rafmacalaba/datause-encoder-data huggingface dataset:
|Employment ratio (num. employed|0.026||0.026|6,812|
|---|---|---|---|---|
|over num. adults in hh)|(0.029)||(0.027)||
|Migration worker in the household|0.005||-0.013|6,812|
|(yes=1)|(0.044)||(0.042)||
|L Si|-0.138||-0.217|3,669|
|og (avngs)|(0.159)||(0.150)||
|Log (Outstanding loans)|-0.405***||-0.415***|4,689|
||(0.142)||(0.133)||
|Covariates,fixed effects||Yes|||

<mark>Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The table presents</mark> estimated coefficients of EGPP participation on household savings and outstanding loans. All regressions include covariates and fixed effects used in Table 8.

# **6.  Program Efficiency**

As shown above, EGPP has helped participating households increase consumption of quality foods and investment in health and leisure while also reducing outstanding loans. Based on the finding that the increase in overall consumption is similar to the amount of resources transferred from the program and that little change was observed in labor supply, there is little sign of substitution effects of EGPP of regular employment and disincentives for work.

The question worth asking now is whether EGPP has achieved these outcomes in an efficient way. Potential efficiency loss (gain) of EGPP compared to alternative options mainly depends on three factors: (i) targeting accuracy; (ii) payment delivery (leakages); and (iii) operational costs and quality of projects under the workfare. In order to assess each aspect, we look into the EGPP sample and delve deeper into the program implementation, and discuss its efficiency compared to other alternative options such as unconditional cash transfer of the same amount through bank accounts.

The first issue of benefits going to the non-poor (e.g., inclusion errors) is one of multiple dimensions to measure targeting accuracy. As the level of benefits (such as wage rates for EGPP) does not vary by household characteristics, the marginal impacts of the benefit would be greater for poorer households that are more credit constrained. Therefore, by improving targeting accuracy, the poverty-reducing and welfare-enhancing impacts of social assistance programs can be enhanced with efficiency gains. Utilizing income quintiles based on per capita consumption,<sup>24</sup> we analyzed

> 24 The latest nationally representative Household Income and Expenditure Survey was conducted in 2010. Based on per capita household consumption, the income cut-off for each quintile is calculated. Using the Consumer Price Index available as part of the World Development Indicators of the World Bank, each income cut-off level was price adjusted and used for the benefits and beneficiary incidence analysis.

29
20 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity Following the latest contributions in the literature, we implemented a two-step econometric approach in which we control for individual sorting of highly skilled individuals into cities. We also take advantage of recent developments in remote sensing science and free geospatial libraries to delineate urban extents and identify metropolitan areas in an automatic and highly standardized way, which guarantees comparability across LAC cities.

The available literature has not arrived to a definitive answer on this topic, and there is evidence in favor of the three potential models: polycentric, centralist and regionalist. Our results show that there may exist an optimal level of fragmentation in which the benefits of more responsive government are in balance with the higher costs associated to the presence of multiple local governments within the same functional area. This may indicate that in LAC cities the right model is somewhere in between the polycentric and the centralist governance structures. We found no evidence in favor of the regionalist view, since our results show that the presence of a governance body or integrated public services does not necessarily foster increased productivity.

In line with previous contributions we found that economic performance increases with city size. While evidence for OECD countries indicates that doubling city size may increase economic performance between 2% and 5% (Ahrend et al., 2014); for LAC cities, we found an impact that ranges from 4% to 7%.

# **7. References**

- Abrahams, A; Oram, C, Lozano-Gracia, N; (2018). Deblurring DSMP Nighttime Lights: A new method using Gaussian filters and frequencies of illumination. Remote Sensing of Environment. June 2018,  210, pp242-258.

- Anderson, S. J., Tuttle, B. T., Powell, R. L., & Sutton, P. C. (2010). Characterizing relationships between population density and nighttime imagery for Denver, Colorado: issues of scale and representation. International Journal of Remote Sensing, 31(21), 5733-5746.
# 4. Results

## 4.1. Main results

We begin by estimating the impacts of preschool expansion on plant performance, captured through the TFPR, TFPQ and markup variables. Table 4 shows positive and strongly significant impacts of preschool on productivity. For example, an increase of 1 additional preschool per 1,000 children of eligible age (approximately 1/3 of standard deviation) triggers a 11 percent increase in TFPR for plants in sectors with 41 percent of female employees (which is the average in our sample). For a plant from a sector which employs only women (0.4 percent of firms in our sample), the increase in TFPR would be 30 percent, and for a plant in a sector where only 9-11 percent of employees are women (3 percent of firms in our sample), productivity would go up by approximately 3 percent.<sup>6</sup> The impacts on TFPQ are very similar: building an additional preschool per 1,000 children triggers an 11 percent increase in TFPQ for a firm from a sector with average exposure, or 41 percent of women among employees.

These results are consistent with zero impact on markup: coefficients are low in magnitude and not statistically significant. Indeed, the TFPQ measure captures changes in pure technical efficiency of the firms, carving markup out of the TFPR measure. Combined, these coefficients suggest that access to preschools increases productivity through a boost in technical efficiency rather than product differentiation or market power.

## 4.2. Robustness

We check the robustness of the results to several remaining threats to our identification. First, the baseline results rely on a measure of a plant’s dependence on female work at the sectorlevel rather than at the plant-level. While this choice minimizes endogeneity concerns, it also throws away part of the variation in female work dependence across plants. To address this issue, we re-estimate equation (1) by computing the share at the plant level ( FemShareit0 ) and instrumenting it with the average share at the sector level FemSharest0 , excluding plant _i_ . Table 5 shows the results. The power of the instrument is very high with the reported F-statistics exceeding the recommended minimal threshold (Stock et al., 2002) by orders of magnitude (they

> 6 There are no firms with 10% female employees.

13
Individual Preferences over Immigration Policy.” _Review of Economics and Statistics_ 83(1): 133–45.

- Schmid, Katharina, Ananthi Al Ramiah, and Miles Hewstone (2014), “Neighborhood Ethnic Diversity and Trust: The Role of Intergroup Contact and Perceived Threat.” _Psychological Science_ 25(3): 665–74.

- Steinmayr, Andreas (2021), “Contact versus Exposure: Refugee Presence and Voting for the Far Right.” _The Review of Economics and Statistics_ : 1–18.

- Sumption, Madeleine, and Carlos Vargas-Silva (2019), “Love Is Not All You Need: Income Requirement for Visa Sponsorship of Foreign Family Members.” _Journal of Economics, Race, and Policy_ 2(1–2): 62–76.

- Tize, Carola (2020), “Living in Permanent Temporariness: The Multigenerational Ordeal of Living under Germany’s Toleration Status.” _Journal of Refugee Studies_ .

- UNHCR (2020), “UNHCR - Figures at a Glance.” https://www.unhcr.org/en-us/figures-at-aglance.html (April 22, 2021).

- Ward, Dalston (2019), “Public Attitudes toward Young Immigrant Men.” _American Political Science Review_ 113(1): 264–69.

- Zetter, Roger, and Heloise Ruadel (2016), Knomad Working Paper _Refugees’ Right to Work and Access to Labor Markets – An Assessment (Part 1) | KNOMAD_ . https://www.knomad.org/publication/refugees-right-work-and-access-labor-marketsassessment-part-1 (April 22, 2021).

31
serious of questions that assess how difficult it is for individuals to carry out every day (work related) activities. The radar graph in Figure 2 decomposes the index and plots individual impacts (and the corresponding confidence intervals) for each of these activities in isolation. Perhaps unsurprisingly, the two activities most closely associated with and required for factory work (‘work on feet’ and ‘stand at work bench’) are driving the result on the strenuosity score. It is also worth mentioning that we identify impacts on strenuosity in line with the findings from Blattman and Dercon (2018) despite a considerably shorter evaluation period. We also find a positive impact on the cognitive skills score when considering the simple difference estimator between the treatment and the control group. It is possible that this finding results from a continued cognitive stimulus of the training and work tasks on those individuals in the treatment group who took up job offers.

One possible explanation for the high rate of worker turnover and the small duration of workers’ spells in factories is an ex-ante mismatch of applicants’ job-related expectations and the reality of factory work. Indeed, recent qualitative studies that explore worker and manager interactions in the textile, garment and leather manufacturing industries find that expectations mismatch are key drivers of worker turnover in Ethiopia ( <mark>Hardy and Hauge 2019; B</mark> arrett and Baumann-Pauly 2019).

**<u>Table 4c: Cognitive and Health Related Scores</u>**

|Dependent variables|Obs.|Mean (Control<br>group at<br>baseline)|ITT Difference|ITT ANCOVA|
|---|---|---|---|---|
|Standardized cognitive skills score|806|-.058|.142*|.104|
|||[.934]|(.078)|(.077)|
||||{0.049}**|{0.133}|
|Standardized numeracy score|806|-.125|.106|.050|
|||[1.02]|(.078)|(.074)|
||||{0.097}*|{0.307}|
|Standardized strenuosity score|827|.058|.191**|.197**|
|||[1.06]|(.077)|(.077)|
||||{0.027}**|{0.029}**|
|Standardized strenuosity score (based on|827|-.0009|.192**|.191**|
|dummies for at least "slightly difficult")||[.975]|(.077)|(.077)|
||||{0.027}**|{0.029}**|

Notes. The table reports the intent-to-treat (ITT) estimates of the effect of the job facilitation intervention on cognitive skills and health. 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.

23
6 <!-- Start of picture text --> Flgure 1 East Asia and Latin America<br>Growth Rates: GDP, EXPORTS, DMPORTS<br>(3 Year Moving Average)<br>10<br>GDP 2\1<br>§~~~ If 1'7 \7 IIX<br>18~<br>12<br>EXPORTS ,<br>I 2<br>-41<br>- 70 I 1172 1n 47 1 igis 1 tu7a ivao I :laa Il f I1 hS I §u<br>151 111 *1*5 *I? 1171 1121 1X8 *tUb<br>16~ ~ ~~~67I" 2 n<br>12<br>'a<br>IMPORTS<br>-2<br>-4<br>4 .-<br>-:2<br>"| 18701 :87 § 1#188 a1a1:'4 tusl11218111512<br>1ou1ce:73 1Wor It 1Tbe a18 18 ISO II<br>Source: World Tables V . C<br><!-- End of picture text -->
# **7. India: Another latecomer**

In the catch-up game of the BICs, India is the indisputable laggard. In the early 1980s, India and China were neck and neck on the economic development plane. By the late 1980s, impressed by China’s rapid growth, the Indian government implemented a gradual reduction in tariffs. The proponents of liberalization believed that opening up the economy to global competition would usher in increased efficiency and boost productivity. It opponents contended that competition would wipe out many Indian industries that only served the large domestic market behind tariff walls. As in the case of Brazil and China, both possibilities panned out just as Aghion et al.’s model predicts.

<!-- Start of picture text --> 2005-06<br>2000-04<br>1990-94<br>1980-84<br><!-- End of picture text --> <!-- Start of picture text --> 0.3 0.4<br><!-- End of picture text --> <!-- Start of picture text --> 0.5 0.6<br>Industrial chemicals<br>Food products<br><!-- End of picture text --> <!-- Start of picture text --> Iron and steel<br>Machinery, except electrical<br>Fabricated metal products<br>Source: COMTRADE<br><!-- End of picture text --> <!-- Start of picture text --> Transport equipment<br>Machinery, electric<br>Leather products<br><!-- End of picture text --> In the post-Independence period, the Indian government followed a national self-sufficiency policy that relied heavily on a complex web of government regulations and policies were protectionist, highly interventionist, marked by a plethora of industrial licensing requirements and strong government ownership in industry. India’s trade regime was among the most restrictive in Asia. The 1951 Industries Act adversely affected the technological sophistication of its industries for nearly four decades through industrial licensing that controlled the entry of new firms, expansion of existing ones and closure of inefficient firms. The policy regime was popularly known as the “license raj.”<sup>14</sup> In 1991, a balance of payments crisis triggered several key structural reforms that disciplined and prepared the Indian economy to join the club of emerging market countries.

In the early 1980s, in spite of the legacy of the “raj,” several Indian industries were relatively close to the Korean technological frontier. Indian productivity levels were within 60 percent in Textiles, 90 percent in Wearing apparel, and 100 percent in the Electrical and Nonelectrical machinery industries, i.e. the latter were on the frontier (see figure 4). However, from the mid 1980s until the mid-1990s, productivity levels plummeted to about 20 percent and

> 14 See K.Kochhar, U.Kumar, R.Rajan and A. Subramanian (2006) and P.Topalova (2004).

26
If we consider that it is unlikely that insurance coverage would have fallen in real terms over the period and that the value of goods and risks insured would have been maintained if not increased with GDP then, over the period shown in Figure 2, prices have fallen by over 12% in real terms. Profit margins are expressed in terms of much smaller percentages of premium income so these pricing changes have had severe consequences for ongoing profitability.

Lloyd’s publishes a rate of change in premiums per transaction that is a proxy for rate movements. Reported information from successive quarterly reports is provided in Table 1. Current information by some individual classes of business is shown in Figure 3. There are indications that the period of increasing rates is coming to an end, and that rates are falling in some classes.

**Table 1: Premium Rate Changes in the Lloyd's Market**

|Period (months)|Period end|Percentage increase<br>(total compared to<br>equivalent period in<br>previous year)|
|---|---|---|
|Twelve|Dec-02|16.8|
|Nine|Sep-02|23.0|
|Six|Jun-02|25.3|
|Three|Mar-02|26.7|
|Twelve|Dec-01|24.1|
|Nine|Sep-01|28.5|
|Six|Jun-01|32.0|
|Three|Mar-01|42.5|
|Twelve|Dec-00|n/a|
|Nine|Sep-00|10.0|
|Six|Jun-00|8.4|
|Three|Mar-00|2.2|

Source: Lloyd’s Quarterly Business Reports.

8
Figure 1: Total Bond Sales in Israel <!-- Start of picture text --> 1,800 $ million<br>1,569<br>1,600<br>1,400 1,310<br>1,145<br>1,200<br>962 1,000 924<br>1,000 872<br>785<br>800<br>600<br>400<br>200<br>0<br>1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> _Source: Bank of Israel_ The history of DCI bond issuance reveals that the characteristics of such bond offerings have changed with time. Until the early 1970s, all DCI issues were fixed-rate bonds with maturities of 10 to 15 years (Table 1). In the mid-1970s, DCI decided to target small banks and financial companies in the United States by issuing 10, 7 and 5 year notes in denominations of $150,000, $250,000 and $1,000,000 at prime-based rates. Subsequently, the DCI changed its policy and began to re-target Jewish communities rather than banks and financial companies. The DCI also sold floating rate bonds from 1980 to 1999. The minimum amount on floating rate bonds was set at $25,000 in 1980 and reduced to $5,000 in December 1986. The maturity terms on these bonds were set at 10 to 12 years and interest rate was calculated on the basis of the prime rate. Of the total DCI bond sales of $1.6 billion in 2003, fixed rate bonds comprised 89.5 percent, floating rate bonds 2.9 percent and notes 7.6 percent (Figure 2).

Table 1: Bond Offerings in Israel

|Bond Type|Dates|Maturity|Minimum|Rate Basis|
|---|---|---|---|---|
|Fixed rate|1951-80|10-15 yrs|N/A|4.0|
|Fixed rate|1990 on|10 yrs|N/A|Mkt. based|
|Fixed rate – EDI|1993|10 yrs|$25,000|Mkt. based, 6-month|
|Fixed rate -- Zero Coupon|1993|10yrs|$6,000|Mkt based, at redemption|
|Fixed rate – Jubilee|1998|5-10yrs|$25,000|Mkt. based,6-month|

5
In July 2007, the FSS released proposals (Best Practice) for systemic improvement of mortgage loan reviews for mutual savings banks, credit unions and credit cooperatives, and creditspecialized financial companies. The scope of application for the financial institutions is loans for apartments within areas of high speculation as designated by the government, and areas of excessive investment in the Seoul metropolitan area. DTI ratios of 40 percent to 60 percent are required for mortgage loans on apartments exceeding KRW300 million. The ratios are slightly higher for loans on apartments of lesser value (Table A.1).

Table A.1. Best Practice DTI Ratios, July 2007

|Collateral value|Loan amount|Bank|s, Insurers|**DTI base**<br>Savings banks, mutual finance<br>companies, credit specialized<br>financial companies|
|---|---|---|---|---|
||More than KRW100mil.|40%|Less than||
|More than KRW 300mil.|KRW50mil.–KRW100mil.|50%|60%|(same)|
||KRW50mil. or less||‐||
|KRW300 mil.<br>Surface area more<br>than 85m<sup>2</sup>|<br>More than KRW100mil.<br>KRW50mil.–KRW100mil.|**40%**<br>**50%**|**Less than**<br>**60%**|**45%**<br>**Less than**<br>**70%**<br>**55%**|
|or less<br>|KRW50mil. or less||‐|(same)|
|Surface area less<br>|More than KRW50mil.|**50%, le**|**ss than 60%**|**55%**,less than**70%**|
|than 85m<sup>2</sup>|KRW50mil. or less||‐|(same)|
||**Adjustments**<br>|**to DTI bas**<br>|**e**<br>||
|Fixed interest|Amortization|Cr|edit ratings|Income|
|+ 5%p|+ 5%p|‐ 5|%por + 5%p|‐ 5%p|
|_Source_: FSS.|||||

In September 2009, the continued upward trend in the metropolitan area’s mortgage loans led the FSS to recommend that the banks apply the DTI ratio to cover some non-speculative areas (previously designated speculative) in the metropolitan area (Table A.2).

Table A.2. DTI Ratios of Banks and Insurers, September 2009

|||Current coverae|**DTI ratio**<br>Exanded co|erae|
|---|---|---|---|---|
|Collateral value|Loan amount|g|p <br>|g <br>|
|||Speculative area|Seoul(excluding<br>speculative area)|Incheon,<br>Kyunggi|
|More than KRW600 mil|More than KRW50 mil.|40%|**50%**|**60%**|
|.|KRW50 mil. or less|‐|‐|‐|
|More than KRW 600 mil.|More than KRW100mil.|**40%**|**50%**|**60%**|
|(holding title for more than 3|KRW50 mil.–KRW100 mil.|**50%**|||
|months)|KRW50 mil. or less|‐|‐|‐|
|KRW300 mil.–KRW600 mil.|||||
||More than KRW100 mil.|**40%**|**50%**|**60%**|
|Surface area more than 85 m<sup>2</sup>|KRW50 mil.–KRW100 mil.|**50%**|||
||KRW50 mil. or less|**‐**|‐|‐|
|Surface area less than 85 m<sup>2</sup>|More than KRW85 mil.|**50%**|**50%**|**60%**|
||KRW50 mil. or less|‐|‐|‐|

_Source_ : FSS Press Release, September 2009.

Note: Insurers were recommended with the same criteria in October 2009.

22
**<u>Table 7: Farm Size Productivity Relationship: Net Profit Approach Using Village Market Wages</u>**

|||**Holding level**|||**Plot**|**level**||
|---|---|---|---|---|---|---|---|
|Cropped area (ha)|-0.017*|-0.009|-0.010<br>|0.015<br>|0.035***|0.023*|0.007|
||(1.936)|(0.882)|(0.875)|(1.522)|(2.969)|(1.897)|(0.426)|
|Land value/ha (USD)||<br>0.053***|<br>0.051***||<br>0.070***|<br>0.052***|<br>0.030|
|||(4.197)|(3.987)||(5.145)|(3.822)|(1.417)|
|Dist. from home (min)||-0.000|-0.000||-0.003***|-0.002**|-0.002**|
|||(1.503)|(1.280)||(4.105)|(2.396)|(2.197)|
|Years possessed||0.000|0.003**||-0.001|0.001|-0.002|
|||(0.419)|(2.392)||(1.023)|(0.786)|(0.806)|
|Plot in wetland||-0.057|-0.060||-0.024|-0.019|-0.000|
|||(0.857)|(0.898)||(0.432)|(0.329)|(0.002)|
|Irrigated land||<br>-0.056|<br>-0.051||<br>-0.052|<br>-0.041|<br>-0.093|
|||(0.877)|(0.795)||(0.817)|(0.643)|(0.942)|
|Flat land||-0.058|-0.059||-0.040|-0.039|0.105*|
|||(1.471)|(1.488)||(1.044)|(1.021)|(1.914)|
|Gently sloped land||-0.067*|-0.065*||-0.049|-0.052|0.077|
|||(1.945)|(1.898)||(1.393)|(1.479)|(1.467)|
|Plot in valley||<br>0.176|<br>0.194*||<br>-0.097|<br>-0.062|<br>0.003|
|||(1.534)|(1.696)||(1.177)|(0.759)|(0.029)|
|Sandy soil||0.032|0.031||0.029|0.036|0.024|
|||(0.664)|(0.640)||(0.594)|(0.725)|(0.333)|
|Loam soil||0.100**|0.088**||0.152***|0.140***|0.056|
|||(2.529)|(2.230)||(3.698)|(3.423)|(0.900)|
|Light clay soil||0.136***|0.128**||0.111**|0.105**|0.019|
|||(2.657)|(2.514)||(2.204)|(2.103)|(0.254)|
|Heavy soil||-0.119|-0.111||-0.110|-0.087|-0.014|
|||(1.293)|(1.212)||(1.314)|(1.052)|(0.124)|
|Gravelly soil||0.038|0.035||0.076|0.076|0.014|
|||(0.772)|(0.708)||(1.483)|(1.497)|(0.184)|
|Crop shock||-0.098***|-0.095***||-0.187***|-0.179***|-0.134***|
|||(3.420)|(3.337)||(6.090)|(5.812)|(2.899)|
|Grains|||-0.253|||-0.476***|-0.511***|
||||(1.302)<br>|||(3.317)<br>|(3.013)<br>|
|Tubers|||-0.274|||-0.569***|-0.688***|
||||(1.387)|||(3.928)|(4.037)|
|Tree crops|||-0.045|||-0.138|-0.166|
||||(0.229)|||(0.934)|(0.948)|
|Age of head|||-0.002|||-0.001<br>||
||||(1.287)<br>|||(0.641)<br>||
|Female head|||-0.031|||-0.021||
||||(0.990)|||(0.544)||
|Primary school comp.|||0.046*|||0.055*||
||||(1.656)|||(1.655)||
|Sec. education|||<br>0.066|||<br>0.106*||
||||(1.248)|||(1.757)||
|No of members <=14|||<br>-0.007|||<br>0.007||
||||(0.745)|||(0.686)||
|No of members 15- 35|||-0.007|||-0.013||
||||<br>(0.591)|||<br>(1.044)||
|No of members 35- 60|||<br>-0.011|||<br>-0.031||
||||(0.586)|||(1.405)||
|No of members >=60|||<br>-0.055|||<br>-0.032||
||||(1470)|||(0730)||
|Constant|0.094***|-0.338***|.<br>-0.060|0.176***|-0.320***|.<br>0.220|0.389|
||(4.674)|(3.055)|(0.260)|(5.635)|(2.718)|(1.106)|(1.543)|
|Village fixed effects|<br>No|<br>Yes|<br>Yes|<br>No|<br>Yes|<br>Yes|<br>No|
|Hh fixed effects||||No|No|No|Yes|
|No of observations|3,080|3,062|3,060|7,477|7,308|7,305|7,308|
|R-squared|0.001|0.022|0.034|0.000|0.016|0.031|0.034|

_Note:_ Regressions are for owned plots only (see separate appendix for results with all plots). Absolute value of t-statistics in parenthesis: *** significant at 1%; ** significant at 5%; * significant at 1%.

23