rafmacalaba/datause-encoder-data huggingface dataset:
# **TABLE 10. Wage by worker type. Productivity channel**

||Mana<br>|gers<br>|Skilled W<br>|orkers<br>|Unskilled<br>|Workers<br>|
|---|---|---|---|---|---|---|
||FE|FE‐2SLS|FE|FE‐2SLS|FE|FE‐2SLS|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Productivity|0.040***|0.58*|0.024***|0.53|0.020**|0.58*|
||(0.0099)|(0.34)|(0.0089)|(0.43)|(0.0086)|(0.32)|
|Productivity|0.022**|0.31**|0.014|0.070|0.012|0.11|
||(0.010)|(0.14)|(0.0095)|(0.070)|(0.0099)|(0.072)|
|Prod * High Prod|0.064***|0.36***|0.031**|0.15**|0.031**|0.21***|
||(0.018)|(0.11)|(0.015)|(0.060)|(0.013)|(0.058)|
|Productivity|0.036***|0.40*|0.020**|0.12|0.013|0.32**|
||(0.011)|(0.21)|(0.0087)|(0.15)|(0.0083)|(0.16)|
|Prod * Skills|0.011|0.047|0.014|0.047*|0.022|0.044|
||(0.017)|(0.043)|(0.016)|(0.025)|(0.017)|(0.030)|
|Observations|2,141|2,141|2,221|2,221|3,055|3,055|

Dependent variable: change in log average wage of managers (columns 1 and 2), skilled workers (columns 3 and 4) and unskilled workers (columns 5 and 6). Regressors: change in labor productivity, and change in labor productivity interacted with firm‐level indicator variables defined as in Table 5. Instruments defined as in Table 3. Robust standard errors in parenthesis.

34
## **Table A3:** **<u>Calculation of Maternal Orphans (Before Adjusting for Child EVD Deaths)</u>**

|A|ge 15-19|20-24|25-29|30-34|35-39|40-44|45-49|Total|
|---|---|---|---|---|---|---|---|---|
|_Guinea_|||||||||
|Female deaths|134.7|114.6|95.5|78.4|64.0|53.2|79.2||
|Mean number of living children|0.3|1.2|2.3|3.5|4.3|4.6|5.0||
|Total maternal orphans|41.7|138.7|218.6|277.6|273.1|246.4|397.0||
|Maternal orphans <15 years old|41.7|138.7|218.6|253.3|195.7|124.5|116.5|1,089|
|_Liberia_|||||||||
|Female deaths|238.8|212.1|186.7|156.4|128.5|105.5|126.4||
|Mean number of living children|0.3|1.3|2.3|3.2|4.0|4.6|5.0||
|Total maternal orphans|66.9|267.3|420.0|503.5|507.5|489.3|634.6||
|Maternal orphans <15 years old|66.9|267.3|420.0|459.7|345.6|252.0|227.5|2,039|
|_Sierra Leone_|||||||||
|Female deaths|204.9|183.5|164.3|139.5|113.4|89.4|119.0||
|Mean number of living children|0.2|1.1|2.2|3.2|3.9|4.3|4.6||
|Total maternal orphans|47.1|207.4|363.1|443.8|436.7|384.3|552.3||
|Maternal orphans <15years old|47.1|207.4|363.1|411.7|308.5|186.8|173.8|1,698|
|Total maternal orphans < 15||||||||4,826|

# **5. Repeat process for men aged 15-49**

The same process is carried out for men (see calculations in Table A4), with the difference that the DHS calculate the mean number of living children for men through age 59, due to higher fertility at later ages. Note one exception: The Liberia DHS data for men, unlike those for Sierra Leone and Guinea, only calculate men’s MNLC through age 49. The MNLC for men in Liberia at age 45-49 is closest to that of Guinea, so the growth in MNLC from 45-49 to 50-54 and then to 55-59 is assumed to follow the same percentage growth path as that in Guinea, starting from Liberia’s 45-49 base.

These calculations suggest that, to date, over 5,600 children have lost a father.

14
- 26 -

49. Noland's sample included 45 countries --i.e., 19 developed countries And 26 developing countries. The sample included only countries with over one billion US dollars of manufactured exports in 1980. Estimations were made for a full sample of 45 countries, and subsamples of developed and less developed countries, separately.

50. In the regression results for the developed country subsample, all coefficients estimated were statistically significant at a 1% or 5% confidence level, and R<sup>2</sup> 's were extremely high. However, the endowment similarity variable was estimated with an unexpected positive sign. Noland explained that it reflected the Linder hypothesis that the volume of intra-industry trade increases with similarity in production and consumption. Noland's interpretation was that for the developed country subsample the volume of intra-industry trade so dominated the volume of inter-industry trade that a positive coefficient on the endowment similarity variable was obtained. 51. The studentized residuals obtained from the developed country subsample regressions showed highly abnormal values for Denmark and Switzerland, but the residuals for Japan were comparatively small. Noland thus concluded that, while Japan's actual exports were 16% higher than "predicted" by the model, Japan's actual imports were "nearly exactly the level forecast by the model." 52. The above model ignores differences in natural resource endowments. Balassa (1986b) argued that countries without significant natural resource endowments would exhibit a greater volume of trade because they would both import primary products and engage in intra-industry trade in differentiated manufactures. Noland thus modified the model adding three variables representing natural resource endowments --i.e., arable land, the value of fuels production and the value of production of 13 nonfuel minerals. The
- 43 -

**Table 20 Turkey Commercial Bank Contingencies and Commitments** (US$ million)

|Period||Guarantees|Fx|and Interest Rate||
|---|---|---|---|---|---|
|||and Warranties|Commitments|Transactions|Total|
|1997|TL|8,075|18,875|9,393|36,343|
||FX|21,327|3,850|23,004|48,181|
||**Total**|**29,401**|**22,725**|**32,397**|**84,524**|
|1996|TL|7,213|16,162|4,630|28,005|
||FX|17,222|1,991|15,056|34,269|
||**Total**|**24,436**|**18,153**|**19,686**|**62,275**|
|1995|TL|7,041|7,970|4,794|19,805|
||FX|13,906|633|12,525|27,064|
||**Total**|**20,947**|**8,602**|**17,319**|**46,868**|

Source: The Banks Association of Turkey.

# **VI.2 Capital Flows and Capital Markets Development**

Since financial liberalization in the 1980s, Turkey's securities markets have grown tremendously in size and in sophistication, though the dominance of the government debt securities in the m,arket has remained unchanged. Stock market capitalization has increased from US$1 billion in 1988 to US$62 billion in 1997 (from 1% of GNP in 1988 to 31% of GNP in 1997). The number of listed companies increased from 80 to 258, and turnover ratio increased from 5.5 to 114, in the same period. By the end of 1997, Turkey ranked 29th in total market capitalization, 20th in total value traded, and 29th in number of listed domestic companies, in the world (IFC, 1998).

Although stock market capitalization has surpassed the level of outstanding Treasury bills and bonds, thLe level of outstanding shares issued has been much smaller than debt securities. The value of outstanding shares was only 15% of the outstanding government bills and bonds at the end of 1997. Private sector issues (of which 80% was equity issues) was less than 5% of Treasury bills and bonds issued in 1997. In fact, private sector issues have not shown arny real growth since the early 1 990s, despite the phenomenal growth in market capitalization and high real returns in equity investment.

Secondary market trading of government securities has also grown phenomenally in recent years. Value traded of government securities on the "outright purchases and sales market" in the Istanbul Stock Exchange increased from US$312 million in 1991 to US$35 billion in 1997. Value traded on the "repo-reverse repo market" in the Istanbul Stock Exchange increased from US$4.8 billion in 1993 to US$374 billion in 1997.
8 they face simple or complicated funding structures, depositors and their guarantors need to defend their hoped-for isolation from piobable loss. This is done efficiently --not by setting high initial capital requirerments-- but by making sure that the stakes of lowerpriority svakeholders are rebuilt promptly whenever they decline in value. This means monitoring the value of lesser-priority stakes and pressuring bank managements emther to rebuild or to sunender thesc posido-s when and as advcrse developments compromise them.

Measuring the sensitivity of insider stakes to the asset and liability writedowns that might be implied by changing conditions '; the operational problem that bank supervisors face under the risk-based capital framework. To address this problem, ;upervisors -nust promote adequacy and transparency in bank record keeping.

For credit policy and loan evaluation, this means installing documentation requirements that mandate establishing adequate provisions for loai. losses and explicit files for each of the traditional five C's of credit analysis: cash-flow projections, collateral, char;.;ter evaluation, vulnerability to changing economic conditions, and borrower capital strength.

Explicit and implicit government guarantees of borrower performance are collateral-like "credit enhancements." Especially in developing countries, it is important to separate the value of formal and conjectural guarantees a bank receives from the standalone value of loans it makes to state-owned enterprises and members of governmentally favored economic sectors.

In any developing country, improving the quality of bank information systems needs to be made a high-ranking objectivc of prudential regulation. In more than a few countries, loan-loss provisioning has been ignored and several of the hypothetical "five C" file folders that constitute appropriate credit files have been left emptv. In these countries, setting a nominai 10-percent or 20-percent capital requirement will do less to stabilize the banlking sector than adopting a conscientious program for building databases -at individual banks that ame capable of enhancing capital mea%urement and enforcement over time. However defici-nt a developing nation's ballpoint-pen and carbo.n-paper information system may be ,t the start, its authorities should be urged to throw off limitations on their capacity for measurrng bank capital and risk exposure and to ensure
-2-

# TABLES

|1.|Bank Operations: Employment/Pay Components<br>(Approved as of February 1987)<br>.....<br>.........|.......<br>27|
|---|---|---|
|2.|Bank Operations with Employment/Pay Components<br>(Under Preparation as of February 1987)<br>....<br>....|.....<br>28|
|3.|Countries with Bank Operations in Public Sector<br>Pay and Employment<br>.......<br>....................|.......<br>29|
|4.|Country Economic and Sector Work on Pay<br>and Employment<br>....................................|.. 30|
|5.|Major Diagnosed Problems for Countries with Bank<br>Employment/Pay Interventions<br>.....<br>............|.......<br>31|
|6.|Bank Operations with Employment/Pay Components<br>By Lending Instrument (As of February 1987)<br>.......|..<br>32|
|7.|Bank Operations BrokEt' Down by Pay and Employment<br>Components (Approved or under Preparation as of<br>February 1987)<br>.........<br>......................|.......<br>33|
|REF|ERENCES ............<br>..............................|.<br>34|
_Table 4: Education Production Function Estimation Results, Full Model_

|Independent Variables|2000|2006|2009|2012|
|---|---|---|---|---|
|**_Student's characteristics_**|||||
|Gender (female)|23.024***|37.484***|23.617***|24.964***|
|Not enrolled in 10thgrade(over age)|‐75.138***|‐74.123***|‐64.849***|‐58.565***|
|**_Socioeconomic background_**|||||
|Wealth index|‐2.694|15.153***|9.679***|1.399|
|Mother's highest level of education|||||
|_Complete primary school_|‐23.147*|‐13.503|5.973|16.477**|
|_Complete upper secondary school_|‐17.029|‐0.463|20.580**|16.008**|
|_Complete lower secondary school_|1.07|‐0.514|17.348***|28.020***|
|_Complete tertiary_|‐5.317|2.291|31.514***|41.153***|
|Father's highest level of education|||||
|_Complete primary school_|‐17.789|7.318|5.466|6.889|
|_Complete upper secondary school_|‐21.415|18.147|11.968*|9.34|
|_Complete lower secondary school_|‐7.095|6.68|7.055|8.502|
|_Complete tertiary_|‐13.847|21.692**|9.905|6.24|
|Books at home|||||
|_11‐100_|17.646***|17.220***|14.957***|20.237***|
|_more than 100_|36.722***|34.059***|34.712***|38.043***|
|**_School characteristics_**|||||
|Public school|36.116**|‐57.437|‐12.59|‐51.617***|
|Rural area|‐6.966|‐2.971|‐25.010**|9.147|
|School size|||||
|_200‐500 students_|‐14.008|44.646***|28.698**|0.586|
|_500‐1,000 students_|4.244|53.530***|37.714***|3.688|
|_> 1,000 students_|‐1.246|62.641***|33.401**|4.623|
|Student‐teacher ratio (full time + part‐time)|0.906|‐0.934|‐0.242|0.074|
|Percentage of teachers with tertiary education|0.425|0.108|.426*|.351**|
|Computer with web connection, per student (x 100)|2.621*|1.456|3.297***|.598*|
|Shortage/Inadequacyof instructional material|‐24.078***|‐25.621**|‐1.225|‐8.827|
|**_Institutional setting_**|||||
|Autonomy—firing teachers|52.721***|31.998|8.979|‐0.597|
|Autonomy—formulating the school budget|8.709|‐51.101***|16.39|‐18.889**|
|Autonomy—deciding on budget allocations|2.416|1.083|1.739|13.313|
|Autonomy—establishing student disciplinary policies|‐0.126|‐2.497|‐7.431|1.164|
|Autonomy—establishing student assessment|‐8.651|‐23.714*|9.612|‐0.462|
|Autonomy—determining courses’ content|25.107**|8.181|7.375|2.783|
|Autonomy—deciding which courses to offer|‐4.022|4.037|‐5.277|8.891|

16
**Table A1: FPM transfers and preschool supply, controlling for second order population polynomial**

|||Reduce<br>|d form|||IV<br>|||Obs.|
|---|---|---|---|---|---|---|---|---|---|
||Enrol|lment|#ce|nters|Enrol|lment|#ce|nters||
|Panel A: Municipal supply||||||||||
|Thresholds 1-7|3.646**<br>[1.658]|5.151***<br>[1.425]|0.216**<br>[0.089]|0.197***<br>[0.061]|4.522**<br>[2.053]|6.668***<br>[1.686]|0.268**<br>[0.110]|0.255***<br>[0.072]|21168|
|Thresholds 1-4|4.054***<br>[1.334]|5.025***<br>[1.197]|0.183**<br>[0.077]|0.188***<br>[0.059]|5.411***<br>[1.778]|7.029***<br>[1.531]|0.244**<br>[0.103]|0.262***<br>[0.076]|18365|
|Panel B: Private supply||||||||||
|Thresholds 1-7|0.214|1.494***|-0.008|0.001|0.266|1.935***|-0.010|0.001|21168|
||[0.742]|[0.381]|[0.011]|[0.006]|[0.920]|[0.453]|[0.014]|[0.008]||
|Thresholds 1-4|1.151**|1.168***|0.003|0.006|1.537**|1.634***|0.004|0.009|18365|
||[0.532]|[0.371]|[0.011]|[0.006]|[0.711]|[0.477]|[0.014]|[0.008]||
|State fixed-effects|Yes|No|Yes|No|Yes|No|Yes|No||
|Municipality fixed-effects|No|Yes|No|Yes|No|Yes|No|Yes||

_Notes_ : Reduced form regressions relate the relevant outcome to theoretical transfers. In the IV regressions, theoretical transfers serve as instrument for actual transfers. All regressions include a second order population polynomial and year fixed effects. Robust standard errors clustered at the municipal level in brackets. *, **, *** represent significance at the 10%, 5% and 1% level, respectively.
the time frames that we can consider—the first five to seven years during and after upgrades— the economic effects of the upgrades were mostly neutral with respect to most organized sector location choices regarding urban versus rural settings, the main exception being very high output level plants being more likely to take a rural location near to the improved GQ network. We likewise identify that a key aspect of whether or not an urban or rural setting is expanded upon depends upon the distance from the major nodal cities. Urban parts of districts that are closer to the nodal cities of GQ are activated more, while rural parts of districts farther from the nodal cities experience the greatest stimulus of new entrants.

To study further the urban and rural distinctions, we also extend our earlier work to consider the unorganized manufacturing sector using enterprise data from National Sample Survey. The unorganized sector accounts for about 20% of Indian manufacturing output, but a little over 80% of Indian employments in manufacturing. We extend the scope to include the unorganized sector to revisit a second, independent theme from our earlier work. In Ghani et al. (2012b), we analyze the extent to which the manufacturing sector is moving from urban to rural locations within districts. Among its conclusions, this paper finds that the organized manufacturing sector of India is moving towards rural locations, while the unorganized sector is moving towards urban settings. The predecessor study concludes that infrastructure build-up within districts plays a modest role in location placement, with an emphasis towards retaining higher urban shares. These patterns, and especially the extent to which the unorganized sector is the key force driving urbanization of manufacturing, are important for policy makers to understand in the design of policies to promote urbanization.

Building out a new analysis for the unorganized sector across the GQ network, we find very limited impact of the GQ upgrades outside of the nodal districts. As we describe further below, we see traces of evidence of the organized sector findings repeating themselves in the unorganized sector (e.g., heightened entry rates, forms of industry sorting), but the results are substantially diminished in economic magnitudes. We confirm that these basic patterns are true in both urban and rural settings; they also hold true regardless of the gender of the business owner in the unorganized sector.

Thus, to summarize, we obtain a general conclusion that the massive GQ highway project development had a substantially larger effect on the organized sector than on the unorganized 3
individual access, expenditure and consumption patterns while data at the community level, on the quality and reliability of the service and subjective assessment are missing. The Demographic and Health Surveys gather information on individual access only; information at the community level are recorded by the China Economic, Population, Nutrition and Health Survey together with all the necessary information about household income and consumption pattern, but not on opinions; household opinions about their living standard are instead one of the main focus of the Life in Transition Surveys (EBRD 2006). Designing an ad hoc household survey is obviously an open option, but it is costly and time consuming.

## **_Some Empirical Evidence_**

Miniaci _et al_ (2008a) empirically compare the affordability indices related to the actual and potential budget shares with the one relying on the residual income approach. The authors exploit the information of a standard consumer expenditure survey to assess the affordability of water, gas and electricity in Italy for the period 1998-2005 which has been characterized by major tariff reforms. They identify the reference subsistence level (conditional on family size and regional area of residence) as the median expenditure of those households deemed as relatively poor; and the threshold budget share as the median budget share of the actual utility bills to total expenditure for the relatively poor.

When the actual utility bill to total expenditure is considered, the utilities were unaffordable for 27.9% of the Italian households in 2005. This percentage drops to 5.9% if the ratio is based on the reference subsistence level of consumption. When they consider the residual income approach the fraction of households with affordability problems is 28.1%, but it drops to a mere 3.6% when the households who are above the poverty line but who spend less than the reference subsistence level are excluded (i.e. excluding those falling in area C). The authors show that in the Italian case the underconsumers do not suffer an affordability problem, as they are typically households with high incomes and energy efficient housing conditions.

# **Monitoring Affordability: The Policy Issues**

The status of utility services is often the one of merit goods, for several reasons already mentioned (Le Blanc 2008). In order to avoid underconsumption or, more precisely, 24
# **1. Introduction**

Crises have profound, uneven and long-term effects on aggregate productivity and employment recovery (Reinhart and Rogoff, 2009; Dell’Ariccia et al., 2008). The COVID-19 pandemic, in particular, had an extensive and heterogeneous effect on firms’ operations and performance based on their size, sector, age, and other firm attributes (Apedo-Amah et al., 2020; Cirera et al., 2021). The degree of informality in the context of South Asia has also been a key factor contributing to such differences in effects (Grover and Pereira-López, forthcoming), given its salience in the region (Bussolo et al., 2020). Leveraging three rounds of the World Bank’s Business Pulse Surveys (BPS) in South Asia conducted during the pandemic from May 2020 to August 2022, this paper quantifies the relationship between informality and firms’ investment decisions that may have implications for post-crisis job recovery. Our work suggests that lower investments and employment response among informal firms are potentially attributable to their acute credit constraints and heightened perceived uncertainty.

Evidence from the Great Recession shows that shocks to the financial system can explain the trade collapse (Cetorelli and Goldberg, 2011; Chor and Manova, 2012) and output declines, especially in industries that rely more on banking finance. Such effects are stronger among firms with fewer assets that can serve as collateral or those with limited access to inter-firm finance (Bricongne et al., 2012; Görg and Spaliara, 2014; Iacovone et al., 2019; Paravisini et al., 2015). Although the COVID-19 crisis did not originate in the financial sector, the pandemic-induced mobility restrictions led to supply and demand shocks, and the conditions slowly morphed into a financial recession (Reinhart, 2022). With the increase in risks and uncertainty during the crisis, creditors become more risk-averse and are reluctant to lend in the face of imperfect information regarding default likelihood (“flight to quality”) (Dell’Ariccia et al., 2008). In the pandemic period, this resulted in excess demand and a short supply of credit (Didier et al., 2021).

Although most crises entail uncertainty, the multiple channels through which the pandemic affected firms and consumers generated unprecedented levels of uncertainty (Meyer et al., 2022) due to evolving variants of the virus and the lockdown measures it provoked (Barrero and Bloom, 2020). Uncertainty effectively increases risk, leading to credit crunch and a higher cost of finance. Under uncertainty, investment can be viewed as "real options," and the value of delaying such decisions that cannot easily be reversed is high. This delays investment, borrowing, and hiring decisions (Bloom et al., 2007; Dell’Ariccia et al., 2008; Barrero and Bloom, 2020). For example, firms might prefer hiring part-time workers, as 2