rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7.** LPM of wage employment, without gender role attitudes and personality traits

||(1)|(2)|(3)|
|---|---|---|---|
||Pooled sample|Women|Men|
|Male|0.182***|-|-|
||(7.92)|||
|Age|-0.003|-0.007|0.003|
||(-0.82)|(-1.53)|(0.38)|
|Age_sq|-0.000|0.000|-0.000|
||(-0.54)|(0.18)|(-1.05)|
|Married|-0.003|-0.029|-0.000|
||(-0.11)|(-0.80)|(-0.00)|
|Secondaryeducation|0.133***|0.142***|0.112***|
||(5.22)|(4.66)|(2.63)|
|Male head|0.160***|0.050|0.169**|
||(4.49)|(0.62)|(2.07)|
|Muslim|-0.120***|-0.120**|-0.120**|
||(-3.82)|(-2.60)|(-2.45)|
|Spousepresent|-0.135***|-0.024|-0.142***|
||(-3.60)|(-0.28)|(-2.65)|
|Household size|-0.009**|-0.007*|-0.013**|
||(-2.57)|(-1.86)|(-2.13)|
|Poor|-0.045|-0.044|-0.044|
||(-1.60)|(-1.50)|(-1.03)|
|Urban|0.076**|0.045*|0.108*|
||(2.37)|(1.88)|(1.94)|
|Ashanti(b:Northern)|-0.039|-0.039|-0.045|
||(-1.21)|(-0.88)|(-0.88)|
|Constant|0.394***|0.514***|0.428**|
||(3.81)|(4.13)|(2.35)|
|_N_|2361|1293|1068|

t statistics in parentheses: * p<0.10, ** p<0.05, *** p<0.01. Errors clustered at cluster-level. Base category is “SelfEmployment” 30
|using the<br>oducts by<br>ralization<br>ns (3)-(6),<br> intensity<br> is closest<br>n Table 3.<br>e (_c ∗t_0)<br>(6)<br>Lib<br>0.173***<br>(0.045)<br>-1.846***<br>(0.090)<br>0.012<br>(0.014)|Yes<br>Yes<br>Yes<br>Yes<br>28,739|
|---|---|
|are estimated <br> rate across pr<br>ock market libe<br>ch). In colum<br>e liberalization<br> in a year that<br>s are defned i<br>g country)*tim<br>(5)<br>All<br>0.222***<br>(0.046)<br>-1.516***<br>(0.088)<br>0.079***<br>(0.010)|Yes<br>Yes<br>Yes<br>Yes<br>65,266|
|All regressions <br>baseline hazard<br>t sign of the st<br>ntry fund laun<br> versions of th<br>y-country pair <br> Other variable<br>d at (exportin<br>(4)<br>Lib<br>0.217***<br>(0.047)<br>-1.740***<br>(0.098)<br>0.018<br>(0.015)|Yes<br>Yes<br>Yes<br>Yes<br>28,739|
|ization<br>d at time _t_0. <br>w for diferent <br>year of the frs<br>of the frst cou<br>try level. Both<br> given industr<br>-country pair. <br> errors clustere<br>(3)<br>All<br>0.269***<br>(0.048)<br>-1.515***<br>(0.095)<br>0.079***<br>(0.010)|Yes<br>Yes<br>Yes<br>Yes<br>65,266|
|ket Liberal<br>ket that starte<br>tions also allo<br> based on the <br>DR, the year <br> industry-coun<br> available for a<br> given industry<br>bust standard <br>ively.<br>(2)<br>Lib<br>0.152***<br>(0.024)<br>-1.498***<br>(0.051)<br>-0.004<br>(0.016)|Yes<br>Yes<br>Yes<br>Yes<br>45,699|
|Table 7: Alternative Measures of Stock Mar<br>ard rate for an export spell of product _k_ from country _c_ to the world mar<br>Cox, 1972) and control for exporting-country and time fxed efects. Estima<br>le. In columns (1)-(2), the stock market liberalization measure is a dummy<br>three dates: the year of ofcial liberalization, the year of issuing the frst A<br>asure is a continuous variable capturing the liberalization intensity at the <br> index and IFC Global index. In columns (3) and (4), we use IFC indices<br>rket liberalization. In columns (5) and (6), we use the average value for a <br> column (4) of Table 3. We report coefcients and not hazard ratios. Ro<br> *** denote statistical signifcance at the 10%, 5%, and 1% levels, respect<br>(1)<br>All<br> Distance to comparative advantage<br>0.126***<br>(0.017)<br>-1.063***<br>(0.040)<br>closest year) x Distance to comparative advantage<br>closest year)<br>average) x Distance to comparative advantage<br>average)<br>antage<br>0.078***<br>(0.011)|Yes<br>Yes<br>Yes<br>Yes<br>85,675|
|ndent variable is the haz<br>ortional Hazard Model (<br>product _k_ as strata variab<br>est among the following <br> market liberalization me<br>uted from IFC Investable<br>cial year of the stock ma<br>of controls corresponds to<br> in parentheses. *, **, and<br>BLES<br>eralization frst sign x<br>eralization frst sign<br>eralization intensity (<br>eralization intensity (<br>eralization intensity (<br>eralization intensity (<br>e to comparative adv|of controls<br>ng country FE<br>E<br>t stratifcation<br>tions|
|The depe<br>Cox Prop<br>defning <br>(the earli<br>the stock<br>are comp<br>to the of<br>Full set <br>level are <br>VARIA<br>StM lib<br>StM lib<br>StM lib<br>StM lib<br>StM lib<br>StM lib<br>Distanc|Full set <br>Exporti<br>Time F<br>Produc<br>Observa|

43
insufficien **t** axable income. The above basically single period specification abstracts from the fact that, for instance in the United Statrs, banks that do not have sufficient current taxable income are allowed to carry credits backwards two years, and to carry them forward for seven years. Under current rules, banks can only carry credits forward to reduce future taxes on income in the same basket. Clearly, postponing taking the credits reduces their present value. Also, what is important in this regard is the availability to banks of other ways to shelter income. As pointed out by Frankel [1985], banks were allowed to deduct interest expenses incurred to carry tax-exempt bonds from taxable income during the period under consideration. Hence, foreign tax credits may not have been of immediate importance for the U.S. banks. In a world of income and regulatory uncertainty, the value of tax credits and hence of the parameter 6 is to some extent determined by their option value rather than by their immediate usefulness.6

# 3. The_ ata and Empixical Results

## 3.1 The Data

The data -,et consists of 510 loans originated during the period 19711981, from the World Bank's Debtor Reporting System. All loans are to the private sector, which surely pays the withholding tax, are denominated in U.S. dollars and specify the interest spread above LIBOR. For each loan we know the month of origination, the month of maturity, the loan amount, the debtor country, and the creditor country. Some loans are listed as multiple creditor loans. On a loan by loan basis, we collected the relevant interest withholding tax rates, taking note of bilateral tax treaties between debtor 9
Our paper is also closely related to the literature on the establishment of credit bureaus and credit registries, which play a similar role to collateral registries in improving credit availability. Theory suggests that information sharing can overcome adverse selection and moral hazard problems in credit markets (Pagano and Jappelli, 1993 and Padilla and Pagano, 2000). The ability of creditors to repossess collateral should have a similar effect on reducing adverse selection and moral hazard in credit markets. Using aggregate cross-country data Jappelli and Pagano (2002) show that bank lending is higher and credit risk is lower in countries where lenders share information. Similarly, Djankov, McLiesh and Shleifer (2007) show that quality of credit information is correlated with higher levels of private credit. Using firm level data Love and Mylenko (2005) show that the establishment of credit registries reduces firms’ credit constraints and, more recently, Brown, Jappelli and Pagano (2009) use panel data from 24 countries in Eastern Europe to show that information sharing is associated with improved availability and lower cost of credit to firms. Using bank-level data, Houston et al. (2010) show that greater information sharing leads to higher bank profitability, lower bank risk, a reduced likelihood of financial crisis, and higher economic growth. While the research in support of the establishment of credit registries has flourished in recent decades, to our knowledge, this is the first paper to investigate the impact of introducing of collateral registries for movable assets on firms’ access to finance.

Finally, our paper also relates to a broader literature that investigates the importance of the legal system for the availability and costs of finance. Following the seminal work of La Porta et al (1998), who brought to the spotlight the relationship between creditor rights and financial development, a number of subsequent studies have explored this relationship empirically in various settings. For example, Demirguc-Kunt and Maksimovic (1998) find that in countries 5
# **Learning to Grow from Peers:**

# **Experimental Evidence from Small Retailers in Indonesia**<sup>_∗_</sup>

Patricio S. Dalton<sup>_†_</sup> Julius R ̈uschenp ̈ohler<sup>_‡_</sup> Burak Uras<sup>_†_</sup> Bilal Zia<sup>_§_</sup>

**Keywords:** Business Growth; Efficiency Gains; Small-scale Enterprises; Peer Knowledge; SelfLearning; Social Learning.

**JEL Codes:** O12; L26; M20; O31; O33; O35; O17; M50

> _∗_ We thank the Abdul Latif Jameel Poverty Action Lab (J-PAL) for hosting our study, in particular Ni Luh Putu Satyaning Pradnya Paramita, Dwitri Amalia, Raisa Annisa, and Lukman Edwindra for excellent research assistance. We also thank participants at the 8th Wageningen-Lucerne-Tilburg Development Economics workshop, IPA 8th SME Working Group Meeting at MIT Sloan, 9th Tilburg-Wageningen Development Economics Workshop, ENTER-Jamboree 2018 in Toulouse School of Economics, UCPH Field Days Conference at U. of Copenhagen, NBER 2018 Entrepreneurship Working Group in Boston, SEU 2018 at U. de Montevideo, Innovation Growth Lab 2019 Global Conference in Berlin, and Economics Seminars at EBRD, Radbout Nijmegen, UC Louvain, Stanford, Tilburg, U. Torcuato Di Tella, U. of Glasgow, U. of Gottingen and U. of Florence. Special thanks to Thorsten Beck, Erwin Bulte, David McKenzie, Michelle Brock, Karen Macours, Menno Pradhan, Sarada, Antoinette Schoar, Daan van Soest, and Ben Vollaard for helpful discussions and comments. This paper was produced under the framework of the “Enabling Innovation and Productivity Growth in Low Income Countries (EIP-LIC/PO5639)” project, funded by the Department for International Development (DFID) of the United Kingdom and implemented by Tilburg University. Additional funding was received from the World Bank Strategic Research Program (SRP) and the Tilburg Economics Department. Research on the ground was conducted in collaboration with J-PAL South-East Asia and SurveyMETER. AEA RCT Registry ID: 0001175.

> _†_ Tilburg University, Department of Economics and CentER, Warandelaan 2, 5037 AB, Tilburg, The Netherlands. E-mail corresponding author: `p.s.dalton@uvt.nl` (corresponding author), and `r.b.uras@uvt.nl`

> _‡_ Center for Effective Global Action, UC Berkeley. E-mail: `ruschenpohler@berkeley.edu` .

> _§_ The World Bank, Development Research Group, 1818 H St. N.W. Washington, D.C. 20433. E-mail: `bzia@worldbank.org` .
(MINUSMA).<sup>9</sup> Reaching an accord with so many players and interests at stake can be challenging and concessions need to be made that may lead to a narrow representation of elite interests at the expense of those of the population.

The 2011 World Development Report argues that elite pacts can provide short-term security but that violence often recurs unless the pact broadens and is accompanied by institutional transformation.<sup>10</sup> Thus distinctions have been drawn between inclusive and exclusive elite bargains, depending on the degree of integration of broad coalitions of key elites versus narrow coalitions in terms of access to state structures (e.g. jobs in the government and the army).<sup>11</sup> Elite pacts and peace agreements can bring an end to conflict and violence in the short-term, but if such horizontal pacts are not accompanied by inclusiveness and responsiveness along a more vertical dimension, societies remain vulnerable to the same tensions that brought about violence and instability in the first place.<sup>12</sup> Using household level data collected in northern Mali in August and September 2015, we explore whether the content of the 2015 Peace Accord is inclusive, i.e. whether it reflects the concerns and priorities of the people of the north or whether it is an agreement between elites who fail to represent the interests of the population at large. We look at how the actions outlined in the Peace Accord align with the priorities of local leaders and citizens in the north. We also explore whether the financing priorities reflected in the report of the Joint Assessment Mission (Mission d’évaluation conjointe, MIEC), which operationalizes the Peace Accord, align with priorities identified by households and their leaders.

> 9 Reeve, Richard (2015): Devils in the Detail: Implementing Mali’s New Peace Accord, Oxford Research Group, p. 3.

> 10 World Bank (2011) World Development Report 2011: Conflict, Security and Development, Washington: The World Bank, p. xv.

> 11 See Lindemann, S (2008) “Do Inclusive Elite Bargains Matter? A research framework for understanding the causes of civil war in Sub-Saharan Africa”, CSRC Discussion Paper No. 15. London: Development Studies Institute, LSE.

> 12 Laws, Edward (2012): Political Settlements, Elite Pacts and Government of National Unity. A Conceptual Study, p. 29. Development Leadership Program.

4
only the liquidity of the remaining firms in local markets but also their ability to raise capital, jeopardizing the intended broad-based nature of domestic capital markets.

# **3. Financial Diversification**

The financial diversification dimension of financial globalization can be generally analyzed through both de jure and de facto measures. While the former is based on regulations and restrictions, including capital flow controls, the latter is related to the intensity of cross-border movements of capital. As these two types of measures are not necessarily closely correlated, some recent studies lean toward the more practical relevance of de facto measures.<sup>18</sup> We thus examine two commonly used de facto measures in the financial globalization literature: a stock-based one and a flow-based one.<sup>19</sup> As documented in a number of studies, these de facto measures suggest an increasingly globalized world. Figure 1 shows the level of cross-border capital flows and the stock of crossborder assets and liabilities, scaled by GDP, for several emerging and developed regions during the 1980s, 1990s, and first decade of the 2000s. All regions witnessed an increase in financial diversification over this period, particularly during the latter decade. However, emerging regions still lag behind developed ones. The increase in financial diversification experienced by emerging countries, measured through either the stock or the flow measures, was considerably lower than that observed across developed countries. As a result, financial integration, in its diversification dimension, remains much more developed in developed countries than in emerging ones. For example, foreign assets and liabilities represented about 300 percent of GDP in developed countries in 2000-07, whereas in emerging countries they stood at less than half of

> 18. Kose et al. (2010).

> 19. The stock of foreign assets and liabilities was compiled by Lane and Milesi-Ferretti (2007) and a flow-based one (gross capital flows by domestic and foreign residents) was compiled by Broner et al. (2010).

10
consistent with our evidence from the STE sample. The results in Table 3 thus highlight the need for making a distinction between large and small firms, and the importance of focusing on geographical areas beyond the villages. Indeed ignoring the small towns, as is done in most of the current literature, would lead to a gross underestimation of the positive effects of agricultural productivity growth on structural change in employment.

# **5.3 The Effects on Employment in Large Firms: Disaggregated Analysis**

The results discussed above imply that there are important differences in the way agricultural productivity shocks affect different types of economic activities located in different areas. In this section, we explore whether these differences are also observed at a more disaggregated sectoral levels of employment of larger firms for which we have data from the economic censuses. Within the manufacturing sector, we make a distinction between food processing and beverages, and other manufacturing. Among services activities, we distinguish between trade, education, health and other services. We also examine employment in transport, construction and utilities as a separate category. The fixed effects regression results are reported in Table 4. As before, all regressions include the full set of controls along with upazila and time The results in columns 1 and 2 in Table 4 indicate significant and positive effects of rainfall shocks on both food precessing and other types of manufacturing employment in STEs. The effects on all different types of services are positive and statistically significant at the 5 percent level in the STE sample. In terms of magnitudes, the largest coefficient estimate is found for transport, construction and utilities, followed by education, other manufacturing, other services and health. The smallest impact is observed for the trading services.<sup>24</sup> The estimates from the rural sample in general show a much weaker impact; only in two out of seven types of activities listed in Table 4, the estimated effect is statistically significant at the 10 percent level. The two exceptions are education, and transport and construction; but even in these cases the magnitudes of the effects are much smaller in the rural sample when compared to the corresponding

> 24The weak impact on trading may in part be due to the fact that very few trading enterprises are large in size.

27
income, upper-middle-income, and high-income subpanels comprise data for 28 countries, 28 countries, and 44 countries, respectively (see Table A2 in Appendix A).

The data on CO2 emissions from fuel combustion are collected from the CO2 Emissions from Fuel Combustion 2017 published by the International Energy Agency (IEA) [35], while IEA’s World Energy Balances [36] provides the data on energy consumption across the globe. In this study, we use primary energy supplied defined as energy production plus energy imports plus increase in stocks (which is negative if the stock decreases), minus energy exports and international bunkers. In addition, the data on GDP and population are obtained from the World Development Indicators (WDI) published by the World Bank [2], in which the data on GDP are in 2010 constant prices.

# **3. Results from the Decomposition Analysis**

# **3.1. Decomposition results of global CO2 emissions changes**

The decomposition results are depicted in Fig. 2a. The group with the highest contribution to global CO2 growth during the 1980-2015 period is UMI countries, contributing 71.3%, followed by the LMI, HI, and LI countries, which contributed 21.7%, 9.8%, and 0.3%, respectively.

As shown in Figs. 2b and 2c, only the energy intensity effect (  _CEI_ ) for the 19802015 period is negative, while the income effect (  _CPCG_ ), population effect (  _CP_ ), emission coefficient effect (  _CEC_ ), and energy mix effect (  _CECS_ ) are positive. These results suggest that, while economic and population growth have been driving up CO2 emissions over the 1980-2015 period, improving energy efficiency is slowing down that growth. CO2 growth would have been even faster had there been no improvements of energy efficiency. Note that global CO2 emissions increased by more than 80% over the last 35 years (1980-2015). Total CO2 emissions added during that time are more than 14,000 million tons (Fig. 1c). If the average energy intensity of GDP remained at the 1980 level, global CO2 emissions in 2015 would have been almost 14,000 million tons (or more than 40%) higher than the actual level in that year.

The income or economic growth effect is the primary factor driving CO2 emissions 9
# **Analyzing Female Employment Trends in South Asia**

**January 29, 2020**

**Fatima Najeeb (consultant) Matías Morales (consultant) Gladys Lopez-Acevedo**<sup>**1**</sup> **(World Bank)**

Keywords: Female Labor Force Participation, South Asia JEL: J21, O53 Corresponding author: Gladys Lopez-Acevedo World Bank 1818 H Street , NW Washington, DC 20433 USA E-mail: gacevedo@worldbank.org

> 1 We are grateful to Stuti Manchanda, Hans Timmer, Maurizio Bussolo, Virgilio Galdo and David Newhouse for helpful comments. All errors are our own.

> 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 (IBRD) and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
(WTO, 2019). Combined with the evidence from Figure 8 that there are also no large changes in the growth rate of garment sector employment after Rana Plaza, these figures provide evidence against a potential channel where firms respond to Rana Plaza by changing the capital/labor ratio. We also do not observe a noticeable break in exports for the other main exporters of garment post Rana Plaza, except for China, where the value of exports declined after 2013.<sup>9</sup> Although this descriptive evidence is not a formal proof of the absence of shocks to the garment industry contemporaneous to Rana Plaza, it alleviates concerns that our estimates primarily capture the influence of contemporaneous shocks. It also indicates that international demand for garment products from Bangladesh was not substantially affected by the Rana Plaza event, possibly due to the rapid safety and regulation responses documented in section 2.2. Therefore, while reduced demand could have been a mechanism behind the treatment effects we estimate, they appear instead to be primarily driven by firms’ responses to increased international scrutiny.

# **5.3 The extent to which control groups are treated by Rana Plaza responses**

Even if trends are parallel leading up to Rana Plaza, a violation of the parallel trend assumption required for our triple difference identification strategy would occur if a control group was indirectly affected by the treatment. In the context of the triple difference strategy, this could occur either if garment workers in non-treated (i.e, not exporting) districts are affected, or if non-garment manufacturing workers in treated districts are affected. Table 5 summarizes the potential ways in which both groups could potentially be affected; we discuss each channel in detail in turn.

## **5.3.1 Garment workers in control districts**

Garment workers in control districts might have been affected by Rana Plaza, either because the Rana Plaza reforms affected them directly or because of indirect effects through subcontracting channels or workers flows (in particular, if workers leave garment factories in treated districts for

> 9The decline in Chinese export value has been mostly attributed to a willingness to move away from low-value apparel up to the value chain, and to an increase in labor costs.

20