rafmacalaba/datause-encoder-data huggingface dataset:
are not bound by the ‘substantially all trade’ requirement of Article XXIV agreement. The data confirm that these agreements are much narrower in scope, having typical HS 6-digit coverage rates of less than 20 percent, compared to over 90 percent for RTAs. Reflecting this different standard, two of these agreements (the Asia-Pacific Trade Agreement and the Global System of Trade Preferences) are commonly referred to as “partial scope” agreements.

Table B1 lists the trade agreements in our data and our classification of them into reciprocal vs. non-reciprocal agreements. Because the division of agreements into reciprocal vs. non-reciprocal agreements is a subjective one, we also present an alternative broader classification in the table. Our broad RTA definition includes all Article XXIV agreements plus additional comprehensive agreements between developing countries. It is worth noting that these agreements are not necessarily free trade agreements, as commonly understood. For example, for the Brazil-Mexico agreement, the median tariff is 13 percent (the minimum is roughly 5.5 percent) at the industry level. While we focus on the definition of RTAs as WTO-notified Article XXIV agreements in our main results, we present supplemental results for the broad RTA classifications in Appendix C.

# **B.2.3 Another Look at MFN as a Constraint on Bilateral Applied Tariffs**

An additional salient feature of the data is that tariff preferences are constrained by the MFN rule. When the MFN tariff is low, so too is the potential scope for tariff preferences, since tariffs are then bound between zero and the MFN rate. Given this, we would expect that both the absolute value of the mean preference and the standard deviation of preferences would be low when average MFN rates are also low. In Panel (a) of Figure B1, we see that preferences are indeed near zero when MFN tariffs are low (note the y-axis records negative values, since we define preferences as bilateral applied tariffs minus MFN tariffs). In Panel (b), we see that variability in preferences is rising with mean MFN tariffs. Both these patterns are consistent with MFN-censoring constraining variation in the data.

> notified to the WTO, according to the WTO’s trade agreement database [ `http://rtais.wto.org/UI/ PublicMaintainRTAHome.aspx` ].

54
Measures to improve the _business climate and macroeconomic environment_ are critical in sharing the benefits of innovation. Policy suggestions are aimed at fostering more competition in markets so that the gains from innovation are not captured by a select few. Examples of such policies are: adapting the strength of intellectual property regimes, using competition policy to ensure that innovation is encouraged, and market liberalization to support competition, financial sector development to increase funding of innovation/entrepreneurship, improving infrastructure to support private investment, supporting aggregate demand through stimulatory macroeconomic policy, particularly if actual output is lower than (the new) potential output resulting from innovation. Among these papers, Brnjolfsson and AcAffee (2014) suggest a number of measures for startups such as reducing regulation for ease of entry, growth and exit, facilitating immigration to ensure an increasing supply of highly skilled workers, reducing IPR protection to reduce the share of profits captured by innovators and increase the share accruing to consumers and (potentially) smaller/newer producers, and upgrading infrastructure to support innovation, its commercialization, and access to services. Perez (1983) and Pasinetti (1981) examining earlier decades of technological disruption, contend that the institutional setting of IPR regimes substantially affected market responses after innovation. Strong IPR enforcement could change the balance between R&D and ETC in order to induce more innovation; at the same time, overly strong IPR systems could limit the forces of competition in markets and thus inhibit compensation effects from fully playing out.<sup>41</sup> Mandel and Swanson (2017) suggest a number of measures to maximize the employment effects of innovation and technological adaptation. Though their paper is focused on the U.S. economy, the lessons are generally applicable. They find that even in the United States, the full benefits of

> 41 There is a large literature on the impact of IPR regimes on growth and technology adoption. World Bank (2002), World Development Report, and World Bank (2005) provide good summaries of the literature and evidence, along with policy recommendations.

30
Given the additive separability of the government objective, we can obtain the optimal tariff rate for sector i by maximizing equation (1) with respect to τ i. Consequently, the equilibrium specific and advalorem tariff rates for sector i are implicitly defined as<sup>10</sup> where εi(.) stands for the elasticity of import demand.<sup>11</sup> This expression is a standard one obtained in various political economy models (Helpman 1997). Accordingly, the tariff rate for sector i is an increasing function of the extra political economy weight provided to producers, whereas it is a decreasing function of the import demand elasticity, εi, and import penetration ratio, Mi/AiQi. A tariff is a tax on imports, so just like a tax on a non-traded good, the deadweight loss created is lower the more inelastic the (import) demand is. Thus, a smaller value of εi allows for higher tariffs to be applied. In addition, a relatively larger market for imports creates a greater price distortion potential which should be avoided by the government. Finally, the marginal benefit of a tariff is higher when it applies to more units and more productive processes.

Partially and implicitly differentiating equation (2) with respect to Ai, we obtain the following relationships between tariff protection and productivity I assume that Qi(τ i), Di(τ i) and hence, Mi(τ i) are linear for ease of exposition (i.e. Q<sup>00</sup> i<sup>(τi)=</sup> D<sup>00Althoughinitiallyω≥1istheonlyrestrictiononthevalueofω,Ifurtherassume</sup> i<sup>(τi) = 0).12</sup> Di<sup>0(.)</sup> it to be bounded above such that ω < 2 −<sup>ontheobservationsfromtheempirical</sup> AiQ<sup><u>0</u></sup> i<sup>(.)based</sup> political economy literature. For example, Goldberg and Maggi (1999) estimate ω to be equal to 1.014 for the United States, whereas Karacaovali and Lim ̃ao (2005b) estimate it to be between 1.0025 and 1.0039 for the European Union. Although these estimates might be a bit small, they

> matter: the trade policy and productivity linkage in the presence of unilateral liberalization.

> 10Again note that the specific (first term) and advalorem (second term) tariff rates are equivalent since the international prices are normalized to one. See the appendix (Section A.1) for the derivation of equation (2). 11 0 Here, the import demand elasticity is defined as εi ≡−Mi<sup>pw</sup> i<sup>/Mi,soitdiffersfromthestandarddefinition</sup> which is evaluated at the domestic price. I account for this in the empirical estimations as explained in the appendix (Section A.2).

> 12I have the same assumption throughout the text. This is not a necessary condition for the inequality in equation (3b) to hold or the other results to follow.

5
- Rosenthal, Stuart, and William Strange, “Evidence on the Nature and Sources of Agglomeration Economies”, in Vernon Henderson and Jacques François Thisse (eds.) Handbook of Regional and Urban Economics, Volume 4 (Amsterdam: North-Holland, 2004), 2119-2171.

- Sivadasan, Jagadeesh, “Barriers to Competition and Productivity: Evidence from India”, The B.E. Journal of Economic Analysis & Policy 9:1 (2009), Article 42.

- Trivedi, Pushpa, L. Lakshmanan, Rajeev Jain, and Yogesh Gupta, “Productivity, Efficiency, and Competitiveness of the Indian Manufacturing Sector”, Reserve Bank of India Paper (2011).

- World Bank, “Planning, Connecting, and Financing Cities—Now”, Urbanization Review Flagship Report (2012).

25
10

# d..rections, they moved in unison after disinflation.

# III. TIME SERIES EVIDENCE AND COMPARISONS

In this section, we present time series evidence on changes in the relations between key macro variables that occurred after the program. We assemble and interpret the evidence in the light of the models discussed in the previous section. We start by looking at summary statistics such as means and standard deviations -- the latter taken here as a measure of the degree of variability of different variables -- as well as by examining changes in contemporaneous cross correlations. Then, we investigate how the dynamics of selected variables changed after the program by studying their responses to shocks. We first consider responses to own shocks and later on responses to shocks in other variables. Then, we turn to evidence on the cross sectional variabilities of output growth, real wage growth, and relative prices which provide indication as to whether there has been a change in the type of shocks that are dominant. Last, we investigate whet1-er the persistence of economic fluctuations changed after the program. We conclude the section by summarizing the key findings and by discussing the extent to which they conform with the models.

# A. Summary Statistics

Our discussion in this subsection is mainly based on Table 1 and Figures 1 to 4.

# i. Means

The first two columns of panel la in Table 1 report monthly means for a set of macroeconomic variables before (i.e., 1980:2-1985:6) and after (i.e., 1986:1-1988:12) the disinflation program of 1985. It can be seen that before
OZIER - REPLICATION REDUX – p. 9 of 41

**Figure 3**

<!-- Start of picture text --> How replicable are studies in economics?<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Dewald, Thursby, and Anderson Chang and Li (2015) Galiani, Gertler, and Romero (2018)<br>(1986)<br>Replication conceivable: Are ‐either‐ data ‐or‐ programs available?<br>Replication viable: Are ‐both‐ data ‐and‐ programs available?<br>Conditionally successful: If ‐both‐ data ‐and‐ programs are present, do results replicate?<br><!-- End of picture text --> When is a replication even conceivable? 30 years ago, Dewald, Thursby, and Anderson (1986) were only able to obtain something resembling replication data for just over half of the economics papers for which they sought data; that figure has risen to between 60 and 80 percent; in other words, the rate of data unavailability has nearly been cut in half.<sup>5</sup> Getting usable data and code in hand to make a viable replication attempt was very hard in those days: of 54 received data sets, only 8 satisfied a basic usability criterion (yielding the 15 percent number from the 1986 study), driving the chances of even being able to try a replication below 10 percent. As Figure 3 shows, that viability number has risen considerably, though how high depends on which sample of journals is examined.<sup>6</sup> Galiani, Gertler, and 5 Dewald, Thursby, and Anderson (1986) found that replication datasets were twice as likely to be available if the concerned papers were in the process of being published at the time of the replication attempt, compared to papers that had been published years before the attempt.

6 Datasets and programs failed to combine to produce viable and successful replications for a list of reasons that is, by turns, tragic, comic, incredible, and instructive. Dewald, Thursby, and Anderson (1986) describe a common situation: a regularly updated government dataset is used in analysis, but neither a copy of the relevant vintage of
reports indicate that women’s response to the initiative was positive. For example, from July 2021 to March 2022, the percentage of women traveling by bus in Tamil Nadu increased from 40% to 61% (Sundaram, 2022). According to Goswami (2021), women made the of riders on Delhi buses March 2021 after the up majority Transport Corporation by program’s launch.

To identify the causal impact of the program on women’s labor outcomes, we collated data from several sources. Our main empirical analysis is based on the Consumer Pyramids Household Survey (CPHS) data maintained by the Centre for Monitoring the Indian Economy (CMIE). The rich CPHS data is a panel of about 160,000 households across all major Indian states after 2014. It includes comprehensive information not only on household expenditures, members’ demographic characteristics, and employment status but also on their time use patterns and allocation of time on various activities. We bolster the findings with a large primary survey of women from Delhi that was collected after the program’s launch in Delhi.

Our identification approach compares women in treated states (i.e., Punjab and Tamil Nadu) to their geographical neighbors, serving as our control group. The implementation of the in 2021 variation. To address con- policy provides temporal endogeneity cerns, we implement a synthetic differences-in-differences strategy (SDID) proposed by Arkhangelsky et al. (2021) at the state level. This approach combines the synthetic control method (Abadie and Gardeazabal, 2003) with the difference-in-differences strategy.<sup>4</sup> We compare the pre-trends in our treated and the synthetic control group using event study models and do not discern differential trends.

We examine the effect of the treatment on on In first expenditure commuting. doing so, we determine if women’s travel demand is responsive to the cost of transportation. We find evidence consistent with elastic demand for transportation for women using the CPHS data. Overall expenditure on travel, specifically on a category that includes buses, was reduced for households with women in treated states compared to control states. Findings from the Delhi survey corroborate these results at the individual level. New female users of buses report negligible transportation costs after the start of the policy as opposed to their substantial expenditures on other modes of transport before switching to buses.

We then examine if women in the treated areas change the time spent on traveling and, if they do, the potential reallocation of that time to other margins of time use, such as household chores and labor Diverse outcomes women supply. emerge among

> 4The weights from the SDID approach are used in all survey data-based analyses at the individual level.

2
|(2) Effective tax rate (imputed)|2.5<br>(9.6)|2.9<br>(9.9)|**3.1**<br>(9.7)|**0.5**<br>(2.1)|476|452|496|445|
|---|---|---|---|---|---|---|---|---|
|(3) Effective tax rate (self-|9.1|10.7|5.0|12.4|161|79|167|84|
|reported), given tax>0|(12.7)|(13.3)|(6.6)|(14.7)|||||
|(4) Effective tax rate (imputed),|5.6|10.2|5.2|3.0|276|182|327|166|
|given tax>0|(13.9)|(16.2)|(12.1)|(4.3)|||||
|**Effective tax rate (self-reported), by**|**sector**||||||||
|(5) Manufacturing or sale of local<br>beverages|na|0.1<br>(0.6)|na|0.0<br>(0.2)||113||94|
|(6) Food and beverage services|na|0.6<br>(2.7)|na|1.5<br>(5.9)||111||61|
|(7) Retail trade|0.8|1.1|1.0|1.8|353|389|302|387|
||(4.1)|(5.3)|(3.8)|(7.2)|||||
|(8) Transit services|4.1<br>(8.0)|Na|2.1<br>(4.9)|na|81||108||
|**Effective tax rate (imputed), by sect**|**or**||||||||
|(9) Manufacturing or sale of local|na|0.0|na|0.0||113||94|
|beverages||(0.1)||(0.1)|||||
|(10) Food and beverage services|na|13.9<br>(18.5)|na|5.1<br>(5.6)||100||60|
|(11) Retail trade|**0.5**<br>(2.2)|**0.1**<br>(0.4)|**0.9**<br>(3.2)|**0.1**<br>(0.4)|139|144|198|214|
|(12) Transit services|16.4<br>(23.5)|na|10.3<br>(14.8)|na|80||106||

Source: Author’s calculations using ESS 2018/2019 and 2021/2022. Notes: Imputed presumptive taxes are calculated using the estimated annual turnover and the presumptive tax schedule in the Council of Ministers Regulation No. 410/2017 according to the enterprise activity. This resulted in 599 enterprises with missing values because the activities are not specific enough to assign the presumptive tax values. Significant differences at p _<_ 0.05 are shown in bold. Na indicates sample size is below 50. Numbers in parentheses indicate standard deviation.

# **6. Policy simulation: A hypothetical presumptive tax with an exemption threshold for microbusinesses and a single tax rate on turnover**

Given the regressivity of the presumptive tax, a policy is simulated where the tax burden of a hypothetical tax is estimated with an exemption threshold for _all_ firms earning less than 50,000 birr (which is already in place for some sectors), and a single rate on turnover in tax liabilities for firms earning above this threshold. The value of 2.5% on turnover is chosen in this paper as the hypothetical presumptive tax rate for several reasons. There are tradeoffs to consider in setting the tax rate. It should not be set too high such that it discourages formalization, but it should not set be too low so that it could hinder the migration of a growing business from the presumptive tax system to the standard business tax system (Engelschalk and Loeprick 2016). If the rate is set much lower than the standard tax regime, it could encourage businesses 18
**<u>Table 3: Kitagawa-Oaxaca-Blinder Decomposition of Labor Force Participation</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES||Paid Work||Pai|d+Unpaid|Work|
||Overall|Endow.|Coeff.|Overall|Endow.|<br>Coeff.|
|Male|0.726***|||0.853***|||
||(0.005)|||(0.004)|||
|Female|0.523***<br>|||0.752***<br>|||
||(0.006)|||(0.005)|||
|Raw Difference|0.203***|||0.101***|||
||(0.008)|||(0.007)|||
|Difference due to|-0.009**|||0.001|||
|Endowments|(0.004)|||(0.003)|||
|Difference due to|0.212***|||0.101***|||
|Coefficients|(0.009)|||(0.007)|||
|Individual Lives in an||-0.001|0.020***||-0.000|0.013**|
|Urban Area||(0.001)|(0.007)||(0.001)|(0.005)|
|Individual Age||-0.006|0.750***||0.000|0.285***|
|||(0.005)|(0.140)||(0.001)|(0.103)|
|Individual Age Squared||0.011|-0.317***||-0.001|-0.157***|
|||(0.009)|(0.069)||(0.002)|(0.049)|
|Number of Adults in HH||0.001|-0.034***||-0.000|0.009|
|||(0.001)|(0.012)||(0.000)|(0.008)|
|Number of Children Aged 0-5 in||-0.001|0.012*||0.000|0.012**|
|HH||(0.001)|(0.007)||(0.000)|(0.006)|
|Individual owns a Financial Account||-0.030|-0.009||0.001|0.008|
|||(0.025)|(0.007)||(0.005)|(0.006)|
|Individual is Married||0.013|0.038**||-0.000|0.026**|
|||(0.012)|(0.017)||(0.001)|(0.013)|
|Individual is Divorced/Widowed/||0.006|0.001||-0.000|0.001|
|Separated||(0.006)|(0.001)||(0.000)|(0.001)|
|Individual Completed Secondary||0.004|-0.006**||-0.000|-0.005***|
|Schooling||(0.004)|(0.003)||(0.001)|(0.002)|
|Individual owns a Vehicle (Bicycle,||-0.006|0.004||0.000|-0.002|
|Motorcycle, Car)||(0.005)|(0.010)||(0.002)|(0.008)|
|Wealth PCA - Based on Home Condition||0.000|-0.000||-0.000|-0.000|
|||(0.000)|(0.000)||(0.000)|(0.000)|
|Wealth PCA - Based on Appliances||-0.000|0.000||-0.000|-0.000|
|Ownership||(0.000)|(0.001)||(0.000)|(0.001)|
|Individual has a Disability||-0.000|0.000**||0.000|0.000|
|||(0.000)|(0.000)||(0.000)|(0.000)|
|Constant|||<br>-0.247***|||<br>-0.089*|
||||(0.065)|||(0.045)|
|Observations|22,666|22,666|22,666|22,666|22,666|22,666|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 37
# **4 HIT Data in Action: Agricultural Tariff**

# **Liberalization**

In order to illustrate the use of the database, we explore the welfare and distributional impacts of agricultural tariffs. To do this, we use our simple framework and simulate what would happen to average incomes, and their distribution, if a country were to eliminate its own agricultural import tariffs. This amounts to setting all agricultural tariffs to zero. Note that we work with unilateral tariff cuts and thus we run independent simulations for each of the 54 countries separately.<sup>6</sup> The outcome of this exercise comprises a set of results on the welfare effects for households at different levels of well-being for each country. As a first step in the analysis, these effects will be aggregated to study issues related to the gains from trade (Arkolakis, Costinot and Rodriguez-Clare, 2012; Costinot, Donaldson and Komunjer, 2012; Costinot and Rodriguez-Clare, 2014; Artuc, Lederman and Porto, 2015; Caliendo and Parro, 2015; Melitz and Redding, 2015; Arkolakis, Costinot, Donaldson and Rodriguez-Clare, 2019; Caliendo, Dvornik and Parro, 2018). The results are presented in Table 2 for all 54 countries in our database. The gains from eliminating tariffs amount to 2.50% of real household income on average across countries (column 1). This means that, from a global standpoint, developing countries would win from a unilateral agricultural tariff liberalization.<sup>7</sup> There are two broad mechanisms at play. On the one hand, lower food prices create consumption gains, which amount to a real income gain of 4.64 percentage points on average (column 2). On the other hand, the loss of protection implied by the tariff cuts create income losses, which amount to 2.15 percentage points on average (column 3). Consequently, the overall gains from trade are driven by lower prices and consumption gains that outweigh the income losses from tariff de-protection.

A major distinctive feature of our data is that they allow analysis of heterogeneity in the welfare effects of trade. Heterogeneity takes several forms. There is heterogeneity across

> 6 Although a multilateral liberalization scenario would be feasible, this requires different modeling assumptions about the functioning of global markets.

> 7Note that we ignore impacts on global prices, which we assume to be exogenous given that the countries we are considering only account for a very small share of global trade.

8
associations, giving them lobbying power vis-à-vis government through several channels outside the direct control of government.<sup>10</sup> Figure 1 _: Availability of business reform committees, by region and income grouping_ <!-- Start of picture text --> a.By region<br>Count<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Sub-Saharan Latin America & High income: Europe & Central East Asia & Middle East & South Asia<br>Africa Caribbean OECD Asia Pacific North Africa<br>b. By income<br>Count<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Lower middle Upper middle High income Low income<br>income income<br><!-- End of picture text --> _Source: Reform Committee data_ _Note: Based on the 160 economies where data was collected. Figure 1a and 1b: Count of economies with a functioning reform committee, per region and income grouping. 107 economies have a functioning reform committee. 30 economies do not have a reform committee. Data for 27 economies was not provided from experts or identified by desk research._ For example, in Albania the National Trade Facilitation Committee was established in 2016 as a separate advisory and coordinating group to explicitly improve the business climate (specifically trade policy processes) and facilitate trade under the purview of the Ministry of Finance and Economy. In Brazil, the Special Secretariat for Modernization of the State, under the Federal Government, has the legal mandate to modernize the domestic business environment, including identifying bottlenecks through the creation and coordination of working groups, called _GTA (Grupos Temáticos de Ação or Action Thematic Groups)_ .

9