rafmacalaba/datause-encoder-data huggingface dataset:
constant with respect to time. More recently, observational data has been used to estimate potentially non-constant time-discount functions. This literature, which restricts itself to estimating quasi-hyperbolic discount functions, includes Paserman (2008), Fang and Silverman (2009) and Laibson et al. (2007). We depart from this literature by adopting experimental methods for eliciting intertemporal choices and working with non-parametric measures of patience and “present”-bias.

The experimental literature on time preference is large. Frederick et al. (2002) offer a review. More recent examples include, Andersen et al. (2008), Benhabib, et al. (2010), and Andreoni and Sprenger (2011). As noted above, we adapt to our field environment the methods that Andreoni and Sprenger (2011) developed for use in the lab. A key advantage of their design is that, with a convex choice set, it permits the relatively rapid collection of information on intertemporal choice in the face of several different rates of return. The method thus allows analysis of the consistency of choice with the law of demand as well as quantitative measures of the degree of dynamic inconsistency.

Most recently, experimental studies of time preference have been taken into the field, often in developing countries. Prominent examples include Harrison et al. (2005), Ashraf et al. (2006), and Tanaka et al. (2009). Two such field experiments are closely related to ours and thus merit special consideration. The first, Ashraf et al. (2006), asked a sample of 1,777 in the Philippines hypothetical time preference questions on a survey. Later, a subset of this sample was offered a commitment saving product. Women who exhibited present-biased preference reversals on the survey questions were, as predicted by theory, more likely to take up the commitment saving product. Our paper differs from this prior study, most importantly, by studying directly the link between incentivized intertemporal allocation decisions and revision of prior choices. We measure the extent of preference reversals, as well as the basic consistency of choice with rational economic models, and thus provide a quantitative assessment of the mechanisms behind time inconsistency and the demand for commitment. The second closely related paper, Harrison et al. (2005), elicited time preferences from 253 participants in Denmark. Of this sample, 97 were later revisited and, importantly, asked to perform the same time preference experiment again. Our experiment is distinguished from Harrison et al. (2005) by, among other things, making a participant’s original choice clear and salient. In our view, both studies ask important and interesting questions. However, our goal is not to evaluate the stability of time preference, 30
There is reason to suppose that at least moderate rates of carbon taxation can provide net economic benefits for many lower-income countries (see Appendix 2). Why then have they not already implemented carbon taxes? The explanation likely involves political considerations. Like any other tax, a carbon tax redistributes resources in the economy, and it can be extremely difficult to achieve a sufficient degree of public consensus on those redistributional effects. They include both deadweight losses across the economy and sector-specific transition costs. In the real world these items are not as fungible as assumed in simple economic models.

This creates a case for external donor support of carbon tax implementation in developing countries even when domestic benefits exceed domestic costs at low tax rates, given an interest on the part of donors in increasing the size (for them and the world at large) of benefits from reducing risks of climate change. However, such support does not necessarily undo political barriers to tax implementation. Our focus here is on compensating for deadweight losses from carbon taxation, as these may be the most important barrier with moderate carbon tax rates.

To better characterize the compensation needed to offset those deadweight losses, two basic compensation formulas, or “price points”, will be derived:

- (i) The “incremental (marginal) cost price” (ICP). This is the price per ton of CO2e, reduced in the host country in response to a supported incremental carbon tax, that is needed to be paid to the client country, to make the client accept the tax. A premise for the ICP is that the GHG mitigation induced by the supported carbon tax can be used for compliance purposes by the host country, toward its (not yet fulfilled) Nationally Determined Contribution (NDC) under the Paris Agreement (PA).

- (ii) The “opportunity cost price” (OCP). This is the equivalent cost to the host country of implementing the donor-supported carbon tax, given that the supported tax _cannot_ be used for compliance purposes by the host country toward its (not yet fulfilled) NDC. In this case, credit for the achieved emission reductions is transferred to other parties, and is not available for host NDC compliance. See World Bank 2020a for discussion.

Both price points _represent a minimum compensation to the host country for implementing a given amount of GHG mitigation, based on the additional carbon tax enabled in the host country by the respective carbon tax support program_ . To repeat, the fundamental difference between the two 3
# **1. Introduction**

In much of the world, a great deal of economic activity is informal (Schneider and Hassan, 2016; Loayza, 2016; Elgin et al., 2021). Estimates show that in the typical developing economy about 70 percent of employment is in the informal sector, though this share of labor only makes up about 30 percent of production (Loayza, 2018). This has led economists and policy makers to have broader concerns that such labor is mis-allocated by being informal, dampening overall productivity (Meghir et al., 2015; Ulyssea, 2018). While recent macro estimates point to a decline of the informal sector as a share of GDP, this downward movement is minor, and informality by and large remains persistently high, particularly in developing economies (Ohnsorge and Shu, 2021).

Informality as a whole has received a lot of attention, but recently a rising emphasis has been placed on one portion of the sector: businesses that operate informally. These businesses are the most analogous to their formal counterparts, yet they also are indicative of important distortions in those economies. Businesses that could formalize and be competitive are excluded from an economy’s tax base and may lack opportunities to scale the size of their business; those informal businesses that could not survive after formalizing may employ labor that could be allocated more efficiently elsewhere, and in ways that provide more support (through regular work, income, and benefits) to those workers (Ulyssea, 2018). However, data on most such informal businesses is lacking. The limited data that is available has been used to examine the constraints to business formalization (De Mel et al., 2013; Campos et al., 2015; Benhassine et al., 2018), the characteristics of the informal businesses (Amin and Islam, 2015; Islam, 2019), and the interlinkages of the informal and formal sectors (Benjamin and Mbaye, 2012; Jolevski and Aga, 2019; Amin and Okun, 2020; Amin, 2021).<sup>3</sup> The size and persistence of the informal sector, and the wealth of research studying informal business activity reflects the need for high-quality, representative firm-level data collected in an efficient manner.

Businesses operating informally are frequently small and less productive, but widely abundant (see La Porta and Shleifer, 2014), implying that the typical (modal) business often does not appear in official registrar records, tax rolls, or most often, both. This very nature of informality

> 3 Several of these analyses rely on data reported by formal firms on informal firms’ activity.

2
misalignment is 1 percent, it will be subsequently corrected between 20 and 80 percent by the next year for most countries. Mean reversion of REER is, for instance, faster in China, at about 1.58 years, than in Argentina, at about 2.25 years.

The country estimates of half-lives of deviations from ERER show that these estimates are less than 2.5 years for 34 countries —as predicted by non-linear models of mean reversion. This implies that 42.5 percent of the half-life estimations falls into nonlinear half-life estimations (Lothian and Taylor, 2000; Taylor, Peel and Sarno, 2001) although 2.5 years half-lives are larger in their estimated deviations from PPP while our half-lives are within a shorter range of our results.

Figure 1.3 plots the half-life of deviations from ERER against the level of income per capita. The latter variable is proxied using GDP per capita from World Bank’s World Development Indicators. It suggests that the half-life of deviations from ERER is longer, the higher the level of GDP per capita is. The findings of this paper also support those of the model with non-linear adjustment: deviations from equilibrium may be larger in countries with lower income per capita levels.

Figures 1.4 and 1.5 depict a positive relationship between the half-life of deviations from ERER and the flexibility of the exchange rate arrangement. The data on these arrangements is taken from the database developed by Reinhart and Rogoff (2004) and updated by Ilzetzky, Reinhart and Rogoff (2009). Figure 1.4 shows the scatterplot with the average coarse index while Figure 1.5 presents the plot for the average fine index. In all these cases, the higher the index, the more flexible is the exchange rate regime. Consequently, the figures show that the half-life to ERER is longer the more flexible the exchange rate regime.<sup>7</sup>

# **2.4 RER Misalignments: An Error Correction Model Representation**

From our VECM estimation, we plot the response of the real effective exchange rate to its own impulse and to shocks in fundamentals. As described in equation _(3)_ , we regress the difference of real effective exchange rate on lagged fundamentals and differences of fundamentals. Figures 2.1 through 2.4 depict the response of change in REER (logs) to impulses/shocks to lagged REER (logs), lagged fundamentals and

> 7 The mode of half-life is 2.76 excluding three outliers Chile, India and Senegal while the average halflife is 3.6 years that within Rogoff’s consensus interval.

9
Table 4 presents data on the directions of net trade flows in the base year for the regions in the model. They show that, among the advanced countries, minerals and energy are the major import sector, while capital-intensive manufactures are generally major net export sectors, except for machinery and equipment in the United States, where there is a large deficit with Japan ($58 billion). The US is a net net exporter of agricultural and food processing products and services, the EU has a surplus in food processing, intermediates, machinery, and services, and Japan has an enormous surplus (over $200 billion) in the machinery and equipment sector, with much smaller positive balances for textiles and intermediates. China and ASEAN4 trade patterns exhibit some similarities: They are both net importers of capital-intensive manufactured products (basic intermediates, machinery and equipment), net exporters in labor-intensive manufactures and other primary products, and largely self-sufficient in food grains. The ASEAN4 trade surplus is diversified across mineral and energy products and other resource-based sector as well as in textiles and apparel. The net trade data for the Asian NIEs reveal that these economies are net exporters both of labor-intensive manufactures (like China and ASEAN4) and technology and capital-intensive machinery and equipment (like the industrial countries), and net importers of intermediates and mineral and energy products. At the aggregate level, the Asian NIEs have a trade surplus with the United States, European Union, and China, but a large trade deficit with Japan.

Tables 5 and 6 present data on the market shares of exports and imports, and on the sectoral composition of exports by destination and imports by source. It is apparent that East Asian countries have become important markets for developed countries, especially their manufactured products. The export share of the United States and European Union in the capital-intensive manufactured goods market to the Asian NIEs has exceeded their share to Japan. Asian countries have also become the largest market for manufactured intermediate exports from Japan. The data on the sectoral trade structure further show that most of the trade among the six regions included in the model is concentrated in manufactured goods, notably on labor-intensive consumer goods, basic intermediates, and machinery and equipment.

13 _Lewis, Robinson, and Wang_
_Ways Out of Poverty - DiffiLsing Best Practices and Creating Capabilities_ _12_ biggest factor remains hard to capture. It is not new that unexplained residuals in statistical studies of growth are large for particular countries. But comparing productivity across countries shows the full extent of the elusive "unexplained residual." <!-- Start of picture text --> Figure 6: The difference in output per worker is due to<br>difference In Institutional environment<br>(ratio of the 5 richest to the 5 poorest countries)<br>315<br>Differeice in O%iput Cortributlonof Cordributionof Cortributionof<br>perWorker capitalintensity hrsncEpitae per instiutiornd<br>worker envirorrneTt<br><!-- End of picture text --> Source: Calculatons are based on Hall and Jones (1999).

Various things have to come together to raise productivity and incomes. For example, the more open an economy is-and with it presumably the ability to learn-the more education becomes useful.<sup>3 2</sup> For another example, the higher the level of domestic education, the more productive foreign investment becomes.<sup>3 3</sup> All this points to the power of institutions that enhance the division of labor.

**Regions.** Productivity also differs among regions within countries. Consider the coastal and interior provinces in China, or the North East of Brazil compared with Sao Paolo. Even where labor is mobile and the free movement of goods, services, and capital reigns, significant productivity differences can persist. A study of Italy suggests that longstanding differences between the North and South of Italy reflect differences in trust among people based on long-standing patterns of behavior, what the author calls "social capital."<sup>34</sup> In short, the environment people operate in makes a real difference to productivity.

**Cities and clusters.** Also critical, cities tend to be more productive than other parts of a country. More ideas are generated there, and incomes are higher. Why? Because cities allow for flexible labor markets and work arrangements, production and subcontracting methods, and easier flows of ideas (figure 7).35

31 Denison, 1967; Solow, 1957.

32 Krueger, Alan B. and Mikael Lindahl, 2001; Lopez et al., 1998. 33 Borenzstein et al., 1994; World Bank, 200 la. 34 Putnarl, 1993.

> 35 Glaeser, 1998; Jacobs, 1969.
preceding template 7) should be completed for each subsector where possible, ideally attributing costs and revenues to each sector.

All figures in the income statement, cash flow, and balance sheet should be entered as positive figures unless there is a loss.

# **Public operators’ financial data (template 11)**

Template 11 provides raw financial data from SOEs, public corporations or parastatals, regulatory bodies, or special funds. Henceforth, they are named ―public operators.‖ It should be completed for public operators include SOEs, parastatals, regulatory bodies, and special funds, which have separate accounts. For each public operator, templates should be generated for the five most recent accounting years. Where one enterprise covers more than one subsector, for example, electricity and water supply, template 11 should be completed for each subsector, if possible.

Data should be extracted from audited and/or published accounts from the sources identified in the pre-field stage. These are likely to include: (i) the central unit monitoring public corporations, if it exists; (ii) the national audit institution (auditor general, court of audit, or equivalent); (iii) the Web site of the previous institutions and/or of each of the public operators; and (iv) each of the public operators directly. Note that it is very likely that the most recent accounts are available only from the public operators themselves.

Given the variety in the forms of reporting and the use of accounting concepts, a glossary of accounting terms is provided in annex 4.

**Template 11. Public operators’ financial data**

|**Policy**<br>**code**|**Series**<br>**code**|**Variable**|Unit|**t-4**|**t-3**|**t-2**|**t-1**|**t**|
|---|---|---|---|---|---|---|---|---|
|||**Revenues from sales**|Local<br>currency||||||
|||**Total employee compensation**|Local<br>currency||||||
|||**Purchase of goods and services directly used in production**|Local<br>currency||||||
|||**_Of which fuel_**|Local<br>currency||||||
|||**_Of which power purchase agreement (PPA) fees (if_**<br>**_applicable)_**|Local<br>currency||||||
|ent||**Other purchase of goods and services (that is, those not**<br>**included in line 3 above)**|Local<br>currency||||||
|tatem||**Rent**|Local<br>currency||||||
|come S||**Depreciation and amortization**|Local<br>currency||||||
|In||**Misc. taxes/fees (property and so on)**|Local<br>currency||||||
|||**Other operating expenditures**|Local<br>currency||||||
|||**Income (loss) from operations**|Local<br>currency||||||
|||**Income (loss) from operations as reported by enterprise**|Local<br>currency||||||
|||**Interest paid**|Local<br>currency||||||
|||**(Of which, foreign)**|Local<br>currency||||||

49
YHN 1991 Sector Review 41 lTble 10: Nutrition In Structural and Agricultural Adjustment Operations, FY91

|RegionlCountry|Operations||Nutrition Actions|
|---|---|---|---|
|AFRICA||||
|Burkina Faso|SAL I|-|Collect nutrition data to assist<br>in formulation of nutrition<br>policy.|
|Kenya|Agriculture SECAL|-<br>-|Prepare food security and<br>nutrition strategy.<br>*Adopta nutrition action<br>plan and implement nutrition<br>actions.|
|Rwanda|SAL I|-|Establish a Social Action<br>Fund and prepare FY92<br>Social Action Project,<br>including nutrition<br>components.|
|Tbnzania|Agriculture SECAL|-|Reduce price of food grains as<br>a poverty alleviation measure.|
|Togo|SAL IV|-|*Develop a nutrition action<br>plan.|
|||r-|Change agriculture policy to<br>encourage food crop<br>production for local<br>consumption.|
|Zambia<br>ASIA|Recovery Credit|-|Companion Social Recovery<br>Fund to compensate for<br>adjustment's negative impact<br>on nutrition.|
|Sri Lanka|Economic Restruct|-<br>-<br>-|*Restructure major poverty<br>alleviation programs: The<br>Mid-day meal, Food Stamp<br>and Jana Saviya (poverty<br>reduction -- includes<br>nutrition components)<br>programs.<br>Implement a food stamp<br>program for school children<br>of poor families instead of<br>providing meals to all<br>children.<br>Introduce operational<br>selection criteria to more<br>effectively target the Food<br>Stamp program to the poor<br>and increase the benefits to<br>those households.|
<!-- Start of picture text --> 80 74<br>71<br>69<br>62 62<br>~ 60 59<br>o 53<br>7) 49<br>a<br>E<br>5<br>®40<br>Do<br>&<br>Cc<br>oO<br>o<br>a<br>20<br>0 Informal Formal Total<br>Base wave formality<br>1998-2006 [NNN 2006-2012 MEE 2012-2018<br><!-- End of picture text -->
Table 6. Share of Total Exports Going to Restricted Markets and Export Tax Equivalents associated with the MFA: Percent of Market Prices in Exporting Region, 1992 and 2005.

||||_Tex_|_tiles_|||||_Wearing_|_Apparel_|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||_Sh_|_ares_||_Ta_|_xes_||_Shar_|_es_||_Tax_|_es_||
|**_MFA_**<br>**_Exporter_**|||_U_|_SC_|_E_|_U_|||_US_|_C_|_E_|_U_|
||**_1992_**|_2005_|**_1992_**|_2005_|**_1992_**|**_2005_**|**_1992_**|**_2005_**|**_1992_**|**_2005_**|**_1992_**|**_2005_**|
|KOR|15|_14_|10|_14_|10|_16_|58|_54_|23|_35_|19|_33_|
|TWN|12|_7_|8|_25_|12|_28_|83|_76_|19|_33_|22|_39_|
|HKG|7|4|7|_17_|8|_22_|81|_70_|17|_29_|16|_32_|
|CHN|19|_12_|19|_36_|27|_44_|33|_20_|40|_62_|36|_63_|
|IDN|25|_22_|13|_18_|17|_26_|58|_58_|46|_56_|48|_64_|
|Ln<br>MYS|21|_17_|10|_16_|12|_22_|47|_32_|37|_52_|32|**_54_**|
|**PHL**|50|_47_|9|_12_|10|_24_|84|_80_|33|_43_|28|_48_|
|THA|40|_32_|9|_16_|13|_25_|44|_33_|35|_48_|36|_53_|
|LTN|50|_58_|10|_5_|13|_12_|89|_93_|20|_19_|18|_21_|
|SAS|45|_42_|19|_24_|27|_36_|83|_80_|40|_51_|36|_53_|
|ROW|59|_66_|5|0|6|_6_|87|_94_|16|_15_|10|_15_|

_Sources:_ 1992 shares and export taxes from GTAP data base (Chyc et.al.). 2005 shares and export taxes from updated data base following experiment PO.
Policy ReseaRch WoRking PaPeR 4864

# **Abstract**

For decades the world’s agricultural markets have been highly distorted by national government policies, but very differently for different commodities. Hence a weighted average across countries of nominal rates of assistance or consumer tax equivalents for a product can be misleading as an indicator of the trade or welfare effects of policies affecting that product’s global market. This is especially the case when some countries tax and others subsidize its production or consumption. This article develops a new set of more-satisfactory indicators for that purpose, drawing on the recent literature on trade restrictiveness indexes. It then exploits a global agricultural distortions database recently compiled by the World Bank to generate the first set of estimates of those two indicators for each of 28 key agricultural commodities from 1960 to 2004, based on a sample of 75 countries that together account for more than threequarters of the world’s production of those agricultural commodities. These reveal the considerable extent of reforms in agricultural policies of developing as well as high-income countries over the past two decades.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to better understand trends in policy distortions to agricultural incentives globally. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kym.anderson@adelaide.edu.au or via wmartin1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team