rafmacalaba/datause-encoder-data huggingface dataset:
livestock. Higher temperatures tend to encourage small farmers to shift from cropping to livestock; the returns to both activities fall, but the decline is greater for crops. On the other hand, large livestock producers, who are typically more specialized in beef cattle which are less well adapted to warmer climates, experience sharper losses at higher temperatures.

In wrapping up this survey of a decade of Ricardian analysis in developing countries, Mendelsohn (2009, 16-17) concludes that: The studies generally confirm the hypothesis that tropical and subtropical agriculture in developing countries is more climate sensitive than temperate agriculture. Even marginal warming causes damages in Africa and Latin America to crops. Crops are also sensitive to changes in precipitation. In semi-arid locations, increased rainfall is beneficial. However, in very wet places, increased rainfall can be harmful. If climate scenarios turn out to be relatively hot and dry, they will cause a lot of damage to farms in low latitude countries. However, if climate scenarios turn out to be relatively mild and wet, there will be only modest damages and maybe even beneficial effects. The magnitude of the damage depends greatly on the climate scenario.

The hedonic approach relies on two key assumptions. First, there is long run equilibrium in factor markets (especially land). Second, there are no adjustment costs such that land rents fully reflect the value of climate at any given location. Given these assumptions, the hedonic approach typically utilizes cross-section data to estimate long-run relationships, which are thus sensitive to omitted variable bias. Schlenker, Hanemann and Fisher (2005) examine this issue in considerable detail in the context of the Mendelsohn, Nordhaus and Shaw (1994) findings of warmer temperatures leading to higher economic returns in the US. These authors show that this finding is a consequence of the omission of a variable indicating whether or not the land is irrigated. Since irrigated land tends to be very productive, and the value of pre-existing water rights (and low prices for water) tend to be capitalized into land values, these high value land rents – typically in high temperature zones -- tend to dominate the regression results. Once Schlenker et al. (2005) control for irrigation (by focusing on the non-irrigated lands alone), this 13
schools were invited to an information meeting in which they were informed that there would be unannounced visits by the survey firm to independently collect attendance data between one and three times per trimester throughout the school year. In treatment schools, the initial meeting was also open to pupils and parents of the relevant grades, and the relevant intervention was explained, attendance report cards distributed to the school, and questions answered. The intervention started at the beginning of the 2016 school year (February 5) or as soon as the treatment was announced, if announced after the start of the academic year, which was the case for the vast majority of schools. Reenrollment is automatic for grades such as Grade 7 at which admission is not conditional on passing an exam. In the case of Grade 6, in which enrollment requires students to have passed the end of Grade 5 exam, re-enrollment is not automatic but the official enrollment period ended on January 6, 2016. The treatment announcement visits by the implementing NGO started on January 14 and ended on March 3. Initial visits by Magariro to announce the treatments took place after the start of baseline survey collection in all but one school. But given the delays in completing the baseline survey caused by political tensions between RENAMO and government forces and by heavy rains, in just under 22% of schools, the baseline survey was completed after the initial visit in which the NGO announced the treatments.<sup>17</sup> This may have affected baseline selfreported attendance and monitoring quality, but there is little reason to believe that it should have affected data on baseline socioeconomic indicators or any 17 There is, however, no statistically significant difference in the timing of the treatment announcements across treatment arms relative to the average baseline household interview date. More precisely, when regressing the number of days between treatment announcement and average household interview dates on two treatment indicators (where the third treatment is the omitted category) and a set of district fixed effects, the p-value of a joint F-test of significance of the two treatment coefficients is 0.54.

20
2007; Arellano, 2008, to name but a few.) These frictions and shocks, however, are usually chosen based on intuition or anecdotal evidence.

The purpose of this paper is to identify the type of reduced form distortions that explain the observed business cycle dierences between emerging and developed countries. The logic of the experiment is as follows. At a conceptual level, shocks and frictions in most structural models drive a wedge between certain marginal rates of substitution and marginal rates of transformation relative to a prototype frictionless economy. Then, instead of testing whether a particular structural model generates the data, I estimate these wedges based on the prototype economy and measure their contribution to aggregate uctuations. The estimated wedges, however, cannot be used to identify primitive shocks and frictions: models with dierent primitive shocks and frictions could induce movements in the same reduced form wedge in the prototype economy. Nevertheless, these wedges oer useful information: a successful model should induce reduced form wedges similar to those estimated based on the prototype economy; moreover, these wedges should account, quantitatively, for uctuations in the economic aggregates.

Several authors have used variations of this approach applied to the U.S., including Parkin (1988), Hall (1997), Rotemberg and Woodford (1999), Mulligan (2002), Chari, Kehoe, and McGrattan (2007), and Shimer (2009). In this paper I follow more closely the methodology proposed by Chari, Kehoe, and McGrattan, labeled `Business Cycle Accounting'. In this methodology, the researcher interprets the data through the lens of a small open economy model. This prototype economy is subject to reduced form shocks that are interpreted literally as total factor productivity (the eciency wedge), as labor and investment taxes (the labor wedge and investment wedge), as uctuations in real interest rates (the risk premium wedge), and as government consumption (the government consumption wedge).

I estimate a stochastic process for these wedges using data from a representative emerging country, Mexico, and a representative developed open country, Canada. Next I estimate the realized wedges and study their contribution to aggregate uctuations in both countries.

2
recent papers measuring the effect of _SP_ on labor market outcomes in Mexico. It then discusses relevant papers which evaluate the impact of similar programs in other countries. Overall, there is suggestive evidence that _SP_ may discourage formal employment, but data limitations, endogeneity, and omitted variables has left room for a more rigorous testing of the hypothesis.

Four established working papers are particularly relevant for our study. Two found no effect of _SP_ on labor market decisions, while two provide suggestive evidence of an effect – although using aggregate data or data only covering the Federal District.

Campos-Vazquez and Knox (2008) use aggregate data from 33 urban cities from the ―Encuesta Nacional de Empleo Urbano‖ (ENE) during the period 2001-2004. They do not find any effect of _SP_ on the rate of formal employment in the municipality. They also conduct a parallel analysis using individual-level data from 136 municipalities during 2002-2004 that also fails to find a significant effect. However, their data is only available for individuals from the poorest deciles, thus it captures a segment of society that already has high levels of informality and where it would be less likely that _SP_ would have much effect. Another potential problem is that their period of study may be too premature to find any effect at all.

Parker and Scott (2008) use Rand‘s Mexican Family Life Survey 2002 – 2005 panel. They do find a disincentive effect in rural municipalities, with the percentage change relatively large due to the small base, i.e. the absolute magnitude of the change is small. However, they do not find comparable effects in the urban areas. Using aggregate data from the 2000 and 2005 census, they do not find significant effects. This work has only a limited time dimension and is again looking at the early years of the program.

Barros (2008) measures the effect of _SP_ on health, consumption, and labor outcomes. He estimates the _SP_ effect by using a triple difference equation, taking differences over time, state intensity target (stated objective in 2007 of share of eligible households to enroll), and eligibility (whether no-one in the household holds a formal sector job). He assumes that eligibility in a given state is not correlated with the state‘s target intensity.

7
highly qualified teachers.<sup>13</sup> To incorporate the key feature of decreasing returns to spending as the goal approaches, MAMS uses logistic, S shaped, functions as shown in Figure 1. The ‘flatter’ logistic curve for the case of Honduras indicates that decreasing returns to spending in education are more severe and begin at lower completion rates than in the case of Ghana. This helps explain the counterintuitive situation where Honduras is closer to achieve its education goal but needs more resources than Ghana.

**Figure 1: Honduras is closer to its primary education goal but reaching it may be costlier than in Ghana**

<!-- Start of picture text --> Primary school completion rate, %<br>100%<br>76%<br>Honduras<br>Ghana<br>47%<br>x 1.11x 2.38x<br>Public spending on primary education<br><!-- End of picture text --> Note: the point ‘x’ on the horizontal axis represents the current (2004) public spending on primary education, see values in terms of shares of GDP in Table 3. The points 1.11x and 2.38x represent the spending in 2010 (when every school aged child, in a 6 year primary cycle, has to enter and graduate from grade 1 in order to reach MDG2 by 2015) and are calculated as the compounded growth rates of Table 3; so for the case of Honduras: 2.38 = (1+0.156)<sup>6</sup> .

Similar arguments can be used to compare the health MDGs with the important difference that in this comparison Honduras is in a better position than Ghana. In Honduras, the under-five mortality rate decreased from 59 to 31 per thousand births and the infant mortality rate was reduced from 47 to 23 per thousand between 1990 and 2005/6. While data inadequacies do not permit a precise assessment of the evolution of maternal mortality, the available survey results suggest that considerable progress has been achieved: the maternal mortality rate was reduced from about 180 (per 100 thousand) in 1990 to around 108 in 2000.<sup>14</sup> The rapid pace of reductions in infant and child mortality rates between 2001 and 2005/6 bodes well for the achievement of MDG 4, but continued progress is conditional on maintaining the recent growth of public health expenditures, which grew nearly four times as fast as real GDP between 1999 and 2005. If this growth is not sustained, additional inroads in improving health outcomes are likely to be minor (see, for example, chapter 7 of World Bank, 2007). In Ghana, efforts to reduce child and maternal mortality have practically stalled since 2003, which is even more worrisome since health sector expenditures have risen over the same period (GJAS, 2007). The required additional resources to reach the health MDGs differ markedly

> 13 It is important to underline that MAMS relies on sector studies to assess the empirical strength of these factors and embeds them in its general equilibrium framework.

> 14 Surveys aimed at measuring maternal mortality rates were administered in 1990 and 1997, and the national statistical institute (INE) estimated the rate for the year 2000.

8
Other things equal, lower diamond exports would cause a large drop in mineral revenue, raising the fiscal deficit (Table A2. 2). A 5 percent decline in diamond exports would raise the cumulative fiscal deficit from P 34 billion to almost P 38.5 billion, both expressed in the 2008/09 Pula. Should diamond exports dip further by 15 percent, the cumulative deficit could reach P 48 billion (in the 2008/09 Pula), equivalentto 50 percent of 2008/09 GDP. Faced with the binding the external debt and guarantee limit, the government would draw on the fiscal savings to finance the deficit, while borrowing domestically. The GIA would be depleted in 2011/12 (Figure A2. 1), and thereafter domestic debt would rise rapidly (Figure A2. 2). The

**Figure A2. 1. GIA Balance (in percent of GDP)**

<!-- Start of picture text --> 35<br>30 15% higher<br>25<br>10% higher<br>20<br>5% higher<br>15<br>Baseline<br>10<br>5 5% lower<br>0 10% lower<br>15% lower<br><!-- End of picture text --> Source: Author’s calculation.

government’s net liabilities would rise significantly, from 14 percent of GDP to 26 percent (Figure A2. 3).

**Table A2. 2.  Sensitivity Analysis:  Diamond Exports**

||2010/11|2011/12|2012/13|2013/14|2014/15|2015/16|
|---|---|---|---|---|---|---|
|**Mineral revenue (in percent of G**|**DP)**||||||
|15%higherdiamond exports|11.0|11.7|12.0|12.0|11.7|11.4|
|10%higherdiamond exports|10.7|11.3|11.7|11.6|11.4|11.1|
|5%higherdiamond exports|10.4|11.0|11.3|11.3|11.1|10.8|
|Baseline|10.1|10.7|11.0|11.0|10.7|10.4|
|5%lowerdiamond exports|9.7|10.3|10.6|10.6|10.3|10.1|
|10%lowerdiamond exports|9.4|10.0|10.3|10.2|10.0|9.7|
|15%lowerdiamond exports|9.0|9.6|9.9|9.8|9.6|9.3|
|**Fiscal balance (in percent of GD**|**P)**||||||
|15%higherdiamond exports|-7.9|-4.8|-1.9|0.3|1.5|2.8|
|10%higherdiamond exports|-8.6|-5.5|-2.7|-0.5|0.7|2.0|
|5%higherdiamond exports|-9.4|-6.3|-3.5|-1.3|-0.1|1.1|
|Baseline|-10.2|-7.1|-4.3|-2.1|-0.9|0.3|
|5%lowerdiamond exports|-11.0|-7.9|-5.2|-3.0|-1.8|-0.6|
|10%lowerdiamond exports|-11.8|-8.8|-6.1|-3.9|-2.7|-1.6|
|15%lowerdiamond exports|-12.7|-9.7|-7.0|-4.8|-3.7|-2.5|
|**Cumulative fiscal balance (in bi**|**llions of 2008**|**/09 Pula)**|||||
|15%higherdiamond exports|-18.3|-23.0|-25.0|-24.6|-22.9|-19.6|
|10%higherdiamond exports|-18.9|-24.2|-26.9|-27.4|-26.6|-24.3|
|5%higherdiamond exports|-19.4|-25.4|-28.9|-30.3|-30.3|-29.1|
|Baseline|-20.0|-26.6|-30.9|-33.1|-34.1|-33.8|
|5%lowerdiamond exports|-20.5|-27.8|-32.8|-35.9|-37.8|-38.5|
|10%lowerdiamond exports|-21.1|-29.0|-34.8|-38.7|-41.6|-43.3|
|15%lowerdiamond exports|-21.6|-30.2|-36.7|-41.5|-45.3|-48.0|
|Source: Author’s calculation.|||||||

31
# **Appendix A: Inflation Episodes**<sup>a</sup>

<u>Table A1: Inflation Episodes</u><sup>24,25</sup>

|Country|Trough Date|Trough Inflation (%)|Peak Inflation (%)|Peak Date|
|---|---|---|---|---|
|<br>Argentina|1986:3|19.88|165.60|1989:2|
|Brazil|1985:4|27.61|131.34|1989:2|
||1991:3|63.01|123.53|1993:2|
|Colombia|1983:4|4.02|6.88|1991:1|
|India|1989:1|1.47|3.25|1991:2|
|Indonesia|1985:3|1.11|2.18|1987:3|
||1996:3|1.73|8.51|1998:1|
|Israel|1982:1|21.45|45.68|1984:3|
|Korea|1985:4|0.52|2.17|1991:1|
|Malaysia|1986:4|0.10|1.18|1991:4|
|Mexico|1984:3<br>1993:4|12.48<br>1.96|24.30<br>8.29|1987:1<br>1996:1|
|Peru|1979:4|12.73|24.93|1984:3|
||1986:4|15.72|211.42|1989:3|
|Philippines|1982:2|1.66|7.56|1984:2|
||1986:2|0.27|3.82|1990:1|
|South Africa|1983:1|2.85|4.21|1986:1|
|Thailand|1985:1|0.27|1.53|1990:2|
|Turkey|1982:2|6.68|10.58|1984:4|
||1986:3|7.88|14.52|1988:4|
||1989:3|12.04|17.23|1996:4|
||2000:1|11.12|12.21|2000:4|
|Venezuela|1983:1|1.80|12.94|1989:3|
||1991:4|7.28|16.61|1995:3|

a As was explained in Section 3, the inflation figures presented in the table are the filtered values of actual quarterly consumer price inflation using a nine-quarter centered moving average process.

24 There were inflation episodes in Argentina, Colombia, Israel, Korea, Malaysia, Mexico, South Africa, and Thailand whose start date turned out to be before 1980 and, as a result, could not be included in the investigation. The lightly shaded areas in Figure 2 present some of these episodes, which commenced before 1980 and ended in the early 1980s.

25 We have also considered including Chile in our study. However, the evolution of the inflation rate in this country displays a significantly different pattern since Chile has reduced its trend inflation noticeably during the late 1970s and the early 1980s compared to the countries involved. On this ground, we have not included Chile in our analysis.

22
Random assignment successfully balanced over consumption-related characteristics.<sup>7</sup> All modules were fielded within villages at the same time, so no controls for timing or other covariates are used when examining the village averages. These averages reveal reported consumption to be highest for the ‘gold standard’ design of an intensively supervised, individually-kept diary (Table 2).<sup>8</sup> The variances and ratios reported in the middle columns of Table 2 are inconsistent with the assumptions of classical measurement error. For three of the designs (subset 7-day recall and both household diaries) the variance of the error-ridden variable is less than that of the benchmark variable. This understatement of the variance could not happen if the measurement error was just in the form of white noise.

**Table 2.  Tests for Non-classical (correlated) Measurement Error in Log Consumption**

||Mean|Ratio to<br>Benchmark<br>)<br>(ln<br>)<br>(ln<br>8<sup>_~~x~~_</sup><br>_E_<br>_~~x~~_<br>_E_<br>_k_|Variance|Ratio to<br>Benchmark<br>)<br>var(ln<br>)<br>var(ln<br>8<sup>_~~x~~_</sup><br>_~~x~~k_|ˆλ (S.E.)|_t_-test for<br>Correlated errors<br>0<br>1<br>:<br>1<br>:<br>1<br>_H_<br>_H_<br>λ<br>λ<br>=<br><<br>ˆ<br>Pr(<br>)<br>_p_<br>_t_<br>_t_<br>=<br><|
|---|---|---|---|---|---|---|
|1. Long 14 day|14.104|0.987|0.350|1.081|0.569 (0.068)<sup>a</sup>|_p_=0.000|
|2. Long 7 day|14.225|0.996|0.337|1.040|0.596 (0.064)|_p_=0.000|
|3. Subset 7 day|14.195|0.994|0.320|0.988|0.535 (0.065)|_p_=0.000|
|4. Collapsed 7 day|14.039|0.983|0.343|1.060|0.583 (0.066)|_p_=0.000|
|5. Long usual month|14.084|0.986|0.423|1.307|0.662 (0.072)|_p_=0.000|
|6. HH diary frequent|14.128|0.989|0.289|0.891|0.494 (0.062)|_p_=0.000|
|7. HH diary infrequent|14.155|0.991|0.269|0.832|0.422 (0.063)|_p_=0.000|
|8. Benchmark (indiv, freq)|14.283|1.000|0.324|1.000|||

_Notes:_ The λ<sup>ˆ</sup> are from separate regressions for each module, where the independent variable is village-averaged log annualized total household consumption from the individually-kept, frequent visit diary (the benchmark).

# _N_ =168.

a Standard errors in parenthesis.

- 7 Beegle _et al_ . (2012) find just 13 of 420 pairwise comparisons to have statistically significant differences, at the five percent level, for 15 baseline household characteristics.

- 8 Beegle _et al._ (2012) show understatement by the other designs occurs through both food and non-food; average food consumption is statistically significantly lower than the benchmark for six of the modules, average frequent non-food consumption is significantly lower for four of the modules and average non-frequent non-food consumption is significantly lower for three of the modules.

8
**Step** 5. Using eq. 3 and plugging the mean value of Xi and _Mi,_ we get an estimate of the amount of money the household would be willing to pay for the child's public education. Denote estimate as _MeanPUi._ Again this will be different from the observed average education spending of households for children in public schools, which would reflect the behavior of a self-selected sample of users of public education.

Step 6. Take the difference between _MeanPVi_ and _MeanPUi_ to measure the effect of the government provision of public schools on the education spending behavior of an average household.

Note that this difference would reflect the relative quality and payments (fees and contributions) associated with public and private schools. We can calculate this effect for various income groups by plugging their respective mean values for _Xi_ and _Mi_ into eqs. 1-3.

A key question in this type of analysis is the availability of the trigger variable (Zi) to identify the model, which can be the provincial average learning achievement score of public schools relative to that of private schools, as index of the relative quality of these two types of schools.

# _2.2. TABLES_

**Table 1. Household Expenditures on Educational Service**

|**Public Service**||||**Private Service**||||
|---|---|---|---|---|---|---|---|
|**Explunatories variables**|**Coef.**|**Mean**|**M.W. to P.**|**Explanatories variables**|**Coef**|**Mean**|**M.W. to P.**|
|LogNat per capita H income|0.76 *|7.21|5.46|LogNat per capita H income|0.57*|8.30|4.69|
|Years of Schooling ofHH|0.01 *|591|005|Years of Schooling ofHH|0.02 *|10.11|0.19|
|Region_(rural)_|0.66 *|0.77|0.50|Region_(rural)_|0.06|0.97|0.06|
|Age|0.12 *|11.12|1.30|Age|0.02|13.62|0.32|
|Squared Age|0.00 *|143.74|-0.35|Squared Age|0.00|222.68|-0.13|
|Gender_(female)_|-0.04|0.53|-0.02|Gender_(female)_|0.05|0.46|0.02|
|Number of rooms|0.09 *|2.91|0.27|Number of rooms|-0.02|4.16|-0.08|
|Floor_(notfinishedfloor)_|0.22 *|0.27|0.06|Floor_(notfinishedfloor}_|0.02|0.71|0.01|
|Sewage_(not sewage)_|0.26 *|0.66|0.17|Sewage_(not sewage)_|0.48 *|0.94|_0.45_|
|Number of Children|-0.12 *|2.35|-0.29|Number of Children|-0.02|2.00|-0.04|
|Mill's Ratio|_0.45_*|2.47|1.12|Mill's Ratio|-0.01|0.87|-0.01|
|Constant|-2.67 *||-2.67|Constant|1.87 *||1.87|
|Dependent Variable:||||Dependent Variable:||||
|Log Nat Expenditure||5.60|5.60|Log Nat Expenditure||7.32|7.36|
|Evaluated in pesos||270.64|270.23|Evaluated in pesos||1506.59|1568.72|
|R2|0.42|||R<sup>2</sup>|0.35|||

Source: Own calculations based on ENIGH 96 and DGPyP, SEP

* Significant at 5%, ** Significant at 10%

Italics: Reference category 15
4

# _Table 1_

# Poaoliat Structural Indicators

||Perc_ntaQu of GDP<br>Tra<br>1988<br>1990<br>Percen|de Structur<br>tage of Exp|e<br>orts 1988|
|---|---|---|---|
|Exports|26<br>23<br>Minera|ls|40|
|Imports|54<br>50<br>Raw Ma|terials|30|
|Resource Balance|-28<br>-27|||
||ExDort|s to Non-So|cialist|
|Fiscal Revenues|45<br>51<br>Countr|ies 1988||
|Government Exp.|65<br>64<br>Percen|t of Total||
|<br>(Current Exp.|50<br>52)<br>expor|ts|5|
|(Subs.& Transfers|<br>18<br>20)<br>Share|of Raw||
|Fiscal Balance|-20<br>-13<br>Mater|ials|94|
|(Pct.For.financed|100<br>78)|||
||Import|s from Non-|Socialist|
|Total Absorption|<br>128<br>127<br>Countr|<br>ies 188||
|Investment|42<br>20<br>Percen|t of total||
|Private Consumption|62<br>75<br>impo|rts|33|
|<br>Public Consumption|<br>24<br>22|||
||<br><br>Balance<br>of Payments<br>$millions<br> <br> <br>|||
||1988<br>1990<br>1991|||
|Exports|829<br>468<br>432|||
|imports|1849<br>1047<br>564|||
|Current Acct.<br>-<br>CMEA|1033<br>-644<br>-100<br>Dependence: Mongolia and Easte|rn Europe||
|Exp|ort Share<br>Non-CMEA Exports/|Direct Los|s|
|<br>to|<br> <br>CMEA (e)<br>GDP (e)|from CMEA|Shock/|
||<br>|<br>GDP (e)||
|Mongolia<br>95|<br>1|30*||
|<br>Bulgaria<br>70|<br>15|12||
|C8FR<br>50|<br>15|7||
|Hungary<br>40|<br>22|6||
|<br>Poland<br>40|12|s||

* Aid shock only