rafmacalaba/datause-encoder-data huggingface dataset:
Confirming this intuition, price elasticities of demand have widely been shown to be negative for fossil fuels; that is, higher prices reduce consumption (Dahl 2012; Labandeira et al. 2017). Where elasticities are estimated to be positive, data limitations, time lags, and measurement errors play an important role.<sup>2</sup> While the negative relationship is well established, there is ample heterogeneity in the magnitude of the effect between countries and across fuel types. Using results from Labandeira et al. (2017), we visualize these differences in figure 1, summarizing average elasticity estimates for different energy types and groupings.

**Figure 1. Average price elasticities of energy demand**

<!-- Start of picture text --> Energy Electricity Natural Car fuels Gasoline Diesel Heating Residential Commercial<br>(376) (538) gas (230) (83) (469) (136) oil (44) (710) (61)<br>0<br>-0.1<br>-0.2 Short-run<br>-0.3<br>-0.4<br>-0.5<br>-0.6<br>Long-run<br>-0.7<br>-0.8<br><!-- End of picture text --> _Source:_ Based on data from Labandeira et al. (2017) _Notes: Energy_ refers to a pooled basket of different energy goods. _Car fuels_ refer to a pooled basket of vehicle fuels. Numbers in parentheses indicate the number of observations (studies) in the literature on which these averages are based.

Across different energy types and users, a 10 percent increase in the unit price of energy results in a 2 percent short-run reduction in consumption. This is relatively low, reflecting that consumers adjust their choices, but are likely to face short-run constraints in doing so. For example, they may try to avoid driving for leisure trips, but cannot stop commuting to work.

But in the long run, consumption choices are more responsive to price changes. Average long-run price elasticity estimates in the literature suggest that a sustained 10 percent increase in energy prices leads to a 4–7 percent drop in consumption, depending on fuel type and user (figure 1). It is important to note, however, that demand remains inelastic, and that the response to price changes is sluggish. A relatively more elastic demand in the long versus short run implies that, given time to adjust, consumers will shift away from fossil fuels (Sterner 2007). For example, in the case of transport fuels, consumers could choose to replace fuel-inefficient vehicles or reduce travel (for example, by moving

> 2 For example, when diesel pump prices drop at the beginning of the week and then rise more than they fell, the weekly average diesel price may show a small increase overall, as people may have rushed to fill up their vehicles at the beginning of the week to lock in the low price. Such effects may result in positive elasticities under special circumstances.

4
The dependent variable, r r r i i , is the dummy variable indicating whether an SOE _i_ in city _c_ and two-digit industry PPr _j_ is privatized in year rr _t_ . Since privatization is rarely reversed, each SOE’s observations after the year of privatization are dropped from the sample for the _current_ empirical exercise. A refers to a series of age dummies of the party secretary, being one if the party secretary in city ssi _c_ and year _t_ is _s_ years old, with _s_ taking the values of 51 rr and above. We treat party secretaries younger than age 51 as the reference group. We include city, industry and year fixed effects, and cluster the standard errors at the city level to accommodate for correlation in the error term within a city.

We control for firm characteristics, the party secretary’s background, and the city’s macro conditions (i.e., i ). The firm-level characteristics, all once-lagged, include labor productivity ( _Labor prod_ ii , the logarithm of the ratio of sales to the number of employees), the XX leverage ratio, the logarithm of total employees, and the return on asset ( _ROA_ ). The characteristics of party secretaries include the indicator of having college education ( _College_ ), the indicator of having work experience in the central government ( _Central Experience_ ), and in the provincial government ( _Provincial Experience_ ). The macroeconomic conditions in the city include the GDP growth rate and GDP per capita at the city level.

According to the OLS results in Table 2, the probability of the privatization of local SOEs drops abruptly when the age of the local party secretary passes the threshold of 58. The coefficients of the age dummies for age between 51 and 57 are all statistically insignificant, indicating no differences in the tendency to pursue privatization relative to that of a younger politician. However, when he becomes 58 years or older, the probability of an SOE proceeding with privatization drops by 4 percentage points. This is a large magnitude: relative to the mean of the privatization dummy of 0.209, it represents an increase of the probability of the privatization of 20.6 percent.<sup>17</sup> The results are robust to the inclusion of various sets of control variables, such as lagged firm performance, city-level macroeconomic conditions, and the party secretary’s education and work experience. Local politicians are thus much less likely to pursue privatization of local SOEs after age 58, when they are passed over for promotion.

> 17 The coefficient of the dummy variable of age 60 is insignificant, perhaps reflecting that this age cell has too few observations (see Figure 1a). `13`
institutional details of SNAP payments, and introduces the instrument. Section 3 formally lays out the empirical specifications. Section 4 reports the main results. Section 5 derives pass-through formulas to shed light on the factors that determine the size of the price response, calibrates a theoretical partial-equilibrium framework to compare with the empirical findings, and calculates the incidence of changes in SNAP benefits. Section 6 concludes.

# **2 Data and Institutional Details**

This section gives an overview of the data used for analysis, including data on SNAP benefits, retail prices, quantities sold, household expenditures, and other information. We also explain the formula used by the government to determine SNAP benefits for a given household, which motivates the construction of the IV.

## **2.1 SNAP Benefits**

We use publicly available state-by-month data on SNAP benefits and SNAP participation released by the Department of Agriculture (USDA) Food and Nutrition Service (FNS). We use state-by-month data for our main results; the results are robust to using county-level benefits for the set of available states.<sup>13</sup> Figure 1 plots out the SNAP benefits per population over our sample period from 2006 to 2015, which we use as our variable of interest to capture the amount of SNAP spending retailers receive. We divide total SNAP benefits by population as we are interested in changes in the scale of SNAP that are not driven by changes in population or market size. Substantial variation exists across states, with benefits per population ranging from $ 5 to $ 30 per month. Large discrete jumps in SNAP benefits per population mainly come from two events, the 2008 Farm Bill and the 2009 ARRA, in which maximum benefits for SNAP households were raised by around 8.5% and 13.6%, respectively. Both of these events increased SNAP benefits at the federal level in October 2008 and April 2009, respectively, through the intensive margin (SNAP benefits per recipient) as shown in Figure 2a, with benefits per recipient ranging from $ 80 to $ 150. This led to state-level variation in the percentage change of average SNAP benefits per population, as shown in Figure 3, ranging from around 6% to 25%. SNAP participation rates—ranging from 5% to 25%—were growing over this period, contributing to the smooth rise in the time series through the extensive margin (SNAP recipients per

> 13 County-level benefits are available at the bi-annual level for 33 states (the remaining states do not report them). County-level participation counts are available for all states at the annual level. Data at a higher spatial resolution is not available.

6
the shift to IT.

# **Table 15: Do shocks to current inflation affect inflation expectations?**

||Households'E<br>|xpectations<br>|Profe<br>|ssional Forecas<br>|ters'Expectat<br>|ions<br>|
|---|---|---|---|---|---|---|
||1-quarter<br>ahead|1-year<br>ahead|1-quarter<br>ahead|2-quarter<br>ahead|3-quarter<br>ahead|4-quarter<br>ahead|
|CPI Inflation|0.40<sup>***</sup>|0.41<sup>***</sup>|0.70<sup>***</sup>|0.50<sup>***</sup>|0.30<sup>***</sup>|0.13|
||(3.17)|(3.09)|(10.26)|(5.74)|(3.56)|(1.23)|
|Inflation Targeting|1.18|0.42|0.50|0.34|-0.06|-0.43|
||(0.91)|(0.29)|(0.62)|(0.39)|(0.08)|(0.42)|
|CPI Inflation ×|-0.08|0.05|-0.32<sup>*</sup>|-0.35<sup>**</sup>|-0.36<sup>***</sup>|-0.42<sup>***</sup>|
|Inflation Targeting|(0.48)|(0.20)|(1.95)|(2.31)|(2.83)|(3.57)|
|Constant|6.50<sup>***</sup>|7.10<sup>***</sup>|2.31<sup>***</sup>|3.52<sup>***</sup>|4.83<sup>***</sup>|6.06<sup>***</sup>|
||(5.34)|(5.71)|(3.80)|(4.86)|(7.15)|(6.12)|
|Observations|54|54|45|45|45|30|
|Adjusted _R_<sup>2</sup>|0.12|0.15|0.87|0.72|0.62|0.45|

Note: Robust t statistics in parentheses; *, **, ***, indicate significance at 10, 5 and 1 percent respectively.

# **Do inflation expectations feed into actual inflation?**

To assess whether inflation expectations feed into actual inflation, we estimate the following specification: The expectation term is the expectation of current inflation in the previous quarter. Lagged inflation captures the persistent nature of inflation, and the output gap controls for supply side shocks. As before, the output gap is defined as the difference between seasonally adjusted GDP and potential GDP, expressed as a percentage of seasonally adjusted GDP. 1 captures the magnitude of pass-through from inflation expectations to actual inflation. To compare the strength of any feedback from expectations to ββ actual inflation pre- and post-IT, we interact the expectations term with an inflation targeting dummy.

The estimated results are in Table 16. Pre-IT, there is no pass-through from households’ expectations to actual inflation. However, a one percentage point increase in professional forecaster’s expectations about next quarter’s inflation implies, on an average, an increase in inflation in the next quarter by about 40 basis points. This impact of inflation expectations on inflation has become muted under IT, again consistent with better anchoring.

25
poverty rate from 7 percent in 2016 to an official rate of 5.6 percent in 2022 appears to slightly underestimate the improvements in living conditions for the economically disadvantaged population segments.

Figure 17. Poverty and extreme poverty rates using SWIFT <!-- Start of picture text --> a. 2016 b. 2010<br>33.0 15.0 18.0<br>40.0<br>37.08<br>13.0 16.0<br>28.0 26.46<br>35.0 34.77<br>25.86<br>11.0 14.0<br>23.0<br>9.22 30.0<br>9.0 12.18 12.0<br>18.0<br>7.00 7.0 25.0 10.0<br>9.58<br>13.0 5.0 20.0 8.0<br>Current approach Swift Current Swift<br>approach<br>Poverty Extreme poverty (right axis) Poverty Extreme poverty (right axis)<br>Poverty Poverty<br>Extreme poverty Extreme poverty<br><!-- End of picture text --> Source: Own estimations using the presented approach and SWIFT, based on 2010, 2016 and 2022 HIES

- c. The GDP-poverty elasticity with a neutral distribution

Another approach involved scaling the 2022 observed consumption distribution by a fraction of the real GDP per capita growth, known as the pass-through rate (Mahler, Castañeda Aguilar & Newhouse, 2022). In this case, the exercise involves backcasting instead of nowcasting, yet the procedure remains identical. Data from HIES revealed that between 2010 and 2016 (comparable rounds), the mean per capita consumption increased by 13 percent in real terms. Concurrently, the World Development Indicators (WDI) Database from the World Bank shows that the real GDP per capita growth during this period was 35 percent, resulting in a pass-through rate of 0.37.<sup>16</sup> Utilizing the 2022 consumption distribution and real GDP per capita values for 2010, 2016, and 2022, the backcasting exercise estimated poverty rates at 25.3 percent for 2016 and 31.3 percent for 2010. The backcasted extreme poverty rates were calculated at 8.9 percent for 2016 and 12.2 percent for 2010.

Interestingly, except for the 2010 poverty rate, these backcasting results align well with the findings of this study. The 2010 poverty rate obtained through this alternative approach closely matches that derived from the 2010 HIES using the official welfare aggregate. However, if this consistency holds true,

> 16 Mahler, Castañeda Aguilar & Newhouse (2022) suggest, as a general guideline for international comparability, using a passthrough rate of 0.7. Applying this rate, estimated poverty and extreme poverty rates for 2016 would be 33.1% and 13.1%, respectively, and for 2010, 47.5% and 22.8%. These results show a decreasing trend, but the poverty and extreme poverty levels differ significantly from other robustness checks. For this reason, the chosen pass-through rate of 0.37, specific to Bangladesh, is used in this paper due to its sensitivity to the rate's value.

24
appear to be larger in the public sector than in the private sector (on Ghana, see Glewwe 1996, who also cites similar results for Kenya and Tanzania in Boissiere et. al. 1985).

A second explanation for the sheepskin effects may be that there are differences in the "difficulty" of years within a given level, and therefore in the amount of learning which actually takes place. For example, if there are more rigorous exams at the end of the last year within a cycle, completing this last year may lead to a greater marginal increase in human capital than completing any other intervening year, and may therefore be rewarded disproportionately by rational employers in the labor market. There is some anecdotal evidence in the Philippines that passing the last year within a given cycle may be more difficult than passing preceding years, especially at the university level.

Finally, there may be sharp discontinuities in ability in the transition years between education cycles.'<sup>9</sup> In the Philippines there appear to be very large differences in the direct private costs of education across levels. Table 6 shows that the private unit costs of education increase steeply by level: The mean unit cost of primary education is 898 pesos, while the mean unit cost of tertiary education is 13,334 pesos-almost 10% of mean household consumption. There are even larger differences in the unit costs for a given education level by per capita consumption quintile: The richest households spend more than 20 times as much as the poorest households for every child in primary school, and more than 10 times as much for every child in university.20 To see how these 19 I thank Jere R. Behrman for this suggestion.

20 1 would particularly like to thank Hans Hoogeven for help with this calculation. Disaggregated data on the private costs of education per household are available in the 1997 Family Income and Expenditure Survey (FIES), but not in the 1998 APIS, while data on household composition, including the number of children attending different education levels are available in the APIS, but not in the FIES. I use the panel of households included in both the FIES and APIS to estimate the private unit cost of primary, secondary, and tertiary education by running an auxiliary regression of the total amount spent by a household on education in 1997 on the number of children attending each education level in 1998: Exh = PPh + **_ySh_** + BTh + ch, where Ex is the total amount spent by household h on education in 1997, P, S, and T are the total 20
# Table 6: TSSM awareness

|VARIABLES|Aware of a<br>mason in the<br>community|Aware of a CLTS<br>committee in the<br>village|Agrees with statement:<br>Everybody in this community<br>knows somebody that can be paid<br>to build or improve a latrine|
|---|---|---|---|
||(1)|(2)|(3)|
|HWWS (β1)|0.094***|0.060**|0.027|
||(0.032)|(0.023)|(0.037)|
|TSSM ( β2)|0.183***|0.131***|0.081**|
||(0.036)|(0.026)|(0.035)|
|HWWS + TSSM (β3)|0.188***|0.130***|0.069*|
||(0.031)|(0.028)|(0.035)|
|p-values β1= β2|0.020|0.005|0.138|
|for F-test β1= β3|0.004|0.012|0.251|
|β2=β3|0.871|0.972|0.738|
|_Observations_|3,619|3,619|3,619|
|_R-squared_|0.133|0.288|0.072|
|_Control Mean_|0.143|0.119|0.297|

Notes: Robust standard errors in parentheses clustered at the ward level.*** p<0.01, ** p<0.05,

* p<0.1. Coefficients estimated using a linear probability model including block fixed effects.

42
related to finding indicators that demonstrate both cross-country comparability and relevance.

A final strategy is to develop a database of within-country studies of governance determinants and classify them according to common avenues of inquiry. These can then be subjected to procedures applied by synthetic reviews – clearly identifying the question about governance that is being asked, establishing criteria for the quality of the underlying evidence and assessing findings accordingly.

Ekman (2004) demonstrates how such synthetic reviews can be conducted on a governance determinant. In this case, the focus of the study was the impact of community health insurance schemes on utilization of health care services, out-of-pocket expenditures and health status. By clearly identifying the hypotheses under consideration, pre-establishing criteria for assessing the quality of evidence, and conducting a systematic search for studies, Ekman was able to take studies conducted in specific contexts and determine what evidence was available for generalizable lessons.

It is this latter strategy that is most likely to generate the kind of evidence that policymakers are seeking when they ask how to improve health service provision by improving health sector governance.

# **6. Conclusion**

After reviewing the many ways governance has been used and studied, this paper argues for defining governance as the combination of political, social, economic and institutional factors that affect the behavior of organizations and individuals and influence their performance. Applying this concept to applied research in the health sector requires clarifying which organizations are the subject of analysis (e.g. health insurers, public health services, regulatory agencies) and how their performance is measured (e.g. what are their missions in terms of service provision or health outcomes). It also requires distinguishing governance determinants, such as ownership, decentralization, formal procedures and stakeholder participation, from governance performance (e.g. whether formal procedures are implemented, workers fulfill their responsibilities, or stakeholders have substantive input into decision processes).

Measures of governance performance in public health care provision can be gathered through facility and population surveys. Facility surveys can directly measure certain aspects of governance performance, such as the share of workers who are absent without legitimate excuses. While population surveys are less direct, they can still elicit information about the performance of health facilities in terms of absenteeism or informal payments. The existing literature contains a manageable number of governance performance measures for public health care provision that could form the basis for a ______________________________________________________________________________________

-35-
<!-- Start of picture text -->
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# **Table 11(e): Parsimonious/Reduced VECM – Open Economy:**

``` MOD(40) Estimating the model by FIML The dataset is: C:\EGYMDJAN20151965.in7 The estimation sample is: 1959 - 2013 ``` ``` Equation for: Drm2 ```

|**`Coefficient`**|**`Std.Error t-value t-prob`**|
|---|---|
|**`Drm2_1 0.824396`**|**`0.08354 9.87 0.0000`**|
|**`DDexrtend_1 0.216645`**|**`0.04725 4.59 0.0001`**|
|**`CIa_1 -0.00845390`**|**`0.003614 -2.34 0.0284`**|
|**`DInterestrt_1 0.0200135`**|**`0.009229 2.17 0.0407`**|
|**`DINFL -0.00827521`**|**`0.001606 -5.15 0.0000`**|
|**`Dexrtprmium_1 -0.0697568`**|**`0.02559 -2.73 0.0121`**|

``` sigma = 0.048086 ``` ``` Equation for: Drgdp ```

||**`Coefficient Std.Error t-value t-prob`**|
|---|---|
|**`DDexrtend_1`**|**`0.138573 0.02646 5.24 0.0000`**|
|**`DINFL_1`**|**`-0.00358828 0.0008716 -4.12 0.0004`**|
|**`Drm2_2`**|**`0.449434 0.05028 8.94 0.0000`**|
|**`CIa_1`**|**`-0.0134603 0.002299 -5.85 0.0000`**|
|**`dumm2013`**|**`0.141293 0.02480 5.70 0.0000`**|
|**`DINFL`**|**`-0.00415356 0.0009212 -4.51 0.0002`**|
|**`Dexrtprmium`**|**`0.0269767 0.01445 1.87 0.0747`**|

``` sigma = 0.0292693 ``` ``` Equation for: DDISC ```

|**`Coefficient`**|**`Std.Error t-value t-prob`**|
|---|---|
|**`Drm2_1 4.77892`**|**`1.173 4.08 0.0005`**|
|**`CIa_1 0.243018`**|**`0.05456 4.45 0.0002`**|
|**`dumm1991 4.03556`**|**`0.7739 5.21 0.0000`**|
|**`dumm2008 2.93144`**|**`0.7183 4.08 0.0005`**|
|**`dumm2012 -2.77136`**|**`0.7128 -3.89 0.0007`**|

``` sigma = 0.743813 ``` ``` log-likelihood 164.125872 -T/2log|Omega| 398.25073
no. of observations 55 no. of parameters 18
```

``` LR test of over-identifying restrictions: Chi^2(78) = 204.33 [0.0000]** BFGS using analytical derivatives (eps1=0.0001; eps2=0.005): Strong convergence ``` ``` correlation of structural residuals (standard deviations on diagonal) Drm2 Drgdp DDISC Drm2 0.048086 0.48753 0.12477 Drgdp 0.48753 0.029269 0.35703 DDISC 0.12477 0.35703 0.74381 ```

|**`Single-equation diagnostics us`**|**`ing reduced-form residuals:`**|
|---|---|
|**`Drm2 : AR 1-2 test:`**|**`F(2,47) = 2.0385 [0.1416]`**|
|**`Drm2 : ARCH 1-1 test:`**|**`F(1,53) = 0.056382 [0.8132]`**|
|**`Drm2 : Normality test:`**|**`Chi^2(2) =0.0076428 [0.9962]`**|
|**`Drm2 : Hetero test:`**|**`F(12,42) = 1.5524 [0.1440]`**|
|**`Drgdp : AR 1-2 test:`**|**`F(2,46) = 1.5290 [0.2276]`**|
|**`Drgdp : ARCH 1-1 test:`**|**`F(1,53) = 0.23856 [0.6273]`**|
|**`Drgdp : Normality test:`**|**`Chi^2(2) = 0.95285 [0.6210]`**|
|**`Drgdp : Hetero test:`**|**`F(12,42) = 0.92158 [0.5346]`**|

70
**Figure 16: Volatility of terms of trade growth**

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>Time<br>NON-OECD NON-EAP EAP NON-OECD OECD<br>1970197219741976197819801982198419861988199019921994199619982000200220042006<br>Std Dev Terms of Trade<br><!-- End of picture text --> Sources: author’s own calculations using WDI data. Volatility is measured as the standard deviation of terms of trade growth using a five year window.

# **Sudden Stops and Interest Rate Shocks**

Shocks such as sudden stops where investors rapidly pull their money out of the country affect the terms at which companies and households can get access to credit, increasing uncertainty and causing macroeconomic volatility. In emerging markets sudden stops in financial flows are the most damaging external shock (Becker and Mauro, 2006), although, overall, internal shocks appear to have a much larger impact on volatility than external ones. Large interest rate shocks can also lead firms towards distress or bankruptcy. In addition to making credit, and thus investment, more expensive, adverse interest rate shocks can reduce the tolerance of financial institutions towards risk, excluding groups of borrowers from borrowing, and intensifying output growth volatility. Since firms are interrelated by complex credit relationships, bankruptcy of one firm can also generate “bankruptcy chains” with bad adverse effects (Easterly, Islam and Stiglitz, 2000). The volatility of international interest rates has however fallen progressively since its 1970s peak (Figure 17). The rolling 5-year standard deviation of monthly Federal funds effective rate has fallen progressively since its 1970s peak. Nonetheless, between 2000 and 2004 it experienced a surge of more than 2 percent, from which it has not yet recovered.

20