rafmacalaba/datause-encoder-data huggingface dataset:
production data are converted to US dollars at the current average exchange rates, to make them consistent with trade data from COMTRADE which is denominated in US dollars. These are nominal UD dollar values which include US dollar inflation and changes in the real exchange rates of local currencies against the US dollar.<sup>5</sup> Canada, France, Germany, Japan, and the United States were selected as the industrial countries, and China, Brazil, India, the Republic of Korea, Malaysia, Mexico, South Africa, and Turkey were selected as the developing countries.

Import growth in these countries is decomposed into changes due to demand increases and changes due to market share changes. The contribution of demand changes is estimated assuming a constant share of imports in gross domestic demand between the two time periods, i.e. the market shares do not change. The market share changes are then estimated as the difference between the actual import growth rate and the import growth rate under a constant market share assumption. The periods of 1991/92, 2000/01, and 2007/08 are used as benchmarks to estimate the growth rates and import shares. Two year averages are used to minimize the annual fluctuations in output and trade. 2007/08 is used as the final year both due to data availability, and more importantly, it is the last year before the global financial crisis. The analysis of the impacts of the crisis on trade values requires a separate study.

This simple method has some limitations. First, it assumes that the income elasticity of demand for specific products exported by a group of countries is identical to the average income elasticity for the sectors as a whole. This bias decreases as the number of products exported increases and as product categories get narrower. Second, it assumes that market share changes are independent of demand growth. Normally, one would expect that when the rate of growth of demand accelerates, there would be spillovers to imports that will increase the import shares. This relationship was tested using the both the detailed 3-digit subsector information using imports and exports from developing countries and aggregate global imports for the selected 13 countries. The relationship between the market share changes and demand changes was not significant across industries and countries.<sup>6</sup>

> 5 There is a significant appreciation of the US dollar against the currencies of most the other countries during the late 1990s and depreciation during the 2000s. This appreciation underestimates the domestic production and demand growth in US dollars and overestimates the share of imports, which are denominated in US dollars. Opposite takes place during US dollar depreciations. Thus, nominal US dollar measurement would underestimate real growth during the 1990s and overestimate it during the 2000s.

> 6 The disaggregated estimation was done at the 3-digit ISIC level correlating the 22 sub-sector import growth rates and corresponding import growth rates due to market share changes, with country dummies.  At the aggregate level the relationship

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controlled for. That is, countries with high levels of initial productivity but median levels of the other characteristics are more likely to be in a lower convergence club.<sup>15</sup>

# **4.4 Country characteristics associated with transitioning to higher convergence clubs**

In this section, the pre-conditions for transitioning to a higher convergence club are examined, using the 16 EMDEs who transitioned to Club 1 as informative examples. In this exercise, the problem of endogeneity is less of a concern than in the previous exercise when examining the determinants of club membership.<sup>16</sup> However, the results are consistent with the country features associated with higher-productivity club membership. Multiple approaches suggest that better initial education, deepening economic complexity, and stronger institutions were associated with successful transitions.

**Group averages** . EMDEs that switched into the higher-productivity convergence club were not initially more productive than other EMDEs, and their productivity levels only overtook other EMDEs only in the early 1990s on average ( **Figure 8** ). Their education levels were initially higher but did not accelerate at a faster pace than in other EMDEs. In contrast, economic complexity increased continuously among the EMDEs that transitioned into the high-productivity club, while it has stagnated in non-convergent EMDEs. Measures of institutional quality, such as perceptions of government effectiveness, were modestly higher in those countries that transitioned. Trade openness and levels of investment have significantly overlapped between the two groups for much of the sample—although trade openness did accelerate in transitioning economies from 2000 onwards.

**Logit analysis** . A logit model estimates the probability of transitioning into a higher-productivity club based on country characteristics (Annex E). The logit model was estimated over two separate time periods, 1980-90, just before the transitioning EMDEs overtook the non-transitioning EMDEs, and 1990-2000, just after the transitioning EMDEs began to display accelerating growth relative to other EMDEs. This allows an examination of the conditions in transitioning economies at key junctures in their development.

> 15This finding is consistent with the concept of the ‘middle-income trap’ (Aiyar et al., 2013; Eichengreen et al., 2013; Im and Rosenblatt, 2013). Economies that have progressed to productivity levels consistent with middle-income status risk stagnating if they do not continue to improve educational outcomes, expand to more complex industries, or improve governance.

> 16Examining the determinants of transitioning economies before or during the transition to faster productivity growth trajectories reduces the endogeneity problem between productivity growth and the drivers of productivity growth.

17
16 _Figure 6. Thousands of Water Connections_ <!-- Start of picture text --> 900<br>X 800 -------- - -- - ---- ----- --------------- ---- ----- ---- --- ----------------<br>6 70 0 -- - - - - - - - - - - -- - - - -- - - - -- -- - -- -- -----<br>600 - -- --- -- -- -f -- - --------------- - ~- ~ - ~ --- --- -- -- -- -- -- ---- --- -- - -- - - ------- --------------<br>400<br>1987 1988 1989 1990 1991 1992 1993 1994 1995 1996<br>Source: SEDAPAL Annual Reports<br><!-- End of picture text --> _Figure 7. Percentage of Water Coverage_ <!-- Start of picture text --> 80%<br>70%/ -<br>5(0/o<br>1988 1989 1990 1991 1992 1993 1994 1995 1996<br>Source: SEDAPAL Annual Reports<br><!-- End of picture text --> The efficiency improvements in SEDAPAL did not have a sustained effect on unaccounted-for-water. UFW fell from close to 50 percent in 1987 to a low of about 27 percent in 1992 but then began to increase again (figure 8). By 1996 it was 36 percent.
their external accountant to do their annual accounts if New Zealand were tax free”, fully 45% said “probably not” or “definitely not”, with 49% of the smallest firms giving these responses.<sup>11</sup> Reports from other developed countries display similar broad patterns. Erard (1997) stated for Canada that “the vast majority of small and medium-sized businesses report that they rely on outside professional assistance to comply with their corporate income and capital taxes.” At the same time, when asked about the aspects of CIT compliance that created most problems for businesses, the most frequently cited problem (cited by 58.5% of respondents) was “the cost of professional services.” While not legally required to use professional tax practitioners (as in the case, eg., in Peru), Erard noted: Smaller businesses are likely to lack the technical knowledge to properly complete an income tax return; may not be familiar with the recent tax changes; and would in all probability find it cost-inefficient to attempt to develop this expertise in house. Although the availability of outside tax assistance undoubtedly reduces the overall compliance burden for these firms, the high cost of this assistance is nonetheless the most commonly reported source of compliance problems by respondents.

Similarly, Turner commented that “necessity forces the self-employed tradesman, farmer, shopkeeper, or consultant to engage an accountant, even though the cost of employing such professionals is often regarded as outrageous.”<sup>12</sup> Patterns of outsourcing differ somewhat in developing and transition countries. In Malaysia, “72 percent of public listed companies rely on the use of paid external advisors ... In contrast, 75% of SMEs seem to rely on their internal resources to comply with the tax laws. The findings need to be treated cautiously because both studies were carried out prior to the introduction of the self-assessment system.” (Pope and Abdul-Jabbar, 2008) Similarly, in Yemen, which has not yet enacted “self-assessment” in tax filing except for “large taxpayers,” barely 5% of businesses registered with the tax authorities use any external help. (FIAS, forthcoming) In some East European countries, with relatively complex tax systems, it is normal for companies (i.e., legal entities) of all sizes to have a full time bookkeeper or accountant on the payroll. In a recent survey in Ukraine, over 90% of companies had at least a part-time bookkeeper on the payroll and less than 4% of firms outsource any of their tax compliance work.(FIAS, forthcoming) In the Czech Republic, in a survey of tax compliance costs for businesses, 90% of respondents hired “an external supplier of services” for tax duties. (Vitek, 2004) The question for South Africa is whether it might be beneficial to encourage more outsourcing by SMMEs. The survey results described below shed useful light on this question.

> 11 Colmar Brunton (2005), pg. 89.

> 12 Turner, et. al., pg. 69.

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To create our preferred estimate of the conversion factor, we select the available 15 household surveys conducted in 6 developing and 9 developed countries since 2003 (see Table 15 for list of survey sources) and which have reported data for two categories or more (commercial banks plus one of MOFIs or SSFIs).<sup>20</sup> To assure numbers are comparable across countries, we restrict our sample to only those surveys which use all adults as the survey universe. The mean value of the conversion factor is 2.96 and the 90% confidence interval is from 2.0 to 3.9 (Table 16). As a round number, there appear to be approximately 3 accounts per person on average.

Table 17 also shows two variations to calculate a conversion factor. In the one, we use all countries with household survey data and use both predicted and reported values of account numbers. This expands the sample to 28 countries and produces an estimate of 3.2 total accounts per banked individual for the conversion factor. The final method uses only reported data for commercial banks under the assumption that the number of commercial bank accounts provides a reasonable signal for estimating the number of individuals with bank accounts. This method uses 17 countries and calculates a conversion factor of 2.5 _commercial bank_ accounts per banked individual.

This approach to calculating the conversion factor is admittedly a very rough estimate (as is confirmed by the wide confidence intervals). Though we believe the simplicity of the calculation is a virtue, there are a few simple corrections which we contemplated, but which the data indicated were not warranted.

First, because the Financial Access survey data on number of accounts comes from 2008 data but the household survey data come from various prior years, any worldwide upward trend in account ownership would systematically inflate the conversion factor for older surveys. If older household surveys systematically underestimate the true number of banked adults who currently own accounts, one might expect a negative relationship between survey year and number of

> 20 We calculate the conversion factor as the number of accounts from all sources relative to the number of banked adults. In the conversion factor calculation we drop countries for which we have only commercial bank data and no reported number for government or cooperative accounts (3 surveys) so that the conversion factor better measures this ratio (rather than measuring the number of commercial bank accounts per banked adults). When we do the final calculation, we use predicted values for cooperatives and government accounts in countries where no data were reported.

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The increase in food commodity prices over the last several years is part of a general increase in global commodity prices, including minerals, metals, and energy. Although the food commodity price index rose to a historic high in 2007-08, the price indices for all commodities, and for crude oil in particular, have significantly outpaced it. In fact, between January 2002 and July 2008, the IMF price index for food commodities rose 130%, compared with 330% for all commodities and 590% for crude oil.

Another factor to consider is the increase in energy prices [33]. To this end, the energy impact on food commodity prices should be divided into two factors: the allocation of land to biofuel crops (which reduces food and feed availability and increases the aggregate demand for food commodities), and the increase in energy prices (which increases production costs and reduces the supply of food commodities). See also Hochman et al. [32]. First-generation biofuels, which are derived primarily from corn and sugarcane, compete with food and feed, resulting in higher demand for agricultural commodities and thus in higher prices. The introduction of biofuels, however, also lowers fuel prices [54]. Yet, the literature fails to recognize that lower fuel prices affect farm-level costs. Introducing energy markets, with all its complexity, to our multi-market framework reduces the impact of biofuels on food commodity prices further.

To reiterate, the data show that successive years of positive excess demand led to the gradual depletion of inventory, which reached an historical low in 2008.

The following section discusses the implications of inventory and describes one approach for modeling the demand for inventory in a multi-market equilibrium framework.

# **5. An analytical framework with inventory demand**

The peak of the food crisis marked the depletion of stored grain stocks to historically low levels that had not been witnessed since the 1970s [52, 68]. For storable goods, the ability to adjust the level of inventory can play a crucial role in maintaining price stability and reducing price volatility when there is a supply or demand shock [68]. During periods of excess supply, demand from storers protects producers from rapidly descending prices, while during periods of scarcity; supply from inventory protects consumers from rapidly ascending prices.

We do not focus on the theoretical underpinnings of speculative inventory, which is dynamic and forward looking. Anticipation of future inventory decisions affects current ones, and this complexity is augmented by the inventory constraints, i.e., one cannot borrow from the future or that inventory cannot be negative [68]. Instead we assume that one can estimate an 25
9 variables may alter the affect of some individual level variables is allowed for through the presence of the multiplicative term involving 5. The g and e represent unobservable community and individual influences respectively.

Equations (1) and (2) allow for a two level error structure. Estimation of the parameters of models with such an error components structure specified above has a long history in the sample survey literature as well as the literature dealing with panel data. A textbook discussion of the issues can be found in Hsiao (1986). The method has rarely been applied to control for unobservable community level effects (see Turchi, Guilkey, and Hess (1990), for an exception). The methods of estimation for continuous dependent variables have followed two main strategies that involve either treating the A as fixed or random. The fixed effects estimator has several advantages but is of little use in our context because it does not permit estimation of a, the coefficient vector for the community level variables, which is a parameter of primary importance to us.

We assume that Loth ji and e are independent, identically normally distributed random variables with mean zero and standard deviations a. and a, respectively. The practical implication of these assumptions is that ordinary least squares will result in consistent parameter estimates but the standard errors will be incorrect. Our solution is to estimate the equations by generalized least squares since it is a more efficient estimator and the estimated standard errors are asymptotical!y correct.

The third equation in the model relates fertility intentions to a set of exogenous variables and the number of living children: (3) W 1 , = a3Cj + S3Xj + 83 C Xii + 03(B i-Di) + 3j + E3ij where W.j. is an unobserved continuous variable that represents the strengths of the respondents desire to space children and the other variables are as defined above. The observed dependent variable is WY which is We sorted into ordered categories as W' passes through thresholds that must be estimated. To keep the notation simple, we use the same notation for the vectors of household and community variables as used in equations (1) and (2). These vectors will not overlap completely in the actual empirical work. In fact, if the system of equations is identified, they can not overlap completely. Equation (3) introduces two complications: a limited dependent variable and an endogenous right-hand-side variable. The two estimation methods that have typically been used for ordinal variables are ordered probit and poisson regression. We use the ordered probit method since the error term assumptions that need to be made are the same as are made for equations (1) and (2) and these assumptions are less restrictive than those that are typically imposed in the poisson regression model (see Maddala, 1983). As a practical matter, the methods typically give similar results (see Trevedi and
8 <!-- Start of picture text --> Relative Pri6e<br>IN A Is4.E<br>1"7 1 I9 I *8 IiwO 1 1991<br>Yew<br>Figure 6<br><!-- End of picture text -->

# 3. POLICY CREDIBILITY, DEBT REPUDIATION RISKS AND HIGH INTEREST RATES.

Despite the rapid success in bringing down inflation, domestic real interest rates during the first two years of the Pact were extremely high: on average in excess of 30k. High real interest rates, rather than the debt stock, threatened the stabilization program by increasing the cost of servicing domestic debt, and by hindering investment and, hence, the economic recovery. In this section I argue that behind high real interest rates was: i) the initially low credibility about the fixed/preannounced exchange rate and, to a lesser degree, ii) fears of explicit debt repudiation. The initial lack of credibility led the public to systematically overestimate the exchange rate devaluation, i.e., Mexico in the late 1980's provided a fresh example of the "peso problem" (Krasker 1980). A survey of devaluation expectations conducted by INEGI (the National Institute of Statistics) illustrates this (Figure 7). In the next section I will argue that Mexico's tight fiscal policy and able debt management, helped eventually to bring down real interest rates. In other countries undergoing similar stabilization strategies, e.g., Brazil and Argentina, but without the same ability to tighten fiscal policy and/or manage public debt, high real interest rates led to situations of fast growing domestic debt growth and unsustainable exchange rate/inflation targets (Beckerman 1991).

At the core of the anti-inflation strategy was a first fixed and later on quasi-fixed exchange rate (a preannounced declining rate of devaluation). This, along with price and wage controls, provided nominal anchors for prices and, thus, for inflation. However, at least initially financial markets did not believe that the fixed/qua.,i-fixed exchange rate, and for that matter the stabilization strategy itself, was going to be maintained. This lack of policy credibility led to high devaluation expectations and exchange rate uncertainty as measured by the differential between interest rates on peso-denominated public debt (Cetes) and interest rates on dollar-indexed domestic debt (Pagafes) plus
Table A5: Consistency in Responses on Sensitive Information with Additional Surveys _(Including education level as control variable)_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Cons|istencyw|ith Baselin|e Survey|Cons|istencyw|ith Endlin|e Survey|
||Stress|high|Physical<br>is ef|punishment<br>fective|Stress|high|Physical<br>is ef|punishment<br>fective|
|Phone survey|0.030|-0.071|-0.064|-0.114|0.073|-0.087|-0.075|-0.017|
||(0.079)|(0.142)|(0.074)|(0.127)|(0.090)|(0.157)|(0.089)|(0.158)|
|Mean of Dep.Var (WhatsApp)|0.574|0.574|0.735|0.735|0.566|0.566|0.642|0.642|
|R-squared|0.142|0.147|0.171|0.177|0.173|0.188|0.156|0.178|
|Observations|207|207|207|207|169|169|169|169|
|Individual Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Interviewer FE|No|Yes|No|Yes|No|Yes|No|Yes|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ This table shows the estimated impacts of the phone-based survey relative to WhatsApp-based survey on consistency in responses on sensitive information. “Phone survey” is a dummy variable equal to one if the survey was per call and zero if was assigned to the WhatsApp group. The dependent variables is a dummy equal to one if the participant was consistent on her self-report of stress and her perception about physical punishment effectiveness between pairs of surveys (See more details in Table A8). The control variables include age in years, sex of the respondent and the educational level of the respondent for primary, high-school or tertiary education -the omitted category-. To account for potential time-invariant characteristics within municipalities, we include municipality fixed effects. Robust standard errors are in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

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- 16 -

- f) The results obtained muit finally be adjusted for inflation aa explained in the appendix.

A shortcut of last resort There are circumstances however in which the described adjustments may not be feasible. A lot of additional information is required but may not be available; it is usual for example to find global accounts that collect results of a very different nature for which disaggregated information is not available or it may be very difficult to prepare.

When that is the case, an alternative option is to estimate the Central Bank's results through changes in foreign and domestic assets and liabilities, using

- a) the flow change in foreign assets and liabilities as prepared to produce the balance of payments statistics, converted to the domestic currency. When changes in the exchange rate are important enough, monthly data has to be used to avoid major discrepancies.

- b) the nominal increase in net domestic indebtedness as results from comparing the initial and final balance sheet of the Central Bank. Care has to be taken that the increase in credit to the government corresponds to what the government records as financing from the Central Bank. Regarding credit to the private sector, all increases in lending that represent a mere capitalization of interest should be excluded; when it is the case to obtain a deficit definition that includes net lending as deficit spending, any increase in lending to the private sector has to be excluded.