rafmacalaba/datause-encoder-data huggingface dataset:
25 general tightening of credit conditions. They would therefore shift the purchase date of durables to the prtsent. A perceived increase in permanent income may also give rise to a more-than-proportionate increase in durables purchases, because of the accelerator effect, but this can be expected to be lower than the intertemporal substitution effect.

An examination of tho data for Israel reveals that durable purchases increased tremendously in 1986, following the stabilization of July 1985 (Table 4) which lends some support to the temporariness hypothesis. It is interesting to note that a similar behavior was recorded in Israel in 1981-83 when the prices of durables were reduced temporarily through cuts in sales taxes and through a reduction in the rate of devaluation (in the Tablita fashion).

# 5. Consumption Booms and Self Fulfilling Balance of Payments

# Crises

The foregoing discussion can be related to the theory of rational and self-fulfilling balance of payments crisis, as in Obstfeld (1986). Pessimistic views about the government's policy in the event of a crisis may actually bring it about even if it is completely unrelated to the behavior of the fundamentals under full credibility. In particular, if the public's perception is that an exchange rate collapse will set off an inflationary domestic credit policy (which is not unrealistic) then the crisis may become self-fulfilling.

If agents expect that a balance of payments crisis will involve restrictions on expenditures, in the fashion described above, then the anticipated self-fulfilling crisis may be preceded by a consumption boom. This consideration provides another possible link between confidence in the government's commitment to disinflate and preemptive increases in expenditures.
compared with actual data for all city‐pairs because such data are not available, it is still possible to compare our figures for specific city‐pairs using various sources.

In Table 4, we corroborate our results using data from Tan and Reja (2018). In this paper, the authors conducted interviews with China based companies and collected information regarding the time it takes to ship products by train for certain destinations. While still featuring some differences, our pre‐BRI figures are broadly consistent with the quotes from private companies.

_Table 4: Comparison between shipment time declared by survey participants (Tan and Reja (2018)) and our “pre‐BRI’ results._

||**Survey**|**Our “Pre‐BRI” scenario**|
|---|---|---|
|**Lanzhou to Almaty**|10 days|8.95 days|
|**Lanzhou to Astana**|10 days|8.40 days|
|**Manzhouli to Yekaterinburg**|9 days|‐|
|**Qiqihar to Yekaterinburg**<br>(Qiqihar is 650km east of Manzhouli)|‐|12.2 days|

_Note: This table focuses on rail links_ Along the same lines, Table 5 presents a comparison between our own estimates and quotes that can be found online from a company specialized in maritime transportation.<sup>11</sup> Again, we find that our estimates are not far from the information we can gather from private companies, which is another validation of the method and the assumptions used.

_Table 5: Comparison between commercial information and our “pre‐BRI’ results._

||**CFC**|**Our “Pre‐BRI”**<br>**scenario**|
|---|---|---|
|**Dalian to Hamburg**|34 days|34.04 days|
|**Shanghai to Antwerp**|32 days|32.32 days|
|**Guangzhou to Bremen**|29 days|32.40 days|
|**Shanghai to Rotterdam**|27 days|32.41 days|

_Note: This table focuses on maritime links. Commercial information comes from CFC: cargofromchina.com._

# **3. Effects of the Belt and Road Initiative on shipping times**

In this section, we present the results on shipment times from the network analysis before and after the Belt and Road Initiative. We first construct two scenarios: a lower bound scenario, where exporters cannot switch mode of transport after the BRI infrastructure projects are completed, and an upper bound

> 11 See at https://cargofromchina.com/china‐europe/

10
2 investment regimes.<sup>2</sup> While the intensity with which such programs were pursued varied across countries, the trend was in the same direction. As far as the service sector was concerned, there was increasingly a perception that inefficient service industries constituted a drag on the economy, and that liberalizing access to ser, ice markets was a potentially low cost and Jffective method of improving the quality and efficiency of domestic service sectors.

These unilateral policy developments increased the incentives for developing countries as a group to participate in a multilateral agreement to liberalize trade in services. Such an agreement would allow governments to "lock in" domestic policy changes, while at the same time obtaining "credit" for these changes in the form of increased opportunities to access foreign service markets.<sup>3</sup> The changes in domestic policy occurred in many of the countries that were initially strongly opposed to multilateral disciplines for services. Consequently, these developments enhanced the likelihood of negotiating a far-reaching multilateral agreement to liberalize trade in services.

An analysis of the draft GATS reveals that its scope is limited, however. In large part this reflects the preferences of OECD countries. But developing countries also continued to pursue a cautious negotiating stance throughout the post-1988 period. The unilateral policy changes noted earlier are reflected to only a limited extent in the outcome of ne,otiations. Indeed, the draft GATS can be characterized as consisting of a mix of elements drawn from the _initial_ position papers presented by participants in 1987-88. While it is certainly the case that the influence of developing countries in multilateral trade negotiations is generally limited, the GATS negotiating history illustrates both the substantial inertia that characterizes negotiating positions in such efforts and that developing countries do have some influence in determining the final outcome.

The aim of this paper is twofold. First, to explore the extent to which initial negotiating
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determine whether women who would have chosen long-acting devices belong to the same demographic group as those opting for the new product. Contraceptive coverage for younger women is particularly beneficial, given the disruptive impact of unintended pregnancies on this demographic, especially teenagers.<sup>15</sup> Finally, we are unable to assess the longer-term effects of the intervention.

# **7 Conclusion**

This paper assesses the effects of an intervention designed to incorporate community health workers as key participants in the distribution of a new generation of injectable contraceptives in a rural setting. CHWs in the treatment group received theoretical and hands-on-training followed by eligibility to administer these non-intramuscular injections directly to women at their homes. In contrast, in the control group, the distribution of the new contraceptive was exclusively handled by more skilled workers primarily within health centers.

Our experimental findings indicate a notable change in injectable contraceptive delivery by health centers. On average, we observe roughly 12.72 additional Sayana Press injections as a result of the intervention. This implies an increase of 70 percent over the control group mean. Assuming uniform coverage rates across the six provinces in our sample, this point estimate corresponds to roughly 8% of the overall coverage rate in 2017, estimated to range around 20.8%.<sup>16</sup> However, despite this strong surge in the number of administered injections, our experimental results concerning the overall number of dispensed contraceptives, albeit positive, are statistically insignificant. We explain this finding by substitution effects away from long-acting contraceptive devices. Specifically, we observe a monthly decrease of 2.70 implants and IUDs due to the intervention. In an exploratory analysis that accounts for differences in the protection horizon offered for each of the contraceptive methods dispensed by health centers in our sample, in net terms, we find no evidence that Sayana Press significantly affected the overall contraceptive coverage. If at all, our analysis points to a statistically insignificant net reduction

> 15 Past surveys suggested that injectable users tend to be slightly younger than women adopting long-acting devices (Burundi Institute of Statistics and Economic Studies, 2017).

> 16 This corresponds to a total number of 65.2 new users throughout the evaluated intervention (3 _._ 26 _∗_ 20), which represents approximately 1.7% of the local target population (65 _._ 2 _/_ 3 _,_ 739).

17
# **Table 14. NTM Frequency and Coverage Ratios for Russia, Percent**

||**All NT**<br>|**Ms**<br>|**Sanitary**<br>**Phytosan**<br>**Measur**<br>|**and**<br>**itary**<br>**es**<br>|**Technical Ba**<br>**Trad**<br>|**rriers to**<br>**e**<br>|**Pre-Ship**<br>**Inspection a**<br>**Formali**<br>|**ment**<br>**nd other**<br>**ties**<br>|**Non-auto**<br>**Licensing, Q**<br>**Prohibitions, a**<br>**Quantity C**<br>**Measur**<br>|**matic**<br>**uotas,**<br>**nd Other**<br>**ontrol**<br>**es**<br>|**Price-co**<br>**Measures, I**<br>**Additional T**<br>**Charg**<br>|**ntrol**<br>**ncluding**<br>**axes and**<br>**es**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Sector**|Freq|Cov|Freq|Cov|Freq|Cov|Freq|Cov|Freq|Cov|Freq|Cov|
|01-05 Animal & Animal Products|95.8|95.1|95.8|95.1|82.7|90.2|95.8|95.1|95.8|95.1|95.8|95.1|
|06-15 Vegetable Products|96.5|92.9|95.8|92.9|74.3|84.0|96.5|92.9|14.1|8.2|96.5|92.9|
|16-24 Foodstuffs|95.5|97.0|95.5|97.0|76.7|86.6|95.5|97.0|14.0|33.4|95.5|97.0|
|25-26 Mineral Products|94.6|99.0|68.2|90.0|4.2|6.4|94.6|99.0|20.8|3.9|94.6|99.0|
|27 Oil Minerals|97.7|52.0|2.7|0.0|26.8|7.6|97.7|52.0|22.0|0.7|97.7|52.0|
|28-38 Chemicals & Allied Industries|96.3|97.0|63.0|74.6|81.1|88.7|96.3|97.0|75.7|76.8|96.3|97.0|
|39-40 Plastics / Rubbers|94.2|95.1|26.4|29.4|23.9|33.8|94.2|95.1|16.4|18.2|94.2|95.1|
|41-43 Raw Hides, Skins, Leather &<br>Furs|80.2|94.6|60.5|33.7|64.6|89.3|80.2|94.6|49.4|2.0|80.2|94.6|
|44-49 Wood & Wood Products|88.1|65.1|48.2|23.1|23.5|41.0|88.1|65.1|8.1|7.6|88.1|65.1|
|50-63 Textiles|95.5|91.2|44.6|22.6|95.5|91.2|95.5|91.2|7.6|2.8|95.5|91.2|
|64-67 Footwear / Headgear|100.0|100.0|0.0|0.0|69.4|97.6|100.0|100.0|2.0|0.4|100.0|100.0|
|68-71 Stone / Glass|92.5|89.5|11.1|9.1|50.2|35.1|92.5|89.5|33.2|7.8|92.5|89.5|
|72-83 Metals|95.9|95.5|3.3|9.8|20.9|33.3|95.9|95.5|11.1|9.1|95.9|95.5|
|84-85 Machinery/ Electrical|94.3|79.8|0.4|0.8|94.3|79.8|94.3|79.8|42.8|42.9|94.3|79.8|
|86-89 Transportation|98.5|98.7|2.7|2.0|66.1|83.6|98.5|98.7|11.0|0.0|98.5|98.7|
|90-97 Miscellaneous|96.0|90.4|1.2|0.3|52.0|69.4|96.0|90.4|12.1|9.1|96.0|90.4|

_Source:_ UNCTAD.

_Note:_ “Freq” denotes the frequency ratio—the percentage of tariff lines covered by an NTM. “Cov” denotes the coverage ratio—the share of imports by value covered by NTMs. The most recent UNCTAD data for Russia are for 2009, compared with 2012 for Kazakhstan and the EU. Since country NTM profiles change slowly, and since countries are more likely to add NTMs than drop them, these data are likely to still be useful. Import data used for the coverage ratio represent mirror data averaged over 2008, 2009, and 2010, as reported by Russia’s trading partners. Often there are multiple NTMs for the same product, even within categories (e.g., multiple SPS or TBT measures).

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 41
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|||**Table A**|**.15: Robustness t**|**o alternative m**|**etrics forpublic-**|**private compar**|**ison**|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||**(1)**<br>**E**|**(2)**<br>**ffort**|**(3)**|**(4)**<br>**Diagnosis**|**(5)**|**(6)**|**(7)**|**(8)**<br>**Treat**|**(9)**<br>**ment**|**(10)**|**(11)**|
||Time spent|Checklist|Gave diagnosis|Correct<br>diagnosis<br>(conditional)|Correct<br>diagnosis<br>(unconditional)|Correct<br>treatment|Palliative<br>treatment|Unnecessary<br>treatment|Correct<br>treatment<br>only|Antibiotic|<sup>Number of</sup><br>medicines|
|**Panel A: Bestpublic vs. bestp**|**rivate(by correct**|**treatment)**||||||||||
|Is a private provider|1.632***|11.288***|0.235***|0.033|0.079|0.162**|0.074|0.169|-0.014|0.143|1.147***|
||(0.388)|(2.855)|(0.090)|(0.136)|(0.054)|(0.079)|(0.077)|(0.117)|(0.056)|(0.109)|(0.429)|
|R-squared|0.453|0.417|0.430|0.714|0.363|0.592|0.447|0.353|0.218|0.435|0.463|
|Number of observations|286|286|192|76|192|192|192|192|192|192|192|
|Mean of public|2.547|16.000|0.271|0.154|0.042|0.271|0.521|0.708|0.042|0.250|2.063|
|Mean of private|3.613|24.551|0.438|0.238|0.104|0.438|0.535|0.750|0.049|0.292|3.014|
|Mean of sample|3.352|22.458|0.396|0.224|0.089|0.396|0.531|0.740|0.047|0.281|2.776|
|**Panel B: Bestpublic vs. bestp**|**rivate(by checkli**|**st items)**||||||||||
|Is a private provider|3.216***|16.987***|0.263**|0.119|0.079|0.141|0.034|0.167|-0.027|0.222|1.581***|
||(0.916)|(5.003)|(0.116)|(0.160)|(0.056)|(0.095)|(0.104)|(0.139)|(0.028)|(0.156)|(0.503)|
|R-squared|0.586|0.501|0.610|0.823|0.487|0.616|0.699|0.468|0.540|0.473|0.674|
|Number of observations|191|191|129|63|129|129|129|129|129|129|129|
|Mean of public|2.481|18.832|0.333|0.133|0.044|0.200|0.556|0.689|0.022|0.178|1.800|
|Mean of private|4.708|30.269|0.571|0.146|0.083|0.286|0.595|0.845|0.012|0.310|3.381|
|Mean of sample|3.938|26.317|0.488|0.143|0.070|0.256|0.581|0.791|0.016|0.264|2.829|

Notes: *** Significant at 1%, ** Significant at 5%, * Significant at 10%. Robust standard errors clustered at the market level are in parenthesis. All regressions include a constant and SP, case and market fixed effects. Observations are standardized provider-patient interactions. In column (11) the dependent variable is total number of medicines recommended to the patient (dispensed and/or prescribed).
# Table of Contents

|Sum|mary<br>..|ini|
|---|---|---|
|Ack|nowledgments<br>..|iv|
|I.|Introduction .1||
|IH.|A Retrospective of Estimating Chinese Real Income<br>.3||
|ml.|Outine ofBilaterl Compaison Methods<br>.9||
|IV.|Bilateral Comparison Between China and the United States .13||
||1. Collecting and Processing of Data .13||
||2. Matching of Quality .15||
||3. Some Topics in the Bilateral Comparison .16||
|V.|Results of the Bilateral Conpaison .25||
|VI.|Analysis of Errors .28||
|VII.|Concluding Remarks .32||
|Anne|x:||
||Table 1. The Distribution of Sample Data .34||
||Table 2. Estimates of Shadow Rents in China .35||
||Table 3. Estimating Rent in US .35||
||<br>Table 4. Results of Bilateral GDP Comparisons .36||
||<br>Table_5._The Weights Used in the Esimation .37||
||Table 6. List of Items in the Sample ofPmice Retives .38||
|Refer|ences .....|42|

i
Table 4A reports data from round 8 of our survey, using information on the markup of the firm’s main product. We also gathered markup data in round 7. In both rounds, we asked about markups on two goods: those representing the highest and second highest percentage of sales. The results using the round 7 data and the results using the average of the markups of the two main products (weighted by the percentage of sales represented by each product in round 8) are similar to those reported on Table 4A. For retailers, there is a very high correlation between the markups in the two periods (0.78) and the markups of the two goods (0.80, on average). For manufacturers, these correlations are much lower. The correlation in the markup across periods for the manufacturers is 0.28, and the correlation across the two most important goods is 0.25, on average. The lower correlations may be caused by changes in the mix of products sold by a manufacturer or by the seasonality of pricing. The data suggest that asking for markups which are specific to the survey reference period is important in the case of manufacturers.

Table 4B shows that the markup adjustment is most important for firms whose inventories turn over slowly—that is, those with a greater mismatch in the timing of revenues associated with expenses. The median firm in our sample reports that 67 percent of the inputs purchased in the reference month were sold as final goods before the end of the month. For retailers, the median percentage of inputs sold is 74 percent; for manufacturers, 65 percent. Table 4B shows that the Pearson (Spearman) correlation between adjusted profits and adjusted revenues minus expenses is 0.70 (0.77) for firms selling 75 percent or more of purchased inputs in the same month (40 percent of the sample). Adjusting for markups makes little difference for these firms. The Pearson and Spearman correlations are nearly identical, 0.70 and 0.74, respectively. The table reveals a very different story for firms with a slower turnover in inventory. Among firms with selling 60 percent or less of the month’s input purchases in the month (also 40 percent of the sample), the Pearson correlation between adjusted profits and adjusted revenuesexpenses is only 0.14. Using markups to adjust for timing increases this correlation to 0.66, or nearly the same level obtained by either method for firms with fast turnover of inventories. Thus, table 4B helps isolate the circumstances under which using markups provides a significant increase in the matching of reported revenues, expenses and profits.

12
and lower margins. But its economic effect on spreads is relatively small. A one standard deviation increase leads to less 0.7 percentage point lower spreads and 0.3 percentage points lower margins, or 15% of the standard deviation of spreads and 6% of the standard deviation of margins.

Next, we extend the model specification with four key macroeconomic variables. Specifically, we include the inflation rate, as measured by the CPI, the 91- days Treasury bill rate deflated by inflation, the change in the exchange rate to the dollar as well as a proxy for GDP growth. The results in column 4 indicate that the exchange rate enters negatively and significantly in the spread regressions, suggesting a widening spread in times of an appreciating Shilling. Also, higher inflation leads to an increase in the nominal spreads. Margins are higher in quarters with higher GDP growth, inflation-adjusted T-Bill rates and an appreciating Shilling.

The negative effect of the change in the exchange rate is surprising since the variable captures expectations of exchange risk, for which we would expect a positive sign. As alternative measures for exchange rate risk, we used the quarterly standard deviation over the previous 3, 6 and 12 months, respectively. These three measures are all positively influencing the spread whereas only the 12-month standard deviation is significant in the margin specification, suggesting that exchange rate volatility results in higher spreads.

But overall the economic effects of the macroeconomic characteristics on the spreads and margins are small so this does not change our main findings that bank characteristics such as overhead costs, market share, foreign ownership and loan portfolio composition are the driving factors of spreads and margins. Take the example of exchange rate depreciation. One standard deviation in exchange rate changes results in 0.6 percentage points lower spreads and margins. Similarly, one standard deviation in GDP growth results in 0.4 percentage point higher margins.

18
Table 10 COte d'lvoire: Mean Per Capita Total Household Expenditures with and without Household-Size **Weights** by Socio-Economic Group. 1985-1988

|||1985|||1986|||1987||1988|
|---|---|---|---|---|---|---|---|---|---|---|
||A.|B.|%|A.|B.|%|A.|B.|%|A.*|
||||Diff.|||Diff.|||Diff.||
|Export Crop Farmers<br>|175,840|194,418|_10.57_|166,601|182,715|9.67|158,791|158,434|-0.22|140,906|
|Food Crop Farmers<br>|167,235|171,802|2.73|155,292|165,034|6.27|151,795|153,771|1.31|132,809|
|Public Sector Employees<br>|394,987|431,171|9.16|356,881|401,988|12.64|349,514|364,820|4.38|254,402|
|Formal Private Sector Employees<br>|346,100|364,835|5.41|314,902|328,060|4.18|367,494|385,322|4.85|278,708|
|Informal Private Sector Employees<br>|223,698|230,593|3.08|169,723|191,132|12.61|189,422|186,294|-1.65|144,153|
|Self-Employed<br>|217,439|234,910|8.03|200,874|217,634|8.34|206,577|212,771|3.00|174,333|
|lnqctive<br>|233,905|253,049|8.18|214,911|233,127|8.48|201,489|201,330|-0.08|191,040|
|Unemployed<br>|320,954|306,624|4.46|247,561|228,037|7.89|193,179|203,729|5.46|190,503|
|C6te d'lvoire|221,313|237,853|7.47|204,183|223,905|9.66|210,286|216,965|3.18|173,072|

- A. Mean per capita expenditure computed without household-size weights. B. Mean per capita expenditure computed with household-size weights. * Since 1988 data were obtained entirely under the revised sampling frame and sampling procedures, no household-size weights are applied.

20