rafmacalaba/datause-encoder-data huggingface dataset:
We assigned interventions to _at least_ one of 12 intervention categories.<sup>10</sup> Around 60 percent of programs included a component focused on teachers’ professional development, with more of a focus on this kind of component in low- and middle-income countries. This is not surprising as many interventions that aim to improve the quality of preprimary education—such as a change in curriculum or pedagogy—will require teachers to receive some training prior to program implementation. Similarly, offering a new service will also initially entail training. Close to half of all programs provided subsidized or free access in studies from high-income countries, compared to only 3 percent among studies from low- and middle-income countries. In both contexts, around 70 percent aimed to target a disadvantaged population.

These interventions include both small-scale pilots implemented by non-governmental organizations (NGOs) and nationally scaled government programs. Daycare programs with education components represented very few evaluated programs; formal preschool was the dominant program in both high-income and low- and middle-income contexts, although community preschool accounted for more than 40 percent of programs in low- and middleincome contexts. While a majority of programs in both contexts took place in publicly managed schools, a sizeable fraction had a combination of public and private management. A higher fraction of programs in studies from high-income countries took place in classrooms with a teacher who was formally qualified to teach preprimary students than in studies from low- and middle-income countries, where teachers were formally qualified to teach in preprimary settings in only half of the studies in the sample.

# **_3.1.3.3 Outcomes_**

We focused on outcomes related to school participation and child development, extracting average treatment effects and their precision for each intervention (Table 4). When available, we also captured outcomes of teachers, such as classroom practice or self-reported measures of wellbeing. We extracted a total of 798 outcomes at the child and teacher levels. In one study that relied on a very large administrative dataset with a panel structure (Rossin-Slater & Wust, 2020), outcomes were aggregated to combinations of geographic unit and birth cohort. Sample sizes

> 10 These categories included (i) teacher professional development, (ii) subsidized or free access, (iii) change in curriculum, (iv) change in pedagogy, (v) provision of materials, (vi) provision of new staff, (vii) provision of health and nutrition services, (viii) preschool construction, (ix) parental engagement, (x) community outreach, (xi) preschool day extension, and (xii) teacher payments.

15
9 growth ‘function’ that transforms period-0 income into period-1 income for a given group of period-0 equals is estimated more precisely as the number of observations used increases); and an ‘appropriation effect’, which tends to decrease _V_ as _w_ is increased (as _w_ is raised, increasingly disparate individuals are grouped together as equals, making for an ever-larger error in the decomposition). Van de Ven et al. argue for choosing _w_ such that _V_ is maximized—the point where the gains from averaging are balanced against the losses associated with appropriation. This would point to optimal bandwidths of 80 RMB in the case of China and 100 Dong in the case of Vietnam.

_Table 2: Decomposition results—China 1989-97_

|Bandwidth(RMB)|_V_|_H_|_R_|_V%_|_H%_|_R%_|
|---|---|---|---|---|---|---|
|30|-0.1912|0.0034|0.2115|-806%|14%|892%|
|40|-0.1960|0.0057|0.2140|-827%|24%|903%|
|50|-0.1982|0.0049|0.2170|-836%|21%|915%|
|60|-0.1973|0.0066|0.2144|-832%|28%|904%|
|70|-0.1977|0.0088|0.2126|-834%|37%|897%|
|80|-0.1989|0.0078|0.2148|-839%|33%|906%|
|90|-0.1974|0.0092|0.2120|-833%|39%|894%|
|100|-0.1985|0.0100|0.2122|-837%|42%|895%|
|110|-0.1983|0.0110|0.2109|-836%|46%|890%|
|120|-0.1972|0.0108|0.2101|-832%|46%|886%|
|130|-0.1962|0.0117|0.2081|-827%|49%|878%|
|140|-0.1963|0.0124|0.2076|-828%|52%|875%|
|**Countries**|**GATS Commitments**|**Doha Offers**|**ASEAN Framework Agreement**<br>**on Services (AFAS 8th Package)**|**Blueprint 2015**|**Applied policy (as of 2012),**<br>**reviewed by government**|
|---|---|---|---|---|---|
||indirect investment in these operators<br>is allowed.|||||
|**Thailand**|Must be locally incorporated. Foreign<br>equity limit is 20% of the registered<br>capital. Due to scarce resources, the<br>number of licenses may be limited.<br>Since 2006, new commitments will be<br>introduced, conditional on new<br>communications acts.|Offers reflect<br>commitment, except:<br>Starting in 2006, new<br>commitments will be<br>made, as the Thai<br>Communications Acts<br>are beingrevised.|Facilities based: Foreign equity<br>limit is 25%. Number of licenses<br>may be limited. Head office and<br>management must be in Thai<br>territory.|By 2015, allow for foreign<br>(ASEAN) equity participation of<br>not less than 70%.|Foreign majority owned or<br>controlled providers may only<br>offer services on a resale basis,<br>and such offerings are limited to<br>certain defined services. No other<br>restrictions.|
|**Vietnam**|Facilities-based: Upon accession, joint<br>ventures with telecom service suppliers<br>duly licensed in Vietnam will be<br>allowed. Foreign equity limit is 49%.<br>Non-facilities based: Upon accession,<br>joint venture with telecom service<br>suppliers duly licensed in Vietnam<br>with foreign equity limit of 51% is<br>allowed.|Did not submit an<br>Offer.|None, services must be offered<br>through commercial arrangements<br>with an entity established in Viet<br>Nam and licensed to provide<br>international telecommunication<br>services.|By 2015, allow for foreign<br>(ASEAN) equity participation of<br>not less than 70%.|Foreign equity limit is 70%<br>(including facilities-based<br>services). Approval required for<br>majority control. The State of<br>Vietnam holds dominant shares in<br>telecommunications service<br>providers with network<br>infrastructure.|

Source: Uruguay round commitments, Accession schedules, and Doha round offers are from the WTO website. ASEAN Framework Agreement on Services (AFAS): 8th Package of Schedules; Financial Services, 5th package of schedules; and ASEAN Economic Community Blueprint, Jan 2008.

27
<u>Table A.3: Augmented Dickey-Fuller (ADF) Unit Root Test – Annual data</u>

||||**Level**||**1**|**st Differenc**|**e**||
|---|---|---|---|---|---|---|---|---|
|**Fundamental**|**Variable**|**None**|**Constant**|**Constant**<br>**and**<br>**Trend**|**None**|**Constant**|**Constant**<br>**and**<br>**Trend**|<br>**Stationarity**|
|REER|reer|-1.22|-1.52|-1.68|-5.00|-5.07|-4.98|I(1)|
||reer_adj|-0.63|-2.36|-2.23|-5.06|-5.00|4.98|I(1)|
|Terms of trade|tot|0.29|-1.17|-4.01|-5.94|-5.94|-5.85|I(1)|
||tbY|-2.16|-2.59|-2.47|-5.18|-5.10|-5.05|I(1)|
|Productivity|tw_gdppc|-1.10|-2.53|-2.60|-5.05|-5.06|-5.03|I(1)|
|differential|tw_gdppc_adj|-1.88|-0.72|-1.79|-4.47|-4.73|-4.69|I(1)|
||agr_tw|-0.47|-2.75|-2.70|-6.43|-6.33|-6.23|I(1)|
||agr_tw_xchn|-0.31|-2.79|-2.73|-6.19|-6.08|-5.99|I(1)|
||tnt_ag_ind|-0.91|-0.32|-1.46|-5.34|-5.40|-5.63|I(1)|
||tnt_ag_ind_xchn|-0.65|-0.91|-1.81|-5.39|-5.37|-5.42|I(1)|
||tnt_ag|-1.38|-0.93|0.05|-4.22|-4.16|-4.16|I(1)|
||tnt_ag_xchn|-0.17|-1.15|-1.06|-3.83|-3.77|-3.70|I(1)|
||tnt_ag_xchn (2)|-0.16|-1.16|-1.03|-3.80|-3.74|-3.67|I(1)|
||tnt_ag_xchn(3)|0.70|1.43|-1.11|-3.93|-3.87|-3.80|I(1)|
|International|resY|0.16|-1.61|-2.80|-7.91|-7.84|-7.84|I(1)|
|reserves|res_extdebt|-0.60|-1.88|-3.67|-5.32|-5.24|-5.02|I(1)|
||res_imp|0.48|-1.00|-2.93|-6.47|-6.51|-6.38|I(1)|
|Trade openness|tradeY|0.73|-1.73|-2.67|-7.06|-7.12|-7.04|I(1)|
||impY|0.71|-1.14|-2.67|-6.42|-6.46|-6.35|I(1)|
||imp_vol|0.68|-0.50|-2.94|-4.27|-4.31|-4.29|I(1)|
|Money supply|m2Y|-0.05|-2.04|-2.48|-4.41|-4.34|-4.36|I(1)|
||m2_res|-0.17|-2.32|-2.94|-5.92|-5.82|-5.72|I(1)|
|Net foreign|nfaY|-3.38|-3.37|-4.42|-9.06|-8.92|-8.78|I(0)|
|assets|xdebtY|0.11|-3.06|-2.85|-5.49|-5.40|-5.48|I(0)|
||xdebt_cpi|0.84|-2.27|-1.59|-4.67|-4.64|-4.69|I(1)|
||xdebt_exp|-0.35|-0.92|-2.40|-4.47|-4.41|-4.59|I(1)|
||cabY|-2.43|-2.46|-2.65|-5.36|-5.28|-5.21|I(1)|
||cab_cpi|-1.78|-1.97|-1.95|-4.89|-4.81|-4.76|I(1)|
|Government|ggconsY|1.03|-0.03|-1.33|-4.58|-4.62|-4.91|I(1)|
|spending|ggconscpi|2.06|0.43|-2.15|-4.85|-5.37|-5.46|I(1)|
||_||_Critical_|_values:_|||||
||1%|-2.58|-3.46|-4.00|-2.58|-3.46|-4.00||
||5%|-1.94|-2.87|-3.43|-1.94|-2.87|-3.43||
||10%|-1.62|-2.57|-3.14|-1.62|-2.57|-3.14||

# Figure A.1: Trade Shares

<!-- Start of picture text --> Trade Shares of Argentina's Major Trade Partners (%)<br>30<br>Brazil United States China Germany Chile<br>25<br>20<br>15<br>10<br>5<br>0<br>Source: IMF DOTS<br>1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> 35
Tobin, J. (1965). Money and economic growth. _Econometrica 33_ , 671–810.

# **A Proof of Proposition 1**

To prove this result, note that, for _k_ 0 _>_ 0 sufficiently small, _k_<sup>_ss_</sup> ( _ns, πh_ ) = 0. To see this, consider the range _k_ 0 _< k_ min( _πh_ ), where _k_ min( _πh_ ) is defined as follows. Let _k_ 0( _s, πh_ ) be defined by: Note that _k_ 0( _s, πh_ ) is defined so that, if initial capital levels are _k_ 0( _s, πh_ ) and inflation is _πh_ , a small firm that accumulates all of its retained earnings will be able to purchase an indivisible investment good after exactly _s_ units of time. The left hand side of the equation is the nominal value of the firm’s retained earnings: at period _t_ , each of the _ns_ production unit has _e_<sup>_−δt_</sup> _k_ 0( _s, πh_ ) units of depreciated capital, and output is sold at the nominal price _pt_ . The right hand side is the nominal price of the indivisible investment good at time _s_ . Using the assumption that _π_ = _πh_ the above equation can be rewritten as: Note that _k_ 0( _s, πh_ ) is decreasing in _s_ : trivially, if the firm can accumulate enough retained earnings in _s_ units of time with _k_ 0( _s, πh_ ), it can do so with any larger initial capital stock. Thus, if the firm has a lower initial capital level, it takes longer to accumulate the necessary reserves to finance the indivisible investment.

Thus, since _k_ 0( _s, πh_ ) is decreasing in _s_ , the limit _k_ min( _πh_ ) = lim _s→∞ k_ 0( _s, πh_ ) 25
# **References:**

Akin, John S., David K. Guilkey, and E. Hazel Denton, 1995. "Quality of Services and Demand for Health Care in Nigeria: A Mulitinomial Probit Estimation," Social Science and Medicine. 40(11): 1527-1537.

Al-Kabir, a. 1984. "Effects of Community Factors in Infant and Child Mortality in Rural Bangladesh," World Fertility Survey Scientific Report No. 56. Voorburg: Intemational Statistical Institute.

Alderman, Harold, and Paul Gertler, 1989. "The Substitutability of Public and Private Health Care for the Treatmnent of Children in Pakistan," LSMS Working Paper No. 57. The World Bank, Washington, DC.

Alderman, Harold, and Victor Lavy, 1996. 'Household Responses to Public Health Services: Cost and Quality Tradeoffs," World Bank Research Observer, 11(1):3-22.

Anand, Sudhir, and Martin Ravaillon, 1993. "Human Development in Poor Countries: On the Role of Private Incomes and Public Services," Journal of Economic Perspectives, 7(1):133-50.

Barrera, Albino. 1990. "The Role of Maternal Schooling and its Interactions with Public Health programs in Child Health Production." Journal of Development Economics. 32:69-91.

Barro, Robert J. and Jong-Wha Lee. 1996. "International Measures of Schooling Years and Schooling Quality." American Economic Review. Pagers and Proceedings 86 (2): 218-23.

Benefo, Kofi, and T. Paul Schultz. 1996. "Fertility and Child Mortality in Cote d'lvoire and Ghana." World Bank Economic Review 10(1):123-158.

Bidani, Benu, and Martin Ravaillon, 1997. 'Decomposing Social Indicators using Distributional Data," Journal of Econometrics, 77:125-139.

Bitran, Ricardo a., and D. Keith Mclnmes, 1993. "The Demand for Health Care in Latin America," EDI Seminar Paper No. 46. The World Bank, Washington, DC.

Bobadilla, Jose-Luis, Helen Saxenian, 1993. "Designing an essential national health package," Finance and Development. 30:10-13 Bolduc, Denis, Guy Lacroix and Christophe Muller, 1996. "The Choice of Medical Providers in Rural Benin: a Comparison of Discrete Choice Models," Journal of Health Economic , 15:477-498.

Caldwell, John C., 1986. "Routes to Low Mortality in Poor Countries," Population and Development Review, 12(2): 171-220.

Caldwell, John C., 1990. "Cultural and Social Factors Influencing Mortality Levels in Developing Countries," The Annals of the American Academy, 510:44-59.

CIA, 1994. "The World Factbook 1994" CIA.

Comes, Richard, 1995. "Measuring the Distributional Inpact of Public Goods," in Dominique van de Walle and 51
districts, and 249 of its 322 Divisional Secretariat Divisions (DSDs).<sup>21</sup> The DSD identi...er in the HIES (2002) allows us to examine the behavior of wages at a more disaggregated geographical level. From the 16,924 households in the survey, about 25,886 individuals participated in the labor force. Our sample consists of adults (age 21 to 65 years) who are labor force participants in the rural subsample consisting of 243 DSDs. The HIES 2002 has complete employment, wage and other information for 22,323 individuals in this age range. Our estimation is based on the rural sample consisting of 12363 individuals. In addition to employment and wages, the survey collected information on education, age, gender, ethnicity and religion. The HIES 2002, however, has only limited information on farming (farm size and income only).

A key piece of information for our analysis is the amount of land under LDO restrictions in a DSD. We draw this information from the Agricultural Census of 1998. We estimated percentage of agricultural land under LDO leases (including permits and grants). The geographic information including travel time from surveyed DSDs to major urban centers with population of 100 thousand or more are drawn from the Geographical Information System (GIS) database. The travel time is estimated using the existing road network and allowing di¤erent travel speed on di¤erent types of roads.

A critical variable for our instrumental variables analysis is the historical district level malaria prevalence rate. The data on historical malaria prevalence are taken from Newman (1965). The measure for malaria prevalence used in this paper is called Gabaldon’s endemicity index (see column 2 in Table 4, P.34, Newman, 1965). This index is based on the estimates of enlarged spleens in children due to malaria, and is a good indicator of the degree to which malaria is high and permanent in a district.

Sri Lankan provinces di¤er considerably in terms of access to large urban centers (with population equal to or more than 100 thousand).<sup>22</sup> The average travel time to the urban center is 2.50 hours in our sample.

> 21 Data collection in the North and Eastern provinces was not possible due to on-going civil con‡icts at the time of survey ...eld work.

> 22 Sri Lanka has 7 cities with population more than 100 thousand. These are Colombo, Kandy, Dehiwala, Ja¤na, Kote, Moratuwa and Negombo. Except for two cities (Kandy and Ja¤na), all other large urban centers are clustered around Colombo, and in the Western coast.

20
|Average daily caring time|Caregiver of child under the<br>age of 5|-|4,404|
|---|---|---|---|
|Child Development||||
|||Z-score computed child||
|ASQ communication skills-for-age z-score|Children under the age of 5|development index based on<br>caregivers responses to certain|3,628|
|ASQ gross-motor skills-for-age z-score|Children under the age of 5|child’s reactions to specific stimuli<br>and achieved milestones. The<br>questions were based on the Ages<br>and Stages Questionnaire and the|3,624|
|ASQ social-personal skills-for-age z-score|Children under the age of 5|skills measured included three<br>domains: communications, gross-<br>motor and social/personal skills.<br>ae|3,627|
|||g.||
|Average number of times child was left at the charge of<br>another child|Children under the age of 2|-|688|
|Average number of times was child left alone|Children under the age of 2|-|688|
|Child Health||||
|||ALRI symptoms: constant cough or||
|ALRI prevalence (48 hours)|Children under the age of 5|<br>difficulty breathing, and raised<br>|4,563|
|ALRI prevalence (7 days)|Children under the age of 5|respiratory rate (according to WHO)|4,563|
|Diarrhea prevalence (48 hours)|Children under the age of 5|Diarrhea symptoms: 3 or more stools<br>per day and stools were loose or<br>|4,563|
|Diarrhea prevalence (7 days)|Children under the age of 5|watery, or blood and/or mucus<br>were present in stool.|4,563|
|Anemia (Hb < 110 g/L)|Children under the age of 5|Percent of children under 5 with<br>hemoglobin levels < 100 g/L.<br>Hemoglobin counts were altitude-<br>adjusted|4,083|
|Anthropometric Measures||||
|BMI-for-age z-score|Children under the age of 5|Z-scores were adjusted taking into|4,347|
|Head circumference-for-age z-score|Children under the age of 5|<br>account child’s age; values outside<br>|4,375|
|Length/height-for-age z-score|Children under the age of 5|reasonable bounds were not<br>idd flli|4,354|
|Arm circumference-for-age z-score|Children under the age of 5|consere (oowng|4,342|

72
**Graph 1. Percentage of localities with implemented activities in T1 and T2**

<!-- Start of picture text --> 100%<br>90% 84% 85% 84% 84% 83% 83%<br>80%<br>70% 68% 64% 68% 68% 68%<br>60% 60% 60%<br>60% 53%<br>50% 46%<br>40% 32% 35% 34% 32% 33% 32% 31%<br>30%<br>20%<br>10%<br>0%<br>0%<br>Contacted Approval to Had at least one Had two Poster hung by Interacted by Received Received<br>hang poster meeting meetings time of last visit phone, reminders of behavioral<br>to locality WhatsApp or meetings (at messages (at<br>text (at least least one least one<br>one person) person) person)<br>T1 T2 Total<br>% of localities in T1/T2<br><!-- End of picture text -->

# **Results**

## _4.1 Main experimental outcomes_

Table 5 shows the estimates of T1 and T2 as compared to T0 for different outcome variables, and Table 6 shows additional estimates of T1 versus T2 for the same outcomes.

**Table 5. Treatment effects of locality level variables, as compared to T0.**

||Applications|Female<br>leader|All-women<br>group|Selected|Total people|Total<br>women|
|---|---|---|---|---|---|---|
|T1|2.308|1.113|0.124|-0.002|7.110|3.508|
||(0.667)***|(0.422)***|(0.106)|(0.007)|(2.945)**|(1.676)**|
||[0.001]|[0.010]|[0.247]|[0.765]|[0.018]|[0.039]|
|T2|2.792|1.889|0.556|0.023|15.578|9.889|
||(0.748)***|(0.588)***|(0.197)***|(0.023)|(4.954)***|(3.115)***|
||[0.000]|[0.002]|[0.006]|[0.320]|[0.002]|[0.002]|
|Adj Base Mean|.159|.0821|.001|.001|1.671|.740|
|N|113|113|113|113|113|113|
|Specification|OLS|OLS|OLS|OLS|OLS|OLS|
|Base|T0|T0|T0|T0|T0|T0|

* p<0.1; ** p<0.05; *** p<0.01. P-values in squared brackets. Robust standard errors clustered by agrarian unit in parenthesis. Covariates include: adult population in the locality, the proportion of adult woman in the locality, the proportion of woman of 3 years or older speaking an indigenous language, the localities’ marginalization index of 2010, the number of grants awarded from CONAFOR between 2007-2017, and a dummy of state.

On average, localities in T1 and T2 submitted 2.3 and 2.8 more applications, respectively, than localities in T0. While T2 had a higher average number of applications submitted than T1, the difference was not statistically significant. Given the adjusted base mean of T0 was 0.159 applications (only one application was submitted from the 36 T0 localities, compared to 214 applications among localities in T1 or T2), this increase is quite substantial in relative terms. It represents a 1,452% increase in application rates for T1 localities and a 1,756% increase for T2 in comparison to the control group T0.

12
mortgage lending has increased its share in high-income countries and now exceeds consumer credit. This appears to contradict the maturity data that show a decline in the share of longer-term credit, but it may be due to the different sample coverage. The decline in the share of consumer lending in high-income countries confirms the decrease in short-term lending observed in the maturity data. The share of commercial and consumer loans appears to be particularly low in LAC compared to other regions.

To further explore how the patterns of credit allocation differ in LAC with respect to countries elsewhere, we conducted a benchmarking analysis. Assuming that financial development ( _X_ ) depends on economic development ( _Y_ ), policy variables ( _P_ ), and structural characteristics ( _Z_ ) and that economic development depends on lagged policy variables, which are autocorrelated in time, in the reduced form, we can benchmark using only economic, development, and external factors.

We estimated equation 4, regressing the share of credit to GDP on a regional LAC dummy and other structural factors found to explain financial development and excluding outliers as previously defined. We followed de la Torre, Feyen, and Ize (2013) and included population, population density (people per square kilometer of land area), age dependency ratio (old and young) as percentage of working-age population, and inflation (annual percentage) in the structural variables. Dummies to control for offshore financial centers, transition economies, and oil exporters are also included.<sup>21</sup> While endogeneity of GDP per capita and credit to GDP results in

> 21 GDP per capita is obtained from IMF WEO. Source for total population, population density, age-dependency ratios, and inflation is the World Bank WDI. The offshore dummy takes a value 1 for all years if the country is an offshore center and zero otherwise based on the IMF classification. The transition country dummy takes a value of 1 for all years if the country is a transition country and zero otherwise based on FinStats. The fuel exporter dummy takes a value of 1 for all years if the country’s average fuel exports as fraction of exports is in the 75th percentile or higher in the world distribution and zero otherwise based on World Bank WDI.

15
Rogoff, K. 2021b. “Risks to Central Bank Independence.” in Independence, Credibility, and Communication of Central Banking, edited by Pasten, E. and R. Reis, Central Bank of Chile.

Rogoff, K. 2022a. “ <mark>The Age of Inflation: Easy Money, Hard Choices.” Foreign Affairs November/December.</mark> Rogoff, K. 2022b. “Rethinking Central Bank Design.” in Karl Brunner and Monetarism, edited by Moser, T. and M. Savioz, Cambridge: MIT Press.

Rogoff, K. 2022c. “Emerging Market Sovereign Debt in the Aftermath of the Pandemic.” Journal of Economic Perspectives 36 (4): 147-166.

di Giovanni, J., S. Kalemli-Özcan, A. Silva, and M. A. Yildirim. 2023. “Pandemic-Era Inflation Drivers and Global Spillovers.” NBER Working Paper w31887, National Bureau of Economic Research.

Smets, F., and R. Wouters. 2007. “Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach.” American Economic Review 97 (3): 586-606.

Uhlig, H. 2017. “Shocks, Sign Restrictions, and Identification.” Advances in Economics and Econometrics 2: 95-112.

Wheeler, C.M., J. Baffes, A. Kabundi, G. Kindberg-Hanlon, P. S. Nagle, and F. Ohnsorge. 2020. “Adding Fuel to the Fire: Cheap Oil During the COVID-19 Pandemic.” Policy Research Working Paper 9320, World Bank, Washington, DC.

Wong, B. 2015. “Do Inflation Expectations Propagate the Inflationary Impact of Real Oil Price Shocks? Evidence from the Michigan Survey.” Journal of Money, Credit and Banking 47(8): 1673-1689.

Wu, J. C., and F. D. Xia. 2016. “Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound.” Journal of Money, Credit, and Banking 48 (2–3): 253– 29.

Yilmazkuday, H., 2014. “Gasoline Prices, Transport Costs, and the US Business Cycles.” Journal of Economic Dynamics and Control 45: 165-79.

Yilmazkuday, H., 2021. “The Great Trade Collapse: An Evaluation of Competing Stories.” Macroeconomic Dynamics 25 (4): 1053-89.

23