rafmacalaba/datause-encoder-data huggingface dataset:
wages were delayed vs. 21% in the national urban sample. In addition, the average level of wage arrears is higher in mining municipalities (7.3 months compared to 4.2 months in the national urban sample).

149. Concerning past unemployment, the same percentage (30%) of employed respondents in the mining and in the nation-wide samples said that they had been unemployed during the last five years, but the average length of unemployment among respondents in the mining sample was 26 months as opposed to 19 months for respondents in the national urban sample _150. Housing and communal services._ Residents of the two mining communities are considerably more likely to live in apartments than residents of similarly sized cities in the rest of the country, and consequently are more dependent on communal services for the provision of various essential services such as heat and water. In the mining sample, 71% of the respondents reported living in an apartment as opposed to 45% in the comparable urban sample.

> 151. Respondents in the mining sample noticeably more often gave negative assessments of their housing conditions than respondents in the national sample. In the mining sample, 46% of apartment-dwellers said that the condition of their housing was very bad or even dilapidated, requiring either major repairs or demolition, as opposed to 21% in the national urban sample. Also, considerably more respondents in the mining sample reported having no hot water in their apartments (79% vs. 43%). Respondents in the mining sample were also more likely to have no central heating (21% in mining towns vs. 12% in the other cities) or gas for heating (75% vs. 63%) and less likely to have a telephone (56% in the mining sample did not have telephones vs. 43% in the national urban sample).

152. For respondents who receive these services, the analysis demonstrated that the functioning of all basic services is considerably worse in mining towns than in the rest of the country. Twenty-nine percent of the respondents in the mining towns as compared with 11% in the national urban sample reported that their electricity service does not function well. Of those connected to central heating in the mining towns, 69% vs. 43% in the nation-wide sample said that the service does not work well. Cold water is delivered with interruptions or not delivered at all to those who have water connection in 64% of the cases in mining towns as compared with 35% in Ukraine. And almost all respondents (95%) in the mining sample with connection to hot water reported bad delivery of hot water compared to 68% in the national urban sample.

44
- Magat,W., K.W. Viscusi, and J. Huber, 1987. Risk-dollar trade-offs, risk perceptions, and consumer behaviour. In: Learning About Risk [Viscusi, W. and W. Magat (eds.)]. Harvard University Press, Cambridge, MA, 83-97.

Malthus,T. R. (1965).First essay on population. New-York: Kelley. (Original work published 1798)

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- Milanovic, M., 2011. The Haves and the Have-Nots. _A Brief Idiosyncratic History of Global Inequality_ , Basic Books, 2011

- Muller, NZ., R. Mendelsohn and W. Nordhaus, 2011, Environmental Accounting for Pollution in the United States. American Economic Review 101:1649-1675.

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Nordhaus, W.D. 1974. "Resources as a Constraint on Growth." American Economic Review 64: 22-26.

- O’Neill, B. C., Riahi, K. & Keppo, I. Mitigation implications of midcentury targets that preserve long-term climate policy options. Proc. Natl Acad. Sci. USA 107, 1011–1016 (2009).

- Otto, V.M., and Reilly, J., 2008. Directed technical change and the adoption of CO2 abatement technology: the case of CO2 capture and storage. Energy Economics. 30, 2871–2890. doi:10.1016/j.eneco.2007.07.001.

- Pagiola S., von Ritter K. and J. Bishop, 2005. Assessing the Economic Value of ecosystem Conservation, Others 0502006, EconWPA.

- Parry, I. W. H. & Bento, A. M., 2000. "Tax Deductions, Environmental Policy, and the "Double Dividend" Hypothesis," Journal of Environmental Economics and Management, Elsevier, vol. 39(1), pages 6796, January.

- Popp D., R.G. Newell, and A.B. Jaffe, 2009. Energy, the Environment, and Technological Change, NBER Working Papers 14832, National Bureau of Economic Research, Inc

- Porter, M., and C. van der Linde (1995), Toward a New Conception of the Environment-Competitiveness Relationship, Journal of Economic Perspective 9(4), 97–118.

36
<!-- Start of picture text --> A Cross-Entropy<br>80°N-<br>60"NON] = . ae => pceese a aI ~/_<br>ont yen _— P ai 2 rm a oeos . = eos B ae<br>0°- gpmaga, Sapey. - ad ' Neewy ~<br>20°S- ee bic ‘es Age 4 7<br>Ps y * » 2<br>2g. pee wow oy<br>40°s ; < 7<br>Rural per capita<br>80°N-<br>ON = am "bey,<br>20°N - go> AS ‘fare,<br>40°S - f sy<br>120°W 60°W O° 60°E 120°E<br>Agr GDP (2010 US$)<br>§) 200,000Low Agricultural- 399,999 GDP / NA 86 00,000 - 79 99,999 | 42 ,000,000 - 35 ,999,999<br>|, 400,000 - 599,999 || 1,000,000 - 1,999,999 >= 6,000,000<br>B Correlation between models<br>80°N -<br>6O°N - ; “so<br>Mea<br>20°N- ;<br>: Sig Po at a L<br>i f Sp, a edad B.<br>20°S - ; Po we P<br>40°S - ,oa age - Be * Age ?.<br>120°W 60°W o° 60°E 120°E<br>Spearman correlation<br>05 0.0 0.5<br><!-- End of picture text -->
6 In each LSMS-ISA country, all sample households are administered a multi-topic Household Questionnaire.<sup>9</sup> Agricultural households, who are defined as owning and/or cultivating land and/or owning livestock, are additionally administered an Agricultural Questionnaire. The latter records (i) geo-referenced plot locations and Global Positioning System (GPS)-based plot areas, (ii) collects plot-level information on input use, cultivation and production, (iii) identifies the household members that manage and/or own each plot, and (iv) most importantly, solicits individual-disaggregated labor input at the plot-level. The information is collected separately for each agricultural season in the country (if there is more than one), and often in two visits, with information on the post-planting/pre-harvest and the post-harvest outcomes collected during the first and second visit, respectively.

While the data are collected at the plot-level in all countries, the quantification of the labor input at the plot level differs slightly across countries. The surveys in Malawi, Nigeria and Ethiopia collect data on the number of weeks of work provided by each household member on each plot, differentiated by activity (land preparation and planting, weeding/fertilizing/other non-harvest activities, and harvesting). This is complemented by further probing regarding the average number of days per week and the average number of hours per day worked on each plot, separately for each activity. The surveys in Tanzania and Niger solicit the number of days worked by each member of the household on each plot, separately for each activity domain. Finally, in Uganda, the total number of days worked on a plot (by all household members and across all activities) are collected first. Subsequently, the household members who worked on the plot are identified. In the analysis, the total number of days worked are distributed equally among all household members

> 9 The Household Questionnaire geo-references the dwelling’s location and collects individual-disaggregated information on demographics, education, health, employment, anthropometrics, and control of off-farm income as well as data on housing, food and non-food consumption and expenditures, and asset ownership, among other topics.
credit and productivity growth holds for all but small firms, those that have fewer than 50 employees, which is similar to the patterns in Beck, Demirguc-Kunt, and Maksimovic (2008).

These findings suggest a sweet spot or ‘Goldilocks’ region where mid-size firms and those in countries at middling levels of development tend to rely relatively more heavily on trade credit as a source of finance that can help spur productivity.

# **5. Conclusions**

Though trade credit is often as important as bank loans in terms of firms’ short-term financing, our understanding of it remains rather limited. In this paper we survey existing studies of trade credit, especially those related to economic development. The evidence suggests that trade credit plays an important role in many developing countries as well as for many creditconstrained firms in developed countries, especially during monetary contractions.

In most countries/regions, there are many firms, especially those that are young, small, or informationally opaque for other reasons, that cannot obtain adequate bank credit. The informational advantages of suppliers (and sometimes customers) allow them to use and benefit from such decentralized and dispersed knowledge (Hayek 1945), and to direct firm resources in the form of trade credit to trading partners that are worthy of such resources. For instance, there is reasonably consistent evidence—from the US, Africa, China, and other transitional countries-that suppliers grant more trade credit to their customers when they have (1) a larger stake in their customer’s financial success (e.g., when the customer accounts for a larger share of the supplier’s profit; when the supplier sells differentiated products; or when the customer is in a strong bargaining position relative to the supplier so that the opportunity cost of losing such an important customer is a real threat to the profit of the supplier), (2) a stronger informational advantage, and (3) better access to finance (than their customers). In contrast, when informational frictions between suppliers and customers are less severe (e.g.., when suppliers have a longer relationship with or are geographically close to the customer, or the customer is a publicly listed company), trade credit is found to be less prevalent (Dass et al. 2015). Furthermore, both suppliers and customers use trade credit to signal hidden information— less established suppliers use trade credit to allow buyers to verify product quality (and thus facilitate trade); customers pay trade credit early to obtain a discounted price which also signals their 24
Table 5: robustness tests: cumulative loans

|variation|DPL_>_0|closing year|controls|EFW|Heritage|
|---|---|---|---|---|---|
|equation no.|(1)|(2)|(3)|(4)|(5)|
|log of number of cumulative loans|.341|.444|.331|.394|2.623|
||(.115)<sup>_∗∗∗_</sup>|(.127)<sup>_∗∗∗_</sup>|(.129)<sup>_∗∗_</sup>|(.134)<sup>_∗∗∗_</sup>|(1.413)<sup>_∗_</sup>|
|year|-.018|-.039|-.036|.045|-.130|
||(.009)<sup>_∗∗_</sup>|(.011)<sup>_∗∗∗_</sup>|(.019)<sup>_∗_</sup>|(.009)<sup>_∗∗∗_</sup>|(.101)|
|log GDP per capita (PPP)|.727|.980|.899|.442|12.193|
||(.148)<sup>_∗∗∗_</sup>|(.260)<sup>_∗∗∗_</sup>|(.263)<sup>_∗∗∗_</sup>|(.204)<sup>_∗∗_</sup>|(2.566)<sup>_∗∗∗_</sup>|
|aid over GDP|1.564|1.458|2.019|1.961|-34.705|
||(.556)<sup>_∗∗∗_</sup>|(.576)<sup>_∗∗_</sup>|(.706)<sup>_∗∗∗_</sup>|(.973)<sup>_∗∗_</sup>|(11.410)<sup>_∗∗∗_</sup>|
|Political Rights|-.008|-.012|.010|-.079|-.751|
||(.021)|(.026)|(.021)|(.042)<sup>_∗_</sup>|(.358)<sup>_∗∗_</sup>|
|log of gross IDA|.|.|.077|.|.|
||||(.023)<sup>_∗∗∗_</sup>|||
|IMF arrangement|.|.|-.022|.|.|
||||(.025)|||
|debt service (% of GNI)|.|.|-.004<br>(.004)|.|.|
|log of population|.|.|.941|.|.|
||||(.734)|||
|lagged election|.|.|-.009|.|.|
||||(.019)|||
|election cycle|.|.|-.011|.|.|
||||(.014)|||
|election frequency|.|.|.008|.|.|
||||(.010)|||
|_country fxed efects_|_yes_|_yes_|_yes_|_yes_|_yes_|
|Observations|1443|1182|1235|1040|1607|
|Countries|114|121|105|95|132|
|_R_<sup>2</sup>|.143|.136|.191|.547|.195|

Note: Panel regression results from several robustness tests. Dependent variable: CPIA clusters A and B average. Variable of interest: (log of) number of cumulative market reform loans. * significance at 10%; ** significance at 5%; *** significance at 1%. Standard errors clustered by country.

26
|Participants|13*|(a) Report numbers of individuals at each stage of study—eg numbers<br>potentially eligible, examined for eligibility, confirmed eligible, included in<br>the study, completing follow-up, and analysed<br>_See the relevant text: “The analysis in the study is at the level of the_<br>_provider. We used the full sample of providers available from the surveys._<br>_In total, the sample includes information from 89,826 vignettes from_<br>_13,754 providers and absenteeism observations from 22,747 providers_<br>_across 8,061 facilities in 10 countries.”_|
|---|---|---|
|||(b) Give reasons for non-participation at each stage<br>_The surveys were carried out in partnership with national governments and_<br>_so facilities participated._|
|||(c) Consider use of a flow diagram<br>_Not applicable._|
|Descriptive data|14*|(a) Give characteristics of study participants (eg demographic, clinical,<br>social) and information on exposures and potential confounders<br>_Because the paper covers samples in ten different countries, and descriptive_<br>_data on those samples are provided in country-specific studies, we provide_<br>_overarching characteristics of the country samples (e.g., in Table S1) as_<br>_compared to Africa as a whole._|
|||(b) Indicate number of participants with missing data for each variable of<br>interest|
|||_Not applicable, given that facilities participated in the surveys._|
|Outcome data|15*|Report numbers of outcome events or summary measures<br>_As discussed in the text, measures include (1) diagnostic accuracy, (2) rate_<br>_at which the correct prescription is given to the patient, (3) rate of provider_<br>_absence, (4) availability of drugs for treatment, (5) probability of care_<br>_readiness, namely (i) whether there is at least one provider working in the_<br>_clinic that can correctly diagnose and prescribe treatment for the_<br>_condition(s); (ii) health worker attendance; and (iii) whether or not the_<br>_facility has a minimum set of drugs to treat the condition(s)._|
|Main results|16|(a) Give unadjusted estimates and, if applicable, confounder-adjusted<br>estimates and their precision (eg, 95% confidence interval). Make clear<br>which confounders were adjusted for and why they were included<br>_The paper includes unadjusted estimates and confidence intervals: “(1)_<br>_Across all conditions and countries, health care providers correctly diagnose_<br>_64% (95% CI 0.62-0.65) of the cases. For diabetes, 35% (95% CI 0.33-0.37)_<br>_of the health workers provide a correct diagnosis (Figure 1A; Table S7). For_<br>_the three main killer diseases for children in Sub-Saharan Africa, accounting_<br>_for approximately one-third of all deaths among children under age 5 in the_<br>_countries surveyed, average diagnostic accuracy is 57% (95% CI 0.54-0.59)_|

41
content of exports, on a sector basis? The most important sector is machinery at 19.4 percent, a share that has slightly decreased from 1992-2007. Collectively, machinery, transport equipment, metals, and chemicals account for 45 percent of all exports, on a value added basis. If we compare the values in Table 5 with those in Table 6, it is clear that a large share of total value added, in the goods sectors that dominate trade on a gross value basis, comes from service inputs to manufacturing.<sup>5</sup> Table 6: **Sector shares of total value added exports – World Trade Direct and indirect exports through backward linkages**

||1992|1995|1997|2001|2004|2007|
|---|---|---|---|---|---|---|
|Agr, Forestry, Fisheries|0.044|0.039|0.033|0.030|0.027|0.025|
|Processed Foods|0.032|0.043|0.039|0.036|0.038|0.037|
|Energy Extraction|0.078|0.055|0.062|0.061|0.092|0.121|
|Minerals nec|0.018|0.015|0.006|0.006|0.011|0.015|
|Beverages and Tobacco Products|0.009|0.008|0.008|0.008|0.008|0.007|
|Textiles|0.024|0.024|0.027|0.025|0.023|0.019|
|Wearing Apparel|0.023|0.023|0.020|0.021|0.017|0.016|
|Leather Products|0.012|0.013|0.011|0.012|0.009|0.009|
|Wood Products|0.017|0.018|0.017|0.017|0.017|0.015|
|Paper Products, Publishing|0.023|0.026|0.022|0.022|0.020|0.017|
|Chemical, Rubber, Plastic Products|0.077|0.091|0.085|0.091|0.102|0.098|
|Mineral Products nec|0.009|0.011|0.017|0.017|0.011|0.010|
|Ferrous Metals|0.021|0.026|0.022|0.019|0.022|0.025|
|Metals nec|0.014|0.016|0.022|0.020|0.022|0.027|
|Metal Products|0.016|0.018|0.018|0.018|0.019|0.019|
|Transport Equipment|0.089|0.084|0.088|0.095|0.095|0.087|
|Machinery and Equipment nec|0.208|0.229|0.239|0.234|0.214|0.194|
|Manufactures nec|0.024|0.022|0.021|0.025|0.017|0.017|
|Electricity, Gas, Water|0.001|0.002|0.004|0.004|0.004|0.006|
|Construction|0.002|0.004|0.006|0.004|0.005|0.007|
|Trade and Transport Services|0.161|0.144|0.126|0.116|0.106|0.101|
|Other Commercial Services|0.090|0.065|0.087|0.098|0.104|0.111|
|Public Services|0.008|0.026|0.019|0.019|0.017|0.016|
|Dwellings|0.000|0.000|0.000|0.000|0.000|0.000|
|Total|1.000|1.000|1.000|1.000|1.000|1.000|

Finally, before turning to individual countries and regions, we look at the import intensity of cost structures across industry. Strictly speaking, this is not a measure of value added contents, but at this stage we have calculated the share of imported inputs in gross costs across sectors. This involves a variation of equation (5), adjusted to track import costs downstream rather than domestic value added. What this yields is a measure of the extent to which individual sectors have become

> 5It is also the case that we should expect, in a world without f.o.b and c.i.f margins and trade taxes, that values in Tables 6 and 3 ought to be the same.

13
**FIGURE 10: CHANGE IN EXPORTS BY REGION**

<!-- Start of picture text --> 25<br>20<br>Ports<br>15<br>Customs<br>10 Regulations<br>Services<br>5<br>0<br>% change<br>ECA LAC MENA<br>OECD East Asia South Asia SS Africa<br><!-- End of picture text -->

**_<u>Source: Wilson et al., 2004.</u>_**

On the whole, as Table 9 shows, from improvement in all trade facilitation measures the highest export gain is attained by South Asia (40.3 percent). LAC is in fourth place (20 percent), after ECA (30.0 percent) and East Asia (24 percent). High gains for South Asia emanates from high export gains due to improvement in port efficiency, and service sector infrastructure. Likewise, the LAC region gains in its exports mainly come from reforms in port efficiency and service sector infrastructure. In both cases, the gains are generated from their own improvements, rather than the improvements by trading partners. In the LAC region, Mexico gathers an export gain in the amount of $17.3 billion (i.e. the highest in the region) and Paraguay realizes a gain of 74.8 percent. Mexico and Paraguay's high gains again come from the improvement in ports and service sector infrastructure.

# **6.3 Conclusions (iii)**

In conclusion, the results from this section suggest that the scope and benefit of unilateral trade facilitation reforms are very large and that the gains fall disproportionately on exports. Some of the policies required to improve the four trade facilitation indicators are easier to adopt than others. But the message is clear: there are significant gains to be made from improving the components of trade facilitation, even if the improvements are unilateral.

31
important in determining volatility. Specifically,<sup>_BANK_is a measure of the importance</sup> of banks residing in that country. One possible<sup>interpretation of this result is that if</sup> international capital flows exacerbate investment<sup>volatility, a stronger banking sector</sup> reduces their relative importance.

# **3.2. Sensitivity Analysis**

The previous section establishes that higher<sup>values of</sup><sup>_PRIVATE_</sup> are associated with lower values of consumption and GDP volatility,<sup>that higher values of</sup><sup>_BANK_</sup> are associated with lower values of output,<sup>consumption and investment volatility, and that</sup> higher values of _LLY_ are associated with higher<sup>values of consumption volatility. In this</sup> section we examine how robust these results<sup>are to different data selection and estimation</sup> techniques.

As explained in Section 2, our main data set<sup>contains a measure of financial devel-</sup> opment at the beginning of a time period<sup>and a measure of subsequent macroeconomic</sup> variability. However, the years we use to<sup>calculate initial financial development imme-</sup> diately precede those years which we use to calculate<sup>subsequent volatility. In order to</sup> determine if our results are sensitive to the<sup>time period in which we measure financial</sup> development, in Table 4 we repeat the estimations<sup>presented in Table 2, but this time we</sup> 'measure financial development with a five year<sup>lag between the last year we use in the</sup> financial development measure and the first<sup>year we use in the variability measure (i.e.,</sup> financial development is measured over the periods<sup>1951-55, 1961-1964, 1971-1973, and</sup> 1979-1982). These results confirm our initial<sup>findings for consumption and output vari-</sup> ability, however, in this case _BANK_ becomes<sup>insignificant in the investment regression</sup> while _PRIVATE_ becomes significant.

# [See Table 4.]

Although our data set is a relatively large macro<sup>data set, our analysis still may be</sup> sensitive to outliers. We therefore provide<sup>another estimate of the relationship between</sup> financial development and fluctuations this<sup>time using a robust regression technique that</sup> 11
|Malaysia|2010<br>1997<br>1993|6<br>20<br>55|14<br>4|2<br>2|3<br>7||||
|---|---|---|---|---|---|---|---|---|
|Mauritius|2009<br>1995|14 *<br>9|7||||||
|Mexico|2008<br>1997<br>1993|6<br>28|1|2|2<br>7||2||
|Morocco|2011<br>1996<br>1990|2 *<br>13<br>51||2 *|1||24||
|Mozambique|2009||||||3||
|Nepal|2012|1|||||||
|Nigeria|2011<br>1998<br>1991|17<br>2|9<br>14||||||
|Pakistan|1995||17|1|||||
|Paraguay|2008|1 *|1 *||||1||
|Peru|2008<br>1994||1 *||||2<br>6||
|Philippines|2012<br>1993|5<br>7|4|1<br>1|||||
|Senegal|2011<br>1994|2 *<br>10|1 *|5|||||
|Singapore|1996<br>1992|1<br>1|||||||
|South Africa|2009<br>1998<br>1993|5<br>36||1<br>3|5||3||
|Sri Lanka|2010<br>1995|4<br>23|||||||
|Taiwan,China|2010||||1||||
|Thailand|2007<br>1995<br>1991|3<br>11<br>36|6|1<br>2|8<br>12|2**|3||
|Tunisia|2011<br>1994|54|||||2|22 *|
|Uganda|1995|3|||||||
|Uruguay|2012<br>1998<br>1992|3||1|1|4**|31||
|Venezuela|2008<br>1996|24 *<br>2|3 *<br>3|1 *|||13||
|Zambia|1996||1||||||
|Zimbabwe|2011<br>1995|23||||100***|||

Notes: Frequency ratio of percentage in HS 2-digit products affected by each NTM.

All countries report several product categories—usually 15-30% of tariff lines as subject to licensing and/ or prohibitions for health, sanitation, safety or defense reasons. Only licensing and/or prohibitions for economic reasons are reported here. * in percent at HS 6-digit level; ** import monitoring; *** exchange control Sources: GATT TPR (1989-1994); WTO TPR (1995-2012); UNCTAD/World Bank NTM database.

16
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