rafmacalaba/datause-encoder-data huggingface dataset:
|Hakkio-Rush<br>(1991)|U.S.<br>1950:JI-<br>1988:IV|Real gov't revenue and<br>spending inclusive of real<br>interest.|Real interest rate<br>is stationary.<br>Gov't spending<br>and revenue are<br>difference-<br>stationary.|Test whether gov't<br>revenue and<br>spending inclusive<br>of interest are<br>cointegrated.|A cointegration<br>relationship is<br>found for the<br>entire sample, but<br>not for the sub-<br>period 1976:III-<br>1988:IV. Hence,<br>recent fiscal<br>policy is not<br>sustainable.|
|---|---|---|---|---|---|
|Corsetti-<br>Roubini<br>(1991)|OECD<br>1960-89|Net general gov't debt; ex<br>post return on debt.||Test stationarity of<br>discounted debt<br>and the existence<br>of positive drift or<br>time trend.|Results are mnixed<br>for OECD<br>countries studied.|
|Ahmed-<br>Rogers<br>(1995)|U.S. 1792-<br>1992<br>U.K.<br>1692-1992|Real gov't tax revenue,<br>expenditure, and real interest<br>payments|**st+j**G,* and<br>st+j T,+1are<br>difference<br>stationary where<br>s,, is the lender's<br>marginal rate of<br>substitution.|Test whether real<br>gov't tax revenue,<br>expenditure, and<br>real interest<br>payments are<br>cointegrated with<br>vector<br>(-1,1,1**)**.<br>I|For both the U.S.<br>and the U.K., a<br>cointegration<br>relationship with<br>vector<br>--<br>**(-**1, 1,) is found,<br>implying that U.S.<br>fiscal policy is<br>sustainable.|

# Testing NPG condition

The literature testing the NPG condition originated with the pioneering work of Hamilton and Flavin (1984). They tested a version of the NPG condition where the _ex post_ real interest rate on government debt in each period r, was replaced by the average real interest rate denoted by r. This involves rewriting the government budget constraint (3) as: where the error term v, captures the deviation of the interest rate from its average value. Iterating forward yields: 22
86

# LOU INDEBTED COUNTRIES

|udg|etDeficIt/GDP||
|---|---|---|
|Yr|Projected|Actual|
|**86**|N/A|-10.40|
|8S|- 10.30|*11.10|
|**88**|- 10.00|.10.00|
|89|- 9.40|-11.00|
|90|:- 9.30|*10.80|
|91|- 8.80|- 9.10|
|92|6<br>8.10|N/A|
|Avg.|-9.56|10.40|

||Current Acet/|GDP||
|---|---|---|---|
|Yr|Projected|Actual|Act TOT|
|**86**|N/A|-13.50|<br>*13.SO|
|ST|- 9.60|-13.90|<br>-13.30|
|A|-10.70|.12.30|<br>-11.10|
|89|<br>-12.50|-12.70|<br>-11.30|
|90|<br>-12.10|-12.70|<br>-11.10|
|91|<br>-11.90|-12.40|<br>-12.00|
||-11.20|N/A|_VA2_<br>N/|
|Avg.|O36|2.80|-|

Gross Investment/GDP

|Yr|ProJected|Actual|
|---|---|---|
|86|N/A|11.60|
|B7|13.30|12.00|
|Be|14.20|13.00|
|89|13.20|12.80|
|90|13.90|12.40|
|91|13.30|11.60|
|92|13.40|N/A|
|Avg.|13.S8|12.36|

|Do|<br>tfcSavings/GDP|
|---|---|
|Yr|Prolected<br>Actuat|
|**86**|N/A<br>0.70|
|87|1.10<br>1.10|
|Be|2.10<br>3.70|
|89|3.10<br>2.90|
|90|3.60<br>1.80|
|91|3.70<br>1.70|
|92|4.10<br>N/A|
|Avg.|2.72<br>2.24|

|RealExrt|<br>GUFS Growth Rate|
|---|---|
|Yr|Prolected<br>Actual|
|86|N/A<br>9.20|
|r7|8.10<br>S.90|
|as|2.90<br>-0.90|
|89|8.40<br>-0.80|
||6.00<br>2.00|
|VI|8.70<br>1.10|
|92|6.00<br>N/A|
||6.82<br>1.02|

||Real GOP Growth<br>**Rate**|
|---|---|
|Yr|<br>Projected<br>Actual|
|86||/A<br>2.90|
|_.r_|1.30<br>-0.20|
|as|4.10<br>6.00|
|89|<br>3.70<br>t.80|
|90|<br>3.60<br>1.60|
||1<br>3.10<br>3.20|
|92|<br>3.90<br>N/A|
||3.16<br>2.48|
understanding beyond traditional bargaining models that emphasize material resources and outside options (Bloch and Rao, 2002; Farmer and Tiefenthaler, 1997; Tauchen et al., 1991) to highlight the critical role of identity threats. We build on the nascent literature examining non-material drivers of IPV (Adams et al., 2024; Tur-Prats, 2019) by connecting it to broader work showing how identity and social norms can shape behavioral and economic outcomes and responses to policy (Akerlof and Kranton, 2000; Bursztyn et al., 2020; Guarnieri and Tur-Prats, 2023; Karing, 2024; Oh, 2023). While previous studies have documented backlash against women’s empowerment — whether through cash transfers, labor market opportunities, or legal reforms — these interventions primarily operated by shifting the material balance of power between spouses (Anderson and Genicot, 2015; Angelucci, 2008; Bhalotra et al., 2021; Bulte and Lensink, 2019; Erten and Keskin, 2021). Our results reveal that perceived threats to male identity alone can trigger significant increases in IPV - a finding that helps reconcile the mixed empirical evidence on IPV prevention programs with theoretical predictions that improvements in women’s bargaining power can either increase or decrease violence (Eswaran and Malhotra, 2011; Tauchen et al., 1991).

Second, this study provides methodological insights for evaluating couples-based IPV prevention programs through a novel application of blinded, community-level randomization. While couplesbased training programs are becoming a more common policy tool globally, existing evaluations face critical limitations that our design addresses. Several previous studies reporting positive or null effects used non-blinded recruitment that sometimes generated large baseline imbalances between treatment and control groups in key observable characteristics such as IPV (Dunkle et al., 2020; Sharma et al., 2020).<sup>3</sup> Other couples-training studies randomized beneficiaries within communities (Boyer et al., 2022; Doyle et al., 2018) but had no pure control group outside treated communities, potentially biasing treatment estimates through contamination. Our design overcomes these two sources of potential bias by implementing both geographic buffers between treatment and control villages and identical recruitment protocols across groups. The importance of this approach is evident in our results: we find larger IPV increases in spillover than treatment groups, meaning that without geographically separated controls, we would have (incorrectly) concluded the program _reduced_ violence.<sup>4</sup> Other within-community individually randomized studies have found reductions

> 3For instance, Dunkle et al. (2020) found a difference of 10 percentage points in baseline IPV between treatment and control sectors.

> 4Consistent with our finding of negative spillovers, Chatterji et al. (2020) document negative spillover effects of _Indashyikirwa_

4
# **Table II:** Program impacts on marriage and fertility (beneficiaries)

## **<u>Panel A:  Baseline Dropouts</u>**

||=1|if Ever Mar|<br>ried|Age First<br>Marriage<br>|=|1 if Ever Pr|egnant|Nu|mber of Liv|<br>e Births|Age at First<br>Birth<br>|Desired<br>Fertility<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||During<br>Program|End of<br>Program|Two Years<br>After<br>Program|Two Years<br>After<br>Program|During<br>Program|End of<br>Program|Two Years<br>After Program|<br>During<br>Program|End of<br>Program|Two Years<br>After<br>Program|Two Years<br>After<br>Program|Two Years<br>After<br>Program|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|=1 if Conditional Schoolgirl|-0.140***|-0.157***|-0.107***|0.431***|-0.057*|-0.081***|-0.040*|-0.005|-0.095**|-0.147***|0.272*|-0.172*|
||(0.029)|(0.037)|(0.032)|(0.155)|(0.030)|(0.027)|(0.021)|(0.033)|(0.044)|(0.054)|(0.164)|(0.087)|
|Mean in Control Group|0.291|0.575|0.809|19.644|0.610|0.784|0.924|0.520|0.819|1.380|18.499|3.217|
|Sample Size|698|718|744|500|698|718|744|698|718|744|634|744|
|**Panel B: Baseline Schoolgirls**|||||||||||||
|=1 if Conditional Schoolgirl|0.000|-0.010|-0.035|-0.011|0.008|0.027|-0.024|0.023*|0.003|0.020|-0.144|-0.072|
||(0.012)|(0.024)|(0.027)|(0.148)|(0.015)|(0.027)|(0.034)|(0.014)|(0.022)|(0.036)|(0.136)|(0.064)|
|=1 if Unconditional Schoolgirl|-0.033***|-0.083***|-0.010|0.486**|-0.013|-0.063**|-0.001|0.013|-0.055*|-0.024|0.001|-0.017|
||(0.012)|(0.024)|(0.046)|(0.200)|(0.017)|(0.028)|(0.042)|(0.017)|(0.030)|(0.046)|(0.168)|(0.056)|
|p-value UCT vs. CCT|0.026|0.018|0.613|0.032|0.314|0.009|0.614|0.641|0.075|0.410|0.436|0.477|
|p-value Treatment|0.023|0.004|0.448|0.050|0.600|0.025|0.760|0.209|0.151|0.705|0.547|0.533|
|Mean in Control Group|0.047|0.180|0.402|18.651|0.092|0.247|0.501|0.055|0.199|0.511|18.718|2.974|
|Sample Size|1,967|2,018|2,049|821|1,966|2,019|2,049|1,966|2,019|2,049|998|2,048|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. We correct for inconsistencies in 'ever married' and 'ever pregnant' across rounds.All regressions are weighted to make them representative of the target population in the study EAs. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
Mechanically, the village-level equation (A1.1) can be estimated using the household-level data and specification (A1.4) by 2SLS using as instruments either a vector of village dummies, or identically a vector of village means, ln _y vt_ −1<sup>,</sup><sup>_MLD_</sup> _vt_ −1<sup>(see, for example, Angrist and Pischke (2009) and “Visual</sup> Instrumental Variables”). As long as the weights are correct (i.e., use the number of households in a village as weights in the village-level specification), then the household (2SLS) and village-level (WLS) specifications will yield identical coefficients for α _v_ ,β _v_ . This exercise is conducted Column (9) of Table 2, which is compared to Column (8) of Table 2.

While it is conventional to estimate the village-level (or country-level) regression using village sample averages and measures of inequality (e.g., ln _y vt_ −1<sup>,</sup><sup>_MLD_</sup> _vt_ −1<sup>), there are small-sample and</sup> measurement error issues with using sample averages as “proxies” for population moments (Deaton, 1985). Devereux (2007) shows, however, that the Deaton measurement-error estimator is identical to a Jack-Knife Instrumental Variable (JIVE) estimator, where Jack-knifed sample means serve as instruments for individual-level variables. The intent in that exercise, it should be noted, is NOT to estimate or identify the individual-level specification, but to correct for measurement error in the village-level (aggregate) specification. Adapting Devereux’s framework, the implied “structural” equation at the household-level is: Correcting for measurement error (implementing Deaton’s estimator using Devereux’s Jack-knife result)   simply involves using ln _y vt_ −1( _i_ )<sup>and</sup><sup>_MLD_</sup> _vt_ −1( _i_ ) as instruments for ln _yi_ , _vt_ −1 and _DEVi_ , _vt_ −1 . The implied reduced form equation for _gi_ , _vT_ in this approach is: Except for our inclusion of covariates (which can be easily incorporated into Devereux’s framework), this reduced form (A1.6) is identical to our structural equation (1). In other words, our main estimating equation can be seen as “highly similar” to the implied reduced-form associated with Deaton’s measurement error correcting group-means estimator.<sup>1</sup> It is not necessary that our model be imbedded

> 1  There is one other bit of slippage: in this discussion we treat ln _~~y~~ vt_ −1 as a known parameter. Of course, it is not. However, because the sample mean is inside the non-linear logarithm function, the linear algebra associated with the JIVE estimator does not hold exactly (without treating it as a constant). In our empirical work, however, we use the Jack-knifed version of _y_  _vt_ −1( _i_ ) inside the logarithm to address “in spirit” the same small-sample (measurement error) problem. Note that this only effects the degree to which our equation (1) is an _exact_ reduced form for the Deaton/Devereux specification.

Inequality and Growth in Rural China, Appendices: Page 2
the true culprit. This is particularly true for young children, who spend only one hour per day in cooking areas, on average. Living-area pollution is only moderately below cooking-area pollution and follows the same cycle, so most daily inhalation of particulates occurs in the living areas. Adult males have lower exposures simply because they are out of the house for many more hours per day.

Second, we should qualify our results with a cautionary note about the impact of very intense pollution on women and children during peak cooking periods. It is possible that peak pollution during a few hours per day causes disproportionate health damage. Currently-available scientific evidence suggests that health damage is associated with daily average exposure levels, not peak hourly exposures. However, the evidence is far from conclusive, and it is mostly derived from research on outdoor pollution effects in industrial economies.<sup>7</sup> Given the intensity of hourly pollution “spikes” during cooking in many Bangladeshi households, further research on this issue seems justified.

# **5.  Reducing Exposure for Young Children**

We use the case of infants (age 0-1) in our typical household to illustrate some implications of our results. We focus on infants because their documented vulnerability to indoor pollution seems to be the greatest. Our example applies to both male and female infants, since Table 4 shows that gender only makes a slight difference for daily exposure (216 ug/m<sup>3</sup> for females vs. 214 for males).

Figure 4 presents a simple experiment with data for the typical household. Starting with the status quo, with infants spending 3 hours outside in mid-afternoon, we optimize the 3-hour outside period by switching to other times (e.g., 8:00 AM) that provide the greatest relief from indoor

> 7   Studies of short exposures to outdoor particulate concentrations suggest some impact on heart rate variability and the rate of heart attacks.  However, a recent study in Palm Springs, California suggests that the short-period effect disappears when 24-hour average exposure is controlled for.  Similarly, average exposures seem to dominate day-to-day variations in daily time series studies.  Our thanks to Dr. Bart Ostro, Chief, California Office of Environmental Health Hazard Assessment, for his insights.

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||||PaRe|
|---|---|---|---|
|3. CONS|ERVATI|ON<br>AND INDIGENOUS PEOPLE: DIVERGENCE .......|<br>23|
|3.1.|INTROD|UCTION...........<br>.<br>....................<br>.........<br>..|23|
|3.2.|EXCLUS|IONS FROM CONSERVATION<br>AREAS ..........**.**..|<br>23|
|3.3.|CONDIT|IONAL OCCUPANCY OF PARKS AND RESERVES ....|.25|
|3.4.|COMPET<br>ISSUE|ITION FROM SPORTSMEN: THE SUBSISTENCE<br>.......................................<br>o..........|27|
|3.5.|ANIMAL<br>FUR-BE|PRESERVATION<br>CAMPAIGNS: WHALES, SEALS,<br>ARERS ..<br>..............................<br>. ...|.<br>28|
|3.6.|CONCLUD|ING COMENTS ...........<br>..<br>............|... 29|
|4. INDI|GENOUS P|EOPLE AND CONSERVATION: CONVERGENCE......|31|
|4.1.|INTROD|UCTION. ..................<br>.................|31|
|4.2.|INDIGE|NOUS ENVIRONMENTAL<br>RESEARCH AND MANAGEMENT.|<br>32|
||4.2.1.|The Alaska Eskimo Whaling Commission.....|32|
||4.2.2.|The Eskimo Walrus Commission .............|33|
||4.2.3.|CaribouManagement Boards .<br>...............|<br>33|
||4.2.4.|Makivik Research Centre ...<br>................|34|
||4.2.5.<br>4.2.6.|Arctic Research Establishment,<br>Pond Inlet.<br>Indigenous Conservation in the Pacific|<br>35|
|||Region ...... o..........<br>.......................|.........<br>36|
|4.3.|INDIGE<br>MANAGEM|NOUS INVOLVEMENT IN CONSERVATION<br>AREA<br>ENT...<br>......................<br>..........|37|
||4.3.1.<br>4.3.2.|Papua New Guinea......<br>.....<br>.......<br>.<br>Kakadu and Coburg National Parks,|37|
||<br>4.3.3.|<br>Australia ...... #..........................<br>.......<br>The Annapurna Conservation<br>Project,|.<br>38|
||4.3.4.|Nepal<br>................<br>*<br>.......................<br>Amboseli National Park, Kenya<br>...........|...<br>39<br>. 40|
|4.4.|CONCLU|DING COMMENTS .....................<br>*..<br>....|40|
# **2.3 The Returns to Education in Brazil**

Prior to exploring whether trade liberalization has contributed to the growing<sup>skill</sup> premium during our sample period, we summarize the trends in the returns<sup>toeducationduring</sup> the 1980s and 1990s. Using the same data as this paper (but focusing<sup>on workers in all sectors of</sup> the economy rather than just manufacturing), Blom, Holm-Nielsen, and Verner<sup>(2001)</sup> investigate the returns to education for Brazil from 1981 to 1998. Their main<sup>findingsare</sup> summarized in Figure 3, which reports the growth in the returns to education<sup>relative to 1982for</sup> workers with complete elementary education, complete lower secondary<sup>education, complete</sup> secondary education, and tertiary education. These estimates are based<sup>on thecoefficientsfrom</sup> earnings regression that controls for age, age squared, gender, whether<sup>a personis self employed,</sup> and whether the person works in the formal sector. Several interesting<sup>findingemerge.First,</sup> workers with complete elementary and lower secondary education have experienced<sup>a26% and</sup> 35% decline in their return to education relative to 1982, respectively.<sup>Second,the returnsto</sup> complete secondary education have not changed substantially relative<sup>to1982.However,since</sup> the returns to elementary and lower-secondary education have actually declined<sup>duringthesame</sup> period, this translates into a growing return to secondary education relative<sup>to theless educated</sup> workers. Third, the returns to tertiary education have increased by 24<sup>percent relativeto1982.</sup> Given that the returns to elementary and lower secondary education have actually<sup>declined, this</sup> translates into an even bigger increase in premium associated with complete university<sup>degree</sup> relative to the return earned by relatively uneducated workers. The<sup>above findingsarealso</sup> confirmed for a sample of urban and rural workers by Green, Arbache,<sup>and Dickerson (2001)</sup> using data from a nationally representative labor survey (PNAD).

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9. Financial integration was also promoted by greater economic alignment among Euro Zone countries, resulting from the observance of convergence criteria for inflation, fiscal balances, exchange rate and interest rates that had been established as pre-conditions for the adoption of the euro. However, it was the adoption of the euro itself in 2002 which brought about a marked increase in financial integration. Table 3 shows the substantial growth in balance sheets in particular in non-core countries, following the adoption of the euro.

**Table 3: Growth in balance sheets in European Union measured by external position vis-à-vis all sectors as percentages of GDP**

||**Assets**|
|---|---|
|**Period**|**Core countries**<br>**Non-Core countries**<br>**Non-Euro**|
|**1990–1995**|47.40<br>22.52<br>58.97|
|**1996–2001**|61.96<br>55.74<br>76.40|
|**2002–2010**|118.31<br>96.15<br>131.37|
||**Liabilities**|
|**Period**|**Core countries**<br>**Non-Core countries**<br>**Non-Euro**|
|**1990–1995**|41.79<br>19.89<br>63.26|
|**1996–2001**|63.40<br>60.31<br>84.35|
|**2002–2010**|102.44<br>107.22<br>148.44|
|Note: Core countri|es include Austria, Belgium, France, Germany, and the Netherlands. Non-Core countries include|
|Greece, Ireland, Ita<br>Source: Bank for In|ly, Portugal, and Spain. Non-Euro countries include Sweden and the United Kingdom.<br>ternational Settlements.|

10. Kalemli-Ozcan et al (2009) investigate in greater detail the impact of the euro on financial integration. They find that following the adoption of the euro bilateral bank holdings and transactions increased by roughly 40 percent among the Euro Area countries. The significance of the euro for this result is underscored by comparing the increase in bank holdings in the 12 countries that first adopted the euro with the three EU 15 countries (Denmark, Sweden and United Kingdom) that have not joined the currency union.

11. After quantifying the total effect of the euro, Kalemli-Oczan also analyze the deeper roots of the impact of the euro on financial integration. Specifically, they ask whether the euro promoted financial integration by (i) reducing the currency risk and eliminating exchange rate fluctuations, (ii) through legal harmonization or (iii) through higher trade. They find that the euro‘s impact on capital flows was largely driven by the elimination of the currency risk. In addition, they confirm that the 27 directives of the FSAP had a major impact on financial integration and on the intensity of capital flows across member states.

12. They also investigate the hypothesis of whether cross-border banking integration is driven by an increased volume of transactions in international trade. As goods and assets move 6
- National average (by size and class) gap filling or equalization standard to replace Williamson’s index. Pool can be adjusted by affordability and allocation determined by standard.

- Gap filling or equalization by size and class of local governments and possibly equal per capita grants to villages.

- Fiscal capacity measurement based upon potential revenues plus tax shares and other transfers and 50% of resource revenues.

- Fiscal needs measurement to discontinue wages compensation (basic allocation), discontinue use of HDI and the Williamson’s index and other indexes. Instead consider need measurement based upon service/client population for each service category.

# **_Alternative 1: A Simpler Yet More Objective Gap Filling Approach_**

To simplify DAU and to ground it in local comparative context, one requires meaningful grouping of local governments. One alternative in this regard is to have the following classes or clusters. Please note that the use of sophisticated statistical techniques in clustering as recently advocated by the World Bank and the Indonesian academics simply adds more complexity without having any clear advantages to a simpler and transparent approach advocated here.

**Table 7. Characteristics of Each Cluster: Province, C1-C4, and D1-D4**

|**Cluster**|**No.**<br>**of**<br>**juris-**<br>**dicti-**<br>**ons**|**Total**<br>**popu-**<br>**lation**<br>**(mil.)**|**Min.**<br>**pop.**<br>**(mil.)**|**Max.**<br>**pop.**<br>**(mil.)**|**Group**<br>**ave-**<br>**rage**<br>**pop.**|**Min.**<br>**area**<br>**(km**<sup>**2**</sup>**)**|**Max.**<br>**area**<br>**(km**<sup>**2**</sup>**)**|**Group**<br>**average**<br>**area**<br>**(km**<sup>**2**</sup>**)**|**Per**<br>**capita**<br>**2009**<br>**own**<br>**revenues**<br>**(IDR)**|**Per capita**<br>**2009**<br>**expendi-**<br>**ture**<br>**(IDR)**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Provinces**|32|228|0.76|43.1|7.12|3,133|319,036|59,696|164,181|666,348|
|**Cities**|||||||||||
|C1|11|19.2|1.02|2.63|1.74|119|683|302|148,971|853,093|
|C2|13|8.8|0.52|0.88|0.68|39|2,938|589|168,587|1,405,204|
|C3|58|12.4|0.10|0.50|0.21|16|2,400|352|189,369|2,493,247|
|C4|11|0.7|0.03|0.10|0.07|23|9565|1426|221736|4336555|
|<br>**Districts**|||||||,|,|,|,,|
|D1|100|71.3|0.01|3.68|0.71|425|1,891|1,232|100,068|1,725,989|
|D2|99|52.1|0.01|4.09|0.52|1,901|3,802|2,710|94,200|2,345,628|
|D3|99|34.4|0.01|2.43|0.35|3,803|7,155|5,218|122,226|3,383,613|
|D4|100|26.1|0.02|1.54|0.26|7,248|49,958|19,603|202,034|5,431,698|

Notes: Cluster classification: **Provinces** —one group: P1: Provinces excluding Jakarta. **Cities** ( _Kota_ )—4 groups: C1 with population over 1 million; C2 with population 500K to 1 million; C3: Population 100500 K; C4: Population under 100K. **Districts** ( _Kabupaten_ )—4 groups: D1: vast area—top quartile in area; D2: large area—2<sup>nd</sup> quartile in area; D3: medium area—3<sup>rd</sup> quartile in area; D4: small area—4<sup>th</sup> quartile in area.

12