rafmacalaba/datause-encoder-data huggingface dataset:
unique choice profiles<sup>4</sup> (Kuhfeld, 2010; Vermeulen et al. 2008). Carlsson and Martinsson (2008) investigate the effect of the number of choice sets and the design of the first choice set as context dependence in a choice experiment, and they found no significant impact on estimated marginal willingness to pay. We created a block design where each of the surveys were separated into blocks of six choice profiles, giving fourteen unique REDD+ surveys with six questions each. Carlsson et al. (2010) test for learning and ordering effects in CE surveys and show that dropping the first choice question can decrease the error variance of estimates. We follow this suggestion and add an additional choice question before the six choice questions in each survey and drop the first choice question when conducting the analyses to account for possible learning effects. In order to account for possible ordering effects we reversed the order of the questions in half the surveys and obtained 28 unique versions of the REDD+ survey.

# **_Model and Estimation_**

The standard conditional logit model (CL), which has been used for analyzing discrete choice models for many years, assumes that the respondents have homogeneous preferences (estimate a mean value for the sample for each attribute). To account for heterogeneity of preferences we also use a mixed multinomial logit model (MMNL)<sup>5</sup> that estimates both a mean and a standard deviation for each attribute ( Carlsson _et al_ . 2003; Hensher and Greene, 2003; Train, 2003, Hensher _et al_ . 2005; Dissanayake 2014). Assuming a linear utility, the utility gained by person _q_ from alternative _i_ in choice situation _t_ is given by where _X qit_ is a vector of explanatory variables. Following standard practice for logit models we assume that ε _qit_ is independently and identically distributed extreme value type I. We assume the density of β _q_ is given by _f_ ( β | Ω ) where the true parameter of the distribution is given by Ω .

10
a surplus of 9.6 percent of GDP in 2010–13 to a deficit of 14.3 percent in 2015–16) and Equatorial Guinea (where the deficit widened from 3.9 percent of GDP in 2010–13 to 17.4 percent in 2015– 16). Other notable countries with a large primary deficit in 2015–16 are Niger (7.1 percent of GDP) and Botswana (11.5 percent of GDP). In contrast, Ghana, the Central African Republic, and Cabo Verde experienced an important reduction in their primary deficits. The primary deficit in Ghana was cut from 6.7 percent of GDP in 2010–13 to 0.4 percent in 2015–16, that is, a reduction of 6.3 percentage points of GDP. The primary balance of the Central African Republic shifted from a deficit of 1.8 percent of GDP in 2010–13 to a surplus of 1.1 percent in 2015–16.

**Figure 5. Primary Balance Across Sub-Saharan African Countries, 2010-13 vs. 2015-16**

_(Percent of GDP)_ <!-- Start of picture text --> 10<br>5<br>SYC<br>CAF<br>GHA ZAF 0 COD<br>‐10 ‐5 UGA 0SDNTCDMUS MRT GAB 5 AGO COM 10 15<br>CPV GINSENTZABFALSOGMB GNBCMRMLIMDGCIVETHRWANGAMWI<br>SLE<br>MOZ KENBDI ZMB ‐5 ZWE<br>NAM BEN<br>TGOLBR NERSWZ<br>‐10<br>BWA<br>‐15<br>Primary Balance (% GDP), 2010‐13<br>Primary Balance (% GDP), 2015‐16<br><!-- End of picture text --> _Notes: Red dots represent resource rich countries while blue dots represent non-resource rich countries. Calculations based on data from Kose et al. (2017)_ In most countries in the Africa region (36 of 44), the public debt burden increased in 2015–16 compared with 2010–13 (figure 6). The median increase in general government gross debt was about 14.9 percentage points. The largest increases from 2010–13 to 2015–16 were in Mozambique (from 44 to 102 percent of GDP), Cabo Verde (from 86 to 131 percent of GDP), and 16
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- 12 -

beef every year. Swaziland produces 44,000 head or 12,000 tons of beef. In these countries, the cattle industry is not only the center of the rural economy but also an important export earner. Kenya is one of the largest beef producers in Africa. The livestock sector contributes to about 1 percent of GDP, despite the large number of processed cattle averaging over 2 million head or 320,000 tons per annum.

Figure 4. Beef Production in Africa, 2004 (In thousand tons) <!-- Start of picture text --> Zimbabwe Cameroon Angola Namibia Chad<br><!-- End of picture text --> <!-- Start of picture text --> Mozambique Niger Guinea Botswana Ghana Mauritania<br><!-- End of picture text --> <!-- Start of picture text --> Rwanda Benin Eritrea Malawi<br><!-- End of picture text --> Source: _FAOSTAT_ .

In terms of exports to the EU market, following Botswana, all beneficiaries from the beef protocol but Kenya, have exported the majority of African beef and products to the European market. Exports from other Sub-Saharan African countries remain very limited.<sup>20</sup> Beef export prices differ significantly across countries, possibly due to horizontal differentiation and different food safety standards. While the average export price is 2.1 euro per kilogram for Botswana, some other exporters have much higher or lower prices ranging from 0.5 to 4.7 euro per kilogram.<sup>21</sup> 20 Provided that the current preferences were removed, large cattle producers in the region, such as Ethiopia, Kenya and Sudan, would have a good potential for exporting beef to the EU even without the preferences. There are several countries that have significantly increased beef production in recent years, such as Angola, Cameroon and Chad. They may also foster the cattle industry as a strategic export sector.

21 This implies that beef and veal are somewhat product-differentiated. A certain degree of product differentiation is methodologically important to construct our empirical model. See Section III.
Table A.8. Citizens’perception of changes in the quality of health care over the last year

|Dependent<br>variable|Overall quality|Sta¤ politeness|Availability of<br>medical sta¤|
|---|---|---|---|
|Speci...cation|(1)|(2)|(3)|
|Program impact|0.09**|0.08**|0.09***|
||(0.04)|(0.03)|(0.03)|
|Mean in control group|0.47|0.53|0.51|
|Controls|Yes|Yes|Yes|
|District ...xed e¤ects|Yes|Yes|Yes|
|Observations|3343|3343|3343|
|R<sup>2</sup>|0.09|0.05|0.06|

a. *** [**] (*) denote signi...cance at the 1 [5] (10) percent level. b. Dependent variable in speci...cations: (1) Dummy variable indicating improvement in overall quality; (2) Dummy variable indicating improvement in sta¤ politeness; (3) Dummy variable indicating improvement in availability of medical sta¤.

c. Robust standard errors in parenthesis. Disturbance terms are clustered within catchment areas. d. Control variables include: Distance to nearest local council from the health facility, distance to other government health facilities in the area and electricity at the health facility.

As of 2001, public health services are free of charge. However, the survey evidence indicates that patients still encounter varying costs, although a large majority of patients do not pay (informal) user fees. In the pre-treatment data, 7 percent of the households surveyed reported having to pay user charges for outpatient services; approximately 15 percent had to pay for injections (when needed); and 67 percent paid for delivery.<sup>51</sup> In table A.9, we report the program impact on these informal charges. The intervention had no signi...cant e¤ect on the share of households that needed to pay for drugs (regression 1) or delivery (regression 4). However, it had an impact on general outpatient services (regression 2) as well as on injections (regression 3).

felt he/she was free to express herself when being examined.

> 51Average payment (for those that had to pay) was UGX 1,435 (USD 0.80) for out-patient service, UGX 370 (USD 0.21) for injections, and UGX 4,955 (USD 2.75) for delivery.

39
interpretation are very different. Their activities are instead allocated to different types of entities – for example, banks are typically responsible for trust and custody services<sup>33</sup> , specialized fund management companies are responsible for collective investment schemes, while some other activities (e.g. payment of public pensions) are not normally associated with financial institutions. However, as can be seen in the Figure below, fiduciarias have proved quite successful in Colombia, potentially because they are perceived as a relatively efficient response to contract enforcement problems and general distrust concerns.

Figure 5: Evolution of Fiduciarias by Asset Type (1996-2005) <!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Inversion Inmobiliarios Administracion Fiducia en Garantia Other Social Security<br>% of GDP<br><!-- End of picture text --> _Source: Superintendencia Financiera, World Bank, own analysis._

**This recommendation would allow market choice and greater efficiencies, but conflicts of interest should be addressed via a stronger prudential framework** . The proposed reform would permit banks to choose whether to offer fiduciary activities directly, while it would allow niche players to continue operating in this market via fiduciarias. Assuming that some fiduciarias currently owned by financial groups are subsumed into the group bank, the main benefit of this reform would be greater operational efficiencies (i.e. similar to the leasing recommendation); these have to be weighed against the good experience and innovative products of stand-alone fiduciarias in recent years, although the final decision should be voluntary/market-driven and not imposed by regulation. Smaller banks might also decide to enter this market and provide greater competition by offering fiduciary services in-house. These benefits on balance likely outweigh the risks of additional conflicts of interest and explicitly extending the bank safety net to cover fiduciary activities. In particular, conflicts of interest primarily apply to collective investment schemes that are not part of this proposal. Some conflicts of interest that already exist<sup>34</sup> could be exacerbated and would need to be addressed via a

> 33 Mexico allows banks that are unrelated to the loan originating bank to manage the ‘fiducia en garantía’. 34 Potential conflicts of interest are apparent in the transactions between the fiduciaria and the rest of the financial conglomerate. For example, the fiduciaria typically manages the securitization vehicle and the

25
homogeneity between the different survivor functions. Survival rates are lowest in SubSaharan Africa. It is interesting to note that export flow survival rates for East Asia and the Pacific, the most successful developing region of the past 2 to 3 decades, are similar to those of the high-income OECD countries.

The evidence in this section is suggestive in nature. We now proceed to a deeper investigation of the factors that may shorten or enhance the life of an export flow and that may explain differences in survival rates. We are particularly interested in why export flows in low-income countries have substantially lower survival rates than those for middle and high-income countries? We start by reviewing the available literature on the determinants of the duration of export flows before proceeding to an econometric analysis of our trade flow data.

# **3. The Literature on the Determinants of Export Survival**

International trade theory has little to say about the duration of trade flows. Recent developments in trade theory have been focused on heterogeneous firms and entry into exporting and how reductions in trade costs shift resources away from lower productivity firms towards higher productivity exporters. There has been little attention as to why after entering firms may, within a short period of time, cease exporting activity. Similarly, the literature on the export behavior of firms, based extensively on information from firm surveys, has tended to concentrate on differences between exporters and non-exporters and whether the observed higher productivity of exporters is a cause or result of entry into foreign markets. Much less is available on the evolution of exporters after they enter into trade activities (see Tybout (2003)), reflecting in part the lack of continuous surveys.

Hence we do not have recourse to a well established theory on export survival. There are however, a number of studies that identify issues that are pertinent as factors explaining why some export flows may not survive. Issues relating to the information and market knowledge needed for successful entry into exporting are likely to be important in explaining exit. If firms have less than perfect information about the fixed costs of exporting a product to a particular market or there is some uncertainty about the value of these costs, then firms with relatively low productivity that are marginal entrants into 8
<!-- Start of picture text --> AFR APD EAP EC ECA LCR MNA NAM SAR<br>| in i, | | || " ,|<br>-25<br>Scenario<br>£o a Zeroinvest<br>= Gi Invest<br>50 Oo Maxinvest<br>75<br>2030 2050 ©2030 2050 §92030 2050 92030 2050 92030 2050 §9=2030 2050 §=2030 2050 + 2030 2050 2030 2050<br><!-- End of picture text -->
|**Table 20**|**. Welfar**<br>National<br>_change fro_|**e Impact a**<br>Northwest<br>_m base year,_|**s % of In**<br>Central<br>_%_|**come**<br>South|Volga|Urals|Siberia|Far East|
|---|---|---|---|---|---|---|---|---|
|Average|4.5|5.6|4.4|4.5|4.9|3.9|3.8|5.1|
|10|4.1|5.5|4.0|4.0|4.0|3.5|3.5|4.8|
|20|4.2|5.4|4.0|4.2|4.3|3.6|3.8|4.0|
|30|4.2|5.4|4.0|4.2|4.2|3.6|3.6|4.8|
|40|4.3|5.3|4.2|4.1|4.4|3.6|3.7|4.8|
|50|4.3|5.6|4.2|4.3|4.5|3.5|3.7|4.7|
|60|4.4|5.4|4.4|4.3|4.4|3.6|3.6|4.5|
|70|4.4|5.4|4.5|4.4|4.4|3.6|3.6|4.6|
|80|4.4|5.5|4.3|4.3|4.7|3.6|3.8|4.9|
|90|4.4|5.2|4.4|4.4|4.6|3.7|3.7|4.9|
|100<br>Source: Au|4.6<br>thors' calcu|6.1<br>lations.|4.4|4.8|5.3|4.1|3.8|5.7|

63
for the self-employed. This may be due to wealthier households being more connected to government networks and having savings, but the differences are not statistically significant.

**Table 8. Which coping mechanisms were used by whom?**

||||Househol<br>|d Heads<br>||Househo<br>|ld Business<br>||
|---|---|---|---|---|---|---|---|---|
|Sample:|Sold<br>assets|Any<br>assistance|Public<br>assistance|Private<br>assistance|Sold<br>assets|Any<br>assistance|Public<br>assistance|Private<br>assistance|
|Farm|-0.017|0.031|0.009|-0.007|0.034|0.015|-0.008|0.008|
|business|[0.031]|[0.033]|[0.030]|[0.009]|[0.018]*|[0.032]|[0.032]|[0.007]|
|Nonfarm|-0.009|-0.002|-0.003|0.001|-0.006|-0.080|-0.035|-0.008|
|business|[0.030]|[0.029]|[0.018]|[0.011]|[0.021]|[0.032]**|[0.027]|[0.007]|
|$2.15 to|0.077|-0.030|-0.008|-0.003|0.080|0.025|0.000|-0.002|
|$3.65|[0.048]|[0.031]|[0.031]|[0.017]|[0.053]|[0.045]|[0.026]|[0.014]|
|$3.65 to|-0.071|0.021|0.044|-0.008|-0.055|0.079|0.031|-0.003|
|$6.85|[0.052]|[0.034]|[0.036]|[0.020]|[0.050]|[0.058]|[0.026]|[0.017]|
|Above|-0.049|0.104|0.095|0.012|-0.032|0.123|0.065|0.009|
|$6.85|[0.054]|[0.070]|[0.068]|[0.023]|[0.044]|[0.068]*|[0.047]|[0.021]|
|Male|0.023|-0.044|0.019|-0.009|||||
||[0.026]|[0.045]|[0.032]|[0.009]|||||
|Adult|0.023|0.099|0.033|0.022|||||
||[0.035]|[0.046]**|[0.028]|[0.014]|||||
|Constant|0.037|0.030|-0.139|0.101|0.048|0.027|-0.056|0.093|
|||[0.081]|[0.102]|[0.025]***|[0.048]|[0.050]|[0.037]|[0.015]***|
|Obs.|685|1,727|1,098|1,553|827|2,256|1,321|1,879|
|R-squared|0.261|0.258|0.152|0.220|0.22|0.218|0.141|0.226|
||ETH,|ETH, MWI,|ETH, MWI,<br>|ETH,<br>|ETH,|ETH,<br>MWI,|ETH, MWI,|ETH,<br>MWI,|
|Countries|NGA|NGA|NGA,<br>SLE|MWI,<br>NGA|NGA|NGA,<br>SLE,ZWI|NGA, ZWE|NGA,<br>ZWE|

Source: HFPS-Wave 1.

Notes: Urban observations only. Adult defined as 25 years old or more. Omitted categories: Below 2.15 USD-a-day, female, youth. Robust standard errors clustered at the subnational geographic units between brackets. *** significant at 1%, ** 5%, * 10%.

_What is the correlation between the use of coping strategies and declines in enterprise income?_ To directly test if coping strategies were adopted in response to a decline in household enterprise earnings, Table 9 presents how the probabilities of reporting different coping strategies differed for enterprise-owning households who reported farm or non-farm income declines from those that did not experience an income loss. Only households with enterprises are included in the sample. As in the previous sub-section, country surveys included different coping strategies, which affects the sample for each estimate.

20
44 PHN 1991 Sector Review Figure 2: Tot-l Project Resources for Nutrition in Bank-Asisted Operations, FYS1-91 <!-- Start of picture text --> US$ millions<br>500<br>450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50 _<br>81 82 83 84 85 86 87 88 89 90 91<br>Fiscal Year<br><!-- End of picture text --> Figure 3: The Bank's Nutrition Lending, FYS1-91 <!-- Start of picture text --> US$ millions<br>300<br>250<br>200<br>150,<br>100.<br>50<br>8 1 8 2 8 3 8 4 8 5 8 6 8 7 8 8 8 9 9 0 9 1<br>Fiscal Year<br><!-- End of picture text --> _Tese_ figues ar lasedo an sm_ent of the total moure allocated to nutrition In nutdtin prject and nutrition ouponentm of proects desipated as PHN, _bealth,_ or populatin prc$eca, and education, food security and WID projectL They do not, bemuse of dffcties of quantlfication, Iclude structurl adjusment or agricultue sector autment pojL (For lstng the latter, see lbbl 12.) 7btal prect m o allocated to nuaition were estimated from cost tables in Staff Appmbial Reports or, In case whee thse wre not suficekntly detaied, they we upplied by the pec' task mamg. Bank nutrition klndig amounts are baed an a pmrated share of Bank fnancing of _eU_ prject se. If tbe nkloan comprbi SO pecent of the to poect and 15 percent o the prect b nutrtion, then l5 percent at the 50 peent b attributed to nutrition
# **Figure 1 Estimates of Trade Costs in Industrialized Countries**

<!-- Start of picture text -->
- Freight costs<br>21 Transport costs  - Time value<br>Border related trade  - Policy   - Language   - Currency<br>44<br>barriers  - Information   - Security<br>Retail and wholesale<br>55<br>distribution cost<br>100%<br>Production<br>costs<br><!-- End of picture text -->

_Note_ : The breakdown of costs is expressed in ad-valorem equivalent terms: 1.7=1.21*1.44*1.55  1.

_Source_ : Estimates are drawn from Anderson and Van Wincoop (2004).

To illustrate the variability of trading costs across regional groups, Figure 2 shows the average costs of export and import procedures by group of countries presented in the World Bank’s (2008) Doing Business report.<sup>7</sup> Among the developing countries in the data set, those in Sub-Saharan Africa have the highest costs on average. The costs of import and export procedures in Africa are about twice as high as those in high-income OECD countries.

> 7 To ensure comparability across countries, these figures represent the official fees levied on a dry-cargo, 20-foot, full container load expressed in U.S. dollars and associated with completing the procedures to export or import the goods. Costs include the costs of documents, administrative fees for customs clearance and technical control, terminal handling charges, and inland transport, and exclude tariffs as well as other trade-related taxes.

5
cost of insolvency, the enforcement of priority of claims, the extent to which the efficient outcome is achieved, and the degree of court involvement in the process.

Rather than just summarize _de jure_ regulation _,_ our measures capture to a large extent its actual burden on economic agents. However, a caveat is necessary: our indices, as well as their components, do not provide full information about the quality of regulation; in particular, they do not reflect completely either the extent to which regulation corrects for market failures or the political and social context in which regulation is implemented. These distinctions are important because, as already noted, the economic impact of regulation may be dependent on institutional country characteristics.<sup>10</sup> We attempt to account for this issue in the econometric section of the paper.

# **Regulation around the world**

How do entry, growth and exit regulations vary across world regions? Although governments oversee and extract revenues from business activities in all countries --and quite heavily in many--, there is considerable variation in the intensity of regulation across regions. Table 1 presents the summary statistics of the seven indices mentioned above. Industrial countries tend to adopt heavy fiscal regulation, medium labor regulation, and low regulation in trade, financial markets, entry, bankruptcy, and contract enforcement. Developing regions cannot be characterized as simply as the OECD as they show varying patterns for each type of regulation. For example, with respect to the OECD, labor regulation is almost 16% _lower_ in East Asia and the Pacific, while 26% higher in Latin America and the Caribbean.<sup>11</sup> The most striking difference between the OECD and developing-country groups is in trade regulation, where all developing regions have indices at least 150% higher than the OECD group, reaching an enormous 710% difference in the case of the Middle East and Northern Africa. On the other hand, in the case of fiscal regulation, differences across regions are not nearly as pronounced.

Which countries regulate the most? Although heavy regulators are mainly found among developing countries, OECD countries rank highest in fiscal regulation: Belgium ranks number one, Italy number two, and France is number four (Syria is number three).

> 10 See, for instance, Claessens and Klapper (2002).

> 11 These numbers are calculated on the basis of Table 1, for example, the mean of the “Labor” indicator is 0.44 in the OECD and 0.37 in the EAP region, so that the difference is (0.37-0.44)*100/0.44 = -15.9%.

9