rafmacalaba/datause-encoder-data huggingface dataset:
This matter has yet to be resolved. As Al Harberger allegedly mentioned: _"I know of no one who has canceled his vacation because the government has increased its bond issue, but I know people who have canceled their vacation because the government increased taxes "._ Yet the empirical evidence seems to tilt in favor of the Ricardo/Barro approach. A survey of empirical studies on the deficit-interest rate _'found that only four of twentyone studies provided evidence supporting a positive, statistically significant impact of deficits on interest rates.... another ten studies found no evidence_ .... _the remaining seven studies produced mixed results that also sometimes indicated no link"_ (Bradly 1986).

Bradley (1986) links interest rate changes to government expenditures: _"In this article we demonstrate that a crucial difference exists between the economic impacts of increased government spending and the economic impacts of increasing the bond stock Using a simple dynamic model, we find that increases in the stock of government bonds, by themselves, do not force interest rates. When the debt increase is caused by a rise in spending, however, we find that, after a lag, both interest rates ...... rise. "_ Barro (1990) draws a distinction between temporary and permanent variations in government purchases, arguing that the interest rate and outputs are mostly affected by temporary changes in government expenditures.

The issue concerning the impact of government deficit and fiscal policy on interest rates is far from being resolved. Most of the empirical evidence is inconclusive and pertains mostly to the US. Nevertheless, it seems safe to conclude that, at least for Latin America, an increment in the primary surplus would tend to reduce interest rates due to its contribution to reducing country risk. Also, a reduction of government expenditures, for example, through privatization of SOEs or reduction in bloated bureaucracies, would help to (a) increase the rate of income growth (allocation effect) and (b) reduce the interest rate (crowding out effect).

# **Description of Some Debt Statistics for Selected Countries**

Tlhe remaining sections will present some variables relevant to the debt sustainability of Brazil, Chile and Costa Rica, with passing references to Germany and Argentina.

# **Brazil**

Brazil is an interesting country given its broad experience with inflation, debt problems and extensive debt-financing strategies. In the previous debt sustainability exercise, we have demonstrated that k is a key variable. The following illustration will show the evolution of k for Brazil.

Chart 1 18
of Credit Supply on Trade: Evidence from Matched Credit–Export Data,” Review of Economic Studies 82, 333-359.

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Rauch, J.E., 1999, “Networks versus Markets in International Trade,” Journal of International Economics 48, 7-35.

- Reinhart, C., and K. Rogoff, 2009, This Time Is Different: Eight Centuries of Financial Folly. Princeton University Press.

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- Sachs, J.D., and A. Warner, 1995, “Economic Reform and the Process of Global Integration,” Brookings Papers on Economic Activity 1, 1-118.

- Strieborny, M., 2017, “Suppliers, Investors, and Equity Market Liberalizations,” Working Paper.

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34
Using firm-level data for 73 developing countries, the paper finds that the likelihood of a female top manager in a firm is positively associated with more favorable education enrollment rates in primary, secondary and tertiary education for women relative to men. In addition, the empirical findings in this study have important policy implications as it helps policy makers identify factors that promote women in top positions at private firms. The results also add another dimension to the debate on how closing the gender gap in education can help reduce gender inequality in income levels and job opportunities. Gender disparity in top managerial positions is a relatively under researched area, especially for developing countries. The present paper takes one step towards filling this gap. Our results are interesting for another reason, in that they show a much stronger positive relationship between education enrollment and the likelihood of a female top manager among SMEs than large firms. Hence, our findings contribute to ongoing debate on the cost-benefit calculus of policies aimed at promoting SMEs. We hope that the present paper motivates more research in the area in the future.

23
# _Question 13: Interface State-Government_

This general question asks entrepreneurs on their overall perception of the state and the bureaucracy. Is the state an opponent, a neutral agent, or a helping hand for the private sector? Though this question has 6 possible answers, these were clearly divided into 3 parts: opponent, neutral agent, and helping hand. The graph shows the polar case of the percentage of entrepreneurs who ticked 5 and 6 (opponent).

13. Please rate your overall perception of the relation between government and/or bureaucracy and private firms on the following scale.

"All in all, for doing business I perceive the state as": Helping Neutral Opponent hand agent Now 1 2 3 4 _56_ 10 years ago 1 2 3 4 _5_ 6 <!-- Start of picture text --> 45<br>40<br>35<br>80<br>25 - -<br>20-<br>15<br>10<br>5<br>iI DC S9A MNA CEE IAC SSA CiS<br>ONowl<br>I:10 (5) vaso<br><!-- End of picture text -->

## **Percentage of firms who ticked (5), or (6) for question 13**

The percentage of entrepreneurs who thought that the state was an _opponent_ is highest in Latin America (40 percent). At the other extreme, only 5 percent of entrepreneurs in SSEA feel in this way about their relation with the state. When asked to rate this relationship 10 years ago, 20 percent of businessmen thought of the state as an opponent in this region. In MNA, the developed countries, and the CIS the relation between the state and the business community seems to have worsened in the past 10 years ago (5 years in the case of the transition economies).

# _Question 14: Frequency of Corruption_

This question probes the overall frequency of corruption without distinguishing between different types of corruption. To obtain a less biased answer, the question asks respondents to rate the frequency of irregular payments in their line of business, without asking whether he or she has ever paid bribes personally.

30
**Table 10. Summary of key findings and match with the hypotheses**

||_Pop_<br>_level_|_Modern_<br>_GDP per_<br>_capita_|_Modern_<br>_Fiscal_<br>_Capacity_|_Modern_<br>_sector_|_Modern_<br>_Human_<br>_capital_|_Support Hypotheses_|
|---|---|---|---|---|---|---|
|Fast pop<br>growth before<br>TP||||||Malthusian transition|
|Taiping (vs not<br>Taiping)|-*||+*|+*|+* (2sls)|Malthusian transition.<br>The war-induced-state-capacity.|
|Late (vs Early )<br>Taiping|initial -*;<br>recovery<br>+*|+*<br>(early -*)|+*|+*|-* on<br>health|Stationary-bandit property-<br>rights. Malthusian transition.|
|Early Taiping|-*|-*||||Against the naïve Malthusian<br>transition hypothesis.|
|More idle land<br>in Early than in<br>Late Taiping||||||Stationary-bandit property-<br>rights.|
|Taiping-<br>Capital-Close|initial -*;<br>recovery<br>+*|+*|+*|+*|+*|Distance-based stationary<br>bandits.<br>Malthusian transition.|
|Initial post-war<br>fiscal capacity|-*|+*||+*|+*|Likin-as-fiscal-capacity**.**<br>Not: Likin-as-tax.|
|Initial<br>Likin*Early<br>Taiping||+* (sml)|+* (sml)|||Malthusian transition.|
|Initial<br>Likin*Late<br>Taiping||+* large|+* large|+*|+*|The fiscal-and-institutional-<br>capacity complementarity.|
|Initial<br>Likin*Capital-<br>Close|||||+*|The institution-induced skill<br>demand.|
|No. of wars<br>since 1776||-*||||Inconsistent with War-and-<br>Development hypothesis.|
|Initial human<br>capital||+*|+*|+*|+*|The HC-long-term-development<br>view|

55
Table 8: Specific Commiitmnents by the Largest Developing Country GATS Members (percentage)

|Total or<br>Average|_|Argentina|Brazil<br>__:|Chile|China|Colombia|<br>India|Indonesia|Israel|Malaysia|Pakistan|Philip-<br>pines|Poland|South<br>Africa|Thailand|Vene-<br>zuela|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|183.7|Count (No. of conmiitments)|208|156|140|196|164|132|140|180|256|108|160|212|288|260|156|
||Market Access:||||||||||||||||
|17.1|Average sectonal coverage|26.4|7.5|9.8|13.9|13.9|7.2|13.7|19.1|28.1|8.2|21.1|21.5|32.6|19.4|13.6|
|29.6|CountVGNS list total|33.6|25.2|22.6|31.6|26.5|21.3|22.6|29.0|41.3|17.4|25.8|34.2|46.5|41.9|25.2|
|57.6|Av. Sec. Cov./[CountVGNS list]|78.6|29.8|43.2|44.1|52.4|33.7|60.7|65.8|68.0|47.2|81.9|62.7|70.1|46.2|53.9|
|67.3|Count of l's (no restrictions)|136|19|36|42|62|10|42|90|100|30|102|79|150|59|53|
|36.6|No restrictions/count|65.4|12.2|25.7|21.4|37.8|7.6|30.0|50.0|39.1|27.8|63.7|37.3|52.1|22.7|34.0|
||National treatment:||||||||||||||||
|18.8|Average sectoral coverage|26.4|8.5|10.7|14.6|13.2|8.4|10.8|19.2|33.4|8.2|24.1|28.3|35.2|26.8|13.6|
|29.6|Count/GNS list total|33.6|25.2|22.6|31.6|26.5|21.3|22.6|29.0|41.3|17.4|25.8|34.2|46.5|41.9|25.2|
|63.3|Av. Sec. Cov./[Count/GNS list)|78.6|34.0|47.5|46.2|50.0|39.4|47.9|66.1|80.9|46.8|93.4|82.8|75.9|63.9|53.9|
|90.5|Count of l's (no restrictions)|136|27|48|54|62|31|30|91|166|38|144|164|182|132|53|
|49.3|No restrictions/count|65.4|17.3|34.3|27.6|37.8|23.5|21.4|50.6|64.8|35.2|90.0|77.4|63.2|50.8|34.0|
||MA and NT comparisons:||||||||||||||||
|1.5|No. of NT 's/No. of MA I's|1.0|1.4|1.3|1.3|1.0|3.1|0.7|1.0|1.7|1.3|1.4|2.1|1.2|2.2|1.0|
|64.3|No. of matched l's (MA=NT=1)|136|17|36|37|62|10|28|89|90|30|91|79|149|58|53|
|_35.0_|Matched I s/count|65.4|10.9|25.7|18.9|37.8|7.6|20.0|49.4|35.2|27.8|56.9|37.3|51.7|22.3|34.0|
|10.4|Matched Is/GNS total|21.9|2.7|5.8|6.0|10.0|1.6|4.5|14.4|14.5|4.8|14.7|12.7|24.0|9.4|8.6|
|2611|GDP ($ billion)|229|395|41|506|49|242|126|70|57|473|52|84|115|110|61|
|11.3|Share in world GDP|0.99|1.71|0.18|2.19|0.21|1.04|0.55|0.30|0.25|2.05|0.23|0.36|0.50|0.48|0.27|
|1.6|MA cov. (weighted by GDP)|0.26|0.13|0.02|0.31|0.03|0.08|0.08|0.06|0.07|0.17|0.05|0.08|0.16|0.09|0.04|

22
try of new US exporters of electronics products into the EU. Schmidt and Steingress (2019) quantify the effect of the harmonization of standards on French firm exports. Exploiting harmonization events based on a novel cross-country standard equivalences database, they show the introduction of harmonized standards increases trade through entry and higher exports by existing exporters.<sup>9</sup> Our contribution to this literature is to show the heterogeneous effect of provisions for the harmonization of SPS in PTAs on firm exports, namely the stronger benefit for exports of small firms relative to large firms.

A third, smaller literature examines the effects of regulatory trade barriers on firm-level trade. Fontagné et al. (2015) estimate the impact of SPS concerns raised in the WTO Committee on SPS - which are SPS measures perceived as de facto trade barriers - on French firms’ exports using customs data. They show that SPS concerns reduce firms’ exports at the extensive margin (the probability of exporting) and at the intensive margin (export value) but increase firms’ export prices. The negative impact of SPS concerns is lower for larger firms exporting to multiple destinations. Fontagné and Orefice (2018) focus on TBT concerns raised in the WTO committee on TBT and show they have a negative effect on French firms’ export participation that is magnified for multi-destination firms able to divert their exports towards destinations with no TBT. Fernandes et al. (2019) estimate the effect of product standards (on pesticides) on firms’ export decisions using customs data for 42 developing countries. They show that an increase in the stringency of product standards in the destination country, relative to the exporting country, lowers firms’ probability of exporting, deters exporters from entering new markets, and fosters exit from existing markets. Smaller exporters are more affected in their market entry and exit decisions by the relative stringency of standards than larger exporters. Macedoni and Weinberger (2019) use a large panel of countries to show that countries have fewer exporters selling to destinations with larger numbers of standards,

> 9To rationalize their results, they propose a trade model with heterogeneous firms and endogenous adoption of product standards. Product standards reduce information asymmetries and ensure the compatibility of inputs and devices and this increases demand by consumers. This increases firms’ incentives to produce goods that meet the standard despite high investment costs and higher marginal costs to produce meeting the standard. Their model’s prediction is that a new standard (through harmonization) leads to both entry of exporters as well as increases in exports of existing exporters.

7
Table 1 shows the production levels prior and following the reforms for the sample cases.<sup>9</sup> Since each case has a separate reform date, five years prior to the reforms - including the reform year - and five years following the initiation of the reforms are used as pre- and post-reform periods. In seven out of the nine cases, output increased substantially following the reforms. The exceptions are coffee in Kenya and Tanzania. These results partially support the fact that there is significant short run supply response capability in SSA, and that the reforms, by changing the incentives for the producers, led to these output increases.

**Table 1: Production Before and After the Reforms**

|_Cdit_|_Ct_|Per|iod|Produ|ction („000|mt)<br>|
|---|---|---|---|---|---|---|
|_ommoy_|_ounry_|_Pre-_<br>_Reform_|_Post-_<br>_Reform_|_Pre-_<br>_Reform_|_Post-_<br>_Reform_|_2004-_<br>_2008_|
|Coffee|Kenya|1988-92|1993-97|87|82|47|
|Coffee|Uganda|1988-92|1993-97|121|172|167|
|Cashew|Mozambique|1990-94|1995-99|23|39|41|
|Cashew|Tanzania|1988-92|1993-97|26|59|90<sup>10</sup>|
|Coffee|Tanzania|1990-94|1995-99|47|46|53|
|Tea|Kenya|1988-92|1993-97|187|229|336|
|Tea|Tanzania|1989-93|1994-98|90|105|141|
|Cotton|Zambia|1990-94|1995-99|47|79|152|
|Tobacco|Tanzania|1991-95|1996-00|22|35|52|

The key constraint on the commodity sector performance was insufficient producer incentives. By eliminating most of the intermediate agencies and reducing and eliminating export taxes and restrictions, the agricultural policy reforms led to higher prices for the farmers. They fit into the categorization of the growth accelerations through the elimination of major constraints, and as expected they lead to spurts in output growth.

We also include the production levels for the last five years in Table 1 as a proxy for the longerterm sustainable growth outcomes. They are indicative of whether the initial supply responses were maintained and improved upon. Of course, given the cyclical behavior of production in many cases, output in the last five years might not give a clear sense of the sustainability of the growth process.<sup>11</sup> It is just another indicator in addition to our judgments on status of the sector around 2008. The case studies and the discussions below indicate that four cases of can be thought of as showing sustained growth. These are Zambia cotton, Kenya and Tanzania tea, and

> 9 The details of the output movements along with the behavior of producer prices are analyzed in the following sections. For the further details, the reader is referred to the original papers.

> 10 Cashew production in Tanzania is higher in the last five years but it is much lower than the peak production attained in the post-reform period (see Fig. 9).

> 11 Commodity prices have been increasing for the last few years which might skew the results of the last five years.

5
## **Box 4: Added‐value of community activities in Brazil**

A World Bank Independent Evaluation Group study looked at the AIDS/STDs Control Project in Brazil as part of an effort to understand what difference NGOs make to the performance of World Bank–supported projects. The study hypothesized that NGO effectiveness is the product of the existence of an enabling environment for their participation; a positive relationship between NGOs, governments and the World Bank; and NGOs’ local knowledge, flexibility, and innovation (Boyd and Garrison 1999).

More specifically, communities are typically involved in the following types of activities:

# **_Service Delivery_**

- **_Prevention_** : This area includes education; information and awareness raising; life skills enhancement; behavior change; action to change harmful traditional practices and cultural or gender norms and to reduce stigma and discrimination; access to safe means of protection (condoms and lubricants, needles and syringes) and their distribution; HIV testing and related counseling; activities targeting groups at elevated risk, in particular, marginalized populations who fear authority and public service because of their illegal behaviors; and activities aimed at preventing mother‐to‐child transmission (PMTCT) of HIV.

- **_Referral and an increased utilization of services_** : Communities serve as an interface between outreach activities and HIV counseling and testing (HCT), treatment, care, and follow‐up. The contribution by the community response to prevention is to facilitate access to HCT and is not limited to promoting uptake of services.

- **_PMTCT and treatment_** : Communities can play a supporting role in medical care by referral to health facilities, monitoring of HIV and TB treatment adherence, and other activities that complement government response, treatment education, and literacy.

- **_Care and support_** : Communities can aid in social, psychological, and spiritual support; counseling; child care, day care, and respite care; home‐based care; palliative care; nutrition support; social support, including for orphans and vulnerable children (OVC) and families; support group and self‐help activities; and economic support. They can also help ensure adherence to treatment.

- **_Impact mitigation_** : Community efforts can positively impact savings and credit, grants, vocational training, income generation, material and welfare support, agricultural support, food assistance, nutrition gardens, legal support and referral, and welfare support and referral.

12
Finally, we add variables for the gender of interviewer and respondent along with an interaction term (Specification III). This results in very small decreases in the ICC across most of the variables and reduces the mean ICC from 0.137 for Specification II to 0.128 for Specification III.

# **Table 4: ICCs for interviewer area clusters and Models 1 -3**

||**Area**|**Interv**|**iewer cluster**|**s**|
|---|---|---|---|---|
||**clusters**|**I**|**II**|**III**|
|Number of people in HH|.047|.015|.013|.013|
||(.021)|(.011)|(.011)|(.011)|
|Age of HH head|.088|.039|.021|.022|
||(.025)|(.018)|(.013)|(.013)|
|Months out of past 12 worked in primary|.174|.101|.032|.022|
|occupation by HH head|(.033)|(.035)|(.016)|(.013)|
|Days per month worked in primary|.057|.12|.117|.117|
|occupation by HH head|(.022)|(.039)|(.039)|(.039)|
|Hours per day worked in primary occupation|.072|.359|.335|.316|
|by HH head|(.024)|(.076)|(.074)|(.072)|
|Meals per day for HH members over 5|.1|.235|.227|.223|
||(.026)|(.062)|(.06)|(.06)|
|HH expenditure on sorghum (including|.118|.087|.072|.072|
|sorghum flour) in last 7 days (SDG)|(.029)|(.032)|(.028)|(.028)|
|HH expenditure on sugar or high-sugar|.145|.27|.212|.209|
|products in last 7 days (SDG)|(.031)|(.067)|(.058)|(.058)|
|HH expenditure on matches in last 30 days|.071|.15|.115|.081|
|(SDG)|(.024)|(.046)|(.039)|(.03)|
|HH expenditure on laundry detergent in last|.118|.23|.221|.203|
|30 days (SDG)|(.028)|(.061)|(.059)|(.056)|
|Total mean ICC<sup>7</sup>|.099|.161|.137|.128|
|Easier questions, average ICC|.068|.027|.017|.018|
|More difficult question, average ICC|.174|.101|.032|.022|
|Most difficult questions, average ICC|.097|.207|.186|.174|

# _Design effects_

We use these ICCs to calculate interviewer design effects using an average interviewer workload of 46.5 interviews. To calculate interviewer design effects, we use the ICC results from Specification II. Using the design effect formula from Section 1, we find a mean design effect of 7.2. In other words, clustering of responses by interviewers results in survey data with average variances that are 7.2 times higher that they would be without interviewer effects. Design effects range from 1.6 for number of people in the household to 16.2 for hours per day worked in primary occupation by the household head. By comparison, there were 10 interviews per area cluster which together with the mean ICC of 0.099 results in a mean design effect of 1.9.

The design effects found here are high compared to O’Muircheartaigh and Campanelli (1998) who found a mean interviewer design effect of 1.8 with a high of 5. However, this was based on a smaller survey cluster size of around 24 respondents per interviewer and the highest ICC

> 7 Calculated as the arithmetic mean of the ten ICCs.

9