rafmacalaba/datause-encoder-data huggingface dataset:
both the time series and cross sectional dimension of the data. We re-estimate the investment model over moving windows of **16** years of data and use the re-estimated model to compute the globalization measure. This approach accommodates the difficult problem of identifying structural breaks, and possibly multiple breaks, in a large cross section of countries and with many variables.

Consistent with the hypothesis of increased world capital market integration, the analysis reveals that global factors have increased in importance in explaining the **dy-** namics of the cross section of foreign direct investment and the level of aggregate foreign investment over time for developing and developed countries. For the full sample of countries in **1999,** the globalization measure was **62** percent, which represents an **8** fold increase since **1985.** Fhrthermore, developing countries' exposure to global factors has increased faster than that of developed countries and the gap is narrower at the end of the 1990s, particularly so for Latin America. Interestingly, we find a significant decline in global factors as drivers of direct investment around the time of the debt crisis of the 1980s for developing and developed countries alike, but no noticeable change due to the recent Mexican, Russian, or East Asian crises. Finally, using the liberalization variables in Bekaert, Harvey and Lundblad (2002), we show that our globalization measure is explained to a significant extent **by** the level offi nancial liberalization.

The increased exposure to global factors that we find is associated with increased flows of direct investment into emerging market economies. While these findings constitute evidence of greater worldwide market integration, we also find that growth in local productivity, trade openness, financial depth, low government burden, and macroeconomic stability are important domestic drivers.

The sequence oft he paper is as follows. Section 2 briefly presents severalt heories of foreign direct investment. This section appeals to appendix **A** for three case studies (Enron International, Intel International, and Johnson and Johnson) intended at providing explicit examples of global factors at work as drivers of foreign direct investment and to 'See for example Bekaert and Harvey **(1995),** and Bekaert, Harvey and Lumsdaine (2002) for alternative approaches.

4
<u>Table 1: Descriptive statistics (continued)</u>

|||Obser|vationsfrom|all,ma|jor, andmin|or refugee-|hostingro|und-EAs||Mean|
|---|---|---|---|---|---|---|---|---|---|---|
|||All|||Major hos|ts||Minor host|s|Difference|
||N|Mean|SD|N|Mean|SD|N|Mean|SD|Major-Minor|
|**Panel (d): Control variables**|||||||||||
|Head is male (=1)|13871|0.711|0.453|5586|0.694|0.461|8285|0.723|0.447|(-)***|
|Head’s age|13871|44.995|15.571|5586|46.126|15.265|8285|44.233|15.728|(+)***|
|Head's education in years|13797|3.411|5.025|5564|3.907|5.402|8233|3.075|4.724|(+)***|
|Head is married (=1)|13873|0.705|0.456|5587|0.690|0.462|8286|0.715|0.451|(-)***|
|Head is born in the residence region (=1)|13873|0.875|0.331|5587|0.829|0.376|8286|0.906|0.292|(-)***|
|Household size|13925|4.809|2.422|5590|4.903|2.428|8335|4.746|2.416|(+)***|
|# of young dependents in the HH|13925|2.128|1.787|5590|2.043|1.770|8335|2.185|1.796|(-)***|
|# of elderly dependents in the HH|13925|0.185|0.430|5590|0.196|0.439|8335|0.178|0.424|(+)**|
|Total annual rainfall (mm)|13883|1114.200|494.981|5588|986.405|461.425|8295|1200.291|498.236|(-)***|
|Rainfall in wettest quarter (mm)|13883|561.641|219.022|5588|523.581|234.982|8295|587.281|203.622|(-)***|
|Change in greenness in_Meher_(EVI)|13883|41.767|14.529|5588|39.626|16.617|8295|43.209|12.735|(-)***|
|Peakof EVIvaluein_Meher_|13883|0.441|0.120|5588|0.423|0.127|8295|0.454|0.113|(-)***|

_Notes_ : Consumption expenditure measures are in per capita terms. HH, N, SD, EVI, and NFE stand for household, number of observations, standard deviation, Enhanced Vegetation Index, and non-farm enterprise, respectively. Rainfall and greenness related variables consider the fiscal year immediately before the survey year in each round of the survey. Rainfall related variables are measured at the household-round-level while greenness related variables are at the zone-round-level (averaged by zone-round). Despite this, greenness related variables are reported here at the household level. _Meher_ is the main growing season in Ethiopia running from June to September. The symbols (+) and (-) indicate whether the corresponding mean difference is positive and negative, respectively. Asterisks: *, **, and *** indicate statistical significance of the mean difference at 10%, 5%, and 1% levels, respectively, based on the standard two-sided t-test. The source of all the variables in panel (d) is the ESS.

39
the cost-reduction effects. Warren and Findlay (2000) suggest computing the pro-competitive impacts using price-cost margins (or “net interest margins”). Ideally, we would also like to capture the cost-reduction effects by comparing actual costs to a constructed estimate of costs if services were provided according to a ‘world’s best-practice’ cost function. Unfortunately, none of these measurements are attainable for Tunisia, as is the case for most countries.

The services barriers given in Table 3 are based on industry studies in Tunisia and extensive discussions with Tunisian industry experts, country economists and government officials and on Zarrouk (2000). The financial services barriers estimates are taken from our observation that the level of monetary intermediation in the banking system is about 30% lower than in comparable countries (Bahlous and Nabli 2000) and on Goaied’s (1999) estimation of the cost inefficiencies in the financial sector. This is in line with the estimates of Kalirajan, et al (2000) for the banking sectors in Chile, Singapore, South Korea, and Thailand. The price wedges in insurance, communications, and transportation reflect the high level of benchmark regulation in those sectors and comparisons with markets in similar countries (Vittas 1995, World Bank 2000). The distribution and retail sectors show large inefficiencies and are very fragmented, making our 5% inefficiency measure conservative. Many professional services are subject to a nationality requirement, thus restricting foreign participation, and it is possible that our 10% estimated price wedge is low. While the construction, hotel, and restaurant sectors are viewed here as already substantially liberalized and subject to a small price-cost wedge, foreign participation remains subject to the investment code and projects are granted upon approval of a _Cahier des Charges_ . Remaining sectors (health and education, public services, and other services) are taken to be exempt from potential liberalization.

13
elimination of barriers to capital movements have stimulated governments to compete for FDI in global markets as well as reinforced the role of tax policy in this process. This recent competitive trend has to be offset by the increasing pressure that governments face to harmonize their tax policies within regional (or international) agreements. A second important issue has been the recognition that tax policies of the home and host countries are interconnected and that this link influences the behavior of multinationals. There has been a great deal of evidence, especially after the changes in the U.S. tax laws during the late 1980s, that home country tax policy affects both the multinational firm's behavior and the effectiveness of tax policy in the countries where these firms operate and invest. Last but not least, there has been a growing attention to the costs associated with tax incentives -and not only to their possible benefits. Tax incentives are likely not only to have a direct negative impact on fiscal revenues but also, and frequently, create significant possibilities for suspicious behaviors from tax administrations and companies. This issue has become crucial in emerging countries where budgetary constraints as well as corruption are certainly more severe than in industrial countries.

**Table 1**

Types of Incentives Used by Region

|**Region/**<br>**Major**<br>**Incentives**<br>**(countries)**|**Africa**<br>**(23)**|**Asia**<br>**(17)**|**Latin**<br>**America**<br>**&**<br>**Caribbean**<br>**(12)**|**Central**<br>**&**<br>**Eastern**<br>**Europe**<br>**(25)**|**Western**<br>**Europe**<br>**(20)**|**Other**<br>**Countries**<br>**(6)**|**Total**<br>**(103)**|
|---|---|---|---|---|---|---|---|
|Tax holidays|16|13|8|19|7|4|67|
|Accelerated<br>depreciation|12|8|6|6|10|5|47|
|Investment|4|5|9|3|_5_||26|
|allowances||||||||
|Import duty<br>exemption|15|13|11|1<br>3|7|4|63|
|Duty drawback|10|8|10|12|6|3|49|

Source: UNCTAD, 1995 3
understand if there is heterogeneity among informal businesses in terms of supplying to other businesses, we checked whether it correlates with productivity or owners’ education. Informal businesses that have a higher than the median level of sales per worker are equally likely as those below the median to supply to other businesses, suggesting a limited association with productivity. In contrast, owner’s education does have some predictive power. In particular, the owners with a college degree are more than twice as likely as those without a degree to supply products or provide services to other businesses (31% vs. 12%).

Not only is the share of informal businesses that supply to other businesses very low, but the size or frequency of such transactions appears to be small. Informal businesses in Somalia and Zambia were also asked whether their main customers are individuals or businesses, and 96% and 94%, respectively, responded that their main customers are individuals with little variation within countries. By comparison, the same measures for formal micro-enterprises in Somalia and Zambia are 85% and 91%, respectively, with the difference between the two types of businesses in Somalia being statistically significant. These patterns of informal businesses mainly transacting with individuals, with a very low share selling to other businesses, are consistent with a bleak picture of informal businesses being largely isolated from supply chains with little opportunities to tap into for growth (e.g., La Porta and Shleifer, 2014).

Developing business relations with customers is likely crucial for informal businesses, regardless of the types of customers they have. This is because the lack of formal registration may limit their ability to ensure that the transactions made with customers or suppliers are enforced by parties outside of the transaction itself, including courts or other institutions. Informal businesses are thus more vulnerable in terms of both being able to convince the customers that their side of the transaction will be fulfilled and ensuring that customers fulfill their side of the transaction as well (e.g., pay as much as agreed and on time). To investigate informal businesses’ relations with their customers, the Informal Sector Enterprise Surveys ask whether the businesses’ typical customer is someone known as a repeated customer, someone known personally, or someone unfamiliar. Figure 9 shows the shares of businesses’ typical customers in each country (panel A) and sector (panel B). Except for Mozambique, the least common type of customer for each country is someone unfamiliar, suggesting that impersonal market exchanges comprise a minority of business transactions for informal businesses in these countries.

23
of formal dependent employment creation, tends to flow into unemployment, self-employment (often informal), or informality, which has been observed in Brazil during the last crises (Firpo and de Pieri, 2018; World Bank, 2020).

**Multiplier effect caused by BF coverage** The second methodological challenge relates to potential reverse causality, namely that the changes in coverage of BF cash transfers might affect levels of local employment through a local multipliers effect (Gerard _et al._ , 2021). At the national level, the number of beneficiary families remained more or less stable in our observation period (in December 2012, 13.9 million families were registered in BF compared to 13.2 million families in December 2019); however, we do observe variation at the local level, which might cause biases in our results (Ministerio da Cidadania, 2022).

**Shift-share instrument** For both challenges, we propose to use a shift-share instrument after Bartik (1991). In particular, we instrument local employment growth using national employment growth across more than 600 industries, interacted with the local share of each respective industry in the previous period. The instrument is plausibly exogenous, as national employment growth at the industry level is unlikely to be correlated with local economic shocks or unobserved local factors, affecting local employment growth only through the area’s industry composition. The Bartik instrument can be expressed as:

where _zλτt−_ 1 is the share of private sector employment in industry _λ_ in municipality _τ_ in _t−_ 1 and _gλ_ is the national growth rate of private sector employment in industry _λ_ at time t.<sup>25,26</sup> We compile different variations of the shift-share instrument. We start by using annual growth rates. However, since we have monthly information on exits, we switch to semester growth rates. In an additional step, we exclude the growth rate of the municipality for which the industry is calculated. In other words, if we calculate the instrument for municipality _τ_ in industry _i_ , we exclude employment levels of industry _i_ in municipality _τ_ when computing the national growth rate (leave-one-out estimator, illustrated by _−τ_ in model (2)). This gives additional credibility to the instrument since we can exclude concerns that national growth rates of a specific industry

> 25We follow the same procedure as above and winsorize the national employment growth by industry at the 99.5 percentile.

> 26We acknowledge that we could also work with initial shares at time t = 0, however, since we are looking at a long time period during which there were changing economic situations, looking at the shares of the previous semester seems more reasonable (Borusyak _et al._ , 2022).

15
# **-** **<u>Appendix table A7: Likelihood of completing the follow up survey</u>**

|<br>PSG beneficiary<br>Applied to other programs|<br>0.097***<br>(0.025)<br>0.178***<br>(0.027)|0.102***<br>(0.026)<br>0.176***<br>(0.027)|
|---|---|---|
|Owner respondent|-0.021|-0.020|
||(0.063)|(0.064)|
|Manager respondent|-0.012|-0.008|
||(0.068)|(0.068)|
|Log holding size (ha)|-0.021**|-0.023**|
||(0.010)|(0.010)|
|FOP|0.128***|0.123***|
||(0.032)|(0.032)|
|Individual|0.063|0.058|
||(0.041)|(0.042)|
|Managing multiple farms|-0.070**<br>(0.031)|-0.071**<br>(0.031)|
|Considered applying for credit|0.071***<br>(0.027)|0.070**<br>(0.027)|
|Personal situation on a scale of 1 to 10|0.007|0.008|
||(0.006)|(0.006)|
|Not directly affect by conflict|0.046|0.048|
||(0.039)|(0.041)|
|East||0.022<br>(0.053)|
|North||-0.019<br>(0.035)|
|South||-0.027|
|||(0.030)|
|West||-0.065*<br>(0.035)|
|No. of obs. (farms)|2,101|2,101|

_Source:_ Original table for this paper. _Note:_ Standard errors in parentheses: * p < 0.10, ** p < 0.05, and *** p < 0.010. Coefficients reported are marginal effects from a probit model. FOP = family-owned business; ha = hectare; PSG = producer support grant.

38
# **III. Data and Methodology**

In our research, we use the List Randomization method, discussed in detail in this section, to estimate both the incidence and the extent of underreporting of domestic violence and physical harassment on private and public buses in Kerala.

The list randomization method, also known as the item-count technique (ICT) and the unmatched count technique, was introduced by Anderson and Miller (1990). Respondents are divided into control and treatment groups, with the former given a set of true-false statements that are both relevant and inoffensive to the respondents’ socio-economic or political context. The treatment group is given one true-false statement in addition to the ones given to the control group. The additional statement is about the sensitive issue. The respondents of each group are asked to report the number of statements they agree with, and it is expected that those in the treatment group will give true answers about the sensitive statement which they might not if questioned directly. The mean value of true answers by the control group is then subtracted from the mean value of true answers by the treatment group to get the average proportion of people who agree that the sensitive statement is indeed true. Examples of application of the list randomization technique include findings on condom use among college students (LaBrie and Earleywine, 2000), voting for bribes (Gonzalez-Ocantos et al., 2012), shoplifting (Tsuchiya et al., 2007), theft at the workplace by employees (Wimbush and Dalton, 1997), welfare and unemployment benefits (Van der Heijden et al., 2000), use of microcredit loans for non-business purposes (Karlan and Zinman, 2012), and illegal migration (McKenzie and Siegel, 2013).

The Kerala Migration Survey (KMS) 2014 was the sixth in the series of an ongoing migration monitoring studies conducted by the Centre for Development Studies, Trivandrum. The first survey was conducted in 1998 (Zachariah and Rajan, 2015). The total sample size in KMS 2014 was 14,575 households, including 10,000 new households and 4,575 households which were interviewed in the previous round in 2010 (Zachariah and Rajan, 2015). Questions about GBV and attitudes on genderrelevant issues were also added to the household questionnaire of the KMS 2014. To implement the list 10
vulnerable than rich ones, so they are likely to lose a larger fraction of their assets than rich households.<sup>20</sup> Second, disaster losses are highly heterogeneous and different households are affected very differently (see Section 3.2).

Before the impact, the welfare in the affected area is given by: Where and are the number of poor and non-poor households in the affected area, and and are their consumption (note that all values noted with in ̃ are for the net present value of the future flux, not the instantaneous value at time t). The function is a “welfare function” that links the net present value of consumption with individual welfare. Using the net present values of consumption to assess welfare is a strong simplification, which is acceptable only if households can smooth the shock over time and if the shock remains relatively limited compared with consumption; these assumptions are thus relaxed in Section 3.2.

After the shock, but before any transfers, the welfare becomes: Where and are the household level consumption losses due to the disaster (also in net present value of the future flux of consumption loss). Since we are looking at the net present value of consumption (and net present value of the consumption loss), it is likely that and are small compared with and , and the nonlinear terms become negligible: Note that these consumption losses can be due to the direct impact of the disaster on households’ productive assets (including their home) or to indirect impacts through the impact on firms (and employment and capital gains) and through government-provided services.<sup>21</sup> If we assume linearity of losses to pre-disaster income, we have ̃ and ̃ ̃. We have the total income in the area ̃+ , and the total loss of income equal to ̃ ̃+ . We thus have ̃ ⁄ and thus:

> 20 One important consideration we are not taking into account here is the differentiated ability of poor and nonpoor households to save in non-tangible assets such as financial products, which are less exposed to natural hazards and can be more easily diversified (spatially and sectorally). If poor households save in kind while non-poor save through financial product, the vulnerability of the former will be larger than that of the latter, and the distribution of disaster losses will not be proportional to income or wealth. In the analysis of hurricane Mitch in Honduras (Carter et al., 2007), poor households are found to be affected less often than rich households, but they lose a larger fraction of their assets if they are affected.

> 21 The impact through government-provided services depends on the ability of the government to fund reconstruction and the need to reallocate budget resources from public services to reconstruction; this will depend on the public sector vulnerability to disasters (Aakre et al., 2010; Mechler, 2004; Mechler et al., 2006).

23
literature consistently finds that cognitive skills and education levels alone do not fully explain variations in labor market outcomes. The analysis is designed to determine whether personality traits are in correlation with wages. In classic human-capital theory, salaries represent the productivity of the individual. Our hypothesis is that non-cognitive skills, as measured by selected personality traits, also play an important part in determining productivity. The analysis compares respondents’ wages at the time of the survey with entry-level wages for their respective positions.

# _c) Estimation of Personality Traits_

A diverse range of definitions can be applied to the concept of non-cognitive skills, and different terminologies are often used to describe similar or overlapping concepts, including socioemotional skills, soft skills, non-cognitive skills, character skills, personality traits, 21<sup>st</sup> century skills, or life skills (Puerta, Valerio and Bernal, 2016). As noted above, this analysis uses the big five personality traits plus measures of grit, hostile attribution bias and decision-making style as in the data collection module developed by the STEP survey (see Table 1 for definitions). All of these traits are independently applied in the literature and are shown to correlate with a range of economic and social outcomes (Pierre _et al_ ., 2014). The STEP survey estimated each trait through three Likert-style 4-point-scale questions, for a total of 24 questions. The default analytical approach for aggregating the trait scores is a simple average, but a number of other techniques may be applied ( _ibid_ .). While some studies use the simple average (e.g., Acosta _et al_ ., 2015), others use factor analysis to recalculate the personality scores (Ajwad _et al_ ., 2014a, 2014b and 2014c; Cunningham _et al_ ., 2016).

This study uses an index of personality traits calculated based on the Rasch model rather than on raw personality trait scores. The Rasch model, which is rooted in item-response theory (Rasch, 1960), is commonly used to analyze binary responses. It allows the estimation of latent, rather than observed, scores by applying different weights to different questions depending on relative differences among the sampled respondents. We use a variation of the Rasch model meant to cover categorical Likert-style responses called the rating scale model (RSM), which allows for estimating latent scores in data with non-binary or ordinal variables (Andrich, 1978).<sup>6</sup> For each of

> 6 The RSM differs from the partial credit model (PCM), as the latter is also a type of Rasch model used to analyze data with more than two responses. For example, I1 with categories 1-3 and I2 with categories 1-4 can be aggregated

7