rafmacalaba/datause-encoder-data huggingface dataset:
trust funds, for a total of 24 companies offering such funds.<sup>18</sup> However, in countries where Islamic finance is less developed, other transitional arrangements may be needed.

The fourth issue concerns _consistency_ of judgment across banks, over time, or across jurisdictions within the same bank. In essence the activities of SSBs are in the nature of creating jurisprudence by the interpretation of legal sources. It should therefore not be surprising to find conflicting opinions on the admissibility of specific financial instruments or transactions.<sup>19</sup> In reality, however, the diversity of opinions is less widespread than might be expected. The CIBAFI sampled about 6000 _fatwas_ , and found that 90% were consistent across banks. The fact that over one hundred _Shariah_ scholars around the world issued these _fatwas_ would suggest an overall consistency in the interpretation of the sources.<sup>20</sup> Further, this high degree of consistency between the _fatwas_ would also point to a substantial independence of SSBs. Nevertheless, as the industry expands, the number of conflicting _fatwas_ on the permissibility of an instrument is likely to increase. This could undermine customer confidence in the industry and have repercussions on the enforceability of contracts.<sup>21</sup> The last and overarching issue relates to _disclosure_ of all information relating to _Shariah_ advisories. In addition to the positive aspects of thus empowering stakeholders, disclosure could be the means to addressing some of the issues discussed in the preceding paragraphs. A transparent financial institution would ideally disclose the duties, decisionmaking process, areas of competence, and the composition of its SSB, as well publish all _fatwas_ issued by the SSB. This would strengthen stakeholders’ confidence in the credibility of SSB assessments. In addition, public disclosure of such information would

> 18The proportion of advisors to company seems to be quite large. Every management company must appoint at least 3 _Shariah_ advisors for one company. This means that every advisor would serve on the SSBs of about two or more fund management companies. Data on _Shariah_ advisors and approved instruments are available at http://www.sc.com.my (as of March 14, 2005).

> 19 A typical example is the financing of leisure activities, which is frowned upon as _haram_ by some Fiqh scholars. Moreover, Islamic jurisprudence is based on different schools of thought that may vary from country to country (the _Shiah_ branch and the Sunni branch, which in turn includes the _Madhahib, Shafie, Hanafi, Hanbali and Maliki_ traditions).

> 20 As quoted in the proceedings of the Fourth Harvard University Forum on Islamic Finance, available at <u>http://www.hifip.harvard.edu/MoreInfo.asp?news_id=34</u> (last visited March 14, 2005)

> 21 It may be hard to invalidate contracts on grounds of a breach of _Shariah_ law, if _Shariah_ has no legal force in the country and there are no widely acceptable codified standards or contract specimens.
# **1. Introduction**

The COVID-19 crisis represented an unprecedented shock for the private sector, affecting businesses through many channels at an uncontrolled speed and spanning a duration of more than two years. In the process, it unveiled the underlying vulnerabilities of firms, sectors, and countries. These types of crises pose pressing questions not only about the magnitudes and responses to economic shocks in general, but also about the heterogeneity of their effects. For instance, evidence from the COVID-19 shock and recovery indicates that small firms were disproportionately affected (Apedo-Amah et al., 2020; Cirera et al., 2021). Given that most informal firms tend to be small, it is crucial to understand not only the effects of a shock on informal firms, but also if those effects are explained by external characteristics such as firm size or by other internal attributes of informality.

_A priori_ , given the characteristics of informal firms, the magnitude and direction of the impact of crises and, in particular, of the COVID-19 shock are not entirely clear. On the one hand, initially, informal firms are expected to exhibit a worse performance due to their higher vulnerability, including the lack of access to finance from formal sources and the difficulty of public support reaching these firms. Specific to the COVID-19 crisis, these firms are concentrated in sectors requiring more face-to-face interactions and were, therefore, more exposed to the lockdown restrictions. On the other hand, informal firms can avoid certain taxes and regulatory burden and have more flexibility in terms of employment (including lower hiring and firing costs), location, resource management, and market competition (Loayza, 2018). Therefore, informal firms and jobs can more easily recover given their lower organization capital and entry costs (Alfaro et al., 2020).

Using several rounds of Business Pulse Survey (BPS) data of the World Bank for six countries in South Asia, this paper quantifies the heterogeneity in the impact of the COVID-19 crisis and the recovery of firms by their informality level. Our results suggest that: _First_ , there is a huge overlap in the productivity distribution across formal and informal firms, suggesting that the reasons for staying informal at the lower end of the distribution might be very different than at the higher end. This also points to the possibility that the shock may not affect all informal firms uniformly.

_Second_ , in line with the existing literature, our BPS data confirms that compared with formal firms, informal firms are smaller, younger, and in sectors with low entry costs. They have lower productivity, with consistently lower levels of management and technological capabilities. Even the better-managed informal firms do not seem to grow in size, especially 2
per private school is much larger in magnitude. The effect of the program on overall enrollment is small in magnitude. Using the average number of government, private and other schools per district in the sample period, our results indicate that 9,824 children per district are not attending school due to the program.<sup>20</sup> Our identifying assumption is that there are no pre-trends in enrollment in districts belonging to different phases prior to MGNREGA’s implementation. DISE data is not available for all states prior to 2005, although major states are covered since 2003. We use data from 2003 to 2005 to check if there are differential pre-trends in enrollment by phases of MGNREGA roll-out.<sup>21</sup> In Appendix Table S4, we control for district-specific changes in enrollment from 2003 to 2005 (pre-treatment years) and allow this to vary over time by interacting with year indicators for the states for which we have pre-program data.<sup>22</sup> The overall effect on enrollment and enrollment by primary and upper primary are similar to those reported in Table 3.

Prior to the program implementation, we observe that the Phase III districts are better in levels. But the growth rate in enrollment is similar. Appendix Figures S2 and S3 show that between 2003 and 2005, the growth in enrollment and number of schools looks similar across districts in different phases. These two tests together show that pre-trends in enrollment are not biasing our results.<sup>23</sup> As we discussed earlier, a concern with the DISE data is that there is measurement error in reporting. Since school headmasters provide the information, it could be inaccu-

> 20In results not shown, we do not find any differences in effects for girls versus boys.

> 21In the Appendix, we discuss that our main results are no different if we exclude or include the states for which we do not have DISE data prior to 2005, we are reassured that the sample for which we have data prior to 2005 is not systematically different. The results are reported in Appendix Table S3. Limited data for a few states is also available for 2001 and 2002 but the coverage is not as expansive. Since data for many states and many variables is not available, we do not use these years.

> 22The study lost 0.7 percent of the sample schools as new districts were carved in 2004 and we are unable to use their pre-trend data. The study also checks the consistency of results when excluding those schools without pre-trends in Appendix Table S3.3.

23Appendix S3 on robustness tests further substantiate our identification by showing in a year-by-year comparison of early versus late districts, that the decline in enrollment occurs in 2006 after the early phases are treated. Appendix Figure S4 depicts this in a graph. We also conduct a placebo test to rule out pre-trends in our results for children’s employment to ensure credibility of our findings. The results are discussed in Subsection D3 in the online Supplemental Appendix.

15
obtained credit the last 12 months (Table 10). Of the richest 20 percent of Haitian farmers 23 percent use irrigation, 13 percent use pesticides or insecticides, 35 percent use fertilizers, and 11 percent have access to (or use) credit. Hence in order to increase productivity of both the poor and rich farmers, access to production enhancing technologies needs to be expanded for example via increased access to credit to finance these technologies.

Table 9: Tools Applied in Farming in Haiti, 2001 (percent)

||1(poorest)|2|Quintile<br>3|<br>4|5(richest)|
|---|---|---|---|---|---|
|Few (0-2 different tools applied)|42.90|33.37|33.17|29.49|26.49|
|Some (3-4 different tools applied)|38.30|42.49|44.23|43.37|43.19|
|Many (5-13 different tools applied)|18.80|24.14|22.61|27.14|30.32|
|_Note:_Onlyhouseholds with access to lan|d. No. observat|ions: 3950|.|||

_Source:_ Own calculations based on HLCS 2001.

Table 10: Technology Applied in Farming of Main Plot in Haiti , 2001 (percent)

|||||Quinti<br>|le<br>||
|---|---|---|---|---|---|---|
|||1(poorest)|2|3|4|5 (richest)|
|Irrigation|Yes|4.41|8.39|15.59|18.04|23.45|
||Rain only|95.59|92.61|86.41|84.96|80.55|
|Pesticides or|Yes|2.03|4.06|7.53|10.81|12.72|
|Insecticides|No|97.97|95.94|92.47|89.19|87.28|
|Fertilizer|Yes|7.47|14.52|22.05|28.25|35.18|
||No|92.53|85.48|77.95|71.75|64.82|
|Credit|Yes|10.50|11.48|12.44|12.29|11.41|
||No|89.50|88.52|87.56|87.71|88.59|

_Note:_ All with access to land included. Irrigation: No. observations: 3629; Pesticides: No. observations: 3932, pesticides used within last 12 months. Fertilizer: No. observations: 3611, fertilizer used within last 12 months, chemical, natural by people and animal, compost and other. Credit: No observations: 4473, credit obtained within last 12 months.

_Source:_ Own calculations based on HLCS 2001.

# **Determinants of Producer Incomes**

After analyzing workers’ labor incomes, this sub-section addresses the determinants of producer households’ income from farm activities in Haiti. Determinants of producer incomes are addressed by analyzing the impact of various individual, assets, production, infrastructural, and geographical characteristics.

Producer incomes generated in agriculture are analyzed by applying an augmented earnings function method. Producer incomes from farm activities are modeled by using log annual incomes, drawn from farming activities, as the dependent variable. Only households with access to land and farming the land are included in the analysis.<sup>19</sup>

> 19 No changes in inventory are taken into account of e.g. livestock nor are production costs taken into account?

20
the more severe the barriers to free capital flows. One would also expect the periods of transition from segmented to integrated markets would be associated with large average adjusted mispricing as asset prices adjust to a different equilibrium level.

Rather than emphasize formal statistical tests, I wish to characterize the cross-sectional and time-series characteristics of the estimated mispricing and relate the behavior of the measures to changes in capital controls in the various markets. Through this characterization of the empirical properties of the mispricing, or market segmentation, measures we should get some sense of the forces causing the measured deviations from LOP. Average adjusted mispricing is estimated for each of the twenty-four national markets (twenty emerging markets and four developed markets). Since the severity of capital controls is likely to vary through time, I estimate a time series of 0's, rather than estimate each country's adjusted mispricing for the entire sample period. The time series of a is constructed by estimating 0 for each (overlapping) eighteen-month period in the sample. That is, data from January 1976 to June 1977 are used to estimate 0r then data from February 1976 to July 1977 are used to estimate<sup>_0_</sup> _7m,_ etcetera. The final period is July 1991 through December 1992. All firms are included in the sample as long as they have at least fifteen monthly observations in the subperiod. The average adjusted mispricing, 06, is plotted for each national market in Figures 1 through 23. Each figure also plots E, for the United States as a reference point. Figures 1 through 3 plot at for the three other developed countries. The values of 0, for the developed countries are generally small. The largest deviations from the value of zero occur for Australia with values around -20, which occur around the 1987 stock market crash. Argentina (Figure 4) begins with very high values of 6, (around 300) in the late 1970's which decline rapidly. There is a sharp rise in 6, in 1986 followed by a sharp decline in 06. The period 1986-1987 coincides with increased investment by foreign institutional investors.

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Figure 12. Decomposition of inequality changes by income sources and period: Ecuador and Mexico <!-- Start of picture text --> a. Ecuador b. Mexico<br>0.0 1.0<br>0.8487<br>-0.2<br>-0.4<br>0.5<br>-0.6<br>-0.8 -0.8196<br>0.0<br>-1.0<br>-1.2<br>-1.3408 -0.5 -0.5556<br>-1.4<br>Other income sources Public Transfer Other income sources Public Transfer<br>2003-2010 2010-2012 2003-2010 2010-2012<br><!-- End of picture text --> Source: Authors’ calculation from SEDLAC (CEDLAS and the World Bank). The figure shows the Shapley Decomposition of inequality changes (Barros et al. 2006 and Azevedo et al. 2012).

Private transfers, and in particular remittances, have also become an increasingly important source of income in several Central American countries in the last decade. With the exception of Panama and Costa Rica, the countries of Central America are a source of large numbers of emigrants and significant remittances inflows. Remittances in most of these countries increased in recent years. For instance, between 2003 and 2012, remittances increased by 35 percent in El Salvador and doubled in Honduras. In 2013, Honduras and El Salvador had the highest remittances as a share of GDP in the region (over 15 percent), followed by Guatemala and Nicaragua (about 10 percent) and the Dominican Republic (7 percent) (Figure 13b). Even still, remittance receipt has not yet fully recovered following the Global Financial Crisis (Figure 13a).

Figure 13. Remittances in Dominican Republic, El Salvador, Honduras, and Mexico a. Remittance Inflows index (Real value LCU, b. Remittances as a share of GDP in 2013 (%) 2008=1) <!-- Start of picture text -->
1.2 20<br>16<br>0.8 12<br>8<br>0.4 4<br>0<br>0.0<br>Dominican Republic El Salvador<br>Honduras Mexico Central America and Mexico South America<br>Remittances Inflow Index Percentage of GDP<br>Honduras El Salvador Guatemala Nicaragua Bolivia Ecuador Paraguay Mexico Peru Costa Rica Colombia Panama Uruguay Brazil Argentina Chile<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>Dominic Republic<br><!-- End of picture text -->

Source: Authors’ calculation from WDI for exchange rates and inflation, and http://www.worldbank.org/migration. For remmitances 20
# 1. Introduction

Recent publications of the Intergovernmental Panel on Climate Change (IPCC) provide strong evidence that accumulating greenhouse gases are leading to a warming world (IPCC 2007). If these greenhouse gases and global warming continue unabated, they are predicted to impose serious costs to agricultural farms in low latitude developing countries (Kurukulasuriya et al. 2006; Seo et al. 2006; Seo and Mendelsohn 2008a, 2007). The international community needs to design an efficient mitigation program to reduce greenhouse gas emissions (Nordhaus 2007). One of the substantive benefits of such a mitigation program is increased food security, especially for people living in the low latitudes (Reilly et al. 1996, McCarthy et al 2001).

Previous research has identified that climate change impacts on agriculture in developing countries will vary from place to place depending on numerous factors. Before policy makers can design appropriate policy responses, they need to have reliable indicators of how impacts will vary across the landscape. This study takes advantage of Agro-Ecological Zones (AEZs) to predict how impacts will be dispersed across Africa. The differential effects of climate change on farms in various agro-ecological zones have not yet been quantified. Specifically, we examine how climate change might affect farm net revenue in different AEZs. Not only does this research provide insight into how climate affects farmers facing different conditions, but the research will also help extrapolate climate change results from an existing sample to the continent from which they are drawn.

The study combines data about AEZs with economic farm data from a recently completed GEF/World Bank study of Africa (Dinar et al 2008). The AEZs are compiled by the Food and Agriculture Organization of the United Nations using information about climate, altitude, and soils (FAO 1978). The GEF/World Bank study measured crop choice, livestock choice, yields, gross revenues, and net revenues of nearly 10 thousand farmers (households) in 11 African countries (Kurukulasuriya et al. 2006, Kurukulasuriya and Mendelsohn 2006, Seo et al. 2006, Seo and Mendelsohn 2008a). Both the countries and the farm households were sampled to represent the various climates across Africa.

This paper differs from the earlier economic research on African agriculture in the following ways. First, it quantifies climate change impacts for each of the 16 Agro-Ecological Zones. The AEZs provide a mechanism to extrapolate from the sample to other similar locations around 2
correlation among U.S. securities is about 0.5 to 0.6,9 this is preliminary evidence that country funds, on average, provide diversification benefits to a U.S. investor. On average, the EMs are also less correlated with the S&P530 in comparison to DMs, thus providing some support to the higher diversification potential of EMs over DMs. As expected, returns on NAV are much more closely associated with returns on local stock market indexes (0.7851) than to returns on S&P500 (0.2143).

# **11.2 - Components of Risk**

A risk components analysis is conducted by regressing NIF price returns on their corresponding local stock market returns (Model 1) and then on the S&P500 returns (Model 2), respectively. The detailed results are reported in Table 9 and are summarized below:

**_Table 11._** _Decomposition of Risk_

||Model I(%)|Model 2(%)|
|---|---|---|
|Average systematic risk|22.8|13.1|
|Average unsystematic risk|77.2|86.9|
|Total risk (variance)|100.0|100.0|
|Maximum unsystematic risk|95.3<br>(India)|100.0<br>(Turkish)|
|Maximum systematic risk|63.1<br>(Emer. Germ)|32.5<br>(Irish 1.)|

Source: **Table** 9.

> 9Chapter 8 of Frank K. Reilly, 'Investment Analysis and Portfolio Management", Third Edition.

9
The conclusions of the literature on _inter-industry_ effects are more clear-cut. Using firm- level panel data from Lithuania covering the period 1996–2000, Javorcik (2004) finds evidence suggesting that FDI presence boosts the productivity of supplying industries, but not the industries in which foreign affiliates operate. She argues that although multinationals have an incentive to prevent knowledge from leaking to their local competitors, they may also have an incentive to provide assistance to their local suppliers in upstream sectors. A one-standard-deviation increase in foreign presence in the sourcing sectors is associated with a 15 percent rise in the productivity of Lithuanian firms in the supplying industry. The productivity effect is found to originate from investments with joint foreign and domestic ownership but not from fully owned foreign affiliates, which is consistent with the evidence of a larger amount of local sourcing undertaken by jointly owned projects. The conclusion with respect to spillovers from fully versus partially owned foreign affiliates is further confirmed by Javorcik and Spatareanu (2008) in the context of Romania. Evidence of positive spillovers through backward linkages is also found in Indonesia by Blalock and Gertler (2008) and in China by Liu (2008) and Du, Harrison, and Jefferson (2011).<sup>11</sup> Typically, the studies of _inter-industry_ spillovers from FDI do not distinguish between pecuniary spillovers and knowledge spillovers. An exception is Javorcik (2004), who made some progress toward this goal by controlling for the demand from foreign affiliates based in downstream sectors.

23
_Table 5: Threshold levels in the Central African Monetary Union – Country Level Analyses_

|**Countries**|**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|
|---|---|---|---|---|---|---|
|**Cameroon**|3*<br>(0.087)|3*<br>(0.098)|6**<br>(0.045)|3*<br>(0.079)|3<br>(0.191)|1<br>(0.202)|
|**Central African Republic**|6***<br>(0.004)|6**<br>(0.013)|6**<br>(0.039)|6***<br>(0.000)|6***<br>(0.004)|6**<br>(0.012)|
|**Chad**|10<br>(0.144)|10**<br>(0.017)|10***<br>(0.009)|3<br>(0.307)|2<br>(0.117)|1<br>(0.103)|
|**Congo, Rep.**|10<br>(0.470)|4*<br>(0.090)|4<br>(0.149)|10***<br>(0.000)|10**<br>(0.015)|10***<br>(0.001)|
|**Equatorial Guinea**|8**<br>(0.021)|7**<br>(0.030)|4**<br>(0.018)|9**<br>(0.019)|4<br>(0.255)|7***<br>(0.000)|
|**Gabon**|1<br>(0.387)|8<br>(0.128)|8<br>(0.449)|11<br>(0.112)|11***<br>(0.002)|11***<br>(0.003)|
|**CAMU-Adjusted**|3.7|4.6|5.2|4.9|5.6|5.9|
|**CAMU-Unadjusted**|5.2|5.6|6.3|6.4|5.6|5.1|

*** p<0.01, ** p<0.05, * p<0.1 for the Wald test of significance associated to the threshold variable. In bracket: p-value of Wald test of significance of the threshold variable.

_Note_ : Regional inflation rate is computed by using a weighted average; with weights being proportional to the GDP at constant prices, 2010 USD of each country in the union. “Adjusted” means that the inflation rate is equal to 3 percent for countries with a p-value of the Wald test (of significance of the threshold variable) above 10 percent. “Unadjusted” average refers to the weighted average without considering the significance of the threshold variable. Models 4, 5 and 6 are ECM models; and Models 1-3 are ARDL models.

Overall, these results show that from the current inflation thresholds there are opportunities to increase the current thresholds that are used by regional central bank in each currency union. Results from the ARDL models and ECMs consistently confirm these findings in both currency unions. In CEMAC countries, the level is broadly consistent with the one found with panel data analyses (Augmented Model 1 - Table 3). The adjusted average suggest that the regional inflation threshold is around 5.6 percent (Model 5). However, the regional inflation rate of CEMAC countries could have been higher if one was considering the best-fitted model with significant threshold coefficients. In WAEMU countries, the regional averages, which is based on country thresholds, are higher than the one estimated with panel analyses. This result reinforces the existence of structural differences that expose countries to asymmetric shocks. Robustness analyses provide similar results by considering inflation rate as an endogenous variable (Appendix 6 and Appendix 7). Based on the above analyses and robustness analyses, it could be concluded that it is possible to increase the threshold levels used by both regional central banks to 5.4-5.6 Page | 21