sector: expansion of Township and Village-ship Enterprises (TVEs), privatization of small and medium state-owned enterprises, modernization of large state-owned enterprises, as well as development of foreign enterprises. Since the late 1990s, the economy has continued rapid and steady growth even during challenging periods such as the Asian financial crisis and the global dot.com bubble. The government further deepened the reforms after its accession to the World Trade Organization.

While many studies of the Chinese economy focus on investigating the factors that condition aggregate productivity and inequality between coastal and inland regions as well as rural and urban areas, this paper takes a closer look at firm level output growth and examines the drivers of its volatility over time. The objective is to understand the micro foundations of the economic growth performance. It aims at shedding light on the sources of output volatility and its evolution over time. The literature on firms’ economic performance is abundant. However, most previous studies focus on the level of productivity. To our knowledge, the present paper is the first analysis of the volatility of productivity in China.<sup>2</sup> This study is based on a firm-level data set collected annually by the National Bureau of Statistics of China. This data set covers about 300,000 industrial firms in 1998-2007.<sup>3</sup> The paper examines the role of different firm characteristics, such as size, age, ownership, and geographic location, in productivity volatility. It follows the methodology developed in Comin and Philippon (2005; 2007) to measure firm productivity volatility as the standard deviation of the annual growth rate of output per worker.

Our empirical work is composed of three parts. First, the paper compares the productivity of three groups of firms - those that survived the entire 10 year period of 1998-2007, those that survived

> 2 We use output per worker to measure productivity.

> 3 In this paper, we use “firms” as shorthand for “industrial firms”.

2
Some of these indicators are reasons for the visit, such as prenatal care or delivery, while others are services provided during the visit, such as giving the tetanus vaccine during prenatal care. The Ministry of Health (MoH) defined these indicators and payments based on health priorities, available budget and the previous NGO experience.<sup>13, 14</sup> Facilities report their monthly indicators to the district P4P steering committee responsible for authorizing payment. For the referral indicators, the facility must also submit verification from the hospital that the referral was appropriate and the referred patient was treated. The committee verifies the reports by sending auditors to the facilities on a quarterly basis on an unannounced randomly chosen day. The auditors review the utilization registry and facility records to verify the data reported is the same as the data recorded in facility records. A comparison of facility records with face-to-face interviews of a random sample of patients reported very little false reporting.<sup>16</sup> The facility’s overall quality enters the payment formula as a multiplicative factor that raises or lowers the payment for all outputs. The quality index is bounded between zero and one. If the facility meets all of the quality criteria, then the index equals one and the facility receives full payment for the Box 1 services. However, if the facility is deficient in some of the quality criteria, then all of the payments are discounted. For example, if the facility only scores 0.80 on the quality index, then it only receives 80 percent of the payment for the P4P services.

The facility’s overall quality is measured as an index of both structural and process measures of quality of care for various types of services (Box 2).<sup>15</sup> Structural measures are the extent to which the facility has the equipment, drugs, supplies and personnel necessary to deliver a specific service, while process measures capture the clinical content of care provided for 3
**Table 5: Interaction term results for business environment variables (Log odds ratios from logit estimation)**

|Dependent variable: R&D Activity|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|**(10)**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Without**|**controlling f**<br>**Info**|**or GDP per**<br>**rmal Compet**|**capita inter**<br>**ition**|**acted with**|**Controllin**|**g for GDP**|**per capita inte**<br>**Competition**|**racted with**|**Informal**|
|Informal Competition (Country-|2.020***|1.853***|8.382***|11.114**|0.206|-4.758|-3.791|4.003|11.120**|3.395|
|level cell average)|(0.538)|(0.538)|(2.450)|(4.449)|(0.954)|(4.750)|(4.231)|(3.798)|(4.838)|(4.584)|
|Informal Competition (Country-|**-0.983****|||||**-1.584*****|||||
|<br>level cell average)*Rule of Law|(0.490)|||||(0.606)|||||
|<br>(lagged)|||||||||||
|Rule of Law (lagged)|0.814***|||||1.216***|||||
||(0.262)|||||(0.373)|||||
|Informal Competition (Country-||**-1.212****|||||**-1.773*****||||
|<br>level cell average)*Control of||(0.530)|||||(0.613)||||
|Corruption (lagged)|||||||||||
|Control of Corruption (lagged)||0.846***|||||1.187***||||
|||(0.272)|||||(0.344)||||
|Informal Competition (Country-<br>level cell average)*DTF (lagged)|||**-0.105*****<br>(0.039)|||||**-0.180*****<br>(0.051)|||
|DTF (lagged)|||0.038*|||||0.077***|||
||||(0.021)|||||(0.028)|||
|Informal Competition (Country-||||**-1.276****|||||**-1.275***||
|<br>level cell average)*Freedom from<br>Regulation (lagged)||||(0.630)|||||(0.725)||
|Freedom from Regulation (lagged)||||0.720*|||||0.719||
|||||(0.415)|||||(0.475)||
|Informal Competition (Country-<br>level cell average)*Profit Tax Rate<br>(%, lagged)|||||**0.140*****<br>(0.050)|||||**0.134****<br>(0.056)|
|Profit Tax Rate (%, lagged)|||||-0.084**|||||-0.081**|
||||||(0.033)|||||(0.036)|

51
succeeded and became the first industrialized nation from the East because of the right choice of targeting countries.

# **Section 2.3 The post WWII era: The US showed the way to Japan and other geese**

The economies of Western Europe and Japan have enjoyed unprecedented growth and technological upgrading in the decades since World War II, or, the so-called Golden Age of Capitalism (1950-1974). During this period, nearly all developing countries pursued ― _dirigiste_ capitalism‖ but except for Japan, Korea and other East Asian Tigers, they did not succeed. _Why?_ The NSE contends that the crux of Japan and East Asia‘s success was that their development followed closely their comparative advantage or was CAF and their governments played the facilitation role (Lin 2010, Lin and Monga 2010, Lin and Monga 2011). Just before WWII, textiles and other light industrial goods accounted for 60 – 75 percent of all Japanese exports. Japan‘s textile industry was at its peak before the Second World War (Ito 1992, p. 24). In the 1960s, when its per capita GDP was about 40 percent of that of the US and it had established a strong industrial base, Japan targeted U.S.‘s industries (Table 2.1). Japan‘s historical labor statistics record that a rising share of labor in Japan‘s manufacturing sector coincided with a declining share of labor in the US‘s manufacturing sectors. In the 1960s-1970s, Japan supported its heavy manufacturing sectors including machinery and automobiles. In the 1980s-1990s, just as the US was upgrading its industrial base, Japan acquired shares in the home appliances, electronics and computer markets (Figure 2.3-2.5).

**Figure 2.3  Structural transformation in Japan: employment in manufacturing increased during 1920-1973, followed by a slow decline as the services sector expanded**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>Services<br>70%<br>60%<br>50%<br>40% Ag, forest&fisheries<br>30%<br>Min&Construction<br>20%<br>10% Manufacturing<br>0%<br>1920 1935 1950 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006<br><!-- End of picture text --> Source: Historical Statistics of Japan, shares of employment by sector. Detailed data by subsectors is available at http://www.stat.go.jp/english/data/chouki/index.htm 21
In this study, we test the hypothesis that cognitive skills, as an indicator of human capital overall, are associated with a lower reliance on emissions in aggregate production technology and estimate the subsequent mitigating effect of education quality on the carbon pricing’s effectiveness and economic consequences including wage inequality. We propose a general equilibrium, overlapping-generations model in which: (1) agents are heterogeneous and their choice of skills is endogenous and (2) the average skill level of an economy affects the share of capital relative to carbon-emitting inputs in the aggregate production function. We then present two empirical applications. First, we test how workers’ cognitive skills associate with their industries’ emissions per output using the OECD’s PIAAC data set with industrial emissions from the EU’s environmental accounts data set. We find that cognitive skills are associated with lower emissions per output and lower or negative growth in emissions per output. This finding is consistent with the hypothesis that a higher skilled workforce is not only associated with industries that are able to produce output with less emissions but also able to innovate over time and reduce the amount of emissions required to produce output.

Second, we estimate: (1) our overlapping-generations model’s aggregate production function parameters using industry level data on output, capital, emissions, and literacy skills from the same two sources and (2) our model’s skills cost function conditional on household wealth using the OECD’s PISA data. We find that higher literacy skills are associated with production technology in which capital has a larger contribution relative to emissions in production. In this sense, capital can substitute emissions through more highly skilled labor. This is consistent with technology imbedded in capital (for example, machines) that can produce output with fewer emissions requiring more highly skilled labor.

One result of this finding is that green technology—technology that enables production with fewer emissions—is skill-biased, in that workers with a higher level of literacy skills contribute more to reducing the production function’s reliance on emissions relative to capital. We then estimate the marginal effects of a carbon tax based on our model’s steady state variables and find that, like other general equilibrium models which do not quantify the direct benefits of reduced emissions for output (such as the benefits to health, agricultural production, and so on), carbon taxes reduce 5
_Table 1. Main parameters for our idealized city_

|**Parameter**|**Symbol**|**Value**|**Obtention method and other relevant information**|
|---|---|---|---|
|Housing production<br>function scale|A|2.014|Calibrated based on data on the structure of Paris in 2008<br>(Viguié, Rozenberg and Hallegatte, 2014) using density data<br>from INSEE (2008) and rent data from CLAMEUR.<sup>2</sup>|
|Elasticity of housing<br>production with respect to<br>capital|b|0.64|Calibrated based on data on the structure of Paris in 2008<br>(Viguié, Rozenberg and Hallegatte 2014), using density data<br>from INSEE (2008) and rent data from CLAMEUR. The<br>calibrated value of 0.64 is very close to the value of 0.65 found<br>for France in Combes, Duranton and Gobillon (2021).|
|Share of household<br>income net of transport<br>costs spent on housing|β|0.3|Calibrated based on data on the structure of Paris in 2008<br>(Viguié, Rozenberg and Hallegatte 2014) using transport cost<br>data from Hourcade and Nadaud (2009) and Rouchaud and<br>Sauvant (2004), income data from Friggit (CGEDD, after<br>INSEE<sup>3</sup>), and real estate data from CLAMEUR.|
|Interest rate: cost of<br>capital|i|0.05|Assumed as a reasonable value. It is difficult to ascertain an<br>interest rate that would reflect the annual financial burden of<br>construction in an urban area that has developed over<br>centuries.|
|Depreciation rate of<br>capital|ρ|0.01|Assuming that structures last for 100 years on average, which<br>is consistent with Hallegatte (2009).|
|Unitary transport cost: fuel<br>and vehicle maintenance<br>(euros per kilometer)|t|0.416|Established from the French tax perception center URSSAF’s<br>reimbursement calculations for using private vehicles for work,<br>assuming an average annual mileage of 12,000 kilometers.|
|Fraction of land that can<br>be built on (net or roads<br>and public spaces)|s|0.62|Based on Corine Land Cover land use data.|
|Number of rich<br>households (millions)|N1|2.45|Assuming the population of the urban area of Paris is divided<br>into two equal-sized groups.|
|Number of poor<br>households (millions)|N0|2.45|Assuming the population of the urban area of Paris is divided<br>into two equal-sized groups.|
|Annual income of rich<br>households (euros)|Y1|80,244|Computed<br>based<br>on<br>the<br>FiLoSoFi<br>database<br>(INSEE‐DGFiP‐Cnaf‐Cnav‐CCMSA2015).|
|Annual income of poor<br>households (euros)|Y0|37,232|Computed<br>based<br>on<br>the<br>FiLoSoFi<br>database<br>(INSEE‐DGFiP‐Cnaf‐Cnav‐CCMSA2015).|

# 2.2 FLOODS AND FLOOD MANAGEMENT POLICIES

Our study of the impact of floods on this idealized city focuses on capital destruction. The model does not account for other consequences of floods, assuming, for example, that it is possible to avoid loss of life and injury through early warning systems. We consider that the agglomeration has two zones: a

> 2 CLAMEUR (https://clameur.fr/) is an association of several public and private organizations that studies the evolution of rents in France.

> 3 <u>https://www.igedd.developpement-durable.gouv.fr/rubrique.php3?id_rubrique=138, accessed on January 13, 2023.</u>

6
countries–38 of them in Europe–during 1990–2012 and find that trade openness increases labor force participation. Dauth et al. (2017) argue that increased trade openness with China and Eastern Europe from 1993 to 2004 allowed Germany to retain manufacturing jobs. Donoso et al. (2015) demonstrate that while Spanish provinces where Chinese imports grew faster experienced a significant decline in manufacturing employment, this decline was compensated by increasing employment in non-manufacturing sectors. Colantone (2012) shows that increased trade openness in the manufacturing sector resulted in job destruction and decreased employment growth in Belgium from 1996 to 2002. <u>Arvanitis and Loukis (2015)</u> report that in Switzerland, trade openness increased the demand for high-skill workers, destroyed employment for medium-skill workers and has no effect on low-skill workers. Balsvik et al. (2015) demonstrate that import competition from China from 1996 to 2007 explains almost 10% of the reduction in manufacturing employment in Norway. Low-skill workers are the most exposed to unemployment and non-participation in the labor market.

# **3. Data and Descriptive Statistics**

Our main data set for the analysis of job tenure is the EU-LFS constructed by Eurostat (2021), based on the results of a large household survey that provides quarterly data on labor force participation by respondents older than 15. National statistical institutes in every EU country conduct these surveys, applying harmonized concepts and definitions, and Eurostat processes the results. This structure allows for cross-country comparisons. Beginning in the early 1990s, the survey covered Iceland, Norway, Switzerland, and the United Kingdom, in addition to members of the European Union. The sample size of the EU-LFS ranges from 4.5 million to 5.2 million observations, depending on the year.<sup>6</sup> Because data on tenure are sparse before the mid-1990s, our sample is based on data for 1995– 2020. We restrict it to workers between the ages of 20 and 65, as often done in the literature on job tenure (see, for example, <u>Gregg and Wadsworth 2002</u> and <u>Burgess and Rees 1998). We group</u> countries into four regions: Western Europe, Northern Europe, Central Europe and the Baltic countries, and Southern Europe.<sup>7</sup>

> 6 Our analysis does not include German data because Eurostat provides no data on Germany to non-EU researchers (Eurostat 2022).

> 7 See <u>table A.1, in the appendix, for a detailed description of the sample and the classification of countries. These groups</u> correspond to the standard categorization used in the World Bank (World Bank 2022b).

8