rafmacalaba/datause-encoder-data huggingface dataset:
regulations, helps reduce the cost of trading (both in terms of exports and imports). In addition, their results suggest that aid to economic infrastructure increases exports, whereas aid to productive capacity appears to have no significant impact on exports. The authors correctly point out that aid for trade is possibly endogenous to exports; particularly aid to productive capacity. For instance, if better performing sectors tend to receive more aid for trade than other sectors, it would generate an upward bias in the aid coefficient. To address this endogeneity, the authors instrument aid for trade with the degree of respect for civil and political liberties measured by the Freedom House Index and with an index proposed by Gartzke (2009) that measures the ―affinity of nations‖<sup>6</sup> . The authors argue that many donors choose recipients based upon development and democratic measures like these which make these indicators correlated with aid but not with exports. However, the authors acknowledge that their instruments are not appropriate for sectoral analysis, as they only vary across country-year and not across country-sector-year.

We propose an alternative identification strategy that allows us to analyze the impact of sectoral aid. To address reverse causality, we exploit the links between the service sector and the manufacturing sector relying on input-output tables. Although input-output data has not been used in the analysis of aid effectiveness so far, a number of studies, particularly in the FDI literature, make use of it. Given the difficulty of finding consistent input-output matrices across countries most studies rely on input-output data from the United States to describe the technological possibilities of firms in a given economy. Acemoglu, Johnson, and Mitton (2009) investigate how contracting costs and financial development determine the extent of vertical integration across countries. Alfaro and Charlton (2009) use new firm level data at the 4-digit sector level and U.S. input-output tables to classify firms between horizontal and vertical

> 6 In preliminary research we also attempted to use other instrumental variables for aid for trade such as immunization rates, gender health access.  However, statistical tests suggested that they are not appropriate instruments for aggregate aid for trade.

7
# Appendix D. Price-Quantity Decomposition: Housing

<!-- Start of picture text --> Figure D.15: Relative Prices & Depreciation (Housing)<br>(A) Relative Capital Prices (B) Depreciation Rates<br>ICT Capital<br>Non-ICT: Non Residential<br>ICT Capital Non-ICT: Residential<br>Non-ICT: Non Residential<br>Non-ICT: Residential<br>1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010<br>Year Year<br>2<br>20<br>1.5<br>15<br>1 10<br>Depreciation Rate (%)<br>.5 5<br>Price Index Relative to GDP Deflator<br>0 0<br><!-- End of picture text --> _Notes:_ Panel A graphs implicit price deflators by capital type relative to the GDP deflator, which were constructed directly from the BEA’s detailed fixed-asst accounts. The BEA GDP deflator is taken from FRED. Panel B depicts asset-specific depreciation rates constructed directly from the BEA’s fixed asset accounts. The dashed vertical lines indicate the year 1968.

# Appendix E. Calibrating the aggregate production function

We specify our objective as follows: Where _gi_ are moment conditions and _wi_ are their corresponding weights. We use tildes to denote simulated variables and moments. Using _p_<sup>_x_</sup> _t_<sup>to denote the price of the input</sup><sup>_x_, the moment</sup> conditions are as follows:
**Figure 5. The income gap in account ownership remains large in some countries in Europe and Central Asia**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>B40 T60 B40 T60 B40 T60 B40 T60 B40 T60 B40 T60 B40 T60 B40 T60 B40 T60 B40 T60<br>Central Central Eastern Northern Russian South Southern Turkey Western Western<br>Asia Europe & Europe Europe Federation Caucasus Europe Balkans Europe<br>the Baltics<br>2011 2017<br>Percent of adults with account<br><!-- End of picture text --> _Source_ : Global Findex database.

_Note_ : The gap is between adults living in the wealthiest 60 percent (T60) and poorest 40 percent of households (B40) within economies.

Unbanked adults are more likely than people with accounts to have low levels of education. In the developing world, about half of all adults have no more than a primary school education; among unbanked adults, the share is close to two‐thirds. In developing economies in ECA, 34 percent of unbanked adults have no more than a primary school education. However, there is wide variation. In Albania and Kosovo, 67 percent of the unbanked have no more than a primary school education. By contrast, in Armenia this share was only 12 percent in 2017, thanks to the huge increase in account ownership among low‐education adults, which rose from 7 percent in 2011 to 50 percent in 2017.

Being unbanked in ECA is also associated with lack of labor force participation. About 60 percent of unbanked adults in developing ECA are out of the labor force. The share of account ownership among people out of the labor force was 12–16 percentage points lower than the average for ECA in 2011, 2014, and 2017. There are a few exceptions. In Azerbaijan and Uzbekistan, where overall account ownership is low (30–40 percent), the share of unbanked adults who are out of the labor force was lower than the ECA average.

# Lack of Trust in Banks Is a Major Barrier to Account Ownership

To help understand why people may remain unbanked, the 2017 Global Findex survey asked adults without a financial institution account why they did not have one. Globally, the most common reason offered was having too little money to use an account. Two‐thirds of unbanked adults across the world cited this as a reason for not having an account, and about one‐fifth cited it as the only reason. About a 11
# **Evidence before this study**

A search has been undertaken and completed among the PubMed, Embase, and POPLINE databases for related studies published from January 2, 2000, to June 30, 2017, with no language restriction.<sup>10</sup> No previous papers estimating the tobacco price elasticity in Moldova or research related to the inequality effects of tobacco tax increases in this country have been discovered. The present study builds on the previous literature on price elasticity estimation and on the health and economic literature on extended cost-benefit analysis.

# **Added value of this study**

The value of this study is twofold. First, this study contributes to the estimation of the tobacco price elasticity per income decile in Moldova using household survey data and the prices paid by consumers. Second, this study directly quantifies the potential impact of a tobacco price rise on consumption increases across income groups and estimates the distributional effects of the price rise, including an associated reduction in medical expenses. The analysis provides evidence of the potential distributional impact of tobacco tax policy on consumption in Moldova, but also on broader health care system goals of improving health outcomes and inequality reduction among income groups within the population.

# **Interpretation of all the available evidence**

Tobacco taxes are deemed to be regressive by many economists and health researchers who argue that lowincome smokers spend a disproportionately greater share of their income on tobacco. We find that, in Moldova, there is a price elasticity of −0.33 and that tobacco taxes benefit the poor because the reduction in medical expenses outweighs the effects of the price increase.

> 10 See Embase (database), Elsevier, Amsterdam, https://www.elsevier.com/solutions/embase-biomedical-research; POPLINE (database), K4Health (Knowledge for Health Project), Johns Hopkins Center for Communication Programs, Johns Hopkins– Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, https://www.popline.org/; PubMed (database), National Center for Biotechnology Information, U.S. National Library of Medicine, Bethesda, MD, https://www.ncbi.nlm.nih.gov/pubmed.

16
# Figure 11 Impulse Response functions: US Real Interest Rate Shock

<!-- Start of picture text --> Response to Cholesky One S.D. Innovations ± 2 S.E.<br>Response of GAP_DR_BK to RATE_US Response of GAP_HT_BK to RATE_US<br>.010 .010<br>.005<br>.005<br>.000<br>.000<br>-.005<br>-.005<br>-.010<br>-.010 -.015<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Response of DLINFL_DUS_DR to RATE_US Response of DLINFL_DUS_HT to RATE_US<br>.04 .04<br>.03<br>.02<br>.02<br>.01<br>.00<br>.00<br>-.01<br>-.02<br>-.02<br>-.04 -.03<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Response of RER_DR to RATE_US Response of RER_HT to RATE_US<br>.04<br>.08<br>.02<br>.00<br>.04<br>-.02<br>.00<br>-.04<br>-.04 -.06<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text --> 40
**Table 2: Village participation–informal sample (monitoring survey)**

||N|Control|CBP<br>group|HBP<br>group|CBP-C|HBP-C|
|---|---|---|---|---|---|---|
|Presence of a primary school in the village|3,524|0.583|0.538|0.636|-0.045|0.053|
|Presence of a preschool in the village|3,524|0.434|0.739|0.395|0.305**|-0.039|
|Presence of a formal (attached) preschool|3,524|0.329|0.179|0.152|-0.15<br>0.455**|-0.178*|
|Presence of a community-based preschool|3,524|0.105|0.56|0.244|*|0.139<br>0.415**|
|Presence of an HBP in the village|3,524|0.281|0.361|0.696|0.08|*|

Note: Columns CBP-C and HBP-C correspond to results of the regression of the dependent variable on the treatment status (CBP or HBP). * 10% significance; ** 5% significance; *** 1% significance.

# <u>HBPs</u>

The monitoring data suggest a high compliance rate of 70 percent in the HBP group, but this rate probably overestimates compliance. It is based on a question to the village chief, who is supposed to organize the program and has strong incentives to report the presence of an HBP in the village. In several villages visited for the qualitative work, the village chief stated that an HBP existed, whereas none of the parents interviewed had heard about it.

# _4.2. Participation (take-up)_

Children’s participation in all three types of programs is low, even when we restrict the analysis only to communities where the programs were implemented. This low level of participation again limits the explanatory power of the analysis.

# <u>FPSS</u>

In villages where a preschool was built (see Table 3, row 4), the average participation is relatively low (around 40 percent). Participation increases slightly (to 43 percent) when we include villages where a preschool class was reported to have been held. Insights from the qualitative work and analysis of the quantitative data shed some light on potential explanations for these low take-up rates.

Hypotheses related to participation for which some empirical support exists include:

- **Budget constraints** : When interviewed, some parents declared that they lack the means to send their child to school. The hidden costs of preschool can include materials (1 notebook, 1 pen, 1 pencil, 1 board) and clothes.

9
and above one million.<sup>4</sup> The rural-urban cutoff point considered by the Census is thus located somewhere between the third and the fourth ranks. But taking all six of them into account supports a richer analysis of the impact of location on female LFPR.

There is a striking difference between the LFPR of men and women along this rural-urban gradation. While male LFPR is relatively stable across the six ranks, female LFPR declines quite steadily (figure 7). The definition of employment does not affect the male LFPR but it does result in a different gradient for the decline in female LFPR along the rural-urban gradation. In smaller villages, female LFPR is much higher based on US than it is based on PS. This is because women in rural areas are more frequently engaged in subsidiary work and casual jobs, typically on the farm. The difference in female LFPR between these different definitions of employment shrinks along the rural-urban gradation, and almost completely disappears in large urban centers.

Figure 7: Female labor force participation declines along the rural-urban gradation, as do female subsidiary jobs <!-- Start of picture text --> Females Males<br>50 90<br>Usual Status<br>40 80<br>Principal Status<br>30 70<br>Usual Status<br>20 60<br>Principal Status<br>10 50<br>LFPR LFPR<br>0-999, rural 1000-4999. rural 5000+, rural 0-50000, urban 50000-Million, urban Million +, urban 0-999, rural 1000-4999. rural 5000+, rural 0-50000, urban 50000-Million, urban Million +, urban<br><!-- End of picture text --> _Notes:_ Estimates for 2011-12.

The relationship between monthly per capita expenditures and female LFPR also varies along the rural-urban gradation (figure 8). In both small villages (0-999) and small towns (0-50,000) there is a decline in female LFPR when moving from the bottom consumption quintile to the top. On the other hand, bigger villages and cities experience a more muted decline in female LFPR.

> 4 Details on the methodology employed to construct the ranks are provided in the next section of the paper.

12
that agents do not make any systematic mistakes. Naturally, residuals of the regression may be correlated across observations within the same time period, this creates bias in the estimated standard errors. Clustering, as described in Artuc (2012), solves this problem. Another potential problem is the correlation between the residuals and other variables. Since residuals capture arrival of new information, lagged variables can be used as instruments because past variables should be uncorrelated with surprise shocks.

We take values of _λ_<sup>_i_</sup> _t_<sup>and</sup><sup>_ω_</sup> _t_<sup>_i_fromthefirststepregressionandthetransitionmatrix</sup><sup>_π_</sup> is already defined exogenously. The remaining parameters _β_ , _ν_ , and _η_ ̄<sup>_i,s_</sup> can be estimated using the Instrumental Variables method.

Our estimation strategy is related to ACM, Hotz and Miller (1993) and Arcidiacono and Miller (2011). The method herein is significantly more efficient compared to ACM, both econometrically and computationally. Unlike ACM, it can handle rich heterogeneity, small sectors, and a large number of structural parameters. Arcidiacono and Miller (2011) is based on the conditional choice probability (henceforth CCP) method introduced by Hotz and Miller (2011) and an expectation maximization algorithm (henceforth EM). Unlike theirs, our method does not use maximum likelihood, it is based on orthogonality conditions. Therefore it does not require parameterization of aggregate shocks, which is a non-trivial convenience when the economy is subject to shocks that are difficult to model (such as the trade shocks in the metal and textile sectors). See Artuc (2012) for a detailed comparison.

# 4 Data

For estimation of the workers’ problem, we use the 1979 cohort of the NLSY and CPS. The CPS sample is from 1983 to 2001 and constructed in a way similar to ACM: We use white males, who are between 23 and 64, and who worked at least 26 weeks in a given year. We have a minimum of 11,857 and a maximum of 20,211 individuals in our final sample between the years 1984 and 2001 (sample size changes every year). In CPS, the reported mobility rates are 5 months’ mobility rather than annual mobility, we correct the transition probabilities. CPS is a repeated cross section, so agents are not followed over years. This 16
There is evidence that courts improve efficiency in the bankruptcy process (Gine and Love forthcoming). For financially distressed firms, bankruptcy laws are supposed to reorganize viable and liquidate nonviable firms to preserve efficiency. Indeed, nearly 90 countries have reformed their bankruptcy systems since World War II. Yet, in developing countries, the reorganization processes are lengthy and costly. Does efficiency improve with lower reorganizing costs by reducing the statutory deadlines for reorganization plans? Gine and Love (forthcoming) exploit the bankruptcy reforms in Colombia in late 1999 to examine that question. By using a unique data set with 1,924 bankruptcy cases filed between 1996 (before the reform) and 2004 (after the reform), they obtained three findings. First, the duration of reorganization proceedings dropped significantly. Second, under the new law, firms filing for reorganization are healthier and more viable than those filing for liquidation. Yet, under the old law, these two types of firms are similar. The new law, thus, better separates viable and nonviable firms and achieves the efficiency-enhancing goal of the bankruptcy procedure. Finally, financially distressed firms recover better under the new law. One year after filing for reorganization, they are more likely to improve performance after the reform. Thus, improving bankruptcy regulations allows financially distressed yet viable firms to improve.

But a good legal system is not a precondition for a country to grow (McMillan and Woodruff 2002). Indeed, an influential theory on the legal system, the development theory, asserts that courts work better in countries with richer and more educated populations (Demsetz 1967). A bettereducated population both raises the efficiency of courts (since education is an input) and the demand for them. Courts are only worthy when transactions and contracts become more complicated and the demand for arms-length enforcement becomes sufficiently high. For poor societies, informal dispute resolutions often prove to be sufficient. Consistent with this theory, McMillan and Woodruff (1999a, 2002) show how entrepreneurs in transitional countries overcome imperfection in the legal systems. In Vietnam, more than 90 percent of firm managers responded that they did not rely on courts for conflicts resolution. Instead, they rely on ongoing relationships and the threat of losing future business. They often emphasize the continuation of existing relationships and punish less severely than expected when one party breaches a contract. Without an effective court system, China substitutes family control of businesses to avoid relying on the court system (Lu and Tao 2009). All of these reduce the reliance on courts for contracts enforcement.

When stakes are high, however, firms still rely more on formal contracts or advance payments, along with community sanctions. Indeed, using the World Bank Investment Climate data of China, Long (2010) finds that a better local court system, as proxied by the city-industry average share of all business disputes resolved through the court, is associated with a higher investment rate, 17
effort is being made to assemble as much as possible into Supporting Documents (SDs) for this study, which would be provided to national compilers as soon as possible.

The issues discussed are complex; and few of the FSU economies have national compilers with much experience with the type of macroeconomic analysis t is study is designed to support. Hence, a workshop for relevant authorities from thc 15 FSU economies, to discuss these matters in greater detail, should be a high priority for the Bank. Quick estimates for 1991 were devised to initiate the Bank's FY93 operational guideline exercise but these estimates are subject to particularly wide margins of error. And the continued, probably sharper, decline experienced by most of these economies in 1992 means that an assessment of Bank lending terms based solely on historical standings could be misleading. A Bank effort to help national compilers with "transitional' technical assistance is urgently needed.

The two major studies of ruble-based national accounts (IDS and CIR), are available separately from IECSE. Preliminary indications are that although IDS relies more on industry reporting and CIR on financial accounts, the two reach much the same results. This is not only reassuring for the historical period; it suggests a form of cross-checking that should prove useful during the transitional period ahead.

23
_9_

# CHART IV: Economic Performance of FiXed and Flexible Exchange Rate Economies

<!-- Start of picture text --> hbcra Pew<br>1*<br>44J -5<br>aD - - t.5<br>4D L i L tiN<br>-1.55<br>o ~~~~~~~~~~~~~~-2<br><!-- End of picture text --> <!-- Start of picture text --> pas<br>5 1<br>45-<br>4-<br>3<br>25- iE4<br>2 -<br>0<br><!-- End of picture text -->
# **Table S5.S: Results for an alternative specification in which a single dummy variable for connectedness (** **_anylink_ ) replaces the three trading relationship dummies in**

# **the baseline specification**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Newprod|invdum|anyrd|it|pcf_tech|suprat|htrain|ftrain|
|anylink|0.00393|-0.0278|0.0580|0.130**|0.0795|-0.0325|0.0719**|0.0147|
||(0.0227)|(0.0295)|(0.0382)|(0.0597)|(0.0914)|(0.0254)|(0.0318)|(0.0611)|
|same_sec|0.00407|-0.00218|-0.0151|-0.0982***|-0.0325|0.00232|-0.0344*|-0.00885|
||(0.0102)|(0.0133)|(0.0186)|(0.0302)|(0.0312)|(0.00624)|(0.0189)|(0.0160)|
|ldist|0.000432|-0.000509|-0.0120***|-0.0218***|-0.0173***|0.00318|-0.00799**|-0.000379|
||(0.00165)|(0.00221)|(0.00301)|(0.00530)|(0.00599)|(0.00195)|(0.00314)|(0.00434)|
|ad_fmage|0.000123|0.000263|0.000370|-0.00317***|-0.00100|-0.000523*|-0.000422|0.000881|
||(0.000283)|(0.000368)|(0.00104)|(0.00120)|(0.00134)|(0.000299)|(0.000663)|(0.000992)|
|ad_meduc|<br>0.00565|<br>0.0409***|<br>0.00750|<br>0.0188|<br>0.0663***|<br>0.00382|<br>-0.000349|<br>-0.0145***|
||(0.00616)|(0.0144)|(0.00929)|(0.0167)|(0.0236)|(0.00339)|(0.00577)|(0.00474)|
|ad_mexper|-4.12e-06|-0.000299|-0.000408|-2.31e-05|-0.000120|0.000258|-0.00155***|0.000637|
||(0.000287)|(0.000265)|(0.00116)|(0.00218)|(0.00240)|(0.000598)|(0.000500)|(0.00108)|
|ad_mfemale|0.00137|0.0358*|0.0985**|0.436***|0.242***|0.0297*|0.0447|0.0847|
||(0.00630)|(0.0187)|(0.0396)|(0.0909)|(0.0752)|(0.0176)|(0.0374)|(0.0541)|
|ad_lemp|0.0232**|0.0525***|0.0396**|0.167***|0.198***|0.0182***|0.0567***|0.0780***|
||(0.00912)|(0.0143)|(0.0175)|(0.0421)|(0.0457)|(0.00569)|(0.0170)|(0.0205)|
|Constant|0.460***|0.373***|0.329***|0.503***|0.807***|0.292***|0.378***|0.0956**|
||(0.0174)|(0.0302)|(0.0357)|(0.0660)|(0.0687)|(0.0158)|(0.0345)|(0.0381)|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 41