rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Consolidated GasW Govunment Revmnues, 19U/1"3

||||AtE<br>|m<br><br>|I.**--**-<br>|||**..-.-..**<br>|**.---**<br>||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**93**|**199**|**M99**|**O 299**|**92**|**299**|**19U**|**1939**|1990|**1991**|**2992**|**299**|
||**-------**<br>|**--**<br>**4U**<br>|**2inof**<br>||**-**<br>|||.....<br>|**--.**<br>**(S**<br>|f<br><br>|**OD)......**<br>||
|Tel It_vs|22.1|**25.0**|24.0|54.0|77.8|112.2|**37.5**|57.9|**52.3**|41.9|36.7|32.1|
|Tax**R1v**|**12.2**|**19.5**|19.2|**30.3**|**64.7**|**95.0**|47.4|49.3|42.4|33.1|31.7|27.7|
|**Pm& Ta**|**3.1**|**9.2**|**l,1.**|**23.0**|**14.3**|12.1|**21.2**|23.2|17.9|17.4|**l.0**|**3.2**|
|**mams**Tax|**L.5**|**1.4**|2.0|**5.0**|10.9|15.7|4.0|4.1|4.2|**3.3**|**5.2**|**_4.S_**|
|Twwuvuw<br>Tax<br>_ Exeio|4.4|**4.4**|4.1|**9.7**|12.3|21.9|11.6|11.2|9.0|7.4|5.9|6.3|
|**CUSem**D|**0.**|**0.3**|**0.4**|1.5|4.0|**9.9**|**0.3**|**0.3**|**1.0**|1.2|1.9|**2.3**|
|**Cc.hib_lm**|**3.6**|**3.3**|4.4|11.4|**13.5**|23.4|**9.5**|**9.6**|**9.6**|*.7|**3.3**|**5.1**|
|06"|0.1|0.1|**0.3**|**0.4**|4.1|2.0|**0.3**|0.3|0.7|**0.**|1.9|0.6|
|Nax<br>**Ravmu**|4.0|**3.4**|**4.7**|4.0|12.3|17.2|10.2|**3.6**|10.4|3.1|5.9|**4.9**|
|Mon|||||||||||||
|ODPg**tma4g**<br>pim*|8.4|39.9|454|111.0|210.0|3500|||||||

Sew.: Bu2,: **Publk Fae Refo im h** Trsis (194), Woi flask Wuig D.C., cp two.

enterprises by directly paying workers a net-of-tax wage, with the implicit income tax being transferred to the enterprise surpluses, or alternatively, the subsidy to the enterprise could be reduced. 12

# 4. CHANGES IN THE TAX BASE IN THE TRANSITION

Table 1 also demonstrates the changing pattern of reNsnues in the early years of transition. These changes reflect the influences of (i) the decline in state enterprise output and

> 12/ The tax instruments under socialism were not used for the typical fiscal policy purposes as in a market economy: see Mario I. Blejer and Gyorgy Szapary (1991).
- **Jametti, Mario and Thomas von Ungern-Sternberg** , “Risk selection in natural-disaster insurance,” _Journal of Institutional and Theoretical Economics JITE_ , 2010, _166_ (2), 344– 364.

- **Kunreuther, Howard and Mark Pauly** , “Rules rather than discretion: Lessons from Hurricane Katrina,” _Journal of Risk and Uncertainty_ , 2006, _33_ (1-2), 101–116.

- **Leeson, Peter T and Russell S Sobel** , “Weathering corruption,” _Journal of Law and Economics_ , 2008, _51_ (4), 667–681.

- **Lindbeck, Assar and Jörgen W Weibull** , “Altruism and time consistency: the economics of fait accompli,” _The Journal of Political Economy_ , 1988, pp. 1165–1182.

- **Mahul, Olivier, José Ángel Villalobos, and Hannah Yi** , “MexicoâĂŹs Indemnity-Based Insurance Against Natural Disasters,” Disaster financing and insurance case study, World Bank 2013.

- **Martimort, David** , “An Agency Perspective on the Costs and Benefits of Privatization,” _Journal of Regulatory Economics_ , July 2006, _30_ (1), 5–44.

- **OCHA** , “Leaving no one behind: Humanitarian effectiveness in the age of sustainable development goals,” Technical Report, OCHA (Office for Coordination of Humanitarian Affairs) Policy and Studies Series, United Nations 2016.

- **ODI** , “ Doing cash differently. How cash transfers can transform humanitarian aid. Report of the High Level Panel on Humanitarian Cash Transfers.,” Technical Report, ODI (Overseas Development Institute, London 2015.

- **Picard, Pierre** , “Natural Disaster Insurance and the Equity-Efficiency Trade-Off,” _Journal of Risk and Insurance_ , 2008, _75_ (1), 17–38.

- **Shavell, Steven** , “A general rationale for a governmental role in the relief of large risks,” _Journal of Risk and Uncertainty_ , 2014, _49_ (3), 213–234.

- **Steinwand, Martin C** , “Compete or coordinate? Aid fragmentation and lead donorship,” _International Organization_ , 2015, _69_ (02), 443–472.

- **Svensson, Jakob** , “When is foreign aid policy credible? Aid dependence and conditionality,” _Journal of development economics_ , 2000, _61_ (1), 61–84.

24
and a systematic sampling rule used to select the households to be interviewed. We administered a screening questionnaire to determine whether the household contained a person between the ages of 18 and 65 who was employed and commuted regularly to his or her place of work. We also required respondents to have at least an 8<sup>th</sup> grade education, in order to understand the risk information provided in the survey, and to have resided in Delhi for at least three months. The questionnaire was administered to 1,200 respondents during October – December of 2005, following two pretests involving 601 households.

# **IV.  Sample Characteristics and Responses**

# **A.  Individual Characteristics of the Respondents**

Descriptive statistics of the sample respondents are displayed in table 2. The top panel of table 2 reports information about demographic and socioeconomic characteristics of the sample. Briefly, we note that the average age of the respondent is 35 and that 95% of the respondents are male. The high proportion of males reflects the fact that only 15% of women in Delhi work outside the home (National Sample Survey, 2005). About 48% of respondents have a high school diploma or vocational degree, and 28% a bachelor’s degree or better. Mean household income is Rs. 135,000 a year, or approximately $15,350 in purchasing power parity terms. Mean earnings (personal income) are approximately $10,250 in PPP terms.

As shown in the table, we place our respondents in three income groups, depending on personal income. Individuals earning less than Rs. 8,000 a month constitute our low-income group, which accounts for 44% of the sample. Individuals with monthly

> 8 Pretests of the questionnaire revealed that changes in the bids on the payment card did not have a

- 10 -
|_Table 1: Benefit-cost ratios f_|_or retrofitting bu_|_ildings_||
|---|---|---|---|
||Base Case<br>Value|B/C if costs/rates<br>halved,<br>benefits/time<br>doubled<br>(sequential)|B/C if costs/rates<br>doubled,<br>benefits/time<br>halved<br>(sequential)|
|Retrofit cost|$80,000|2.0|0.5|
|Probability of earthquake|2%|2.1|0.5|
|Chance of collapse|10%|2.1|0.5|
|Deaths in collapse|10|2.0|0.6|
|Value of statistical life|$250,000|2.0|0.6|
|Discount rate|5%|1.4|0.6|
|Years accounted|30|1.3|0.7|

29
suggest that “most important for the prospects of a nascent insurgency are the government’s police and military capabilities and the reach of government institutions into rural areas.”<sup>10</sup> Military spending would be an important aspect of state capacity to pacify violent conflict via coercion and policing (Hendrix, 2010).

In consolidated democracies, where there is significant oversight over the budget, increasing military expenditure provides public goods for citizens in the form of domestic and international security. In mixed regimes and autocracies, on the other hand, military spending is primarily used for repression, patronage to regime loyalists, or direct rents to the military to prevent possible coup attempts (Collier and Hoeffler, 2007).

In this regard, Herbert et al. (2013) point out that the emergence of rebel groups in the Central African Republic partly stems from the state’s inability to protect its citizens from violence and poverty. The state capacity and authority in many core state functions is too weak. Along the same lines, Hegre and Sambanis (2006) show that the size of military personnel tends to be negatively and robustly correlated with civil war onset.

The opposite could also be suggested, however. Increasing military spending could provide potential military opposition with the needed equipment to overthrow a regime. Larger military budgets may not generate efficiency and loyalty, but may be a result of leaders bribing the military to keep them from staging a coup (Belkin and Schofer, 2003; Acemoglu et al., 2009; Keefer, 2010). Many countries at risk of civil war face other risks (Bodea and Elbadawi 2007; Svolik, 2009; Roessler 2011). Stacking the military with loyalists or ethnic group members, by shuffling, arresting or executing high ranking officers, or by creating multiple and overlapping units suspicious of each other could undermine the quality of the military forces and weaken further vulnerable regimes.

Furthermore, Henderson and Singer (2000) argue that because higher military expenditure crowds out social spending, economic growth and investment, subsequent citizen grievance is more likely to be linked with insurgency and thus fuel violent conflict. Collier and Hoeffler (2007) find that military spending is at best ineffective in reducing the risk of internal conflict. Similarly, the recent work by Taydas and Peksen (2012) find no effect of military spending on civil war onset.

> 10 Previous studies have mainly focused on the duration of violent conflict, relying on military personnel per capita taken from the Correlated of War (COW) Material Capabilities dataset (Hendricks 2010). A key argument is that because the strong military can defeat rebel groups more easily, it contributes to shortening the duration of conflict (Mason and Fett, 1996; Mason et al. 1999; DeRouen and Sobek, 2004). There is also evidence to the contrary (Balch-Lindsey and Enterline, 2000).

6
<!-- Start of picture text --> 4<br>3<br>> Gender<br>e 2 Men<br>fo)<br>a) Women<br>1<br>0<br>0.00 0.25 0.50 0.75 1.00<br>FHS (Married Respondents)<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>3<br>> Gender<br>e 2 Men<br>fo)<br>(a) Women<br>1<br>0<br>0.00 0.25 0.50 0.75 1.00<br>FHS (Single Respondents)<br><!-- End of picture text -->
12 adjustments, changes in the inflation rate associated with changes in government create temporary deviations of real economic activity'from its natural level.

More specifically, the following testable implications can be derived from the model: (i) at the beginning of a right wing (left wing) govemment output growth is below (above) its natural level and unemployment is above (below); (ii) after exr .- ations, pri;s _Z-d_ wages adjust, output and unemployment return to their natural level; after this adjustment period, which should last for no more than a couple of years, the level of economic activity should be independent of the party in office; (iii) the rate of inflation should remain higher -oughout the term of a left wing government. That is, the time consistent (but sub-optimn inflation rate remains higher for left wing parties even after the level of economic activity returns to its natural level beca ise of a "credibility' problem. The public knows that the left has a strong incentive to follow expansionary policies to reduce unemployment. Thus expected inflation is high when the left is in office. In particular, because of rtional expectations, after the inidal adjustment to the new regime expected inflation is high enough so that the government does not have an incentive to inflate more. Thus, actual inflation is equal to expected inflation and unemployment is at its natural level. See Persson and Tabellini (1990) for a recent survey of these 'credibility" models.

In summary, this "rational" model differs from the tradLi nal "partisan" one because it emphaes how differences in growth and unemployment associated to changes of government are only temporary. For example, a left wing or a "populist" government, strongly committed to reducing unemployment by means of expansionary aggregate demand policies is bound to "succeed" only in the short run. After a brief period in which unemployment may actually fall,
# **Table A8: Family labor**

||**Labor**|**Productivity**|**(logs)**|
|---|---|---|---|
||**coef/se**|**coef/se**|**coef/se**|
|Total duration of water shortages last month (in days)|-0.025**|-0.024**|-0.028***|
||(0.010)|(0.011)|(0.010)|
|Family labor Y/N|-0.134|||
||(0.112)|||
|No. of family labor||0.004||
|||(0.046)||
|Share of family labor|||-0.095|
||||(0.143)|
|Log of total number of employees in regular month|-0.513***|-0.547***|-0.540***|
||(0.125)|(0.130)|(0.113)|
|Largest owner has secondary education or higher: Y/N|0.280**|0.288**|0.273**|
||(0.131)|(0.132)|(0.137)|
|Manager experience (years)|0.015**|0.015**|0.015**|
||(0.007)|(0.007)|(0.007)|
|Largest owner has a job in formal sector or looked for one over last 2 years Y/N|0.101|0.105|0.105|
||(0.150)|(0.154)|(0.152)|
|Largest owner started another business in the last three years Y/N|-0.167**|-0.180**|-0.167**|
||(0.068)|(0.072)|(0.067)|
|Firm uses electricity for activity Y/N|0.090|0.085|0.092|
||(0.080)|(0.080)|(0.078)|
|Firm uses machinery in its current operations Y/N|0.036|0.041|0.043|
||(0.085)|(0.087)|(0.085)|
|Firm uses vehicles in its current operations Y/N|0.390***|0.385***|0.383***|
||(0.086)|(0.086)|(0.087)|
|Manufacturing firm Y/N|0.101|0.100|0.094|
||(0.088)|(0.089)|(0.089)|
|Produces or sells under contract from another business or person Y/N|0.341***|0.334***|0.337***|
||(0.104)|(0.105)|(0.107)|
|Firm uses bank account to run business Y/N|0.183|0.189|0.193|
||(0.120)|(0.123)|(0.122)|
|Firm has a loan at this time Y/N|0.005|-0.012|0.000|
||(0.067)|(0.068)|(0.072)|
|Firm pays informal payment to remain unregistered Y/N|-0.104|-0.091|-0.098|

45
32

# **IV.** OECD Nontariff Barriers Facing Africa

Although the previous analysis showed Africa's generally poor export performance could not be attributed to OECD tariffs, the possibility still exists that protectionism had an influence. Aside from import duties, OECD countries employ nontariff restrictions like quotas, variable levies, "voluntary" export restraints, and restrictive licensing arrangements to control imports. Any assessment of the influence of protectionism facing Africa must also examine the potential influence of these measures. Furthermore, since the Uruguay Round made major changes in the ability of GATT members to utilize many of these restrictions one should analyze the incidence of NTBs facing Africa both before, and after, the conclusion of the negotiations.

## A. Pre-Uruguay Round NTBs Facing Africa

What does available information indicate conceming the nature and extent of OECD countries' nontariff barriers on Africa and other groups of countries? Utilizing World Bank-UNCTAD records, Table 7 shows the share of OECD imports from: (i) other OECD countries, (ii) developing countries, and (iii) all sub-Saharan African countries that encounter NTBs.'<sup>5</sup> The latter is reported both with the Republic of South Africa included and excluded due to the relatively high share of manufactures in South African exports, and the fact that this country faced sanctions as a result of its apartheid policies.

As Table 7 shows developed countries' nontariff measures affect a notably higher share of imports from non-OECD countries than they do for OECD intra-trade. Approximately 17 percent of developing counties' exports (excluding petroleum) encounter NTBs, while the corresponding share for OECD intra-trade is under 10 percent. An even greater difference in NTB coverage ratios exists for several product groups. Approximately 53 percent '<sup>5</sup> Laird and Yeats (1990, Chapter 4) describe how this inventory of nontariff measures was constructed and discuss its utility for research and policy studies. In particular, they note that trade coverage ratios are a rough approximation of the importance of NTBs in that they provide no indication of the restrictiveness of the measures. Low coverage ratios, for example, could be associated with highly restrictive NTBs. Laird and Yeats also provide extensive empirical information on the results of NTB inventory studies for industrial countries. UNCTAD (1993, p. 37) tabulates the annual share of developing countries' exports that encountered nontariff measures over the last decade -- it rose from 16.2 percent in 1981 to 18.3 percent in 1991.
<!-- Start of picture text --> Figure 111.11<br>Return on Average Equity _t_t____M_g<br>' 1991 o :N.M, __ _ __<br>25<br>2D-s '- 6<br>2 0 a.<br>15<br>0 0<br>*1994isusedinslc.doef995<br><!-- End of picture text -->

- **Increased Exposure to Risky Sectors.** Real estate lending was high and the banking sector exposure to real estate was greater in countries where the growth of credit was larger than proportional to GDP growth (Figure 111.12). It should be noted that data on real estate lending are not comparable across countries and in several countries likely underestimate the exposure of the banking system to the real estate sector (e.g., as loans to developers are not classified as lending for real estate). But, there were significant differences among countries. Korean banks, for example, did not have large property exposure. Korean banks did, however, increase the share of bonds and other securities in their portfolios to almost 20% (in addition, Korean banks extended large amounts of guarantees on securities issued by corporates). Except for the Philippines, countries increased their exposure to bonds and other securities (see figure 111. 12).

**Figure 111.12**

<!-- Start of picture text --> EKposr of the banidng sator In the property sotr<br>* wty se'ar - ifi s<br>1s 18<br>16 16<br>14 ~12 1<br>12<br>10 ~~~~~~~~~~~~~~~~~~~~~~~10<br>2~~~~~~~~~~~~~~~~~~~<br>4 ~~~~~~~~~~~~~~~~~~0<br>2<br>0 ~ . -u<br>239<br><!-- End of picture text --> Source: Goldman-Sachs, _Banking Research,_ Se'ptember 1997.

39
Table 1: Summary of RAIS matching (2019).

|Matched frm<br>|s<br>||
|---|---|---|
|Firms with a valid tax id|1,302|100%|
|Matched|1,067|82%|
|Not matched|235|18%|
|Matched frms by establi|shment|type|
|Public|129|12%|
|Mixed|238|22%|
|Participated|377|35%|
|Zero employment|323|30%|
|Total matched|1,067|100%|

_Note:_ This table summarizes the number of BOS identified through Orbis that we were able to locate in RAIS, using firm identifiers. In the lower panel, the table illustrates the legal nature information in RAIS of firms successfully located in RAIS, as well as the number of firms which are registered as having zero employment during the whole year of 2019. Data from RAIS and Orbis, 2019.

Table 2: Number of firms by year.

|Year|Firms|POEs|BOS|Public|Mixed|Participated|
|---|---|---|---|---|---|---|
|2016|2,531,848|2,529,989|1,859|910|583|366|
|2017|2,518,208|2,516,404|1,804|877|559|368|
|2018|2,501,936|2,500,773|1,163|389|408|366|
|2019|2,494,135|2,493,215|920|243|291|386|
|2020|2,464,407|2,463,514|893|233|290|370|

_Note:_ This table illustrates the total number of firms, including the distinction between private firms and BOS, in the sample. Data from RAIS, from 2016 to 2020.

Table 3: Sector taxonomy, 2020.

||Number of frms|(%)|Number of BOS|
|---|---|---|---|
|Competitive|2,234,377|90.67|521|
|Partially contestable|34,228|1.39|163|
|Natural monopoly|13,310|0.54|190|
|Excluded|182,492|7.41|19|

_Note:_ Data from RAIS, 2020.

7
(Hakobyan, 2020), the difference is striking—the impact of AGOA suspension on exports is 10-fold larger than that of GSP expiration. This could be partly explained by the longer duration of AGOA suspensions on average, and a larger set of goods eligible for AGOA that Sub-Saharan African countries have comparative advantage in, such as textiles and apparel. At the extensive margin, we find that the probability of exporting AGOA eligible product to the US decreased by 2.1 percentage points. The impact of AGOA suspension was the largest in textiles and apparel products. We find that exports of textiles and clothing products and the probability of exporting these products declined by 88 and 9.4 percentage points, respectively. Furthermore, we find that AGOA suspension had a disproportionate impact on countries with a high pre-suspension utilization rate. The withdrawal of AGOA benefits induces a 65 percent drop in exports with pre-suspension utilization rate above 30 percent (statistically significant at the 5 percent level ).

This study is closely related to the existing literature on the impact of termination of preferential trade agreements. Using Congress’s failure to renew the GSP in 2011, Hakobyan (2020) shows that the revocation of the GSP tariff exemptions resulted in a considerable drop in exports, by about 3 percent on average, with exports of textiles and apparel dropping by about 9 percent. This drop in exports is increasing in the MFN tariff rates and decreasing in the size of exports, with smaller countries experiencing the biggest export declines. Since the 2011 GSP suspension was short in duration (only 10 months), the paper utilizes only three years of data, so it is not clear whether the loss of preferential treatment resulted in a permanent drop in exports. Hakobyan (2017) provides a systematic analysis of the effect of the Competitive Needs Limit (CNL)—suspension of 4
<!-- Start of picture text --> The Economic Effects of Minimum Inport<br>Prices with an AppUcation to Ulruguay'<br>by<br>Federico Changanaquf<br>and<br>Patrick A. Messerlin<br>Table of Chntents<br>1. Introduction<br>2. Theoretical Discussion of the Resource and Welfare Consequences of Minimum Prices<br>2.1 Definitions and background.<br>2.2 Welfare costs of reference and minimum export prices.<br>2.3 Minimum prices' quality-related impact on imports and domestic specialization.<br>a. The competitive case:<br>b. The non-competitive case:<br>c. The welfare effects:<br>3. Estimates of the Extra Protection provided by the Minimum Price Systems in Uruguay<br>3.1 Coverage.<br>3.2 Protection provided by the RP and MEP systems.<br>a. The protection augmentation effect:<br>b. The liberalization erosion effect:<br>c. The price distortion effect:<br>3.3 The effects of reference prices upon Uuguayan textiles producers.<br>3.4 Argentine and Brazilian imports not valid rationale.<br>4. Conclusions<br>Notes<br>References<br>'he authon would like to dhank Bela BaIlu, Michael Connolly. lsime de Mto, Elbi Naino, and Wendy Taas for their<br>vaiy useful comments.<br><!-- End of picture text -->