rafmacalaba/datause-encoder-data huggingface dataset:
|**Tabl**|**e 5c - Bank acco**|**unt margin**|**s**|**Table**|**5d - Loan docu**|**ments m**|**argins**||
|---|---|---|---|---|---|---|---|---|
||**Margin**<br>**Std.**|**Err.**<br>**[95%**|**Conf.**<br>**Interval]**||**Margin**<br>**Std.**|**Err.**|**[95% Conf.**|**Interval]**|
|**Female owner#Loan**||||**Feamle owner#Medium**|||||
|**documents**||||**firm**|||||
|0 0|0.37|0.07|0.23<br>0.51|0 0|0.54|0.08|0.38|0.71|
|0 1|0.62|0.08|0.47<br>0.78|0 1|0.77|0.05|0.67|0.86|
|1 0|0.29|0.04|0.22<br>0.37|1 0|0.63|0.03|0.57|0.68|
|1 1|0.45|0.04|0.38<br>0.53|1 1|0.78|0.06|0.65|0.9|
|**Feamle owner#Medium**|||||||||
|**firm**||||**Female owner#Permit**|||||
|0 0|0.5|0.07|0.36<br>0.64|0 0|0.24|0.17|-0.09|0.57|
|0 1|0.68|0.08|0.52<br>0.84|0 1|0.6|0.07|0.46|0.74|
|1 0|0.35|0.04|0.28<br>0.42|1 0|0.58|0.08|0.42|0.73|
|1 1|0.6|0.05|0.5<br>0.7|1 1|0.65|0.03|0.59|0.72|
|**Female owner#Permit**||||**Female owner#Trade**|||||
|0 0|0.3|0.18|-0.06<br>0.66|0 0|0.58|0.06|0.45|0.7|
|0 1|0.55|0.06|0.42<br>0.67|0 1|0.58|0.11|0.37|0.79|
|1 0|0.4|0.07|0.26<br>0.53|1 0|0.63|0.04|0.56|0.7|
|1 1|0.4|0.03|0.33<br>0.46|1 1|0.69|0.06|0.58|0.8|
|**Female owner#Trade**|||||||||
|0 0|0.48|0.07|0.34<br>0.62||||||
|0 1|0.66|0.08|0.51<br>0.82||||||
|1 0|0.36|0.03|0.3<br>0.42||||||
|1 1|0.49|0.07|0.36<br>0.63||||||

# Outcome: Business Development Practices

We now focus on good management practices (Table 6a). To do so we built two indices measuring business development and financial planning by aggregating different binary variables using a summative approach. The components are described in Appendix B. In this case, coefficients of the covariates in the specifications represent an increase or decrease in the overall value of the index, which translates into more or fewer practices being adopted. The business development index takes values from 0 to 12 and the financial planning index from 0 to 6. Table 6b reports the results of the two regressions, including the interaction terms. Tables 6c and 6d report the predicted values of the interaction terms.

**Firm size, sector of operation, and level of education are all determinants in the adoption of business development**

**and good management practices** . Having a medium sized firm increases the number of adopted practices by 1.04. The high level of education of the entrepreneur is positively and significantly related with the adoption of business development measures (~0.9). A similar trend can be seen for firms belonging to the trade sector, which is significantly related to the number of adopted ‘good-practices’. In this case, a firm in the trade sector will have adopted ~0.8 more good management practices compared to a firm in the non-farming agriculture sector. Tourism is instead negatively related to this index, reducing by 0.61 the number of measures adopted.

**Providing informal HRM practices increases the adoption of business development and financial practices.** While the magnitude of the effect is not very large, ~0.4, there is a significant relationship between the provision of maternity/paternity leave and the number of good management practices adopted. A similar relationship can be

26
society. Given the growing trend of globalization and increased migrant movements, these links need to be better understood, particularly in major migration corridors.

India has been the largest country of origin for international migrants since the turn of the 21<sup>st</sup> century (IOM 2019), as well as being the world’s top recipient of remittances (World Bank 2019). <mark>Migration to industrialized countries from India began to increase with the liberalization of immigration laws in those countries during the 1960s. Since the 1980s, most of this migration has been permanent and comprised of highly skilled and well-educated migrants, with return migration being short-term in nature (Jain, 2013). Furthermore, migrants to industrialized countries are richer and well educated compared to the short-term migrants to the Gulf and Saudi Arabia (Rajan and Amuthan, forthcoming). With the increase in oil prices in the mid-1970s, temporary m</mark> igrant flows surged from India to the Gulf Cooperation Council (GCC) countries: Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates <mark>(Kurian 1979).</mark> Migration to the GCC was partly driven by severe shortage of skills and large-scale dependence on skilled and unskilled foreign labor in these countries.<sup>2</sup> In 2015, 7.8 million Indian migrants out of a total of 11.4 million, or about 68 percent, were destined for the GCC (Chanda and Gupta 2018).

Many of the Indian migrants in the GCC are from the South Indian state of Kerala, although there has been an increase in the number of low-skilled workers from northern states in recent years. Kerala has been exposed to international trade for thousands of years,<sup>3</sup> but emigration on such a large scale is a relatively recent phenomenon. The first Kerala Migration Survey reported that nearly 1.4 million migrants from Kerala were in GCC countries in 1998 and that their annual remittances exceeded Rs. 130 million. This increased sharply to 2.4 million migrants by 2013, with annual remittances increasing to Rs. 710

> 2 <u>https://www.arabianbusiness.com/banking-finance/400036-wkd-uae-saudi-arabia-set-for-near-40bn-talent-shortage-timebomb http://saudigazette.com.sa/article/535108</u>

> 3 Kerala may have been engaged in trading activities as early as 3000 BC with Sumerians and Babylonians (Menon 2007). Phoenicians, Greeks, Egyptians, Romans, Jews, Arabs, and Chinese were attracted by a variety of commodities, especially spices and fabrics. Initially foreigners migrated to Kerala. Jews and trading communities from the Middle East settled in Kerala at the end of the first century. During the 15th century the Chinese had used Kerala as a trading base with the Middle East and East Africa. European arrival started with the Portuguese when Vasco da Gama arrived in Calicut in 1498 followed by the Dutch and the British. The British arrived in the 17th and 18th centuries with the East India Company.

3
Distributional Impact Analysis of Climate Variability in Rural Indonesia

# **Table of Contents**

|I. Introduction ............................................................................................................................................... 3|
|---|
|II. Context ..................................................................................................................................................... 5|
|Climate variability and agricultural productivity in Indonesia ................................................................. 5|
|III. Data and Methodology ............................................................................................................................ 6|
|1. Results from IFLS ................................................................................................................................ 9|
|All rural households ................................................................................................................................... 9|
|Farm households, non-farm households, poor households, and rich households .................................... 11|
|2. Results from SUSENAS ..................................................................................................................... 13|
|IV. Summary and conclusions .................................................................................................................... 15|
|References .............................................................................................................................................. 18|
|Annex 1. Econometric Results ............................................................................................................... 20|
|Annex 2: IFLS Rainfall Maps ................................................................................................................ 31|

2/35
# **Table 6. Difference-in-Differences Equity Issuance Estimates: Small vs Large Connected Firms**

This table shows difference-in-differences (DiD) regressions comparing equity issuances by large (above median) connected firms with those by small (below median) connected firms in the propensity score matched (PSM) sample. Firm size is measured as the average total assets in 2010-12. The table shows regression results obtained by estimating Equation (1) using two different dependent variables: the amount of equity raised over 2012 assets and the cumulative amount of equity raised over 2012 assets. The table shows the DiD coefficients, which estimate, for each year, the average differences in equity raised between small and large connected firms (relative to the 2012 difference). The 2012 coefficients show the differences between small and large connected firms in 2012. The regressions include year fixed effects. Standard errors are clustered at the industry (two-digit SIC) level. ∗, ∗∗, and ∗∗∗ indicate statistical significance at the 10%, 5%, and 1% levels, respectively. The right-side panels exclude state owned enterprises (SOEs).

|Sample:||Small Connected<br>vers<br>Large Connected<br>|(Below Median)<br>us<br> (Above Median)<br>||
|---|---|---|---|---|
||PSM S|<br>ample|PSM Sample, E|<br>xcluding SOEs|
|Dependent<br>Variable:|Equity over<br>2012 Assets|Cum. Equity<br>over 2012<br>Assets|Equity over<br>2012 Assets|Cum. Equity<br>over 2012<br>Assets|
|Y_2005 x Small|0.001|0.025 ***|0.011 **|0.034 ***|
||[0.00]|[0.01]|[0.00]|[0.01]|
|Y_2006 x Small|0.001|0.025 ***|0.011 **|0.033 ***|
||[0.00]|[0.01]|[0.00]|[0.01]|
|Y_2007 x Small|-0.004|0.020 **|0.009 *|0.031 ***|
||[0.00]|[0.01]|[0.00]|[0.01]|
|Y_2008 x Small|-0.001|0.018 **|0.011 *|0.031 ***|
||[0.00]|[0.01]|[0.01]|[0.01]|
|Y_2009 x Small|-0.003|0.014 **|0.009|0.029 ***|
||[0.00]|[0.01]|[0.01]|[0.01]|
|Y_2010 x Small|-0.006|0.006|0.001|0.019 ***|
||[0.01]|[0.01]|[0.01]|[0.01]|
|Y_2011 x Small|-0.004|0.001|0.003|0.011 **|
||[0.00]|[0.00]|[0.01]|[0.00]|
|Small (2012 Diff.)|-0.003|-0.037 *|-0.006|-0.039|
||[0.01]|[0.02]|[0.01]|[0.02]|
|Y_2013 x Small|0.039|0.027|0.037|0.009|
||[0.03]|[0.02]|[0.03]|[0.02]|
|Y_2014 x Small|0.043 **|0.068 **|0.015|0.014|
||[0.02]|[0.03]|[0.04]|[0.03]|
|Y_2015 x Small|0.127 ***|0.215 ***|0.116 ***|0.102 **|
||[0.03]|[0.04]|[0.04]|[0.04]|
|Y_2016 x Small|0.230 **|0.470 ***|0.271 **|0.410 ***|
||[0.10]|[0.10]|[0.10]|[0.12]|
|Y_2017 x Small|0.139 ***|0.594 ***|0.194 ***|0.533 ***|
||[0.04]|[0.13]|[0.06]|[0.15]|
|Y_2018 x Small|0.090 **|0.657 ***|0.102|0.533 ***|
||[0.03]|[0.14]|[0.07]|[0.15]|
|Y_2019 x Small|0.051 ***|0.706 ***|0.084 **|0.580 ***|
||[0.01]|[0.14]|[0.04]|[0.14]|
|Y2020 x Small|0.028***|0.733***|0.011|0.580***|
|_|<br>[0.01]|<br>[0.14]|[0.02]|<br>[0.13]|
|No. of observations|8,448|8,448|5,360|5,360|
|No. of clusters|53|53|44|44|
based on a well-considered strategy and incentive structure. Although the steps taken were gradual, the synergistic effect of many institutional, fiscal, and legislative reforms was much greater. The reform efforts were initiated and implemented by different parts of the government—the FC, the Ministry of Finance (central government), the states themselves, the Planning Commission, and the Reserve Bank of India (RBI). All parties were aware that this was not ―business as usual‖ (World Bank 2004), and there was a sense of urgency about transforming the situation.

Among the states, themselves, there was a move away from competitive populism (Kurien 2000), which included subsidies and lowering tariffs, toward coordination by ending the competitive tax rate reduction and instituting the value-added tax, which proved to be highly buoyant. This, coupled with increases in the states‘ share of central taxes instituted by the Twelfth and Thirteenth FCs and the high buoyancy of the center‘s direct taxes, improved state finances and led to their progress toward the FRL goals. The coordination and consultation among all engaged entities ensured consistency of approach and moved the reforms forward.

There is ample fiscal literature on the states‘ fiscal reform—fiscal rules, the quality of fiscal adjustment, expenditure and taxation reforms, power sector reform, and budget and financial management reforms.<sup>6</sup> This paper focuses on the states‘ borrowing and debt restructuring process, underpinned by the move toward a rule-based framework and market discipline.<sup>7</sup> It concentrates on the perspective of the policy maker during this period and reflects on the key challenge that was to balance the provisions of debt relief with the need to avoid moral hazard and enforce fiscal discipline.

The rest of the paper is organized as follows. Section 2 presents the states‘ borrowing framework as prescribed by the constitution, and changes in the borrowing channels and lending policy for states, while incentivizing market access with a rule-based system. Section 3 summarizes the trends in states‘ deficits, debts, and interest payments in the last two decades,

> 6 See Ianchovichina et al. (2000); Pinto and Zahir (2004); Rangarajan and Srivastava (2008); Reddy (2000); and World Bank (2000, 2004).

> 7 The share of debt of the local governments—the third tier of government—are not be covered in this paper. Local government debt in India is small.  Local governments, with limited fiscal autonomy, are largely dependent on fiscal transfers and on-lending from higher levels of government.

4
**Table 4: Estimated effects of disaster episodes on NPLs by World Bank region**

|Disaster: Ofcial Declaration|||||||
|---|---|---|---|---|---|---|
|<br>horizon|EAP|ECA|LAC|MENA|SA|SSA|
|0 year|-0.48|-0.03|0.32|-0.01|-1.89|-0.63|
||(0.76)|(0.23)|(0.29)|(1.89)|(0.58)|(0.98)|
|1 years|-0.05|-0.22|0.30|-1.07|-0.57|-0.38|
||(1.02)|(0.30)|(0.23)|(1.59)|(1.66)|(1.73)|
|2 years|0.58|0.56|0.12|2.21|3.12***|-0.19|
||(0.42)|(0.54)|(0.23)|(3.09)|(0.75)|(1.90)|
|3 years|0.73|0.48|0.01|1.34|2.06***|3.39**|
||(0.71)|(0.37)|(0.38)|(3.12)|(0.40)|(1.72)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Disaster: Multiple Types|||||||
|horizon|EAP|ECA|LAC|MENA|SA|SSA|
|0 year|0.15|0.31|0.87***|3.79|0.55|-0.08|
||(0.31)|(0.32)|(0.26)|(2.45)|(0.78)|(0.96)|
|1 years|0.63|-0.47|0.78**|-0.09|0.60|-1.00|
||(0.48)|(-0.60)|(0.30)|2.18|(0.87)|(0.82)|
|2 years|1.13*|-0.35|0.46|3.80|1.01|-0.28|
||(0.61)|(0.53)|(0.49)|(3.49)|(1.03)|(1.75)|
|3 years|1.10**|-0.21|0.54|-3.99|-3.68***|1.34|
||(0.50)|(0.81)|(0.56)|(1.13)|(0.25)|(1.79)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Time FE|Yes|Yes|Yes|Yes|Yes|Yes|

The table displays point estimates for the effect of climate-related natural disasters on NPLs by region. Standard errors(in round brackets) are two-way clustered by country and time. Significance levels: *0.10, **0.05, ***0.01.

**Table 5: Estimates of disaster episodes on NPLs by income group**

|Disaster: Ofcial Declaration|||||
|---|---|---|---|---|
|horizon|HI|UMI|LMI|LI|
|0 year|0.32|-0.32|-1.77|5.81*|
||(0.15)|(0.40)|(1.19)|(3.48)|
|1 years|0.26|0.00|-0.56|0.66|
||(0.22)|(0.44)|(1.23)|(3.89)|
|2 years|0.81|0.12|1.50|2.61|
||(0.52)|(0.36)|(1.13)|(2.58)|
|3 years|0.72*|0.34|0.81|1.87|
||(0.37)|(0.44)|(0.98)|(4.28)|
|Country FE|Yes|Yes|Yes|Yes|
|<br>Time FE|Yes|Yes|Yes|Yes|
|Disaster: Multiple Types|||||
|horizon|HI|UMI|LMI|LI|
|0 year|0.00|0.77***|0.59|0.28|
||(0.21)|(0.29)|(0.87)|(1.65)|
|1 years|0.00|0.40|0.30|0.37|
||(0.25)|(0.32)|(0.68)|(1.92)|
|2 years|-0.35|0.42|1.74|-0.55|
||(0.34)|(0.40)|(1.08)|(2.97)|
|3 years|-0.44|0.42|0.53|1.23|
||(0.30)|(0.44)|(1.52)|(3.25)|
|Country FE|Yes|Yes|Yes|Yes|
|<br>Time FE|Yes|Yes|Yes|Yes|

The table displays point estimates for the effect of climate-related natural disasters on NPLs by income groups. Standard errors(in round brackets) are two-way clustered by country and time. Significance levels: *0.10, **0.05, ***0.01.

12
<!-- Start of picture text --> Figure 9 - Which income levels have more systems to address recourse?<br>24<br>21<br>19<br>29<br>57<br>27<br>30<br>11<br>5<br>Low income Middle income High income<br>Financial ombudsman General ombudsman<br>Mediation service<br>Source: Financial Access Database<br>100<br>80<br>60<br>40<br>% of countries<br>20<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Figure 10 - How many complaints per thousand adults do countries receive?<br>Gambia 0.11<br>Botswana 0.11<br>Belgium 0.11<br>Norway 0.13<br>Kuwait 0.14<br>Malaysia 0.14<br>Armenia 0.15<br>Dominican Republic 0.17<br>Sweden 0.17<br>Chile 0.17<br>Azerbaijan 0.17<br>Puerto Rico 0.17<br>Spain 0.19<br>Slovak Republic 0.20<br>Venezuela 0.25<br>Denmark 0.29<br>Netherlands 0.50<br>Switzerland 0.50<br>New Zealand 0.56<br>France 0.57<br>Singapore 0.57<br>Israel 0.58<br>Australia 1.11<br>Colombia 1.44<br>Namibia 1.56<br>Mexico 1.76<br>Korea 1.83<br>United Kingdom 2.52<br>Portugal 2.76<br>0 1 2 3<br>Number of complaints per thousand adults<br>Source: Financial Access Database<br><!-- End of picture text --> 25
consulting, including only 30 on commercial strategy, and this was divided among three consulting companies who operated largely separately from one another, and who each spent a lot of time in diagnosis. Since consulting firms had been hired to work solely on one area, their main focus was on measuring indicators and helping firms in that one area, without regard to whether this change would be the most likely to deliver improvements in overall productivity or in the ability of the firm to export. In contrast, the Technological Extension project in Colombia studied by Iacovone et al. (2022) gave firms 500 hours of individualized or 408 hours of group-based consulting. In those cases the consulting across multiple areas was delivered in a coordinated fashion by a single Colombian consulting organization. This resulted in a 4-14 percent improvement in productivity. In the Indian study of Bloom et al. (2013), textile firms received 781 hours of consulting from an international consulting firm and obtained a 17 percent improvement in productivity.

Moreover, in contrast to these other studies, the firms in this experiment were much more heterogenous in size and industry. As well as making it harder to detect impacts, this heterogeneity likely also affected the quality of the intervention. Our qualitative interviews with firms revealed complaints that the consultants did not have knowledge on the specific industries firms were operating in. For example, a fashion firm noted they spent part of their time educating the consultants on their industry. This is likely to have been particularly a problem for efforts to advise firms on commercial strategy and new export markets, since the consultants would not know specifics of industry trends, quality standards, nor of relevant overseas buyers. Instead, consultants appear to have focused almost solely on narrower applications of lean manufacturing practices, which might have reduced bottlenecks, but which were not clearly linked to sales strategies. We explore robustness of our main results to dropping particularly outlying firm strata (in terms of high exporting) in appendix E.

It is possible that some of these changes will take longer to manifest themselves in firm productivity, and that the COVID-19 pandemic slowed this process down further. It is therefore theoretically possible that the program may have stronger impacts over a longer term period, as was the case of the consulting intervention in Bruhn et al. (2018). But at least in the first two years after the intervention, there is no sign of the program improving exporting through either the direct or indirect channels.

# **6 Conclusions**

Policymakers, academic researchers, and Colombian firms all had priors that consulting services would lead to an increase in firm export outcomes. We have shown how these priors can be incorporated into a Bayesian impact evaluation. Contrary to these priors, our experiment finds no significant impacts on exporting in 2019, and some evidence of 29
30

# Data appendix

Imoorts. Exiorts

|Mij, Xij<br>-|Data on the average annual US dollar value of non-fuel (Total leas SITC 3) imports and exporu<br>between each reporter and partner for 198-82 were extracted from the UNSO COMTRADE database.|
|---|---|
|CztIraphv||
|Distanceij<br>-|The straightline distance between economic center of gravity of the respective countries, from<br>Linneman (1966).|
|Borderij<br>-|=1 if countries ij share a border, 0 otherwise.|
|Economic activity||
|GDPi<br>-|Total GDP in USS of the reporter (importer or exporter) from the World Bank Atlas. The**atlas**<br>method uses a conversion factor other than the ofricial when the official rate is wildly distorted.|
|GDPPCi<br>-|GDP per capita of the reporter from World Bank Atlas.|
|Sizei<br>-|The land area of the reporter in '000 square kilometers.|
|Islandi<br>-|= I if reporter is an island.|
|ODPj<br>-|Total GDP in USS of the partner country from World Bank Atlas.|
|GDPPCj<br>-|GDP per capita of the partner from World Bank Atlas.|
|Sizei<br>-|The land area of the partner in '000 square kilometers.|
|Islandi<br>-|=1if partner is an island.|
|Linder<br>-|The absolute value of the difference in per capita GDP between the source and partner, from Surmmers<br>and Heston (1988).|
|Trade preference arran|zements|
|D ij<br>-|This is a dummy variable =1 if both countries are mcmbers of any type of preferential trading<br>agreement. The arrangements included are: ASEAN, CACM, CEAO, ECOWAS, EEC, EFTA,<br>LAFTA, UDEAC, and Lome preferences.|
|Cultural||
|L ij<br>-|The only cultural factor considered is whether or not the countries share the same language, a very<br>crude, but easily calculated proxy. Dummy variables were included for English, French, and Spanish.|
<!-- Start of picture text --> 0.8<br>L__Jvield<br>0.6 2 Quarterly Demand Shock<br>MW Stocks<br>04 ME Annual Demand Shock<br>HB Residual<br>0.2 |<br>-0.2 2 ER EOE<br>-0.4<br>-0.6<br>-0.8<br>-1<br>1987.5 1986 1988.5 1989 1989.5 1990 1990.5 1991 1991.5 1992 1992.5<br><!-- End of picture text -->
11 Table 2 shows how the households in the Ghana data are distributed across the 15 ethno-religious groups. The table shows that three ethno-religious groups – groups, 3, 8 and 14 – account for 42 percent of the sample. The table also shows the two variables that are used as instrument variables in this paper: (1) international remittances (from African or other countries) received as a percent of household income in the ethno-religious group; and (2) international migrants (to African or other countries) as a percent of the population of the ethno-religious group.

According to Table 2, international remittances and migrants are distributed quite unevenly among the various ethno-religious groups. This uneven distribution suggests that some ethno-religious groups are more “efficient” than others, if we measure efficiency by how much remittance income is received by households in the different groups. For example, focusing on the three largest ethno-religious groups in Table 2 (groups 3, 8 and 14), we see that each group accounts for about 14 percent of the sample. However, each group produces international migrants at different rates (less than one percent for groups 8 and 14, and 3.1 percent for group 3), and receives remittances at different rates (between 5 and 8 percent of observed household income for each group). This variation in the efficiency of migrant networks to generate remittance income is important to our analysis because it helps explain why these variables work well as instruments in our econometric procedure. In the next section we present tests that demonstrate the validity and strength of these instruments.