rafmacalaba/datause-encoder-data huggingface dataset:
**Looking at public spending, the fundamental tradeoff is between current spending, which generates immediate but temporary benefits, and investment, which defers present benefits for the sake of future returns.** The appropriate prioritization of public spending is a complex issue and extends well beyond the scope of this paper; however, a number of important considerations should inform the decision, such as: the estimated rate of return on public investments; the quality of investments and the efficiency of overall public spending; the execution capacity of public agencies; the long-term economic obligations arising from new investment projects; the strength of governance and public institutions; and the country’s current fiscal-policy stance. This fundamental tradeoff may also have significant implications for fiscal sustainability; for example: countries experiencing natural resource boom in many cases increase current spending and – for instance – public sector wages; it should be borne in mind that while natural resource revenues are inherently volatile and uncertain, increases in current spending, like wage hikes, are typically impossible to reverse and may pose a threat to longterm fiscal sustainability if future revenues are unable to support current expenditure levels.

# **INSTITUTIONALIZATION AND IMPROVEMENT OF SECTORAL INTELLIGENCE NEEDED FOR WEALTH ACCOUNTING**

**A final issue is the availability of information to estimate natural capital and total wealth.** In Mauritania there are no comprehensive statistics on wealth accounting, and no attempt to expand, standardize, or regularly update these accounts to reflect the evolution of natural-capital stocks. It is highly recommended that the system of national accounts be updated to include changes in natural capital just as it does man-made capital. The United Nations has developed a System of Economic-Environmental Accounting (SEEA), which encompasses goodpractice standards, concepts, definitions, classifications, and accounting rules for producing internationally comparable statistics on natural resources and their relationship with the economy.<sup>47</sup> The SEEA is, in other words, a statistical standard that provides methodology for measuring depletion and associated macro aggregates and indicators (such as ANS, or Adjusted National Net Income –ANNI-). Ideally, it would be updated periodically in line with strategic national development documents, such as for instance the poverty reduction strategy papers, or other planning documents. Adopting the SEEA system would be an important first step for the Mauritanian government in expanding its capacity to preserve, protect, and enhance the country’s national wealth, building the foundation for prosperity both in the present and for generations to come.

> 47 For more on this, see: www.unstats.un.org.

21
27 percent, suggesting its own current distortions structure by 2004 was pro-agricultural. This is consistent with the NRA and RRA estimates in Table 1, but the modeling results also take into account the impact such a reform would have on China’s terms of trade internationally (since China is a non-trivial player in world markets for numerous goods).

If the rest of the world moved to free markets for all goods, on the other hand, agricultural value added in China would rise by 7.8 percent, while value added in the rest of its economy would rise by just 2.4 percent. Thus the rest of the world’s policies discriminate more against China’s farmers than against its producers of non-farm goods, according to these results (row 2 of Table 5).

If all countries were to reform simultaneously, the results are similar to but not quite the same as the sum of the above, because the effects on the international terms of trade are not additive. The lower part of Table 5 provides the value added results from global liberalization. For China, value added in agriculture would increase by 6.6 percent or by twice the percentage for non-agricultural sectors. The change in the rest of East Asia has the same bias but to a lesser extent. This compares with a much more proagricultural outcome for Latin America, but contrasts with the bias in South Asia, Africa and Eastern Europe where non-agricultural value added would grow at the expense of farmers’ earnings. For developing countries as a whole, the bias of policies in 2004 was clearly against farmers, since their removal would boost net farm incomes by 5.2 percent while value added in other sectors would rise by just 2.1 percent (final row of Table 5).

# **Summary and conclusion**

With import tariffs on farm products continuing to provide an incomplete indication of the extent to which agricultural producer and consumer incentives are distorted in national markets (notwithstanding tariffication under the WTO’s Uruguay Round Agreement on Agriculture), monitoring of government policies causing those distortions remains a complex but necessary task. And for countries that have also distorted incentives for their producers of non-agricultural tradables, those too need to be monitored if impacts of policies on net farm incomes are required (for example, as an input into the poverty and inequality impacts of those policies). The NRA and RRA
11 Mexico MFA

# 2.2 The **Price** Differences

As mentioned above, the implicit valuation of the quota rents is included<sup>in the Mexican</sup> F.O.B. price of exports to the U.S. Thus, we can test for rent-sharing<sup>by comparing the unit value of</sup> U.S. production with the Mexican F.O.B. price, appropriately adjusted<sup>for tariffs, transport costs,</sup> and HTS 9802 tariff concessions. Arbitrage in the U.S. market<sup>will cause these two prices to</sup> equalize if markets are competitive and all goods are homogeneous within<sup>each group. It is</sup> reasonable to assume that Mexican exporters are small, and therefore<sup>price-takers, in the U.S. market.</sup> However, if U.S. importers have monopsony power, this can lead to rent-sharing,<sup>if they can</sup> maintain a lower price for their imports than they pay for U.S. production.

Chart 1 plots the adjusted Mexican F.O.B. price on the vertical axis against<sup>the U.S. price on</sup> the horizontal axis for all six groups of products examined in this study. The<sup>arrow represents the</sup> forty-five degree line. The chart clear!y shows that there is a significant difference<sup>between the two</sup> prices. The U.S. price is above the Mexican price in almost every instance,<sup>inaicating that either</sup> sustained quality or composition differences or rent-sharing may<sup>exist. To test the significance of this</sup> difference, the following regression was run using time series data from<sup>1981 to 1990, pooled over</sup> the six apparel groups: At this point the variation over groups of apparel is not considered,<sup>and so a is maintained constant.</sup> As Erzan, Krishna, and Tan point out, in this equation, the right hand<sup>side variables can be</sup> considered exogenous to the Mexican exporter. If there is no rent<sup>sharing, and markets are perfectly</sup> competitive, and if all goods are assumed homogeneous within g,oups,<sup>then pj,m=pjtUs.</sup> In this case we expect the coefficient on the U.S. price not to be significantly different from<sup>one, and all other</sup> parameters not to be significantly different from zero. If, on the other hand,<sup>rent sharing exists,</sup> implying monopoly power on the part of U.S. importers, or the assumption<sup>of homogeneous goods is</sup> violated (by differences in quality or composition, for example), then<sup>we expect the coefficient on pul</sup>
# 2. ANALYTICAL FRAMEWORK

A complete theoretical treatment of gender differences in human caiital would incorporate all three forms referred to above: health, schooling and job-specific training. This paper explicitly incorporates only investments in health and schooling. This is done because of two reasons: First, an effective treatment of on-the-job training requires a multiperiod model; and second, labor market studies for developed countries have already documented the complementarities between schooling and job training. The focus here is on complementarities between components of human capital accumulated at the level of the household, i.e., health and schooling. Because of these two reasons, investment in job-specific human capital is almost completely ignored.

The Basic Structure A representative household consisting of an adult couple, one female child and one male child (indexed by subscript j - f, m respectively) is the unit of study. Fertility is therefore exogenous. Parents value only their own consumption and the welfare of their children as adults, which is assumed to depend upon each child's income. The parents' utility function is U - U (C, R , Rf; Z) . (1) where C is the quantity of a general consumption good, and R and R are the an f "full" or potential incomes of the male and female child respectively when they are adults, and Z represents household characteristics (tastes, education, location, etc.) that affect the utility obtained by consuming C, R , and R Assume for the sake of simplicity that the utility function has a 5
- Pokhrel, A., Bates, M., Verma, S., Joshi, H., Sreeramareddy, C., and K.R. Smith. 2010. "Tuberculosis and indoor biomass and kerosene use in Nepal: a case-control study." Environmental Health Perspectives 118:558–564.

- Pope, D. P., V. Mishra, L. Thompson, A. R. Siddiqui, E. A. Rehfuess, M. Weber and N. G. Bruce, 2010. Risk of low birth weight and stillbirth associated with indoor air pollution from solid fuel use in developing countries. Epidemiologic Reviews, Vol. 32 (1), pp.70-81.

- Savitz, D.A., J. Cai, E. van Wijngaarden, D. Loomis, G. Mihlan, V. Dufort, R. C. Kleckner, L. Nylander-French, H. Kromhout, H. Zhou (2000). Case-cohort analysis of brain cancer and leukemia in electric utility workers using a refined magnetic field job-exposure matrix. American Journal of Industrial Medicine. Vol. 38, Issue 4, pp. 417-435.

- Smith, K.R., A. Woodward, D. Campbell-Lendrum, D.D. Chadee, Y. Honda, Q. Liu, J.M. Olwoch, B. Revich, and R. Sauerborn, 2014: Human health: impacts, adaptation, and co-benefits. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 709-754.

- Sumpter, C. and D. Chandramohan, 2013. Systematic review and meta-analysis of the associations between indoor air pollution and tuberculosis. Tropical Medicine and International Health, Vol. 18 (1), pp. 101-108.

- Trevor J., V. Antony and S.K. Jindal, 2013. The effect of biomass fuel exposure on the prevalence of asthma in adults in India - review of current evidence. doi:10.3109/02770903.2013.849269.

- van Wijngaarden, E., D. A. Savitz, R. C. Kleckner, R. Kavet and D. Loomis (2001). Mortality patterns by occupation in a cohort of electric utility workers. American Journal of Industrial Medicine. Vol. 40, Issue 6, pp. 667–673.

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22 On balance, these findings imply that, at least for fue' oil, diesel and gasoline, basis risk is likely to be small provided MARAVEN removes some of the inefficiencies in its current pricing system. That is, MARAVEN's product prices should be allowed to become more responsive to product prices in the international markets. However, this judgement should be supporteu by a formal analysis of the basis risk involving **futuMa** price data for heating oil, Wll, and gasoline instead of New York spot prices, and doing the analysis over a longer time period. Heating oil futures contracts are typically used to hedge diesel and (in combination with gasoline futures contracts) some "cracxed" fuel oil prices. For hedging "uncracked" fuel oil, the WTI or Brent crude oil futures contracts are usually used.'<sup>7</sup>

# C. Liquidity

Currently, the futures/options market for crude oil is the most liquid amongst all commodity markets. More crude oil is traded daily on the NYMEX (the light, sweet crude oil futures contract based on the WTI crude) than the total world production of crude oil (about 6580 million barrels in futures versus 60 million barrels of physical).<sup>18</sup> Options on NYMEX's oil futures contracts account for about 25-30 million barrels or half of world crude oil production. In addition, Brent crude oil futures contracts traded at IPE have a daily trading volume of about 40-50 million barrels, and options on these contracts account for 5-7 million "A more appropriate crude oil contract to hedge fuel oil could be the sour crude contract. However, NYMEX's and SIMEX's sour crude contracts do not yet have adequate liquidity.

'Liquidity has expanded rapidly. In 1989 and 1990, the average daily NYMEX crude oil futures contract trading volume was 55 million barrels and 63.4 million barrels respectively, compared to 22 million barrels in 1986.
international payments. Athey et al (2016) also document that bitcoin ownership is highly concentrated. Indeed, even as the popularity of Bitcoin has continued to grow in the past few years, Marakov and Schoar (2021) more recently confirmed that bitcoin ownership and mining capacity are still very concentrated. Auer and Claessens (2020) find that in light of significant regulatory uncertainty, regulatory news regarding crypto-assets has had a significant impact on crypto-assets market prices and trading volumes.

# **<mark>Box 1: Women and Crypto-assets Adoption</mark>**

<mark>In their</mark> _<mark>Global Report on Women and Cryptocurrencies</mark>_ <mark>, Spindler and Rodriguez (2021) conducted a small survey of crypto-asset ownership and adoption factors among 60 women from 31 countries, with a focus on residents of Latin America. 36% of participants in the survey highlighted their interest in the underlying technology, while 14% saw crypto-assets as a long-term investment vehicle. Participants cited a number of factors that increased their interest in crypto-assets, including value fluctuations of their local fiat currency, domestic and regional financial crises, lack of economic empowerment and control over finances within their household, and perceived inefficiency of legacy financial systems. Importantly, 95% of the women respondents had some form of higher education, signaling that educational</mark> background could play a role in crypto-asset adoption.

# _Measuring crypto-assets activity_

To understand the various motivations behind crypto-assets activity, it is important to properly measure it. However, while open distributed ledgers allow anyone to observe the complete historical on-chain transaction data, their pseudonymous nature makes it challenging in practice to match individual persons or businesses to on-chain addresses, unless they, for example, underwent Know-Your-Customer checks to obtain accounts at centralized intermediaries such as cryptoexchanges.

Several studies have taken different approaches to measure crypto-assets activity. To estimate the number of bitcoin and ether owners, Wang (2020, 2021) utilizes on-chain data to count the total number of on-chain deposit addresses<sup>10</sup> which are required to deposit funds into crypto exchanges. One of the heuristics used by Athey, et al (2016) to analyze Bitcoin activity is to associate multiple Bitcoin addresses to user wallets. Estimating country-level activity is also challenging, as cryptoassets networks are global with no on-chain information regarding the geographic origin or

> 10 Deposit addresses are temporary on-chain addresses of users transferring funds to crypto exchanges.

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Sasin (eds), _Migration and Poverty: Toward Better Opportunities for the Poor_ , Washington, DC: World Bank, pp. 101–24.

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22

> - - **(4) V(k)** (l/k)Cvar(ft ft-k)Ivar(ft -t)]

One extreme case of persistence is when ft follows a random walk. In this case, the variance of k-dLfferences in X grows linearly with k and the variance ratio is equal to one. Under these condLtions fluctuations in **i** are permanent and the underlying process is nonstationary. At the other extreme is the case in which it follows a stationary (mean reverting) process. The variance of k-dlfferences in<sup>1</sup> t approaches then a constant equal to twice the variance of the series and the variance ratio approaches zero for large k; in thLs case, fluctuations in X are transitory. Between these two extremes there are cases in which fluctuations in u are partly permanent and partly temporary, as when the series are a combination of a random walk and a stationary component. In these more general cases the variance ratio provides a measure of the relative importance of the permanent component, in that for large k it settles down to the ratio of the innovation variance of the random walk component to the variance of first differences.

Variance ratios adjusted for small sample bias are reported in Table 3 and Figure 9 provides plots of the ratios for four variables: the rate of inflation, the rate of unemployment, the rate of change of the real exchange rate, and the real interest rate. For each variable the ratios are reported for periods before and after the disinflation program. In each case we used monthly data and k - 13. Caution is suggested in regarding the findings as definitive because small samples are being used and standard errors are not provided.

Comparing persistence before and after the program yields two main patterns. On the one hand, no major change in variance ratios shows up for
# Below we first discuss our empirical models. We then present our findings on the

decomposition of salary and grade differentials-including at entry, current and in growth over time-across staff groups. A number of alternative assumptions and samples are tested in order to gauge robustness of the results, as well as to better understand them. A final section interprets the results and discusses their implications for Bank policy and practice on hiring and promotion.

# B. **Models and Decompositions**

We use an approach standard in the literature on labor economics.'

# **_Models_**

The reduced-form model for the log of current salary (Wij ) for the ih staff member in the jth group can be written as: (1) lnWij = Xijaj + eij, where Xij is a vector of individual characteristics-including age, nationality, education, area of specialization, work experience prior to Bank employment and duration of employment in the Bank. The corresponding parameters are aj, while **eij** is a zero mean error term uncorrelated with Xij. The estimated coefficients are used to calculate predicted salaries for the four groups as well as predicted salaries based on the measured characteristics of one group and the parameters of another and vice versa. These predicted values are next used to decompose the differentials in

> 7 The approach is originally due to Oaxaca (1973) and Blinder (1973). A previous study of the World Bank by Oaxaca and Ransom (1993) uses the methodology to explore current salary differentials and promotion rates for staff on board in 1988 and 1992. The present study expands the analysis to examine entry level salaries, growth in pay, current and entry grade and the rate of promotions. It has access to somewhat richer data and the more recent data also allows an exploration of the effects of recent hiring policies on gender and nationality differentials.

6
with potential competition-related problems. COFECE’s research information covers analyses for different types of goods (or categories of goods), for several types of economic sectors (including retailing), in different regional levels. We found that during the period of analysis, COFECE fined industries in poultry, soda, tortilla, avocado and cement. The businesses fined by COFECE were large companies with presence at the national level, which allowed us to analyze the effect of the sanctions in all city markets.

We also use data on the entry of a large retailer store in Mexican cities through time. This large retailer operates several types of retail stores aimed to sell to different types of customers. There are four store brands relevant to this study and we distinguished each for the analysis: 1) the first brand is basic and austere mini- and super-markets that mostly sell groceries and personal care products at the lowest price possible; 2) the second brand sells a wide range of goods, from groceries to electronics and small appliances; 3) the third brand is premium supermarkets focused on higher-end groceries; and 4) the fourth brand stores are a warehouse club.

We are able to follow the openings of these four kinds of stores in all cities in our database. The large retailer provides information on store openings in their monthly sales reports, which are available in their webpage. Thus, we can estimate the effect of the entry of each of the store brands, either as the group as a whole or by type of store. The first brand stores are by far the most common of all this large retailer’s stores, and expanded aggressively during the time period (722 total openings in the sample, including both super- and mini- markets). Then, the second and fourth brand stores opened less frequently (63 and 40 openings, respectively), while third brand store openings were the least common (23 openings).

# EMPIRICAL STRATEGY

The empirical strategy is divided into two activities. First, we estimate indicators to identify and understand how markets are working. This way we can distinguish those markets that seem to be working relatively well from those that work less efficiently. The second step consists in analyzing the effect that the described trade and market structure variables play in the functioning of such markets.

It is important to understand what we mean by market efficiency through the use of the indicators discussed above, in the literature review. Defining efficient markets is the first task of the empirical strategy.

# Efficient market and its characteristics

To identify and understand which markets are functioning well, we first define the performance criteria. Our criteria equate a well-functioning market with an efficient market. More precisely, since all of the indicators of efficiency used are relative, we define a market that is functioning relatively well as a market that is working relatively more efficiently than other markets.

13