rafmacalaba/datause-encoder-data huggingface dataset:
# **3 Main Results**

Our baseline specification is as follows: where _c_ is for country, _y_ is for year. **1** ( _Incumbent Stays_ ) _c_ , _y_ takes the value of 1 if the incumbent party wins the election. ∆ ln( _Crude Oil Index_ ) _c_ , _y−_ 1 captures an oil price shock one year ago. Country fixed effect, _αc_ , capture time-invariant difference in political systems and institutional quality. The specification also includes year fixed effects, _βy_ , which capture global shocks over time, such as the worldwide downturn due to the Global Financial Crisis. Finally, the standard errors in our main tables are clustered at the country level, to address the correlation of oil price shocks within each country. In a robustness exercise, we show that the results are robust to multiway clustering across countries and years (Table A2).

We also examine the effect of changes in import commodity prices on election outcomes: where ∆ ln( _Import ComPI_ ) _c_ , _y−_ 1 captures the lagged change in the import commodity price index. Finally, using the polling data, we test the specification: _Voting Intention Incumbenti_ , _m_ = _αi_ + _βm_ + _μ_ ∆ ln( _Crude Oil Index_ ) _i_ , _m−_ 12 + _εi_ , _m_ (3) where _i_ denotes the election and _m_ denotes the month. _Voting Intention Incumbenti_ , _m_ captures the voting intentions for the incumbent party in a given month.

∆ ln( _Crude Oil Index_ ) _i_ , _m−_ 12 captures the country-specific change in the crude oil price index with a 12-month lag. Lastly, _αi_ and _βm_ are election and month fixed effects.

Table 2 presents our main results on the impacts of oil price shocks on electoral turnover. Column 1 shows that an increase in crude oil prices in the previous year systematically and negatively affects the reelection chance of the incumbent party. A 1% increase in crude oil index reduces the reelection chance by 5.9 percentage points depending on the specification. This is quantitatively large given that the average crude oil shock is -0.16%. To address the concern of potential term limits for individual politicians, incumbency is defined at the party-level, as aforementioned.

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**Table 3.** Sudden and slow onset type distribution.

|||**Sudden**|**-onset**<br>|||**Slow-o**|**nset**<br>||
|---|---|---|---|---|---|---|---|---|
|**Individuals**|**Total**<br>|**PI**|**Risk in**|**duced**|**Total**<br>|**PI**|**Risk i**|**nduced**|
||**Vulnerabilit**||||**Vulnerabilit**||||
||**y**||**CRI**|**IRI**|**y**||**CRI**|**IRI**|
||**(1)**|**(2)**|**(4)**|**(5)**|**(1)**|**(2)**|**(4)**|**(5)**|
|Argentina|100%|50.2%|13.3%|36.5%|100%|49.9%|12.3%|37.8%|
|Bolivia(Plurinational State of)|100%|55.8%|7.0%|37.2%|100%|54.4%|7.5%|38.1%|
|Brazil|100%|57.1%|9.2%|33.8%|100%|57.0%|9.1%|33.9%|
|Chile|100%|36.8%|8.6%|54.7%|100%|36.1%|6.8%|57.1%|
|Colombia|100%|67.6%|8.2%|24.2%|100%|67.6%|8.6%|23.8%|
|Dominican Republic|100%|49.2%|5.2%|45.6%|100%|49.4%|5.6%|45.0%|
|Ecuador|100%|51.5%|5.7%|42.8%|100%|51.1%|7.7%|41.2%|
|El Salvador|100%|50.1%|1.4%|48.5%|100%|50.3%|1.2%|48.5%|
|Guatemala|100%|79.2%|2.9%|17.9%|100%|79.3%|2.9%|17.8%|
|Honduras|100%|75.9%|5.0%|19.1%|100%|75.6%|5.3%|19.1%|
|Mexico|100%|53.8%|6.4%|39.9%|100%|54.0%|6.7%|39.2%|
|Panama|100%|50.6%|12.6%|36.8%|100%|49.8%|13.2%|37.0%|
|Paraguay|100%|43.4%|6.8%|49.8%|100%|43.7%|7.5%|48.8%|
|Peru|100%|59.9%|9.7%|30.4%|100%|59.9%|9.5%|30.6%|
|Uruguay|100%|53.5%|3.4%|43.1%|100%|53.8%|3.5%|42.7%|
|Total region|100%|59.4%|7.9%|32.7%|100%|59.4%|8.0%|32.6%|

Note: Percentages of individuals. Source: Own elaboration based on household survey data, CCKP historical data, ThinkHazard! data, EM-DAT occurrences, and SPEI.

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due to higher productivity<sup>andlowerunit costs,which boost profitability,ratherthan</sup> due to higher profitability<sup>by and of itself.Improvinglabor productivityandlowering</sup> costs is the way<sup>toincreaseboth profitsandemployment.Againit isinterestingto</sup> note that the same<sup>results wasobtained for Lithuania.</sup> Firm which export<sup>their productstend tocreatemore jobs than firmswhichdo</sup> not. This is consistent<sup>withacommonviewthatanaccessto foreignmarkets</sup> facilitates firm expansion<sup>and employmentgrowth.Theemploymenteffectof export</sup> orientation is substantial:<sup>all elsebeingequal,exporting firmsincreasetheir</sup> For employment by<sup>around4percentagepoints fasterthannon-exportingfirms.</sup> example, if an average<sup>non-exportingfirmincreaseditsemploymentbysomewhat</sup> over 2 percent in<sup>2001overthe previous year,anaverageexportingfirmincreased</sup> employment by over<sup>6 percent.This suggeststhatan increasein Croatia'sexportsis</sup> likely to engender<sup>employmentgrowth,directlyaswellasindirectlythrough</sup> productivity improvements<sup>necessary for firmstoeffectivelycompete in theglobal</sup> economy. As with other variables,<sup>thestronglinkbetweenexport growthand</sup> employment growth<sup>hasalso beenobservedin Lithuania.</sup> Ceteris paribus,<sup>firms,which haveaccesstocredit,do not generatemore jobs</sup> than firm, which do<sup>not.Afterall,thedataindicatethatfirms whichreceivedcredit</sup> created _less_ jobs<sup>thanfirmswhichdidnot,although this negativeeffect isinsome</sup> is<sup>statisticallyinsignificant.This suggeststhatcreditdoesnotgoto</sup> specifications firms with growth<sup>potential,but insteadgoestoinefficientbut wellconnectedfinms.</sup> This sort of "soft<sup>lending" toa narrowgroupof privilegedfirmsimplies misallocation</sup> of resources and<sup>hinders rather thanfostersfirm expansionandemploymentgrowth.'7</sup> Given that credit<sup>canplayanimportantroleinsupporting firm expansionandjob</sup> creation, the observed<sup>negative relationshipbetween accessto credit and job creation</sup> should be of concern.<sup>Theissuewarrants further investigation,andif necessary,a</sup> corrective policy<sup>action.</sup>

> the<sup>effect of ownershiponemploymentvanishes onceotherfirm</sup> Interestingly, characteristics are<sup>controlled for.Otherthings being equal, privatefirmsdo not create</sup> 17 In the sample (which<sup>consists predominantlyof smallfirms),only one firm in five wasusing</sup> credit as a source of<sup>financing.At the same time, according to the WorldBusinessEnvironment</sup> Survey (2001), poor<sup>accessto creditis a major constraintto firmdevelopmentin Croatia.Asmuch as</sup> 55 percent of Croatian<sup>firms considered access to credit as an important obstacle, whichsignificantly is</sup> more than in most<sup>other countriesin theregion.For example,in Hungary,Poland or Slovenia only</sup> about one-third of firms<sup>citeaccess to creditas an importantobstaclefor expansion.</sup>

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_Public rental_ . After the privatization and “communalization” waves, the public owners in the region are today overwhelmingly local governments.<sup>63</sup> Public companies, which are now incorporated and taxed as corporations, have also remained owners of apartment stock.<sup>64</sup> Public owners have generally retained the government-built, postwar, often large-panel, prefabricated housing stock. Rental contracts in the public sector are in writing and are subject to taxation (for example, corporate taxation in the case of public incorporated companies). However, in countries with marginalized public rental stock, rent evasion is frequent and often occupants are not even registered.

**Table 10. Rental Investor Categories and Activity Levels**

||**Armenia**|**Lithuania**|**Poland**|**Romania**|**Russia**|**Serbia**|
|---|---|---|---|---|---|---|
|Subsistence<br>landlords|Very active|Active|Active|Very active|Very active|Very active|
|Restitution<br>beneficiaries|Active|Very active|Very active|Very active|Active|Active|
|Small private<br>landlords|Active|Active,<br>including<br>leveraged|Active,<br>including<br>leveraged|Active|Active|Active|
|Small<br>businesses|None|Active,<br>including<br>leveraged|Active,<br>including<br>leveraged|Active|Active|Active|
|Employer|None|Small market|Small market|Small<br>market|Significant<br>market|None|
|Institutional|No<br>institutional<br>construction|Some new<br>construction in<br>major cities|Active new<br>construction<br>in major<br>cities|Active new<br>construction<br>in<br>Bucharest|Some new<br>construction<br>in major<br>cities|Some new<br>construction<br>in major<br>cities|
|Non-profit<br>rental|None|None|Very active<br>(TBS)|None|None|None|
|Public rental|Inactive (small<br>sector size)|New<br>construction<br>program|Almost<br>inactive|Small<br>sector|Active|Inactive|
|Debt investors|No lending|Lending to<br>small private<br>landlords|Lending to<br>small private<br>landlords,<br>PPPs, and<br>institutions|Lending to<br>small<br>private<br>landlords<br>starting|Lending to<br>small<br>private<br>landlords<br>starting|No lending|

_Source_ : Authors’ assessment based on literature review, information from market participants, and household surveys (Table 1) results.

> 63  Through communalization state housing was typically transferred to municipalities, which then decided on the extent of privatization (if allowed by law) or which carried out state-mandated privatization (if imposed by law).

> 64  Especially natural resource companies in Russia.

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percent significance level. Hence, column (3) provides supportive evidence for the hypothesis that reductions in disincentives to agricultural production yielded real increases in agricultural output.

FAOSTAT also provides data on the economically active population in agriculture; i.e. the labor force that is active in the agricultural sector. Unfortunately, no long time-series data exist for actual employment (and unemployment) in agriculture, but data are available from FAOSTAT (2011) on the allocation of the labor force between the agricultural and non-agricultural sectors. Variations in the labor force share in agriculture provide another useful indication of the real effects that variations in the RRA had on the macroeconomy. Column (4) shows that increases in the RRA induced significant decreases in the share of the labor force that is in the agricultural sector. The estimates in column (4) imply that, on average, a ten percentage points increase in the RRA decreased the share of the labor force in the agricultural sector by around 0.01 percentage points. Given that the sample standard deviation of the change in the share of the labor force in agriculture is less than 0.003, this is not a small effect.<sup>15</sup> And, statistically, the estimated effect is significant at the 5 percent level.

In particular, column (4) tells us that reductions in disincentives to agriculture were associated with a significant release of labor from agriculture to the non-agricultural sector. One possible part-explanation for this is that, if work in agriculture has the objective of providing a minimum level of income for the family -- which, in the context of extremely poor Sub-Saharan African countries, appears plausible -- then labor is released from agriculture to allow some members of now-higher-income farm families to search for better prospects in other sectors of the economy.<sup>16</sup> Another possible part-explanation is that a rise in the RRA induces innovation in

> 15 Another way to see this is by interpreting the estimates in column (4) in terms of standard deviations. Recalling that the sample standard deviation for the change in the relative rate of assistance is 0.167, the estimates in column (4) imply that a one standard deviation change in the relative rate of assistance induced a change in the share of the labor force in agriculture of 0.06 standard deviations.

> 16 We note, without further evidence on the causal relationship between the labor force in the non-agricultural sector and GDP growth, that it is unclear whether the re-allocation to the non-agricultural sector had direct positive GDP growth effects. Brückner (2012) shows that in the context of the group of Sub-Saharan African countries (of which many are characterized by unusually large primal cities and strong ethnic divisions) increases in the urbanization rate had a significant negative GDP per capita growth effect.

26
in the Caribbean from LLI (2021).<sup>13</sup> The data offer comprehensive information on ports of call by freight hauling vessels in the Caribbean during the years 2004-2020. Each observation reports the previous five and subsequent five ports of call by the ship in question, as well as a unique vessel ID number. We also purchased from LLI data on characteristics of the 16,097 freight-hauling vessels in our database. The following information is available for nearly every vessel: the vessel identification number, vessel type, flag of registry, year and place of build, the vessel’s owner, and its dead-weight tonnage (DWT). We combine the port of call data with the ship characteristics data to describe the supply of vessels that call in Caribbean or PR ports, as well as those that meet JA criteria, The LLI data understate the volume of barge traffic between the U.S. and PR. We also use data from the National Ballast Information Clearinghouse (NBIC, 2024), which reports data that is similar to that of LLI, but only for U.S. ports. The NBIC data are publicly available, another advantage. We report port-of-call data from both series for 2019 because it is the most recent year of LLI data not affected by the COVID-19 pandemic.

# **3.2 Puerto Rican import data**

Our empirical gravity model relies on data documenting flows of imports into PR. These data are provided by IEPR (2021) for 2010-2017. The data report monthly imports to PR disaggregated by HS10-digit product code, origin country (for foreign imports) and U.S. customs district (for U.S.-origin shipments).<sup>14</sup> The data report the value of imports (defined in FOB terms) and imported quantities (in kg.).<sup>15</sup> 13Taylor (2021) uses these data to study the impact of large vessels on the reproduction rates of Southern Right Killer Wales.

> 14The variables we use to control for export supply and the trade elasticities we include to generate structural trade cost estimates are only available at the HS6-digit level. As a result, we aggregate our trade data to the HS6 level.

15The data do not report port of destination in PR; for the purpose of calculating shipping distances we assume that all seaborne freight traffic goes through San Juan. Our port-of-call data show this to 9
The foregoing factors resulted in a widening of the urban-rural gap in household living standards over time (Nguyen _et al_ ., 2007). Although the absolute poverty rate has declined substantially since the introduction of the _doi moi_ , poverty continues to be far more pervasive in rural than in urban areas. The incidence of household poverty in urban areas reduced from 25% in 1993 to well under 10% by 2004. Though the rural headcount poverty rate has halved over the same period, it remains stubbornly high and was recorded at 25% in 2004.<sup>2</sup> The increasing disparity in urban-rural welfare provides important incentives that stimulate the rural-urban migration flows in Vietnam.

According to the 1999 Population and Housing Census data, 6.5% of the population over five years of age (about 4.5 million people) changed their place of residence between 1994 and 1999.<sup>3</sup> It is not surprising that provinces with the highest population density (in the Red River Delta) and those with low household incomes in the central regions (the North and South Central Coast) had the highest rate of net outward migration. The country’s three largest cities, Hanoi, Da Nang, and Ho Chin Minh City (HCMC), were the main destinations for migrants. For instance, out of a total of nearly one million inward migrants to the Southeast, HCMC received nearly one half of them.<sup>4</sup> An important implication of the increased internal population movement is the significant amount of remittances repatriated by migrants. Le and Nguyen (1999), using the Vietnam Living Standards Surveys (VLSS) data from 1992/93, report that about one-fifth of households received remittances during the 12 months prior to the survey interview date and these were equivalent to, on average, about 38% of their household expenditures.

4
the bottom 50% percentile on the law and regulation dimension, the country has improved on this dimension during the last decade. In the four dimensional ML graph, the green line contains the red line, indicating that over this ten year period Brazil has improved its ranking on each dimension. The principal components show that its rank on the openness dimension has lowered, which probably has to do with Mercosur Argentina and Brazil shared similar rankings on openness in 1995 – or is a result of keeping trade barriers at fixed levels while the rest of the world has liberalized). The ML graph indicates impressive gains in literacy and education in Brazil. It is probably a good bet that this trend will also lead to an increase in Brazil’s rankings on the law dimension in future years (recall that the factors are correlated). The “weighted” data paint a similar picure.

China, being a populous country, will obviously show different rankings for “weighted” versus “unweighted” dimensions. We should be cautious about interpreting the meaning of the innovation potential factors in the “unweighted” versus the “weighted” data. In the unscaled data China ranks high on the innovation potential list because of the sheer strength of its size. The “weighted” data present quite a contrast along the dimension measured by researcher and technical journals. In other words, while China has a critical mass in innovation potential (which may be the reason it attracts foreign direct investment), China still has a long way to go in achieving its full potential on innovation as measured by the scaled data. If it produced patents, researchers and technical journal at the same per capita rate as the more advanced countries, China would probably be an OECD country. Such trends are already in evidence. Along each of these dimensions in the “weighted” data, China is already at the median of the sample and has made strides to move ahead, especially in patent applications. On other dimensions, literacy has not improved greatly. However, the ICT factor leaped from the bottom quartile to close to the median among the sample.

33
# **5. Discussion**

Household surveys in five low and middle-income countries covering Latin America, Eastern Europe, East Asia and the Pacific, and South Asia reveal significant associations between mental health scores and gender, the physical health of the respondent, his/her marital status, and the mental well-being of other members in the respondents’ household and community. These relationships hold (with occasional deviations) across all the countries with roughly comparable magnitudes. In contrast, there is no consistent relationship between mental health scores and socio-economic measures such as the respondent’s education or the per-capita expenditure of the household in which the respondent resides. There is evidence in some countries that lower mental health is associated with reduced labor force participation, especially for women. Mental health is also a significant predictor of health care utilization and thus perhaps burdens a health system ill-equipped to diagnose and provide care.

These results provide the setting for a discussion centered around (a) the validity of mental health modules in multi-purpose household surveys, (b) the possible use of mental health in a broader definition of welfare than traditional measures, and (c) the implications for policy and research on mental health.

# <u>5.1 Including mental health in multipurpose household surveys</u>

Kahneman and Krueger (2006, p.7) in a review of subjective welfare measures suggest that “ _the validity of subjective measures of well-being can be assessed, in part, by considering the pattern of their correlations with other characteristics of individuals and their ability to predict future outcomes_ .” The patterns of correlations between survey mental health measures and age, gender, widowhood, sickness, and the mental health of other family members are remarkably stable across countries and different surveying techniques. We find this to be reassuring, suggesting that survey mental health measures do indeed reflect real psychological morbidity at the individual level. Moreover, unlike other subjective measures of well-being, mental health measures have been further collaborated by studies which show that these general purpose mental health questions are strongly predictive of clinical diagnoses of depression and anxiety disorders.

Moreover, we find mental health status does help predict labor force participation and health care utilization, conditional on other socioeconomic variables typically collected in multipurpose household surveys. Furthermore, the associations between mental health scores and individual/household characteristics are very similar in surveys where questionnaires were fielded on a first visit to households and where they were fielded after a period of acquaintanceship. Finally, shorter modules (such as the GHQ-12) reveal similar associations as the longer SCL-90R, which took one hour to field for non-literate

- 13 -
employment – but only outside SSA. In SSA, more interactions with officials are associated with lower growth of small and medium enterprises than for micro enterprises. In combination with higher rates of bribes among small enterprises, the incentives seem to be to stay below the radar screen of officials.

Taken together, there are two patterns that stand out for SSA. First, the overall impact of a weak investment climate on growth is more muted. For a given level of outages or lack of access to finance, SSA enterprises reduce employment growth by less. Second, this can be understood by more pronounced differences in the response across different sizes of enterprises in SSA. Enterprise responses to potential constraints are more likely to serve to expand growth among the micro enterprises relative to larger enterprises. While this keeps employment growth high among incumbent SSA enterprises, the longer run sustainability of jobs in more labor intensive, less productive activities will need to be addressed as the investment climate is strengthened.<sup>6</sup> After reviewing the literature in section 2, the paper describes the large enterpriselevel dataset used in this work in section 3. The Enterprise Survey dataset has comparable enterprise-level data on both measures of the investment climate and enterprise performance for 104 countries, including 31 from SSA. It also discusses our approach to addressing potential measurement error, endogeneity and omitted variables. Section 4 then describes some of the differences in growth patterns in SSA compared to the rest of the world. Section 5 describes differences in the investment climate, focusing on the four areas of finance, infrastructure, governance and regulation. Section 6 then combines these measures to examine their impact on employment growth. Results are presented in two forms. One tests for differential impacts of investment climate conditions across regions. The second allows for further disaggregation of results, allowing non-linear responses across regions and sizes

> 6 Indeed, micro firms in SSA have significantly lower value added per worker – compared to larger enterprises in SSA as well as compared to micro firms in other LOW regions (Table 1c).

5