rafmacalaba/datause-encoder-data huggingface dataset:
Table 7 Wage regression

|Dependent: log monthly earnings|
|---|
|Work: First First Latest Latest|
|Years of schooling 0.0862 0.0884 0.0980 0.0942|
|(10.93) (11.16) (13.04) (11.55)|
|Tenure first work 0.0196<br>(1.87)|
|Experience 0.0408|
|(6.50)|
|Age started 0.1662 0.1738 0.1291|
|(2.69) (2.75) (2.09)|
|Age started squared -0.0024 -0.0025 -0.0018|
|(1.36) (1.41) (1.04)|
|Age 0.3642<br>(3.12)|
|Age squared -0.0074|
|(2.64)|
|Female -0.1902 -0.1786 -0.1937 -0.1793|
|(6.15) (5.64) (6.34) (5.66)|
|Province dummies yes yes yes yes|
|Number of observations 2024 2002 2067 2031|
|Number of provinces 49 49 49 49|
|R squared 0.2926 0.2979 0.2733 0.2707|

Numbers in parentheses are absolute t values with Huber robust standard errors. Current age is age started first job plus experience (years).

22
Society’ and ‘Measures for a Recycle-oriented Society’ of Tohoku Bureau of METI, a responsible officer admitted that there was no choice other than to select themes that might anticipate demand in the future because of a low level of the accumulation of particular industries in the Tohoku region (Jiang, Harayama, Abe 2003, Tohoku Bureau of Economy 2001).

During the recent decade, universities face with triple identity conflicts: namely, acquiring the status of COE and global fame, contributing to the enhancement of national competitiveness, and contributing to the development of local region (Yonezawa 2003, Motohashi 2005, Tabata 2005).

When two professors<sup>7</sup> , who have inherited an academic attitude of Nishizawa and based on the philosophy of glocalism (extending local initiatives to global endeavors), try to proceed with New Intelligence for IC Differentiation Project and Micro Electro Mechanical Systems Project, they face intense pressure to mobilize their projects into the scheme of central government’s initiative as essential resources to improve the national competitiveness. In the meantime, a strange scene has unfolded in a series of symposiums held by university-industry-government cooperation with a theme of Regional Contribution of Universities, in preparation for making national universities into independent administrative entities. Tohoku University is an integral component of the identity of Sendai in the opinions of those involved in industries and governments of the Sendai area; however, Sendai is not necessarily an integral component of the identity of Tohoku University in the opinions 7 Both were ranked top in 2002 and 2003 respectively, as the researcher having the most promising ability to create new industry according to the Questionnaire on Excellent Universities of Choice of Companies conducted by Nikkei Shimbun.

47
Distributional Impact Analysis of Climate Variability in Rural Indonesia

# _Future Research_

**An extensive agenda remains for future research on Indonesian households’ response to climate shocks.** Future studies could identify the mechanisms by which different types of households are able or unable to maintain their consumption in the face of delayed onset. The results presented above suggest that poor households are more vulnerable to climatic shock than wealthier households.  They say very little, however, about how some households protect their consumption and why others do not. For example, more research could probe migration decisions in response to rainfall shocks.

**Additional research could also probe the role of household income, labor supply decisions, and community infrastructure in mitigating climatic shocks.** Several existing studies examine the extent to which households are insured against income losses due to rainfall shocks in other countries. Additional work could use income data to examine this question in Indonesia, and could examine how rice prices and agricultural wages are affected by variation in the monsoon onset. From a policy perspective, it is important to understand how community characteristics such as the availability of irrigation and credit might play an important role in households’ ability to cope with climatic shocks. Finally, additional analysis could examine heterogeneity in households’ ability to maintain human capital investments, such as children’s school attendance, in response to rainfall shocks.

17/35
United States Environmental Protection Agency (USEPA) (2006), Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2004, USEPA, Washington, DC.

Wang, C., J. Chen and J. Zou (2005), Decomposition of energy-related CO2 emission in China: 1957–2000, _Energy_ , Vol. 30, pp. 73-83.

Weyant, John P. (2004), Introduction and Overview of EMF 19 Alternative Technology Strategies for Climate Change Policy, _Energy Economics_ , Vol. 26, No. 4, pp. 501515.

Weyant, John P., Francisco C. de la Chesnaye and Geoff J. Blanford (2006), Overview of EMF-21: Multigas Mitigation and Climate Policy, _The Energy Journal_ , A Special Issue, 2006, pp. 1-32.

-35-
destruction of firms, missing half of the picture. The increase in exits is only part of the adjustment. Granted, it is the first and most painful part. However, if there are not significant barriers to entry, the other side is that there are new entrants. The exits are often front loaded, but the net gains over time can be substantial.

Wacziarg (1998) uses eleven episodes of trade liberalization in the 1980s to look at the issue of competition and entry. Using data on the number of establishments in each sector, he calculates that entry rates were 20% higher among countries that liberalized compared to ones that did not. This estimate may reflect other policies that accompanied trade liberalization such as privatization and deregulation, so this is likely to be an upper bound of the impact of trade liberalization. However, it is a sizeable effect and indicates that there is plenty of potential for new firms to respond to the new incentives. The evidence also indicates that while exit rates may be significant, entry rates are usually of a comparable magnitude to the exit rates. Using plant level data from Morocco, Chile, and Columbia spanning several years in the 1980s when these countries initiated trade reforms indicates that exit rates range from 6 to 11% a year, and entry rates from 6 to 13%. Over time, the cumulative turnover is quite impressive, with a quarter to a third of firms having turned over in 4 years (Roberts and Tybout, 1996, p.6).

The higher turnover of firms is an important source of the dynamic benefit of openness. In general, dying firms have falling productivity and new firms tend to increase their productivity over time (Liu and Tybout 1996, Aw, Chung and Roberts 1997, Roberts and Tybout 1996). In Taiwan, Aw, Chung and Roberts (2000) find that within a five-year period, the replacement of low productivity firms with new, higher productivity entrants accounted for half or more of the technological advance in many Taiwanese industries.

37
urban market of Tashkent, imputed rent accounted for more than 47 percent of total average consumption in 2018. More than 72 percent of households in Tashkent could not afford to live in their housing if they did not own it, despite enjoying the highest average incomes in the country.

**Figure 9: Imputed Rent Share of Total Consumption, 2018**<sup>**23**</sup>

<!-- Start of picture text --> 100% 80.0%<br>90%<br>70.0%<br>80%<br>60.0%<br>70%<br>50.0%<br>60%<br>50% 40.0%<br>40% 30.0%<br>30% 20.0%<br>20%<br>10.0%<br>10%<br>0.0%<br>0%<br>Budg. share 0_10% 11_20<br>21_30 31_40<br>Rent Share Share above 30%<br>41_50 51% above<br>Andijon Buxoro Farg`ona v. Jizzax Namangan Navoiy Qaraqalpaqstan Qashqadaryo Samarqand Sirdaryo Surxondaryo Toshkent sh (city) Xorazm Jizzax Sirdaryo Namangan Surxondaryo Xorazm Qashqadaryo Navoiy Qaraqalpaqstan Toshkent v. (region) Farg`ona v. Buxoro Samarqand Andijon Toshkent sh (city)<br>Toshkent v. (region)<br><!-- End of picture text --> _Notes: L2CU Baseline, the right-hand graph presents the share of households that allocate more than 30 percent of their budget to housing, a commonly adopted measure of un-affordability._ For urban homeowners, this dynamic does not suggest an immediate budgetary crisis, but does mean that they have very little diversity in the form of their wealth, and that they could achieve higher living standards on other dimensions of well-being if such a large share of their wealth was not tied to housing. For potential migrants however, these high prices are very exclusionary. Currently, very few households outside Tashkent could afford housing there, if it were legal for them to buy it. Thus, the high cost of moving – especially to the capital but also to regional cities – will tend to slow the pace of urbanization if not met by increased affordable housing supply.

> 23 Total consumption per capita is used for the measures adopted here.

27
that a strong banking sector disproportionately increases the hazard rate for products that do not correspond to the comparative advantage of the exporting country, in accordance with theories about disciplining role of debt and superior monitoring abilities of banks (Jaud, Kukenova, and Strieborny 2018).

Columns (5) and (6) of Table 4 include the interaction term of stock market capitalization over GDP (capturing the depth of stock markets in the exporting country) with the distance to comparative advantage. This allows for the possibility that the depth of stock markets interacts with the timing of the opening up of the stock market to foreign investors and/or that deep stock markets differentially affect export survival of products depending on their alignment with the comparative advantage of the exporting country.

The results for either the stock market liberalization or its interaction with distance to comparative advantage are not affected by controlling for additional finance channels in Table 4. Both terms maintain their expected sign and statistical significance. As for the alternative channels, the interaction term of stock market liberalization and dependence on external finance is significant with the expected negative sign in columns (1) and (2), confirming the results of Manova (2008) on export volume also in the context of longterm export survival. The interaction term of distance to comparative advantage with the bank development has the expected positive sign and is statistically significant in columns (3) and (4) while the interaction of distance to comparative advantage with the stock market development in columns (5) and (6) is insignificant, confirming the results of Jaud, Kukenova, and Strieborny (2018) in a different dataset with a different set of exporting countries, a different sample period and a different destination market.<sup>19</sup> Table 5 focuses on alternative non-financial channels that could be correlated with the impact of stock market liberalization on the long-term export survival. Both stock market liberalization and its interaction with the distance to comparative advantage maintain their significant coefficients with the expected signs throughout Table 5.

Columns (1)-(6) of Table 5 look at the impact of economic development and trade

> 19 In this paper, we use SITC data that are slightly more aggregated than the data used by Jaud, Kukenova, and Strieborny (2018) but allow us to look at a longer time span. Additionally, we look at exports to the whole world market rather than focusing on the US destination market alone.

20
Policy Research Working Paper 9142

# **Abstract**

This paper studies how crises prompted firms to switch borrowing across markets, impacting the amount borrowed, maturity, and currency denomination at the firm and aggregate levels. Using data on worldwide debt issuance from advanced and emerging economies, the paper shows that firms shifted their issuances between domestic and international syndicated loans and corporate bonds during financial crises. Firms reduced their borrowing in shockhit markets but increased it in other debt markets. Firms also moved toward longer-term markets, maintaining (or even increasing) their borrowing maturity. As they moved toward domestic markets during international crises, firms reduced the share of foreign currency debt. The opposite occurred during domestic crises. Large firms were the ones that switched between international and domestic markets, affecting aggregate capital raising activity. The analysis of four distinct markets generates patterns consistent with credit supply shocks that are different from those obtained when studying the dynamics of individual markets.

This paper is a joint product of the Development Research Group, Development Economics and the Office of the Global Director, Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jcortinalorente@worldbank.org, tdidier@worlbank.org, and sschmukler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
18

**Table 5. The Incidence of EU, Japanese and US Tariffs on Sub-Saharan African Non-Oil Exports (unweighted averages - all figures in percent).**

||Europe|an Union|Ja|pan|Unite|d States|
|---|---|---|---|---|---|---|
|Exporting Country|African<br>Tariff|Preference<br>Margin*|African<br>Tariff|Preference<br>Margin|African<br>Tariff|Preference<br>Margin|
|Angola|0.3|-3.2|1.8|0.0|0.1|-0.4|
|Botswana|0.1|-2.9|0.0|-2.1|3.5|-1.1|
|Cameroon|0.1|-2.8|0.0|0.0|2.1|-1.1|
|Central African Rep.|0.2|-2.3|0.0|0.0|9.0|-1.1|
|Chad|0.2|-2.9|2.5|0.0|1.6|0.0|
|Congo|0.0|-2.2|0.0|0.0|0.3|-0.6|
|<br>Cote d'Ivoire|0.3|-3.3|1.2|-0.5|3.3|-2.0|
|Ethiopia|0.1|-1.9|1.5|-1.3|2.0|0.4|
|<br>Gabon|0.0|-2.7|0.0|0.0|2.9|0.7|
|Ghana|0.1|-3.1|2.3|0.0|2.6|-0.9|
|Guinea|0.0|-2.9|1.8|-1.9|1.9|-1.0|
|Kenya|0.2|-3.5|2.4|-1.1|3.1|-2.3|
|Liberia|0.3|-1.9|0.0|-0.3|2.5|-1.1|
|Madagascar|0.4|-2.7|0.8|-0.2|0.8|-1.0|
|Malawi|0.1|-3.5|0.0|-0.1|5.4|-0.6|
|Mali|0.2|-3.5|0.0|-1.6|3.1|-2.2|
|Mauritania|0.2|-3.9|3.6|-0.4|1.2|-1.6|
|Mauritius|0.2|-3.4|4.8|-1.1|6.4|-1.8|
|Niger|0.0|-3.0|0.0|0.0|3.3|-1.6|
|Nigeria|0.1|-2.6|3.7|-0.8|5.2|0.7|
|Senegal|0.3|-3.5|3.6|0.1|4.9|-1.2|
|Sierra Leone|0.0|4.0|2.6|-0.7|2.3|-0.2|
|Sudan|0.1|-1.9|0.0|0.0|0.7|-1.0|
|Swaziland|0.5|4.9|6.7|-3.0|3.5|-1.9|
|Togo|0.2|-2.8|9.8|-0.8|0.2|-2.8|
|Uganda|0.6|-3.0|0.0|0.0|2.1|-0.3|
|United Rep. Tanzania|0.0|-2.5|1.4|-1.0|0.0|-2.4|
|Zaire|0.1|-2.4|0.0|_-0.5_|1.3|-1.1|
|Zambia|0.5|-2.9|0.0|-0.6|1.4|-1.4|
|Zimbabwe|0.2|-3.3|1.2|-1.0|4.0|-1.0|
|**MEMO ITEM**|||||||
|Taiwan, China|7.5|4.0|2.2|-2.2|6.8|0.7|
|Republic of Korea|7.8|4.2|2.5|-2.2|7.1|0.7|

*These values show the preferential tariff margin (in points) that the African country has over all other suppliers of the same goods. A negative value indicates the country pays a lower average tariff than other exporters. The United States does not extend GSP tariffs to Taiwan (China), the Republic of Korea. or to OPEC members.

Source: World Bank-UNCTAD SMART Database
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Table 16: IV estimates of the effect of ICT use on other outcomes: Services

||∆ln(numbe|r of workers)|∆ln(|sales-per-wor|ker)|∆ln(Valu|e added-per|-worker)|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|∆Share of labor that uses computer|0.0105***|0.0129***|0.0114***|0.0083***|0.0082*|0.0076***|0.0051**|0.0079*|
||(0.0018)|(0.0019)|(0.0024)|(0.0024)|(0.0048)|(0.0022)|(0.0022)|(0.0047)|
|F- frst stage|72.68|65.53|72.68|65.53|14.45|72.68|65.53|14.45|
|Sargan p-value|0.2512|0.4402|0.1576|0.1417|0.1316|0.1532|0.3116|0.3155|
|∆Share of labor that uses Internet|0.0092***|0.0111***|0.0086***|0.0070***|0.0098*|0.0065***|0.0050***|0.0112**|
||(0.0015)|(0.0017)|(0.0017)|(0.0017)|(0.0051)|(0.0016)|(0.0016)|(0.0051)|
|F-frst stage|148.63|142.38|148.63|142.38|16.80|148.63|142.38|16.80|
|Sargan p-value|0.6603|0.8653|0.1610|0.6076|0.4723|0.2147|0.6847|0.5288|
|∆ln(stock of computer equipment/worker)|0.3003***|0.4844***|0.3197***|0.3094***|0.2430*|0.2515***|0.2305***|0.2615**|
||(0.0549)|(0.0852)|(0.0640)|(0.0877)|(0.1247)|(0.0629)|(0.0859)|(0.1253)|
|F-frst stage|58.04|38.91|58.04|38.91|19.39|58.04|38.91|19.39|
|Sargan p-value|0.4022|0.5239|0.2658|0.2583|0.2334|0.1356|0.1277|0.1139|
|**Controls**|||||||||
|∆ln(capital/worker)|No|Yes|No|Yes|Yes|No|Yes|Yes|
|Age dummies|No|No|No|No|Yes|No|No|Yes|
|Size dummies|No|No|No|No|Yes|No|No|Yes|
|Observations|6,241|6,241|6,241|6,241|6,241|6,241|6,241|6,241|

Standard errors in parentheses * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.
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# **Can Conditional Cash Transfers Improve Education and Nutrition Outcomes for**

**Poor Children in Bangladesh? Evidence from a Pilot Project***

## **Céline Ferré Iffath Sharif**

_Keywords_ : Cash transfers; Conditionality; Nutrition; Poverty; Bangladesh _JEL classification:_ I15; I21; I38; J13, O15

## **Acknowledgements**

We are grateful to Zahid Hassan and his team at DATA for their contribution during the data collection and preparation, the _Shombhob_ pilot implementation team, and Nkosinathi Mbuya and Salwa Khan for their contributions during the design of the nutrition module in the household questionnaire used for the evaluation. Raisa Sara provided excellent research assistance. Helpful comments on earlier version of the paper were received from Maria Florencia Pinto Pablo Gottret, and Cem Mete. Financial support for this research was provided by the South Asia Food and Nutrition Security Initiative (SAFANSI) and the Rapid Social Response (RSR) MDTF of the World Bank.
increase with age, suggesting that on average the unproductive firms exit so older firms are more productive.

Overall, the declining size-productivity covariance in developing countries and the work by Hsieh and Klenow (2014) on the absence of a missing middle in firm size distributions suggest that large firms are equally constrained in the developing countries and more research is needed to understand the welfare implications of relieving the constraints faced by large firms versus small firms and the priorities for reform efforts.

# **IV. Heterogeneity among SMEs with respect to financing needs and profiles**

# **_A. Informal microenterprises_**

Informal firms play a huge role in developing countries. Using different measures of informality, La Porta and Shleifer (2008) estimate that the informal sector accounts for 30-40% of total economic activity in the poorest countries and a higher share of employment. Informality rates tend to be the highest among the smallest firms with the IFC Enterprise Finance Gap Database estimating that about 80% of microenterprises and SMEs are informal.

Research into the characteristics of these firms shows that informal enterprises are small, inefficient, run by poorly educated entrepreneurs and highly unproductive (La Porta and Shleifer (2008)). Hence, they rarely transition to formality and continue without much growth or improvement. Consistent with this, Ayyagari, Demirgüç-Kunt, and Maksimovic (2015a) find that firms in the informal manufacturing sector in India have a very different lifecycle than the firms in the 24
# **Table 4 -- Benchmark Distortions (%)**

||||Borde|r costs|Standards<br>|<br>Regulat|<br>orybarriers|
|---|---|---|---|---|---|---|---|
||Output tax|Tariff|Imports|Exports|for EU exports|All firms|Foreign firms|
|**Business Services**||||||||
|Transport via railways|2.5|||||20.0||
|Transport via pipelines|2.5|||||40.0||
|Air transport|2.5||||||106.8|
|Telecommunications|2.5|||||1.7|0.3|
|Insurance|0.7|||||9.6|15.8|
|Banking|0.7|||||1.5|5.6|
|**Dixit-Stiglitz Goods**||||||||
|Mining and quarrying|2.4||9.1|8.9||||
|Manufacturing|1.5|4.3|9.1|8.9|21.6|||
|**Other goods and services**||||||||
|Agriculture, hunting, forestry and fishing|0.7|10.1|9.1|8.9|15.8|||
|Electricity, gas and water supply|0.8|||||||
|Construction|0.3|||||||
|Wholesale and retail trade and repair|1.2|||||||
|Hotels and restaurants|4.5|||||||
|Other land transport|2.1|||||||
|Auxiliary transport activities|2.1|||||||
|Post and courier activities|2.1|||||||
|Real estate and professional services|1.9|||||||
|Public administration and defence|0.5|||||||
|Education|0.1|||||||
|Health and social work|0.3|||||||
|Other social andpersonal services|6.3|||||||

42