rafmacalaba/datause-encoder-data huggingface dataset:
Though the estimation of _P(X)_ is not the end of the analysis in itself and we do not examine it exhaustively, a few results merit noting. To begin, using the smallest conditioning set _H_ , table 2a suggests that individual entrepreneur characteristics are significantly related to treatment, a perhaps unsurprising result given the small size of the firms.<sup>10</sup> Older workers are more likely to engage in most institutions, as are the more educated. The exception here is that more educated workers are not especially more likely to have received informal credit than entrepreneurs with only primary education.<sup>11</sup> Credit is more likely for entrepreneurs that are household heads and have larger households, while female micro-entrepreneurs are less likely to engage in all except training (perhaps because of the existence of government-sponsored training programs).

When we add variables specific to the firm, table 2b shows that the importance of some individual characteristics falls significantly, suggesting that _H_ and _Z_ cannot be treated as orthogonal. First, the coefficient on the age of the entrepreneur and its significance both fall, possibly due to the inclusion of the time in business variable which is strongly significant in all treatments except training. The two size variables, capital stock and number of workers, enter positively and significantly in virtually all cases. Overall, this is consistent with Levenson and Maloney’s formulation of formality, broadly conceived as participation across a variety of societal institutions, as a normal factor of production for firms operating in a Jovanovic (1982) type environment of noisy selection. In particular, larger and older firms could have a higher demand for the services provided by civil society institutions because they face a lower probability of failing, and a greater capacity for amortizing the fixed costs involved in participation (e.g. the cost of revealing their existence to tax authorities, membership fees, formal accounting procedures, etc.).

More specifically, both time in business and size may capture both greater credit worthiness and the availability of collateral, and hence access to credit. In terms of training, evidence from the US (Lynch and Black, 1995) shows that employers who had invested more in physical capital are more likely to have invested in workers’ instruction.

> 10 For instance, as Berkowitz and White (2002) note for the US, debts are personal liabilities of the firm’s owner, so that lending to a firm is legally equivalent to lending to its owner.

> 11 This is consistent with many studies of credit markets in the US (see for instance Jappelli, 1990 and Blanchflower, Levine and Zimmerman, 2003) that find that age is highly significant for determining whether an individual is credit rationed. For training, many papers have studied individual decisions to be treated but fewer have looked at the firm (see for example Frazis, Gittleman and Joyce, 1998).

14
<!-- Start of picture text --> Figure 2a: Growth-incidence Curve - Urban (2002-2010) Figure 2b: Growth-incidence Curve - Rural (2002-2010)<br>Urban Rural<br>6 Growth-incidence 95% confidence bounds 6 Growth-incidence 95% confidence bounds<br>Growth at median Growth in mean Growth at median Growth in mean<br>Mean growth rate Mean growth rate<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>1 10 20 30 40 50 60 70 80 90 100 1 10 20 30 40 50 60 70 80 90 100<br>Expenditure percentiles Expenditure percentiles<br>Figure 3a: Growth-incidence Curve - Urban (2008-2010) Figure 3b: Growth-incidence Curve - Rural (2008-2010)<br>Urban Rural<br>Growth-incidence 95% confidence bounds Growth-incidence 95% confidence bounds<br>16 Growth at median Growth in mean 16 Growth at median Growth in mean<br>Mean growth rate Mean growth rate<br>13 13<br>10 10<br>7 7<br>4 4<br>1 1<br>1 10 20 30 40 50 60 70 80 90 100 1 10 20 30 40 50 60 70 80 90 100<br>Expenditure percentiles Expenditure percentiles<br>Annual growth rate % Annual growth rate %<br>Annual growth rate % Annual growth rate %<br><!-- End of picture text --> _III.5. However All Trends Reversed Starting in 2008_ Despite an overall improvement in welfare over the past decade, the increase in inequality observed since 2008 has contributed significantly to the population’s overall level of discontent and the general feeling of worsening welfare conditions. The issue of public perception highlighted earlier can also be linked to the economic performance observed in the second half of the decade, especially since 2008. Looking at the data from the 2008 household income and expenditure survey, the figures indicate that the welfare trend observed in the first half of the decade was reversed, with economic growth becoming anti-poor and unequal. Table 1 reveals that growth in welfare between 2008 and 2010 was positively correlated with richer 14
<u>Table 1: Changes in multidimensional inequality and components for three scenarios.</u><sup>*</sup>

||**M**|**LD**|**T**|**heil**|**G**|**ini**|
|---|---|---|---|---|---|---|
||Period 1|Period 2|Period 1|Period 2|Period 1|Period 2|
|_Scenario 1_<br><br><br>|_Mean in_|_come of the first_|_outcome increas_|_es from 100 in Pe_|_riod 1 to 200 in_|_Period 2_|
|M(x�)<br><br>|0.270|0.323|0.281|0.335|0.400|0.434|
|M(x)<br><br>|0.158|0.212|0.170|0.235|0.311|0.360|
|A(x)<br><br>|0.302|0.353|0.302|0.354|0.400|0.434|
|R(x)<br>|0.112|0.111|0.111|0.100|0.089|0.074|
|S(x)<br>|0.032|0.030|0.021|0.019|0.000|0.000|
|_Scenario 2_<br><br>|_Inequality of th_|_e second outcom_|_e increases from_|_Gini 0.3 in Perio_|_d 1 to Gini 0.4 i_|_n Period 2_|
|M(x�)<br><br>|0.270|0.356|0.281|0.360|0.400|0.450|
|M(x)<br><br>|0.158|0.204|0.170|0.211|0.311|0.349|
|A(x)<br><br>|0.302|0.365|0.302|0.366|0.400|0.450|
|R(x)<br><br>|0.112|0.152|0.111|0.149|0.089|0.101|
|S(x)<br>|0.032|0.009|0.021|0.006|0.000|0.000|
|_Scenario 3_<br><br><br>|_Corre_|_lation between o_|_utcomes increase_|_s from 0 in Perio_|_d 1 to_≈_0.3 in P_|_eriod 2_|
|M(x�)<br><br>|0.270|0.270|0.281|0.282|0.400|0.400|
|M(x)<br><br>|0.158|0.200|0.170|0.211|0.311|0.347|
|A(x)<br><br>|0.302|0.302|0.302|0.303|0.400|0.400|
|R(x)<br>|0.112|0.070|0.111|0.071|0.089|0.053|
|S(x)<br>*) The simulate<br>mean 30, Gini|0.032<br>d distributions ha<br>0.3; Correlation: p|0.032<br>ve the following<br>eriod 1: 0; perio|0.021<br>parameters: First<br>d 2: ≈ 0.3.|0.021<br>outcome: mean 1|0.000<br>00, Gini 0.5; S|0.000<br>econd outcome:|

Table 2 presents results for the three inequality measures when the outcome variables in period 2 are based on a simple transformation of the period 1 data: namely, the second variable is unchanged from period 1 while the first is obtained from its period 1 value by an additive or a proportional increase. A uniform increment of 20 units lowers multidimensional inequality, as is seen in the top panel of Table 2: declines from _0.160_ to _0.130_ , declines from _0.173_ to _0.144_ , and from _0.312_ to _0.284_ MMLLMM . That increment also results in a drop in the TTheeiiee MM MM rearrangement component for all three indexes, and the structural component declines for MLD GGiinnii MM and Theil, and remains at 0 for Gini. In contrast, a proportional increase in the first outcome by 20% increases multidimensional inequality as seen in the bottom panel of Table 2. grows from _0.158_ to _0.171_ , grows from _0.170_ to _0.185_ , and grows from _0.311_ to _0.323_ MMLLMM , MM while both the rearrangement and structural mobility components change only slightly. TTheeiiee GG MM MM 25
**3 |** P a g e

1996‐99, when natural‐resource intensive and unskilled labor intensive products were the main levers of export expansion, capital and skilled labor intensive products drove the export expansion in 2003‐07.

The shift in factor content of exports towards capital and skilled labor intensive products has significant implications for future competitiveness of the BiH economy. Since labor costs in BiH are relatively high, unskilled labor‐intensive exports, such as textiles and footwear, face the growing competition from low‐wage countries not only in Asia but also from other Balkan economies. While some of them are likely to withstand this competition thanks to geographical proximity allowing shifting to higher value added apparel, as already demonstrated by some BiH firms, the contribution of these sectors to export earnings is likely to continue falling. Furthermore, in contrast to exports of low processed resource‐based products, industries using intensively skilled labor and capital tend to pay higher wages. In consequence, exports of these sectors boost output growth and help improve living standards and reduce poverty.

Since mirror statistics provide a more adequate picture of BiH export performance than BiH statistics, we use the former. The problem with the BiH foreign trade statistics assessed against other national statistics is that, contrary to widespread practice of under‐reporting imports in developing countries, they under‐report exports while accurately reporting imports. BiH exports statistics have consistently underestimated the extent to which its exports basket has evolved over the last several years.

The paper is structured as follows: Section 1 examines dynamics of BiH export performance since the end of war in 1995 distinguishing three phases: post‐war recovery; stagnation; and current expansion. Levers of export growth performance during the recent expansion phase are addressed in Section 2. Section 3 discusses change in factor intensities of BiH export performance against other SEE economies and the role of FDI in BiH exports. Section 4 concludes.

# **1. Export performance: Recovery, slowdown, and expansion**

Continued expansion of imports and occasionally explosive exports has characterized BiH foreign trade performance since the 1995 Dayton Peace agreement. Despite volatility, one may distinguish three phases in dynamics of BiH exports based on below or above single‐digit growth rates: post‐war recovery of exports in 1996‐1998; slowdown in 1999‐01; and the current phase of export expansion since 2002‐03. Double‐digit growth rates in the values of exports have characterized both expansionary phases, whereas during the slowdown phase growth rates of exports in terms of value fell into a single‐digit territory. However, despite the slowdown in export growth, the share of BiH export in
The most important finding is that regulatory quality is positively correlated with the incidence of episodes as is evidenced by the strongly statistically significant coefficient on the index of economic freedom in columns 5. This finding is robust to controlling for macro-variables, democracy, and whether or not a country is in crisis or at war (column 6). Moreover, the magnitude of this association is remarkable; a one-standard deviation improvement in regulatory quality (i.e. an increase of 1.08 in the Economic Freedom Index) is associated with an increased probability of incidence of 3.6%-5.0%. Note that this is a conditional association; recall that we did not find a significant positive bivariate correlation between the incidence of employment episodes and indicators of overall regulatory quality because countries with better regulation tend to have lower initial unemployment to start with.

Tables 7 and 8 present robustness checks using specifications that replicate those in columns 5 and 6 in Table 6; that is, one that controls for initial unemployment, GDP per capita and GDP per capita squared (referred to as the initial conditions, “IC”, specification), and one that includes all explanatory variables (referred to as the “Full” specification), which we can only estimate on a smaller sample for which all of these variables are available. To conserve space we only present the coefficient estimates associated with our key variable of interest, notably economic freedom.

To start with, as alternative proxies for overall regulatory quality, the ease of doing business rank from the _Doing Business Indicators_ and the regulatory quality index from the _World Governance Indicators_ are used. The results presented in Row A of Table 7 are robust to using these alternative proxies; the ease of doing business rank is consistently negatively correlated with the incidence of episodes, indicating that worse regulation renders unemployment reduction less likely. The indicator of regulatory quality from the _Worldwide Governance Indicators_ assigns higher scores to countries with better regulatory frameworks and is significantly positively correlated with the incidence of unemployment reduction episodes.

Second, we examine the robustness of our results to using more stringent definitions of an unemployment episode by imposing additional criteria for the identification of an episode. We begin by ruling out the possibility that reductions in unemployment are driven by declines in labor force participation. In particular, we impose as an additional criterion for the identification of an episode that labor force participation after the first four years of the unemployment episode is at least as high as it was at the start. While this reduces the number of episodes to 33, the Economic Freedom Index remains a strongly significant predictor of the onset of episodes (see columns 1 and 2 in Row B). Next, we impose a stricter definition of sustainability and require that unemployment does not 12
|**Observations**|830|281|920|355|307|818|263|
|---|---|---|---|---|---|---|---|
|**R‐squared**|0.188|0.687|0.312|0.277|0.272|0.356|0.505|

Notes: Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Survey weights are used in all regressions.

Table 10: Management and total factor productivity by country

|**Y = Total factor prod**|**uctivity**|||||||
|---|---|---|---|---|---|---|---|
||Argentina|Bolivia|Colombia|Ecuador|Paraguay|Peru|Uruguay|
|**Average**<br>**management score**|0.211|‐2.112*|0.315|0.784*|‐1.097|‐0.100|1.772|
||(0.649)|(1.186)|(0.324)|(0.422)|(1.086)|(0.268)|(1.379)|
|**Log(size)**|0.00505|0.448***|0.101**|0.131*|0.120|0.0170|‐0.172|
||(0.113)|(0.153)|(0.0475)|(0.0709)|(0.131)|(0.0454)|(0.184)|
|**Age **|‐0.00657|‐0.0177|0.00192|0.00142|0.000679|0.00372|‐0.00543|
||(0.00612)|(0.0138)|(0.00331)|(0.00347)|(0.00956)|(0.00328)|(0.00421)|
|**Foreign owned**|0.000963|0.000116|‐0.00348*|‐0.00291*|‐0.00658|0.00215|0.00353|
||(0.00384)|(0.00614)|(0.00202)|(0.00151)|(0.00775)|(0.00182)|(0.00316)|
|**Multi‐**<br>**establishment**|‐0.706**|‐0.935*|0.161|‐0.121|‐0.955**|0.0677|0.197|
||(0.271)|(0.476)|(0.140)|(0.237)|(0.435)|(0.143)|(0.434)|
|**Exporter**|0.00598*|0.0143|‐0.000603|0.00213|0.00310|0.000875|0.00124|
||(0.00315)|(0.0165)|(0.00189)|(0.00142)|(0.00448)|(0.00108)|(0.00366)|
|**Top**<br>**manager's**<br>**experience**|0.00369|0.0337|0.000542|‐0.00202|‐0.00510|‐0.00965**|‐0.000723|
||(0.00573)|(0.0317)|(0.00412)|(0.00628)|(0.00702)|(0.00428)|(0.0171)|
|**Female**<br>**top**<br>**manager**|0.00634*|‐0.0191**|‐0.00103|0.00239|0.00403|0.000363|0.00917|
||(0.00337)|(0.00828)|(0.00121)|(0.00175)|(0.00329)|(0.00132)|(0.00555)|
|**Legal status(ref cat**|**= soleproprieto**|**rship): **||||||
|**Publicly**<br>**listed**<br>**company**|0.00133|0.0245**|0.00119|‐0.00663|0.00726|0.000509|‐0.00268|
||(0.00414)|(0.00905)|(0.00823)|(0.00580)|(0.00619)|(0.00243)|(0.00767)|
|**Limited**<br>**Liability**<br>**Company**|0.00933***|‐0.00196|‐0.00208|‐0.00371|0.00726|0.000501|0.00262|
||(0.00337)|(0.00334)|(0.00171)|(0.00332)|(0.00587)|(0.00172)|(0.00524)|
|**Partnership**|0.00143||‐0.00628|‐0.00249|0.0126|||
||(0.00548)||(0.00666)|(0.00481)|(0.00972)|||
|**Limited**<br>**Partnership**|0.00467|‐0.000218|‐0.000619|‐0.00429|0.00469|‐0.00130|‐0.00748|
||(0.00288)|(0.00507)|(0.00197)|(0.00324)|(0.00498)|(0.00204)|(0.00501)|
|**Other**|‐0.00102||0.000149|||||
||(0.00452)||(0.00243)|||||
|**Constant**|1.118***|2.277***|1.769***|1.494***|1.677**|2.816***|3.143***|
||(0.310)|(0.434)|(0.272)|(0.366)|(0.628)|(0.421)|(1.026)|
|**Fixed effects:**|Two‐digit indu|strydummies,|and countryand|region(within|country)fix|ed effects||
|**Observations**|180|52|406|98|38|275|40|
|**R‐squared**|0.776|0.893|0.841|0.908|0.962|0.813|0.899|

Notes: Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Survey weights are used in all regressions.

22
_Table 12: Heterogeneity in drought reports within enumeration areas, by crop type_

||||Drou<br>|ght<br>||
|---|---|---|---|---|---|
|||Allcrops|Maize|Sorghum|Millet|
||2011-2012|39.4|16.1|18.6|13.3|
|Ethiopia|2013-2014|43.8|26.3|24.9|12.7|
||2015-2016|55.8|34.3|29.0|23.3|
||2018-2019|48.6|27.7|26.9|10.5|
||2009-2010|62.6|58.4|76.0|30.0|
|Malawi|2012-2013|25.7|28.0|36.2|30.0|
||2015-2016|33.0|36.3|43.2|40.0|
||2018-2019|17.5|11.7|20.5|16.7|
|Mali|2014|29.7|13.9|20.6|14.4|
||2017|44.5|28.1|32.2|32.2|
|Niger|2011|78.6|41.7|68.1|82.4|
||2014|66.4|28.6|62.6|63.6|
|Nigeria|2019|20.7|8.9|7.6|6.4|
||2008|55.9|51.9|0.0|50.0|
||2010|59.3|57.6|58.1|55.6|
|Tanzania|2012|46.9|44.4|37.9|26.3|
||2014|32.1|29.7|66.7|50.0|
||2019|42.1|44.6|87.5|33.3|
|All countries|Allyears|41.1|31.1|31.5|32.1|

Note: Share of enumeration areas with heterogenous drought reports, pooled and by crop type. Heterogeneous reports are defined as enumeration areas where at least one plot recorded a drought loss and at least one plot did not. Columns 2, 3, and 4 compare only plots that grew the same crop. Enumeration areas with only a single plot with the crop are excluded.

33
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76
16

# **7. Performance**

The product is expected to be piloted in the next season – the Yala season (May - Sep 2019), which depends more heavily on irrigation. To improve its efficacy, it is important to track its performance in terms of pay-outs and compare these pay-outs to the actual experience of farmers in terms of production losses and reasons for those losses for at least two years. It is also important to use additional historical storage data, if possible, and to complement the dataset with other sources of data, including satellite data sources.

Based on the actual rainfall and irrigation tank level data<sup>25</sup> in Anuradhapura, Figure 5 below presents simulated historical pay-outs. For the Maha season, the years of 2003, 2004, 2008 and 2017 were particularly bad years, when crop losses were incurred due to dry spells. The simulated pay-outs show that the pure rainfall index insurance product (blue bars) would have triggered pay-outs in all these years. However, it would have also triggered pay-outs in other years, such as 1995, 2009 and 2015, when pay-outs would not have been necessary for farmers with access to major irrigation. Hence, a pure rainfall index product would have triggered more pay-outs than necessary, leading to a more expensive product and not addressing the lower risk profile of irrigated farming. Used together, the rainfall and irrigation index (red bars) would have triggered pay-outs only in the particularly bad years, which would be a more relevant and affordable product for farmers with access to major irrigation.

**Figure 5. Product Simulated Pay-outs for Anuradhapura (in LKR)**

<!-- Start of picture text --> 12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>Rainfall index Rainfall and Irrigation index<br><!-- End of picture text --> Source: Author-constructed based on meteorological data (Meteorological Department of Sri Lanka) and MCM data (Irrigation Department of Sri Lanka).

25 Based on Huruluwewa tank, from Irrigation Department, Ministry of Irrigation and Water Resources Management, Sri Lanka.