rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 4a: E**<br>Dependent vari|**mployment and Job Search**<br>ables|Obs.|Mean (Control<br>group, follow-up)|ITT Difference|ITT ANCOVA|
|---|---|---|---|---|---|
|**Employment**|Ever employed (yes=1)|827|.936|.004|.003|
|**History**|||[.244]|(.018)|(.019)|
|||||{0.539}|{0.592}|
||Ever employed in self-employment (yes=1)|827|.153|.007|-.005|
||||[.360]|(.028)|(.027)|
|||||{0.539}|{0.592}|
||Ever employed in wage employment (yes=1)|827|.898|.012|.019|
||||[.303]|(.022)|(.022)|
|||||{0.421}|{0.316}|
||Ever employed in factory work (yes=1)|827|.305|.155***|.174***|
||||[.461]|(.038)|(.037)|
|||||{0.001}**|{0.001}**|
|**Current**|Currently employed (yes=1)|827|.462|.112***|.099**|
|**Employment**|||[.500]|(.038)|(.039)|
|||||{0.020}**|{0.041}**|
||Currently employed in self-employment (yes=1)|827|.034|.013|.009|
||||[.181]|(.016)|(.016)|
|||||{0.309}|{0.454}|
||Currently employed in wage employment|827|.432|.101***|.092**|
||(yes=1)||[.496]|(.038)|(.039)|
|||||{0.020}**|{0.044}**|
||Currently employed in factory work (yes=1)|827|.114|.070**|.077***|
||||[.319]|(.028)|(.029)|
|||||{0.022}**|{0.041}**|
||Time employed in the past 6 months (in months)|827|2.95|<br>.569***|<br>.432**|
||||[2.73]|(.206)|(.207)|
|||||{0.020}**|{0.044}**|
||Hours of works in 7 days|827|23.5|5.348***|4.696**|
||||[1.68]|(2.004)|(2.023)|
|||||{0.020}**|{0.052}**|
|**Job Search**|Any job search activity in the past 4 weeks|827|.470|<br>-.073*|<br>-.064*|
||<br>(yes=1)||[.500]|(.038)|(.038)|
|||||{0.052}*|{0.090}*|
||Number of job search activities in the past 4|827|1.39|<br>-.321***|<br>-.312**|
||<br>weeks||[1.68]|(.121)|(.121)|
|||||{0.020}**|{0.041}**|
||Expect any income in 30 days (yes=1)|827|.691|<br>.027|<br>.030|
||||[.463]|(.035)|(.035)|
|||||<br>{0.321}|<br>{0.316}|
||Expect any income in 12 months (yes=1)|827|.979|<br>-.013|<br>-.006|
||||[.144]|(.013)|(.013)|
|||||{0.291}|{0.489}|

Note. The table reports the intent-to-treat (ITT) estimates of the effect of the job facilitation intervention on key outcomes related with employment and job search. Standard deviations in brackets. In the last two columns, we report standard errors in parenthesis and _q_ -values in curly brackets underneath each estimated coefficient. _q-values_ are calculated following the sharpened procedure proposed by Benjamini et al. (2006). *** p<0.01, ** p<0.05, * p<0.1.

20
**Table 8: A Model of Labor Transitions and Social Security in Colombia**

||**Logit**||**Multino**|**mial Logit**||
|---|---|---|---|---|---|
||**transit/no**<br>**transit**|**1 = transit into**<br>**inactivity**|**2 = transit into**<br>**unemployment**|**3 = transit into**<br>**informality**|**4 = transit into**<br>**formality**|
|**Age (years)**|0.012 ***|0.000 ***|0.000 ***|0.017 ***|0.004 ***|
||(0.002)|(0.000)|(0.000)|(0.002)|(0.001)|
|**Age**<sup>**2**</sup>|0.000 ***|0.000 ***|0.000 ***|0.000 ***|0.000 ***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|**Years of education**|0.002|0.000|0.000|-0.001|0.004 ***|
||(0.004)|(0.000)|(0.000)|(0.003)|(0.001)|
|**Years of education**<sup>**2**</sup>|0.000|0.000|0.000|0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|**Gender dummy (male =1)**|0.011|0.000 ***|0.000|0.026 ***|0.010 ***|
||(0.011)|(0.000)|(0.000)|(0.010)|(0.003)|
|**Household size**|0.001|0.000|0.000|0.001|-0.001|
||(0.003)|(0.000)|(0.000)|(0.002)|(0.001)|
|**Household Position**||||||
|**_Household head dummy (head = 1)_**|-0.050 ***|0.000 ***|0.000|0.002|-0.013 ***|
||(0.017)|(0.000)|(0.000)|(0.015)|(0.004)|
|**_Spouse Dummy (Spouse = 1)_**|-0.017|0.000|0.000|-0.018|-0.015 ***|
||(0.019)|(0.000)|(0.000)|(0.015)|(0.004)|
|**_Offspring dummy (Child = 1)_**|0.012|0.000|0.000|0.013|-0.008 **|
||(0.016)|(0.000)|(0.000)|(0.014)|(0.004)|
|**Previous Occupation**||||||
|**_Dummy Unemployed t-1= 1_**|0.323 ***|0.000 ***|0.000 **|0.169 ***|0.046 ***|
||(0.026)|(0.000)|(0.000)|(0.025)|(0.012)|
|**_Dummy Formal salaried t-1= 1_**|0.142 ***|0.692 ***|0.300 ***|-0.149 ***|-0.037 ***|
||(0.022)|(0.095)|(0.095)|(0.005)|(0.003)|
|**_Dummy Informal salaried t-1= 1_**|0.486 ***|0.780 ***|0.219 ***|-0.144 ***|-0.020 ***|
||(0.018)|(0.076)|(0.076)|(0.004)|(0.002)|
|**_Dummy Formal self-employed t-1 = 1_**|0.617 ***|0.817 ***|0.183|-0.150 ***|-0.025 ***|
||(0.026)|(0.127)|(0.127)|(0.004)|(0.002)|
|**_Dummy Informal self-employed t-1 = 1_**|0.306 ***|0.834 ***|0.162 ***|-0.152 ***|-0.023 ***|
||(0.020)|(0.055)|(0.054)|(0.005)|(0.002)|
|**Social Security Status in Previous Occupation**||||||
|<br>**_Dummy Health Coverage t-1= 1_**|-0.045 **|0.000 *|0.000|-0.029 *|-0.002|
||(0.020)|(0.000)|(0.000)|(0.017)|(0.004)|
|**_Dummy Pension Coverage t-1= 1_**|-0.002|0.000 **|0.000|-0.028 ***|0.006 **|
||(0.012)|(0.000)|(0.000)|(0.010)|(0.003)|
|**Preference bias (on Social Security)**|-0.002|0.000|0.000|-0.007|-0.001|
||(0.007)|(0.000)|(0.000)|(0.006)|(0.002)|
|**Preference bias (on Social Assistance)**|0.016|0.000|0.000|-0.007|0.004|
||(0.011)|(0.000)|(0.000)|(0.009)|(0.003)|
|**Number of observations**|9,057|9,057|9,057|9,057|9,057|
|**Wald Test**|1369.95 ***|||||
|**Predicted Probability**|25.85|0.00|0.00|15.49|2.60|
|**Real Probability**|29.19|5.43|2.95|15.25|5.60|

Source: Authors’ estimates from Fedesarrollo (2008, 2009) FSLS.

21
# Impact of Investigations and Duties on Import Prices

<!-- Start of picture text --> Elotroal Machinery Misc.<br>SO<br>1283'41'2'8'41*141*8$41128411204<br>101 82 1 1868 1108 gas a"<br><!-- End of picture text --> <!-- Start of picture text --> Parts for Motor<br>Vehicles<br>Ito<br>3~~~~~~~~~~~~~~~~0<br>8124184104182I I<br>to"i Io 362 is"18818 I3<br>Batteries<br>_ ne<br>110<br>SITC Group<br>CONTROL Group<br>l2341284123412341*34 1234<br>1.01 Im nu2 I I it"4 I, 31 me,<br>Roller Bearings<br>woo n.- _ _<br>?<br>IIa<br><!-- End of picture text --> <!-- Start of picture text --> Appliances for<br>Liquid Flow Control<br>1401<br>ISO0<br>120<br>110<br>CONTROL<br>to<br>lt 8 4 t 2 8 411 2841* i U441 2411 2 84<br>Packaging non Elec.<br>Machinrery<br>o4 341-r1141234,102"4,192i<br>t'' I 106 I 184 I was 31861108<br><!-- End of picture text -->
receiving positive foreign investment. It suggests that smaller and more leveraged firms are less likely to receive foreign investment. The second column reports the results when the dependent variable is foreign portfolio ownership. It suggests that firms which receive foreign portfolio investment are not very different from the average firm. To the extent that this represents the average foreign portfolio investor, it suggests that foreign portfolio investors choose the average market portfolio, as theory predicts.

The third column in Table 4B examines the likelihood of receiving foreign investment above 10 percent. The signs and significance of all of the variables, except for the variable indicating membership in a chaebol, are robust to the 1999 data. The group debt and asset variables were never significant, suggesting that the surviving indebted groups were not capital constrained in 1999. This may also be simply because the most indebted groups drop out of the 1999 sample.

The last column reports the results when the subset that contains firms acquired by foreigners is used. Size and debt again appear to be the main determinants of attracting large foreign ownership. Comparing columns 3 and 4 with columns 1 and 2, exporting is only positive and significant when the dependent variable is extensive foreign ownership. This suggests that informational asymmetries, which should be present for both portfolio and direct investment, are not the reason that foreign investors choose firms that export. Rather, the parent firm provides an intangible asset to the target, perhaps in international distribution and marketing, that another domestic firm would not.

# **6. Conclusion**

In this paper, we examine how firm and industry characteristics affect foreign ownership. We find that foreign acquisitions are larger than domestic acquisitions but are similar to domestic 16
# **Figure B2: Crude oil**

<!-- Start of picture text --> Log scale<br>5<br>Log price Permanent component<br>4<br>3<br>2<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.5<br>Medium-term cycle Transitory component<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 43 —_
Table A4: Heterogeneous effects of savings default on transfers and fees spending

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Panel A:|Male<br>HH head|Any previous<br>bank use|Close to<br>branch|Assets|Above median:<br>Patience|
|Savings Default|69.067|-114.786|-522.847|131.335|428.595|
||(280.881)|(518.859)|(374.370)|(428.443)|(347.383)|
|Observations|310|152|233|226|190|
|Mean of dependent variable in Cash group|1267.63|1465.71|1402.56|1401.66|1030.78|
|R-squared|0.192|0.277|0.187|0.136|0.229|
|Panel B:|Female<br>HH head|No previous<br>bank use|Far from<br>branch|Assets|Below median:<br>Patience|
|Savings Default|-370.776|-74.800|225.796|-332.797|-329.216|
||(434.824)|(277.759)|(308.838)|(256.943)|(327.598)|
|Observations|162|320|239|246|282|
|Mean of dependent variable in Cash group|1176.21|1121.67|1069.51|1064.67|1361.72|
|R-squared|0.066|0.061|0.084|0.134|0.117|
|P-value: equal efect of savings default|0.372|0.943|0.111|0.334|0.098|

The top panel includes respondents from female-headed households, those with above-median values for number of transactions at NBS in the 90 days preceding the transfer, those who live above the median distance from the bank branch, those with above-median values of a principle-index of asset ownership, and those with above-median patience, as measured at baseline by amount required to prefer a transfer in one month instead of immediately. P-values are from tests of equality of Savings Default coefficients in Panels A and B. All specifications include village and week-of-first-survey fixed effects, and the value of the outcome measured at survey 1. Exchange rate: MK 420 per USD. Robust standard errors in parentheses. * <u>p</u> _<_ 0.10, ** <u>p</u> _<_ 0.05, *** <u>p</u> _<_ 0.01 46
The size of these effects is important: a 1 percent employment-intensive growth in agriculture raises the headcount poverty by around 6.5 percent (the headcount poverty goes from 45 percent to 48 percent) to 9 percent. A 1 percent employment-intensive growth in manufacturing decreases poverty by 5 percent to 7 percent. Productivityintensive growth in agriculture reduces poverty by 4 percent to 9 percent.

**<u>Table 7: Poverty Changes and the Sectoral Productivity Intensity of Growth</u>**

||(1)<br>Benchmark|(2)<br>Controls|(3)<br>Non Upper<br>MiddleIncome|(4)<br>Non<br>LAC|(5)<br>Positive<br>Growth|(6)<br>Negative<br>Growth|
|---|---|---|---|---|---|---|
|Productivity-intensive Growth in|||||-||
|Agriculture|-7.227***|-7.131***|-4.141**|-9.452**|7.112***|-9.590|
||(-3.00)|(-3.45)|(-2.78)|(-2.64)|(-3.88)|(-0.88)|
|Productivity-intensive Growth in|||||||
|Secondary Sector|0.596|0.813|3.398*|3.624|5.081**|1.328|
||(0.33)|(0.53)|(1.82)|(0.90)|(2.69)|(0.34)|
|Productivity-intensive Growth in|||||||
|Tertiary Sector|-0.726|-1.149|-1.156|-0.643|-1.324|4.769|
||(-0.72)|(-1.17)|(-1.04)|(-0.29)|(-0.97)|(0.68)|
|Change in Gini (%)||2.810***|1.887***|2.783***|2.003***|3.094|
|||(3.34)|(4.50)|(3.99)|(3.67)|(0.99)|
|Constant|0.0277|0.0412|-0.0493|0.0458|-0.0894|0.340|
||(0.43)|(0.64)|(-0.88)|(0.31)|(-1.36)|(1.21)|
|Observations|98|98|58|37|68|30|
|AdjustedR-squared|0.045|0.174|0.232|0.185|0.317|0.072|
|*p<.10,**p<.05,***p<.01.T-|statisticsinpar|entheses.|||||

# **_4.2 The Determinants of Growth Patterns_**

In the previous section we found that there are important differences between countries in the type of growth experienced, and that these differences may be significant for the poverty impact of growth. It is thus important to find out whether the observed growth patterns are the result of the underlying labor market characteristics.

In this section we explore the determinants of manufacturing and secondary sector laborintensive growth and agricultural productivity-intensive growth. In particular, we explore whether education, the share of workers in agriculture, labor market regulation, trade and some investment climate indicators are correlated with poverty reduction. To do this, we regress our measures of employment and productivity-intensive growth against the value of the variable at the beginning of the period (see Equation 6). The dependent variable is measured in percentage points of growth. The independent variables are measured in levels at the beginning of the spell. By using the level of the variable at the beginning of 23
the ICRG indicators did not prove to be significant either. Hence, these results are not reported in any of the tables.

To investigate the possibility of slope heterogeneity across countries, we followed the approach used in Haque, Pesaran, and Sharma (1999). Specifically, the slope coefficients in each country are assumed to be fixed over time, but varying across countries linearly with the individual sample mean of GDP per capita. The final results (Tables 15 and 16) only include the statistically significant interaction terms, in order to avoid co linearity arising from the correlation between original explanatory variables and the auxiliary variable capturing the interaction of these with the sample mean of GDP per capita. Hence the estimated model is: _VRSTEit_<sup>=</sup> _f_ ( _WAGEit_ , _GOVEXPit_ , _GDPPCit_ , _URBAN it_ , _AIDSit_ , _GINI it_ , _WAGEGit_ , _GOVGit_ , _GINIGitCONS_ ) where _VRSTEit_ = Variable returns to scale DEA efficiency score for single output and multiple output cases

- _WAGEit_ = Wages and salaries (% of total public expenditure)

_GOVEXPit_ = Total government expenditure (% of GDP) _PUBTOTit_ = Share of expenditures publicly financed (public/total) _GDPPCit_ = GDP per capita in constant 1995 US dollars _URBAN it_ = Urban population (% of total) _AIDSit_ = Dummy variable for HIV/AIDS _GINI it_ = Gini Coefficient _CONS_ = Constant

* _WAGEGit_ = _WAGEit GDPPCi_ * _GOVGit_ = _GOVEXPit GDPPCi_ * _GINIGit_ = _GINI it GDPPCi_ − 1 _T GDPPCi_ = _T_ ∑ _t_ = 1 _GDPPCit_

Results show that the interaction terms are significant, especially for the health regression, implying that there is a heterogeneous response of efficiency scores to the different explanatory variables. This confirms Greene’s (2003) results on the WHO data. One of the key results of this section is that the negative association between the size of government expenditure and efficiency is stronger in countries with higher per-capita GDP. Similarly, this happens with the wage variable. Results are somewhat similar to those of the homogeneous slopes, though statistical significance of many of the coefficients is lower. This is the result of co-linearity between the auxiliary variables and the original set of explanatory variables. This problem deserves further work in the future.

30
# **1. Introduction**

Financial inclusion, typically defined as the use of formal financial services, has become a subject of growing interest for researchers, policy makers, and other financial sector stakeholders.<sup>1</sup> Financial exclusion is problematic when it is involuntary. In other words, exclusion deserves policy action when there are individuals whose marginal benefit from using financial services exceeds the marginal costs, but who are excluded by barriers—such as high account fees, large distances, and lack of suitable products—that result from market failures. The market failures could be due to a host of factors, such as imperfect information, noncompetitive markets, shortcomings in the contractual environment, and lack of physical infrastructure.

A growing body of research using field experiments shows that financial inclusion can have significant beneficial effects for individuals, providing both an economic and a political rationale for policies that promote financial inclusion.<sup>2</sup> In particular, the evidence is most compelling when it comes to the use of bank accounts. Having a bank account increases savings (Aportela, 1999), female empowerment (Ashraf et al., 2010), and consumption and productive investment of entrepreneurs (Dupas and Robinson, 2009).

In addition, previous studies focusing on the unbanked in the US have argued that not having a bank account can have a wide range of harmful effects. For instance, the lack of a bank account can make liquidity management and payments difficult, which could result in high fees associated with the use of money orders or check-cashing services (Lusardi, 2010). Also, cash transactions present financial and personal risks for those unbanked, since individuals have no

> 1 In its most recent communiqué, the G20 agreed to “take the financial inclusion agenda forward” and “to assist countries, policymakers and stakeholders in focusing global efforts on measuring and sustainably tracking progress on access to financial services globally.” Furthermore, according to a recent survey of bank regulators across 143 jurisdictions, 67 percent of regulators are charged with promoting financial inclusion (Cihak et al., 2012). For more information See item 9 in: http://www.mof.go.jp/english/international_policy/convention/g20/g20_120420.htm. 2 See Karlan and Murdoch (2009) for a review of the literature.

2
Despite its importance, little is known about the rural dibao program’s performance and poverty impact. Several reports provide descriptive analyses and preliminary evaluations of the program’s successes and challenges, but they are based on older data (World Bank 2011; Luo and Sicular 2013). Studies have been done on China’s urban dibao program (e.g., Chen, Ravallion and Wang 2006; Gao, Garfinkel and Zhai 2009; Wang 2007; Ravallion 2008, 2009); however, the urban and rural programs are distinct, address different levels and types of poverty, and face somewhat different challenges.

For our analysis we use nationwide household‐ and village‐level survey data matched with county‐level administrative data on the dibao program from the Ministry of Civil Affairs (MOCA). The data are for 2007‐2009, the years during which the rural dibao program was expanded to reach full nationwide coverage. Using these data, we outline major features of the program, estimate its poverty impact, and carry out targeting analysis. We find that although the program provided substantial income benefits to beneficiaries, its overall poverty impact was limited. Moreover, targeting analysis reveals that the exclusionary and inclusionary targeting errors were quite large.

In settings such as rural China where measurement of household income is difficult, administrators of conditional transfer programs often rely on observable correlates of income to determine eligibility (Chen, Ravallion and Wang 2006). We therefore estimate the relationship between household characteristics and dibao participation, which provides insights into the correlates of dibao selection, and carry out propensity score targeting analysis. Propensity score targeting analysis, which evaluates the program’s targeting performance based on observed selection in relation to correlates of income, should yield better targeting performance than conventional targeting analysis. We find that although propensity score analysis reduces the magnitude of exclusion and inclusion errors, the targeting errors remain large.

How can the rural dibao program’s poverty impact be improved? In recent years spending on the program has grown substantially, which raises the question of whether and how expansion of the program can increase its impact. We therefore carry out simulations of increases in the dibao budget to analyze the consequences of increased program spending on the poverty headcount, gap and squared gap. Specifically, we simulate the impact of (a) 3
produced t-shirts branded with the logo of some U.S. university. Actions by concerned student groups resulted in a slew of reforms: doing away with child labor, reducing the length of the work day, improved lighting and other work conditions, and so on.

# **Figure 2: Different Pathways to Social Upgrading**

<!-- Start of picture text --> Social Upgrading<br>A<br>+ B<br>Measurable<br>standards<br>C<br>Types of work<br>0<br>Small scale, low skilled, Moderate High skilled, Knowledge-<br>Household- labour- skilled, varied technology- intensive work<br>based work intensive work labour intensive work<br>intensities<br>work<br>-<br>Measurable<br>standards<br><!-- End of picture text --> Adapted from Barrientos et al. (2011).

Alternatively, social upgrading can occur along Pathway C, where almost the entire burden is borne by economic upgrading. In this case, risks for workers are reduced as small-scale household work gets turned over into high-tech and knowledge-intensive work, for instance, as in the case when a weaver of traditional rugs takes to computerized design and manufacturing. In the intermediate case of Pathway B, social upgrading within labor-intensive industries like apparel can be achieved with lower risks to workers if outside institutions like the Better Work 14
agent is choosing his occupation by comparing the earnings from each activity.<sup>31</sup> Given preferences and budget constraints, the stochastic optimal control problem of the agent that can operate in sector _s_ is given by The value function of the optimal control problem satises the Hamilton-Jacobi-Bellman (HJB), which can be used to characterize the solution of the agent's problem where _μ_ ( _zst_ ) and _σ_<sup>2</sup> ( _zst_ ) are the drift and difussion process of _zst_ , and where _Mst_ ( _ast, zst, t_ ) is the optimal saving rule in period _t_ , The HJB equation is a second order dierential equation. The value function _Vs_ depends on _t_ due to the fact that prices may be changing along the transition path.

For the quantitative part, I will assume that _log zst_ follows a mean reverting diusion process given by where as before _Wst_ is a Brownian Motion. In this particular process, the parameter _ψ_ measures the speed of reversion and _log z_ ̄ is the long run mean. One particular property of the process is the

> 31In order to do this computation, agents need to know the aggregate state of the economy. In particular, they need to know prices, and how the dierent choices that they are making will aect earnings. In a more complicated model with uncertainty about those variables, the agent would be choosing based on expectations about potential earnings.

22