rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure III-2: GDP Growth and Credit Cycle Peaks**

<!-- Start of picture text --> SAU: Growth of GDP and Credit Cycle Peaks EGY: Growth of GDP and Credit Cycle Peaks<br>1967 1977 1987 1997 2007 2017 1967 1977 1987 1997 2007 2017<br>year year<br>Growth of GDP Peak Growth of GDP Peak<br>USA: Growth of GDP and Credit Cycle Peaks<br>1967 1977 1987 1997 2007 2017<br>year<br>Growth of GDP Peak<br>60 1 15 1<br>.8 .8<br>40 10<br>.6 .6<br>20 5<br>percent .4 percent .4<br>local_peak_SAU local_peak_EGY<br>0 0<br>.2 .2<br>-20 0 -5 0<br>8 1<br>6 .8<br>4 .6<br>percent 2 .4<br>local_peak_USA<br>0<br>.2<br>-2<br>0<br><!-- End of picture text --> As the third step in the relationship between credit cycles and recession we run a few simple regressions where the main dependent variable is the growth of GDP .The results of OLS regressions in

> Table **III-1** and Table III-2 show the relation between the peaks in credit cycles and the GDP growth and non-oil GDP for oil exporters.  If the credit cycle peak can be used as an indicator for recessions, we expect to see negative and statistically significant coefficients in Tables III-2 and III-3. However, the coefficient of interest is non-significant for almost all countries except for the Islamic Republic of Iran, which is slightly significant, but positive. Hence, the strength of credit cycles in predicting the decreases in overall GDP for oil exporters is very limited.

11
**Table 12: Existing and Planned Standards for Diesel Sulfur Contents in Selected Countries** Unit: PPM (milligram of sulfur per kilogram of diesel)

|Country|1996|1998|1999|2000|2002|2003|2004|2005|2006|2007|2009|2010|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|USA|500||||||||15||||
|EU|500|||350||||50|||10||
|Japan|500|||||||50||10|||
|Australia|2,000||||500||||50||||
|Bangladesh|>5000||||5,000||||||||
|Cambodia|>5000|||2,000|||||||||
|China|5,000|2,000|||||||||||
|India|5,000|||2,500||||500||||350|
|Indonesia|5,000||||||||||||
|South Korea|2,000|500|||||||||||
|Malaysia|5,000|3,000|||500||||||||
|Pakistan|10,000||||5,000||||||||
|Philippines|5,000|||2,000|||500||||||
|Singapore|5,000|500|||||||||||
|Sri Lanka|5,000|||||3,000|||||||
|Thailand|2,500||500||||||||||
|Vietnam|10,000|||||2,000||500|||||

Source: Krylov et al. (2005) 32
> <sup>15</sup>

with fewer continuous years of competitive elections is starkly different from that of older democracies: they are more corrupt, spend more on public investment and government jobs and exhibit lower secondary school enrollment, rule of law and bureaucratic quality – relations that are robust to a variety of specifications and endogeneity controls. These policy differences can be best explained by the greater difficulties that competitors in younger democracies confront in making impersonal credible commitments to voters prior to elections. From the _DPI_ , one can calculate one how many years a country has continuously held competitive elections (where both the Executive and Legislative Indices of Competitive Elections equal seven). The value of this variable ( _continuous years of competitive elections_ ) in 1975 is therefore used in the regressions below.<sup>4</sup> A large literature has also argued that voter information is critical to the effects of elections on incumbent behavior. Following the empirical research in this literature (see, e.g., Adserà, et al. 2003), _newspaper circulation_ from the _World Development Indicators_ is therefore used as a proxy for the extent of voter information and its effect on growth. The 1975 (initial) values of all political variables are used here, with the exception of newspaper circulation. To counter spotty coverage in any given year, the average of newspaper circulation over the period 1975 – 2000 is employed below.

# **Instruments**

The literature uses a variety of instruments to identify the effects of institutions: distance from the equator (Hall and Jones 1999 use this to instrument for their index of social infrastructure), colonial heritage, years since the creation or independence of a country (Persson, Tabellini and Trebbi 2003), settler mortality and urbanization in 1700 (Acemoglu,

> 4 The 1975 value of this variable in the DPI is actually taken from Clague, et al. (1996); values subsequent to 1975 are updated according to the methodology explained in the text.  The use of values of _persistence_ averaged over the 1975 – 2000 period does not change the results reported below.
could be adding more value to the output. For instance, if the farmer sells her output to a direct consumer, we are certain that the farmer does not participate in a value chain. By contrast, if the output is sold to an exporter or trader, it is more likely that these types of buyers add some value and the farmer is deemed to be participating in a value chain.

To capture value chain participation in the context of our sampled farmers, their buyers were also consulted, because most farmers are not aware of value addition that is done in the remaining stages of the value chain. In the case of contract farmers, a face-to-face interview was conducted with the aggregator or the group’s leader. In the case of randomly selected farmers, follow-up calls were made with their buyers after the data collection. The results show that farmers without any contracts are less likely to produce acceptable products for GVCs. This seems to be confirmed by the respondents to the surveys (figure 5). At the same time, we recognize that farmers without a contract can develop linkages with GVC participants, which we tried to capture in the interviews.

**_Figure 5: Farmers’ Knowledge about Their Participation in Value Chains (%)_** a. Random sample

<!-- Start of picture text --> 93 96<br>90<br>7 5743 2773 72 5 4 50 919 100 10 1 82 8 4852<br>Ghana Kenya Zambia<br>known to be on VC Not known to be on VC<br>Source: Authors' calculations based on survey responses<br>b. Purposive sample<br>57<br>49 51<br>25 37<br>43<br>51 22 66 63 49<br>100 35 77 23 98 2 100<br>Ghana Kenya Zambia<br>known to be on VC Not known to be on VC<br>Source: Authors' calculations based on survey responses<br>No contract Formal Informal Drop-out No contract Formal Informal Drop-out No contract Formal Informal<br>Formal Informal Drop-out Formal Informal Drop-out No contract Formal Informal Drop-out<br>100<br>80<br>60<br>% of farmers 40<br>20<br>0<br>80<br>60<br>40<br>% of farmers<br>20<br>0<br><!-- End of picture text -->

# b. Purposive sample

_Source:_ Staff calculations based on survey responses. _Note:_ VC = value chain.

The key findings from the interviews on contract farming are summarized as follows: 23
# 5 RESULTS

## _5.1 MainResults: OutcomesofDisplacement_

We compile the main results of the project into Figure 3, which plots effect estimates (points) and95%confidenceintervals(lines)foreachofthesixtypesofoutcomes. Overall,afewdistinct patterns emerge.

First, displacement seems to have substantial negative welfare effects: IDPs report lower levelsofincometoday,aremorelikelytosaytheirincomeisnotenoughtosubsiston,andexperience food insecurity more frequently.<sup>19</sup> They are also more likely to be at risk of eviction, and to expect to have a harder time transferring their property, patterns that reflect the economic damages of displacement. We also test for whether displacement causes shifts in general riskseeking behavior, but find no evidence of differences between IDPs and non-IDPs.<sup>20</sup> Our evidence also suggests displacement may increase levels of social cohesion and mobilization: IDPs are more likely to say they’ve recently helped solve a local problem, support collective action (“ _Would you get together with other rural families to demand the state improve your situation?_ ”), are more willing to pay taxes if they were used to help the needy, and are more likely to say they’ve attended a variety of local meetings<sup>21</sup> . These findings echo prior work on the mobilizing effects of victimization (Blattman, 2009; Bauer et al., 2016), though to our knowledge these patterns have not been documented with respect to displacement.

Displacementalsoappearstoinfluenceattitudestowardspeace-building. Peoplewhohave been displaced are more likely to express support for the peace process ( _“to what extent do you support the implementation of the peace accords with the FARC?”_ ) and to be optimistic about the ability of ex-FARC members to reintegrate into society ( _“How optimistic or pessimistic do you feel about demobilized FARC members successfully reintegrating into society?”_ ) than those who weren’t displaced, even as they are _less_ likely to say they feel safer now. This latter pattern bearing on expectationsoffuturesafetyismoremixed: theyarenomorelikelytofeartheformationofnew groups, nor to expect less conflict after the end of the peace process.<sup>22</sup>

> 19These patterns are in line with prior work showing that IDPs have extreme poverty rates over three times higher than the national average (CODHES, 2016).

> 20Wepickedthreeitemsfroma“Risk-AcceptanceIndex”usedintheAmericanNationalElectionsSurvey(ANES) (Kam, 2012). The items we selected were very weakly correlated, and thus we did not combine them into an index. 21The “women’s meetings” item was only administered to respondents who identified as women.

> 22Wedelvemoredeeplyintotheeffectsofdisplacementonpeaceattitudesthroughaconjointexperimentthat randomly varies the attributes of hypothetical peace agreements (Hainmueller, Hopkins and Yamamoto, 2014). Overall, we find no evidence that the displaced and non-displaced react differently to the peace agreement attributes, using the preference heterogeneity test in Leeper, Hobolt and Tilley (2020) (Table A.4). Attribute-level results available in Table A.5.

14
**Figure 4. Poverty and gender equality**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>R 2 = 0.4287<br>20<br>10<br>0<br>0.6 0.7 0.8 0.9 1 1.1<br>Female-to-male ratio in HDIs, 1997<br>Poverty line: US$2/day<br>Poverty headcount ratio (in percent)<br><!-- End of picture text --> _Note_ : Scatter plot for 73 developing countries. Poverty headcount ratio statistics are based on the international poverty line of $2.16 per day (1993 PPP $) and are obtained from the World Bank's World Development Indicators database. Male and female HDIs were calculated using data from the 1999 HDR statistical database. See Klasen (2006) for the formula used to obtain the gender-specific HDIs. When the poverty headcount ratio statistic for a given country is unavailable for 1997, the closest year to 1997 with available data (in the 1998-2003 period) is used.

Within countries, a comparison of poor and rich households measured using consumption expenditures shows that gender inequality in at least two key resources – education and health – are greater among the poor than among the rich. This correlation between household poverty and gender inequality has been found across the developing world (Filmer, 1999; World Bank, 2001; Strauss and Thomas, 1995).

# _Headship and household poverty_

Because there are gender inequalities in resources, rights and voice, it is often expected that female-headed households are more likely to be poor than male-headed households (Buvinic and Gupta, 1997). However, the empirical evidence on this is mixed. Buvinic and Gupta (1997) review 61 studies on headship and poverty and find female-headed households to be more commonly used UNDP Gender-related Development Index; UNDP, in its 2006 Human Development Report, proposed the use of gender-disaggregated HDIs to more accurately measure gender equality). As robustness checks, we examine the relationship between poverty and gender equality when gender equality is measured using the ratio of the gender-related development index to the human development index (the GDI-HDI ratio, another measure developed to replace the GDI as a measure of gender equality—see Klasen, 2006 for details) or the gender empowerment measure (the GEM) and when poverty is measured using the lower international poverty line of $1.08 dollars per day (1993 PPP $). When we do this, the relationship becomes weaker, with the correlation coefficient falling into the range of -0.34 to -0.55, but it remains consistently statistically significant. Note that the GEM as a measure of gender equality has serious weaknesses, limiting its usefulness in comparing gender equality across countries (see footnote 3 below and Klasen 2006 for a discussion).

29
# **3.2 Worker Transitions: Variable Creation**

An industry (skill) switcher is an individual that in the subsequent observation is employed in a different industry (has a different skill). The set of worker transitions contains five alternatives: the worker changes industry retaining the same skill; moves up the skill ladder while remaining in the same industry; moves down the skill ladder while remaining in the same industry; moves up the skill ladder and moves industry; moves down the skill ladder and moves industry. Skill classification follows the ILO’s skill definition described in Section 2.1 and industry classification follows the NACE-Rev.2 at the 2-digit level.

# **3.3 Descriptive Statistics**

Moving industry and/or skill appear to be important forms of labor reallocation. Around 17 percent of all worker-year observations are of some kind of switching. Figure 3 provides some detail on (average) rates of switching (expressed as a percentage of total number of observations). Switching by changing both industry and skill is least common, on average around 3 percent of all worker-year observations are of this type. Switching in one dimension alone (either skill or industry) is more common. The rate of industry switching conditional on retaining the same skill is similar to that of changing skill conditional on staying in the same industry, on average around 7 percent of all worker-year observations are of each of these types. Considering each of these forms of reallocation independently the (unconditional) switching rate increases to around 10 percent (either for industry or skill).

As Figure 3 makes clear, moving up the skill ladder is more common than moving down, but both movements are important – the share of all workers who are upgraders and downgraders is 9 and 3.8 percent, respectively. The difference is driven by differences in the fraction that moves skill while staying in the same industry. Whereas the unconditional rate of industry switching and its components are similar in both cases, the average rate of skill upgrading, conditional on staying in the same industry, is almost twice as high as that of skill downgrading conditional on staying in the same industry.<sup>13</sup>

> 13Detailed tables of transition patterns across industries are available from the authors on request.

11
||(0.069)|(0.068)|(0.088)|(0.103)|
|---|---|---|---|---|
|Education level of household head_Secondary|<br>-0.061|<br>-0.067|0.137|0.186|
||(0.090)|(0.090)|(0.123)|(0.123)|
|Education level of household head_University|-0.408***|-0.416***|0.619***|0.596***|
||(0.120)|(0.129)|(0.148)|(0.182)|
|Education level of household_Other|-0.194***|-0.273***|0.387***|0.569***|
||(0.060)|(0.053)|(0.097)|(0.084)|
|Land ownership|-0.152**|-0.132**|0.263**|0.256***|
||(0.068)|(0.058)|(0.103)|(0.092)|
|Household head age|-0.003|-0.002|0.036|0.025|
||(0.020)|(0.008)|(0.029)|(0.019)|
|Household head age-squared|0.000|0.000|0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Household size|0.077***|0.091***|-0.175***|-0.182***|
||(0.013)|(0.013)|(0.022)|(0.024)|
|Household size-squared|-0.001***|-0.002***|0.004***|0.004***|
||(0.000)|(0.001)|(0.001)|(0.001)|
|Household head Unemployed|-0.003||-0.055||
||(0.145)||(0.134)||
|Household head employment: Manufacturing||0.201||-0.125|
|||(0.123)||(0.137)|
|Household head employment: Services||-0.137*||0.183**|
|||(0.077)||(0.087)|
|Household head employment: Education||-0.012||-0.112|
|||(0.169)||(0.157)|
|Household head employment: Defense/Security||-0.125||0.213**|
|||(0.098)||(0.091)|
|Household head employment: Public<br>Administration||<br>-0.323*||0.249|
|||(0.165)||(0.159)|
|Constant|-0.131|-0.247|1.897**|2.211***|
||(0.466)|(0.274)|(0.706)|(0.498)|
|Observations|703|673|703|673|
|R-squared|0.296|0.353|0.860|0.868|

*** p<0.01, ** p<0.05, * p<0.1.

Notes: Robust standard errors in parentheses. Reference is 2015 for survey year; male for gender of household head; no education for household head educational level; employed for household head employment status; and agriculture for household head’s sector of employment.

25
||**US offer**<br>_Note_: Currently, H1-B visa holders<br>can extend their stay up to a<br>maximum of 6 years. New<br>legislation requires H1-B dependent<br>employers to advertise positions in<br>the US before petitioning a foreign<br>worker.|**EU offer**|**Canadian offer**<br>**Australian offer**|**Additional coverage in Model Schedule**|
|---|---|---|---|---|
|_Contractual_<br>_service_<br>_suppliers_||Employees of a juridical<br>person, on a contract that has<br>been tendered or advertised,<br>admitted for a period not<br>exceeding 6 months in any<br>12 month period, with no<br>mention of an economics<br>needs test.<br>Commitments are subject to<br>the application of a<br>numerical ceiling, except<br>where otherwise indicated,<br>for a particular sub-sector.<br>Commitments apply to a<br>specific list of professions.<br>**Independent professionals,**<br>**practicing as self-**<br>**employed, on a contract**<br>**that has been tendered or**<br>**advertised, admitted for a**<br>**period not exceeding 6**<br>**months in any 12 month**<br>**period, with no mention of**<br>**an economic needs test.**<br>Commitments are subject to<br>the application of a<br>numerical ceiling, except<br>where otherwise indicated,<br>for a particular sub-sector.<br>Commitments apply to a<br>specific list of professions.|Professionals, seeking to<br>engage as part of a services<br>contract granted by a<br>juridical person or a service<br>consumer in Canada and<br>obtained by a juridical<br>person or a service supplier<br>of another WTO member, to<br>be**admitted on being**<br>**granted a work permit for**<br>**an initial stay of 1 year, or**<br>**the time necessary to**<br>**complete the contract,**<br>**whichever is less, and**<br>**extensions are possible.**<br>**No labor market test**<br>**required for** **qualifying**<br>**professionals, and their**<br>**spouses and common law**<br>**partners.**<br>**Commitments apply to a**<br>**specific list of professions.**<br>**There will be a limit of 10**<br>**entrants per project in the**<br>**case of senior computer**<br>**specialists.**<br>Professionals must possess<br>the necessary academic<br>credentials and professional<br>qualifications, which have<br>been duly recognized, where<br>appropriate, by the<br>professional association in<br>Canada.|Employees of a juridical person should be<br>admitted for short periods of stay up to a<br>maximum of 1 year.<br>Independent professionals should be<br>admitted for short periods of stay up to a<br>maximum of 1 year.|

Source: Compiled by authors from WTO documents and national websites.

12
# **Table 2, continued: Services Firms in Core versus Non-core Regions**

||**Core**|**Non-**<br>**core**|**p-value**<sup>**+**</sup>|**Core/**<br>**Non-**<br>**core**|
|---|---|---|---|---|
|**Investment Climate, continued**|||||
|**_Transport and tradefacilitation_**|||||
|Average time to clear direct exports through customs(days)|5.4|7.3|0.316|0.74|
|Average time to clear imports from customs(days)|12.0|17.2|0.301|0.70|
|Average time of inventoryof most important input(days)|20.1|11.5|0.027**|1.75|
|% of firms identifyingcustoms & trade regulations as a major constraint|15.0|18.5|0.510|0.81|
|% of firms identifying transportation as a major constraint|19.0|19.8|0.847|0.96|
|**_Regulations and Tax_**|||||
|Senior management time spent in dealing with requirements of<br>government regulation(%)|13.4|13.1|0.870|1.03|
|Average number of visits or required meetings with tax officials|1.1|1.3|0.344|0.85|
|% of firms identifyingtax rates as major constraint|39.4|53.9|0.033**|0.73|
|% of firms identifyingtax administration as major constraint|28.1|43.9|0.025**|0.64|
|**_Permits and Licenses_**|<br>||||
|Average time to obtain operatinglicense(days)|31.2|41.9|0.336|0.74|
|Average time to obtain import license(days)|14.3|22.2|0.123|0.65|
|Average time to obtain construction-relatedpermit(days)|104.4|75.1|0.194|1.39|
|% of firms identifyingbusiness licensingandpermits as major constraint|19.6|28.9|0.148|0.68|
|**_Corruption_**|<br>||||
|% of firms expected topayinformalpayment topublic officials|18.7|14.3|0.309|1.31|
|% of firms expected togivegifts toget an operatinglicense|16.0|16.2|0.979|0.99|
|% of firms expected togivegifts in meetings with tax officials|11.6|13.0|0.767|0.89|
|% of firms expected togivegifts to secure agovernment contract|13.0|18.2|0.342|0.72|
|% of firms identifyingcorruption as a major constraint|41.4|55.7|0.031**|0.74|
|**_Access to Finance_**|<br>||||
|Finance from internal sources(%)|69.9|59.6|0.111|1.17|
|Finance from banks(%)|19.0|23.1|0.445|0.82|
|Finance from trade credit(%)|5.6|11.1|0.082*|0.51|
|% of firms with line of credit or loans from financial institutions|41.7|36.8|0.443|1.13|
|% of firms usingbanks to finance investments|24.1|27.7|0.581|0.87|
|% of firms usingbanks to finance expenses|31.0|37.5|0.502|0.83|
|%of firms identifyingaccess to finance as a major constraint|26.8|32.6|0.405|0.82|

Source: Own calculations. Data: Enterprise Survey Indicators. NB:<sup>+</sup> t-test of difference in means. p*<0.1, p**<0.05, p***<0.01.

Infrastructure-related indicators confirm the existence of congestion costs in agglomerations for services firms, especially with regards to electricity. The number and duration of power outages per month is higher in the core. Moreover, the time to obtain an electrical and water connection is almost four times as high for services firms in the core compared to firms in non-core regions. As a result, the percentage of services firms producing their electricity from a generator is almost 9 14