rafmacalaba/datause-encoder-data huggingface dataset:
nevertheless, is insignificant in other specifications when we consider the household’s asset index. This, at first glance, seems like a counterintuitive result but it is not. Female headed households are mostly in the South, which is the richer part of the country. The variable, thus, reflects the big divide between North and South rather than increasing gender equality. Households where the head has never married are significantly less likely to be poor, all else being equal. These households accordingly also have higher per capita expenditure levels. Consequently the head’s age is positively and significantly related to being poor, although the effect is decreasing with age. All else being equal, households where the head has a job which is not related to agriculture are significantly less likely to be poor than those where the head does not have work, and those where the head is involved in agricultural work. Accordingly, households where the head is employed in non-agricultural activities have higher per capita expenditure levels.

When the asset index is included in the previous specification (not reported), the result shows a significant and negative relation to being poor. Also, the household’s asset index is positive and significant in the expenditure per capita equation. The sign of the coefficient for the health shock dummy included are somewhat counterintuitive. Part of the reason why the sign is negative may be explained by the fact that the measure of poverty is built around expenditures and not income. Hence households that face a health shock may be forced to increase their expenditure in order to cope with the event. The funds for this increase may come from loans or from sales of household owned items.

The results from the GME estimation procedure resemble those from the logit specification (Table 3). The use of GME is justified because of the improved properties the estimator possesses and helps test the robustness of the analysis. As mentioned above, GME works well when the sample is small, covariates are highly correlated, and is more efficient than its maximum likelihood counterparts (Golan et al. 1996).

Households located in the North West, South East, and South South zones are significantly more likely to be considered poor in the second period than households in the North Central zone. Households in South West are significantly less likely to be poor. Households in urban locations are less likely to be poor and have higher expenditure per capita than those in rural areas. The effect of education is significant for those who completed secondary level schooling. Not surprisingly, expenditure levels for all are significantly lower for households with no education when compared to higher educational attainments.

Table 4 compares the various thresholds estimated by different specifications. Ten percent probability to fall into poverty is the threshold used in VAMC’s studies. Households below 16
Even among these 92 firms, 53% (unreported in the table) had a sufficient cash balance to cover their liquidity crunch. Thus, a very small percentage of the firms in the US sample had a positive increase in sales from 2009 to 2010 while the overall change in credit (short-term debt, long-term debt, net capital stock issuance, cash generated from cutting dividends and cash from investing and operating activities) was negative.<sup>23</sup>

# **_5.2 Firms’ Financing and Investment Decisions during Recovery – US Case_**

In this section we repeat the analysis in section 4.2 for the US case. We examine how positive and negative cash flow shocks affect firms’ investment, financing, and distribution decisions jointly, subject to the accounting identity that sources of cash equal uses of cash. We re-estimate the system of equations in (7) over 2006-2010 using a sample of US firms from Compustat. We start with the balanced sample of 3,338 firms and remove the top and bottom 1% outliers for all the variables in the system. To estimate the effects during the crisis and recovery periods separately, we estimate the system by including four time dummies for 2006-2007 (pre-crisis period), 2008 (crisis year), 2009 (crisis year) and 2010 (recovery year) and interactions of Operating cash flow (OCF) with each of the four dummies. In all regressions, we use de-meaned values of all variables (to proxy for firm fixed effects) and control for firm size (Log Total assets) and Tobin’s Q. The system is estimated as a seemingly unrelated regression (SURE) model subject to the constraints that across the system, in each year, the operating cash flow coefficients sum to one while the other coefficients sum to 0. That is, Σ Coefficient of OCF + Σ Coefficient of (OCF x 2006-2007 dummy) = 1; Σ Coefficient of OCF + Σ Coefficient of (OCF x 2008 dummy) = 1; Σ Coefficient of OCF + Σ Coefficient of (OCF x 2009 dummy) = 1; Σ Coefficient of OCF + Σ Coefficient of (OCF x 2010 dummy) = 1;

> 23 As is the practice in the literature (e.g. Gatchev, Pulvino and Tarhan, 2010), we replace missing data with zero to avoid dropping observations with missing Compustat variables . When we re-compute Table 4 after dropping observations with missing data on long-term borrowing, net capital stock issuance and other financing, our sample is reduced to 1609 firms of which 34.62% are candidate Phoenix miracles. When we examine alternate sources of credit and further drop observations with missing data on the different investing and operating activities in the Statement of Cash flows, our sample is reduced to 890 firms and we find that only 14.16% of the firms had a recovery in sales without a recovery in credit.

26
3 FOREIGN DIRECT INVESTMENT IN KENYA but new jobs as well. Chinese companies have a higher number of jobs per project because they have fewer projects than other countries. Between 2003 and 2015, FDI from China created 166.92 jobs on average; total FDI generated 100 jobs per project.<sup>10</sup> FDI has a strong ability to create jobs, and table 3 shows some of the progress.

<!-- Start of picture text --> 6000<br>4000<br>2000<br>0<br>China Egypt India Japan Nigeria South AfricaSouth Korea Spain UK United States<br>Country<br>Jobs Created<br><!-- End of picture text --> Figure 29: China is fifth largest creator of jobs. India creates the most jobs (2003-2015) Source: fDi Intelligence from The Financial Times Ltd 2015 Table 3: FDI Projects and related employment (2007-2011)

|Year|Number of Projects|Employment|Employees per<br>project|
|---|---|---|---|
|2007|55|2,847|51.8|
|2008|73|4,341|59.5|
|2009|121|37,045|306.2|
|2010|129|15,753|122.1|
|2011|145|13,289|91.6|

_Source:_ Adapted from UNCTAD/DIAE/PCB/2012/6

**Chinese firms already offer basic skills training** 60 percent of Chinese companies offer formal training programs on skills, safety, and hygiene for local staff; 64 percent of all foreign firms in Kenya offer formal training (SACE 2014; World Bank Enterprise Survey 2013). Companies in the manufacturing sector and state enterprises are more likely to offer formal training than private companies and the service sector. A few Chinese projects aim specifically to provide skills training for Kenyans, but businesses report that high turnover harms efforts for skills training and promoting Kenyans to higher level management because the high staff turnover raises the cost of training employees (SACE 2014). The BPI suggests that companies engage local employees in formal contracts, regardless of skill level, to build more trust and reduce the incentive to switch jobs and vandalize property. The language barrier also presents a challenge in training, but the growing interest in Chinese langauge study among Kenyans should slowly improve communication

> 10Kenya gained 40,646 jobs and 406 projects from FDI in total between 2003 and 2015.

29
# **1. Introduction**

Vertical specialization has been one of the most notable trends in the international organization of production during the last few decades (Helpman, 2006; Spencer, 2006; Desai, 2009). Thanks to reductions in communication, transportation and other trade barriers, multinational firms have sliced up their supply chains and have dispersed their production activities across multiple countries. This means that a single final good is often worked on in many countries, with each sequential node in the supply chain performed in the location that is most advantageous for the process.

A prominent question in the literature on vertical specialization is the role of trade costs on trade within global production networks (GPNs). In a seminal theoretical paper, Yi (2003) has formally demonstrated that intra-GPN trade should be more sensitive to changes in trade costs than regular trade since vertical specialization leads to products crossing borders many more times before reaching the final consumer. Yi (2003) has used this insight to explain how a relatively small reduction in tariffs could explain the rapid growth of world trade in the second half of the twentieth century. Furthermore, Rubin and Tal (2008) and Rubin (2009) have built on this theory to conjecture that rising oil prices will lead to a major slowdown in the growth of world trade and especially intraGPN trade. Finally, Jacks, Meissner and Novy (2009) and Yi (2009) have used the notion to attribute the large trade collapse during the recent Great Recession to the impact of rising trade costs associated with evaporating credit, increasing non-tariff barriers and home bias in government stimulus plans on global supply chains.<sup>1</sup> Largely due to data limitations, empirical research on the sensitivity of intra-GPN trade on trade costs has been scant.<sup>2</sup> In this paper, we take advantage of a unique data set on China’s processing trade regime for the period from 1988 to 2008 to analyze the role of trade costs on intra-GPN trade. Under this customs regime, firms are granted duty exemptions on imported raw materials and other inputs _as long as they are used solely for export purposes_ . As a result, the data set provides, for each Chinese processing location,

> 1 This argument has been contested by Hoekman, Martin and Mattoo (2009) and Kee, Neagu and Nicita (2010), among others. See the discussion in section 6.

> 2 A notable exception is Hanson, Mataloni and Slaughter (2004), who have examined the role of trade costs for the decisions by U.S. multinationals to export intermediate goods to their foreign affiliates for processing.

2
# **Table 1. Structure of the financial system**<sup>**3**</sup>

|**Type of financial institutions**|**Total as**|**sets (in mil**<br>**Denars)**|**lions of**||**Structure**||**Numb**|**er of instit**|**utions**|**Tota**|**l assets /**|**GDP**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**2005**|**2009**|**2010**|**2005**|**2009**|**2010**|**2005**|**2009**|**2010**|**2005**|**2009**|**2010**|
|**Depository financial institutions**|**142,391**|**271,825**|**308,265**|**89.7%**|**89.7%**|**n.a.**|**34**|**28**|**26**|**48.3%**|**66.4%**|**72.7%**|
|Banks|140,436|268,543|305,290|88.4%|88.6%|n.a.|20|18|18|47.6%|65.6%|72.0%|
|Savinghouses|1,955|3,283|2,975|1.2%|1.1%|n.a.|14|10|8|0.7%|0.8%|0.7%|
|**Nondepository financial institutions**|**16,427**|**31,347**|**n.a.**|**10.3%**|**10.3%**|**n.a.**|**23**|**76**|**n.a.**|**5.6%**|**7.7%**|**n.a.**|
|Insurance companies|13,618|12,202|n.a.|8.6%|4.0%|n.a.|10|13|n.a.|4.6%|3.0%|n.a.|
|Insurance brokers|n.f.|n.a.|n.a.|n.f.|n.a.|n.a.|n.f.|12|n.a.|n.f.|n.a.|n.a.|
|Insurance agents|n.f.|n.a.|n.a.|n.f.|n.a.|n.a.|n.f.|5|n.a.|n.f.|n.a.|n.a.|
|Leasingcompanies|2,276|9,115|n.a.|1.4%|3.0%|n.a.|2|9|n.a.|0.8%|2.2%|n.a.|
|Pension funds|n.f.|8,752|12,494|n.f.|2.9%|n.a.|n.f.|4|4|n.f.|2.1%|2.9%|
|Pension fund management companies|174|333|n.a.|0.1%|0.1%|n.a.|2|2|2|0.1%|0.1%|n.a.|
|Brokerage houses|359|754|n.a.|0.2%|0.2%|n.a.|9|18|n.a.|0.1%|0.2%|n.a.|
|Investment funds|n.f.|152|n.a.|n.f.|0.1%|n.a.|n.f.|8|n.a.|n.f.|0.0%|n.a.|
|Investment fund management companies|n.f.|40|n.a.|n.f.|0.01%|n.a.|n.f.|5|n.a.|n.f.|0.01%|n.a.|
|Private equityfunds|n.f.|n.a.|n.a.|n.f.|n.a.|n.a.|n.f.|n.a.|n.a.|n.f.|n.a.|n.a.|
|Private equityfund management companies|n.f.|n.a.|n.a.|n.f.|n.a.|n.a.|n.f.|n.a.|n.a.|n.f.|n.a.|n.a.|
|**Total**|**158,818**|**303,172**|**n.a.**|**100.0%**|**100.0%**|**n.a.**|**57**|**104**|**n.a.**|**53.8%**|**74.1%**|**n.a.**|

n.f.- not functioning n.a. - not available

> 3 Source of data for all tables and charts: National Bank of the Republic of Macedonia (www.nbrm.mk).

3
Currently, Singapore and the Republic of Korea, top performers with regard to the TWP processing time, are the only economies with a complete online TWP processing system.

75 % of the economies which have advanced online capability (meaning the system allows more than only downloading the TWP application form) have average TWP processing times which are lower than the global average of 8 weeks. Figure 8 shows what the specific characteristics of application systems in surveyed economies are. In 80% of the surveyed economies there is no advanced application system (a system which allows more than only downloading the application form: e.g. the ability to monitor the application process).

**Figure 8. Characteristics of the application system in the surveyed economies (in %)**

<!-- Start of picture text --> 20%<br>35%<br>45%<br><!-- End of picture text --> <!-- Start of picture text --> Advanced online capability (the<br>system allows more than<br>downloading the TWP<br>application form)<br>Only the TWP application form<br>can be downloaded online<br>No online component (all<br>documents need to be<br>physically obtained)<br><!-- End of picture text --> Source: FDI Regulations Database

# • **One-Stop Shops**

Before the applicant can apply for a TWP, some economies require a number of approvals or documents from competent authorities in the economy where s/he wants to work. This requires coordination on the part of the applicant and may also impact the total time required to obtain the TWP.

Having a one-stop shop where the processing unit manages the relationship between the different competent authorities is applicant-friendly and normally results in shorter timelines. Figure 9 shows that 80% of economies with one-stop shops have TWP processing timelines which are at or below the global average of 8 weeks.

14
# **Table A9: Impacts of overall health and physical health**

|Dependent variable:|Employme|nt status|Weeklylabor inco|me ($1,000AUD)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Overall health issue|-0.020**||-0.002***||
||(0.008)||(0.001)||
|Physical health issue||-0.010||-0.030*|
|||(0.165)||(0.017)|
|_First stage of 2SLS_|||||
|Trauma experience*Year<br>after arrival|-0.001***<br>(0.0002)|-0.0001***<br>(0.00003)|-0.001***<br>(0.0002)|-0.0001***<br>(0.00003)|
|Kleibergen-Paap F stat.|24.517|7.652|24.517|7.652|
|AR 95-CIs|[-0.040, -0.005]|[-0.386, 0.545]|[-0.005, -0.001]|[-0.106, -0.002]|
|Dep. Mean|0.455|0.455|0.716|0.716|
|Other controls|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|
|Observations|3,687|1,863|3,687|1,863|
|Number of individuals|1,609|1,147|1,609|1,147|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Columns (1)-(2) and IV-FE model in Columns (3)-(4). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. The critical value of the F-test from Stock and Yogo (2005) is 16.38.

41
a credit card is more expensive than paying more than the minimum, and 29 percentage points more likely to know that payday loans are more expensive than credit cards or hire purchase. All of these impacts are significant at the 1 percent level, so as a result, the average knowledge score, which is a mean of these four questions, also shows a positive and significant effect. There is some suggestion of a decline in this knowledge on several questions at the 6 month follow-up survey, but even at 6 months migrants who were assigned to training are more likely to understand the exchange rate commission and to know that payday loans are more expensive than credit cards or hire purchase. Consistent with other studies of financial literacy (Carpena et al, 2011), we find no impact on computational measures of financial literacy such as the ability to correctly calculate the APR on a payday loan or to understand compound interest (which was not taught in the course).

We also see some increases in financial knowledge about remittances for the other two groups: East Asian migrants are 12 percentage points more likely to know that it is cheaper to bundle remittances into a larger transaction, and 10 percentage points more likely to know the cheapest method for remitting. Sri Lankan migrants saw an increase in knowledge of the cheapest method, but no increase (from a high base) in knowledge on remittance bundling. The Sri Lankan results should be caveated by the high and differential attrition rates. Recall that credit issues were not covered in the financial literacy training for the other two groups. Consistent with this, we find no increase in knowledge about credit among the East Asians (and the questions weren’t asked in the Sri Lankan sample).

# **3.2 Impact on Information Seeking and Budgeting**

Our follow-up surveys asked each month whether respondents had used any source of information to compare the costs of remitting across methods or products, and if so, what information source they had used. Since our intervention focused on the use of several internet comparison sites, we are particularly interested in whether participants use the internet more to compare remitting costs as a result of the intervention. Finally, our surveys at 1 month and 6 months also asked whether individuals always keep track of how much they spend each month. The financial literacy course made no mention of doing this, and we would thus not expect to see 15
Speci...cation (3) includes district ...xed e¤ects (�d) and facility and household variables (X) controlling for pre-treatment di¤erences across health facilities and communities that were present despite randomization. This increases the precision of the coe¢ cient estimates.

For a subset of variables, we can also stack the pre and post data and explore the di¤erence-in-di¤erences in outcomes, i.e., we estimate:<sup>17</sup> where POST is a post period dummy, �j is a facility speci...c ...xed e¤ect, and �DD is the di¤erence-in-di¤erences estimate (program impact).<sup>18</sup>

# 7 Results

## 7.1 Pre-intervention di¤erences

Prior to the intervention, the treatment and the control group were similar on most characteristics. We report the test of di¤erence in means across control and treatment groups in table 1. At the baseline, we do not ...nd any statistically signi...cant di¤erences in utilization (number of outpatient treated and deliveries per month), households’ use of di¤erent service providers (including drug shops) in case of illness, waiting time, equipment usage, government funding of clinics, citizens’ perceptions of sta¤ behavior, catchment area characteristics (such as the number of villages and households in catchment area), distances from the health facility to the nearest local council and government facility, or health facility characteristics (such as type of water source, availability of drinking water at the facility, whether a separate maternity unit is available, electricity shortages). In one out of ...ve measures of monthly supply of drugs (i.e., Quinine), the treatment group, on average, has a marginally higher supply in the year prior to treatment. In one out of four user-charge measures, there is some evidence (the estimate is signi...cant at the 10 percent level) that patients served by the treatment facilities are more likely to pay for service delivery. Overall, though, the randomization appears to have been successful.

> 17It is a subset of variables since the post intervention surveys collected information on more variables and outcomes.

> 18A slightly more restricted di¤erence-in-di¤erence (DD) speci...cation substitutes the facility ...xed e¤ects for Tjd. In that case, time invariant factors will be captured by Tjd. Both DD speci...cations yield identical point estimates of �DD.

15
weather. The ICRISAT Indian village surveys (Singh et al., 1985) provide detailed time-series information on agricultural production and investments for farm households over a period of up to ten years. Begun in the crop year 1975-76 in six villages in three agroclimatic regions of the Indian semi-arid tropics, the survey collected longitudinal information approximately every three weeks on all transactions (purchases, sales, production, investment) for 40 households in each village, 30 of which were cultivating households. In addition, on approximately July 1 of each year a complete asset survey was undertaken for all survey households. As part of the survey, daily rainfall information was also obtained. At the conclusion of the survey, there were ten years of information collected for three of the villages, seven years. for one, and nine years of information for two of the original six villages. In 1980-81, two other villages (each with 40 households) were added to the survev and were surveyed for four years; another two villages (80 households) were added in 1981-82 and were surveyed for three years. It is thus possible with these data to construct a number of alternative measures of rainfall incidence and variability, and to measure asset portfolios, total farmer wealth and farm profits.

The semi-arid tropics in which the ICRISAT study villages reside are characterized by low levels of erratically-distributed rainfall. As a consequence, agricultural incomes are low and quite variable--in the six original villages for which there is from seven to ten years of information, the average coefficient of variation in total farm profits (net of the value of family labor) was 127.5 In contrast, our analysis of the earnings of U.S. white males aged 25-29 in 1971, surveyed in seven rounds of the National Longitudinal Survey of Youth, indicated that the average coefficient of variation in earnings was only 39. Total wealth holdings in the six villages are very unequally distributed. Based on sample-period averages of real wealth for 9