rafmacalaba/datause-encoder-data huggingface dataset:
and cultivated area). If we were to run a new experiment, the chances of obtaining null or negative effects would be non-negligible. For example, the treatment effect on fertilizer has a 50% chance of being between _−_ 0.00 SD and +0.17 SD, a 25% chance of being negative, and a 25% chance of being larger than +0.17 SD. The posterior intervals of the predicted effects are considerably wider than the confidence intervals from the pooling model (by a factor from 2 to 5). Findings are similar using automatic priors (Figure A7), and estimates of _τk_ +1 along the different covariates are very uncertain too (Figure A8). Overall, this evidence suggests the existing evidence base offers limited insights to predict the effects of index insurance offers in new settings because of the sizable heterogeneity in treatment effects across studies. Conducting new studies in such settings is particularly worthwhile.

# **6 Conclusion**

Farmers in low- and middle-income countries are exposed to severe risks and live in areas where formal insurance markets are generally absent. In recent years, a solution that has attracted considerable attention to help smallholder farmers manage risks is index insurance. In this paper, we aggregated evidence from six experiments on index insurance and explored the external validity of their results. We find positive but highly heterogeneous effects on farmers’ productive investments, with no evidence that this heterogeneity can be meaningfully explained by basic household characteristics. This suggests that most of the heterogeneity in treatment effects might stem from more complex household characteristics or from contextual variables such as study characteristics or product design. Overall, we uncover moderate external validity and limited insights to predict the impact of index insurance in new settings. We estimate a sizable likelihood of finding null or even negative effects in new settings.

These findings should in no way be interpreted as evidence that efforts to promote index insurance should be abandoned. In fact, they tend to confirm that index insurance has the potential to boost smallholders’ productive investments. We estimate that interventions expanding access to index insurance typically boost productive investments by 0.06–0.11 standard deviation on average. However, our results also suggest that effects are highly uncertain, and, together with evidence on the limited uptake of index-based products, they emphasize that governments and development agencies should remain cautious before investing in their widespread promotion. Recent innovations in the design and implementation of index-based 21
**Figure 5 - Net Effects of Tobacco Taxes**

# **6. Discussion**

Tobacco-related illness accounts for 6 percent of total health care expenditure in the EU and an important share of health care expenditure in Moldova. The relevance of tobacco consumption in Moldova is not only economic, but social because it is the leading factor in mortality in the country. The negative health and economic impacts of tobacco highlight the relevance of tobacco tax policies that could diminish tobacco consumption.

Tax policies are one of the best instruments to reduce tobacco consumption and increase government revenue, but critics view this approach as regressive. This paper shows that tobacco tax increases are not regressive, but progressive and pro-poor because they could eventually boost incomes among lower-income population groups.

Using four years of household budget surveys, the study calculated the price elasticity of tobacco for the population in Moldova, obtaining an average price elasticity of −0.33 and estimates for the 10 income deciles. The elasticity for the lowest income group is −0.53 and −0.13 for the highest income group. This appears to be the first tobacco price elasticity estimate across income groups ever produced on Moldova.

To extend the analysis, other elasticities were created to simulate the short-term and long-term elasticity scenarios. Using an extended cost-benefit analysis, the study simulated a price increase of 25 percent and obtained the expenditure increases associated with the higher price as well as the reduction in medical expenses caused by the reduction in tobacco consumption. The results show that, in all cases, the tobacco tax increases are progressive, while the reduction in medical expenses outweighs the price increases. Moreover, in the upperbound elasticity, the net gains of the tobacco tax policy are positive for the lower income groups.

15
**Table 10: Regression results for split sample with post-crisis dummy variable** Dependent variable: Log of spread-to-benchmark at launch

||(1)|(2)|
|---|---|---|
|VARIABLES|U.K. law|U.S. law|
|IG dummy|-0.306|-0.346***|
||(-1.36)|(-4.66)|
|S&P Rating|0.085**|0.107***|
||(2.16)|(5.25)|
|Log(size)|0.071|0.082|
||(0.87)|(1.59)|
|Years toMaturity|0.013|0.006**|
||(0.87)|(2.51)|
|Post-crisis|-0.159|-0.521**|
||(-0.47)|(-2.25)|
|VIX|0.024**|0.021***|
||(2.41)|(3.90)|
|GDP growth (_t-1_)|-0.000|-0.022***|
||(-0.03)|(-3.13)|
|EAP|0.090|0.076|
||(0.34)|(0.81)|
|ECA|0.506**|0.120|
||(2.22)|(1.15)|
|LAC|0.857***|0.240***|
||(3.06)|(3.13)|
|SAS|0.408*|0.341***|
||(1.85)|(2.92)|
|SSA|0.528**|0.430***|
||(2.46)|(4.49)|
|Observations|109|381|
|R-squared|0.609|0.639|

Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Year dummies and a constant term were included in all specifications, but not reported. The default region is Middle East & North Africa (MNA); the regional dummies represent East Asia & Pacific (EAP), Europe & Central Asia (ECA), Latin America & Caribbean (LAC), South Asia (SAS) and Sub-Saharan Africa (SSA).

This leads us to posit some plausible explanations for the observed lower spreads of U.S. governing law dollar-denominated sovereign bonds in the post-crisis period. This could be a consequence of a reduced ability to directly tap U.S. investors for entities that do not register with the SEC. Also it could arise from a combination of self-selection and signaling problems. The post-crisis period saw many hitherto unrated sovereigns issuing bonds (Jaramillo and Tejada 2011). While the first-time issuer exercise and the regional dummies captured impacts associated with those factors, there could be an additional effect in play. Some of these issuers resorted to U.K. law to avoid the higher costs, disclosure requirements, perceived stronger investor protection and regulatory burdens associated with U.S. law (Coffee 2007, Jackson 2007). At the same time, investors chose to take this as an indication of lower transparency and quality. In any event, it is remarkable that investors started holding a negative view of governments that do not have SEC registration or U.S. governing law only after the crisis. This may have arisen due to increased risk-aversion and investor consciousness of the higher stringency of U.S. law in the post-crisis period.

19
**Table 6. Income & Transfers**

||**Midl**|**ine Survey**<br>||**End**|**line Survey**<br>|<br>|
|---|---|---|---|---|---|---|
||Control<br>Group<br>Mean|Treatment -<br>Coeff.|Control<br>_p-value_|Control<br>Group<br>Mean|Treatmen<br>Coeff.|t - Control<br>_p-value_|
|**Labor & Non-labor income in last 7 days**|||||||
|Total income (in FDJ)|8,312|3,216 ***|_0.000_|9,110|-96|_0.843_|
|Amount (in FDJ) of beneficiary woman's labor income|1,441|3,010 ***|_0.000_|1,112|-237|_0.210_|
|Amount (in FDJ) of other HH members' labor income|6,394|357|_0.349_|7,772|44|_0.920_|
|Amount (in FDJ) of husband's labor income|7,855|-1,211|_0.150_|10,271|270|_0.673_|
|HH had non-labor income|0.109|-0.047 ***|_0.001_|0.043|-0.005|_0.616_|
|Amount (in FDJ) of non-labor income|748|-111|_0.563_|287|88.57|_0.409_|
|**Intra-HH transfers in last 7 days**(as declared by woman)|||||||
|Beneficiary woman gave money to husband|0.067|0.062 ***|_0.000_|0.032|0.001|_0.879_|
|Amount (in FDJ) beneficiary woman gave to husband|88|305 ***|_0.000_|184|28.75|_0.752_|
|Husband gave money to beneficiary woman|0.574|-0.035|_0.155_|0.549|-0.014|_0.605_|
|Amount (in FDJ) husband gave to beneficiary woman|7015|159|_0.770_|7445|537.9|_0.433_|

Notes _:_ Coefficients are from an OLS regression of the left-hand side variable on a treatment dummy, controlling for strata dummies, survey-time dummies, and baseline traits (see Table 3 note). ***, **, * indicate significance at 1, 5 and 10 percent. Husbands’ labor income is reported both separately as well as being included in other household members' labor income.

45
performance evaluation, as well as her total outreach (number of clients); this raises concern over the content of their interactions with farmers. Introducing “softer” measures of performance in their assessment is therefore of interest both in reinforcing satisfaction with and demand for the service.

# 3   Experimental Design and Data

The experiment took place in 3 of Rwanda’s 30 districts: Karongi and Rutsiro districts in the Western province and Rwamagana district in the Eastern province. The sampling frame is all registered clients of OAF for Season 2013A, comprising 228 farmer groups.<sup>14</sup> We randomly sample 180 of the 228 groups, and 8 farmers within each of those groups.<sup>15</sup> With 1.8% attrition across two survey rounds, the final sample for analysis is 1,460 farming clients, in 20 cells.

We worked with OAF and the Ministry of Agriculture to introduce and test a variety of farmer feedback tools between September 2012 and June 2013, thus covering two agricultural seasons.<sup>16</sup> We devise a randomized-controlled trial (RCT) to test two types of feedback tools: scorecards and logbooks. A third intervention cross-cuts the scorecard treatment and explores the impact of announcing scorecards to FOs to capture the pure monitoring effect of adding a feedback tool. Table 1 summarizes the study design.

> 14 While the sampling frame is the population of OAF clients in 2013A, we use the OAF client registry in the subsequent two seasons (Season 2013B and Season 2014A) to track retention of members over seasons and the addition of new members.

> 15 The average of 8 individuals per farmer group gives us sufficient power to detect fairly small effects; at 80% power and assuming intracluster correlation 0f 0.1, the minimal detectable effect size is .43 standard deviations on the outcomes of interest for as few as 12 control groups (this is the most conservative estimate of power for the experiment, as comparisons between treatment arms will be higher powered than comparisons to the pure control group).

> 16 Cell is an administrative division in Rwanda above village, and the level at which OAF organizes clients.

10
<!-- Start of picture text --> Figure 4. Private Capital Flows to Asian Countries in Sample, 1991-93<br>INDIA INDONESIA<br>CUMULATIVE 199i-93<br>CUMULATIVE 1991-93 Privat LogTerm Debt<br>Pitjem LorgiV-Tm Deb<br>Po_/foa .quiy Fkows<br>2.4%<br>budi Formn knvue<br>Far*lb EqA rbvvi Direct Foreign Irrmest<br><!-- End of picture text --> KOREA CUMULATIVE 1991-93 <!-- Start of picture text --> THAILAND<br>CUMULATIVE 1991-93<br><!-- End of picture text --> <!-- Start of picture text --> Prhvate Long-Term Debt Piate Long-Term Debt<br>Pdmie Long-Term Debt W~~~~~5A%<br>< :jS' ~~~~~~~Direct D:rsd Foreign Frelin lvest, 16st -- Porfolio Equity Fbws<br>0.7%<br>Portfollio Equity Flows<br>26.6% Dir.rt Fbregn IrweeL<br>43.1%<br><!-- End of picture text --> Source: The World Bank, World Debt Tablea 1993-94
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31 increase of around 20% in the average gross wages and salaries after SHI adoption in a given country. We find no evidence in Table 7 of SHI significantly increasing unemployment or reducing employment; nor do we find any effects of SHI adoption on either the informal economy measure or our informal employment proxies (self-employment and agricultural employment as shares of the total).

# **_Endogeneity of SHI vis-à-vis labor market outcomes_**

We undertook the same type of informal test for endogeneity discussed above for the health sector outcome variables. The coefficient on the lead SHI dummy was significant at the 5% level for two of the outcomes: registered unemployment rate and TransMONEE’s employment-to-population ratio. Moreover, the coefficient for the lead SHI dummy was significant at the 10% level for our measure of the size of the informal economy. These results suggest that the statistical insignificance of at least some of the SHI impacts estimated in our previous models might be due to endogeneity. Therefore, in contrast to what we found for the health outcomes, endogeneity seems to be a reason for concern in our labor models, and we tackle this issue by implementing our IV methods in what follows.

# **_Results for the instrumental variables (IV) approach_**

The results for the registered unemployment, TransMONEE’s employment-to-population ratio and informal economy models estimated by 2SLS and GMM are reported in Table 8.<sup>34</sup> As for the health outcomes, the two instruments used seem to be extremely relevant for all labor models, with partial _F_ statistics above 250. Additional tests (not shown) confirm that our instruments are relevant and strong in the context of these labor models: for the three cases,

> 34 As in the random trend specification, the IV models are estimated using the natural logarithm of the dependent variable at the left-hand side.
Figure 41: Yield by Land Area Quantile for the Four Major Crops in Sudan, 2014/15

## (a) Sorghum

<!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br><!-- End of picture text -->

- (b) Millet

<!-- Start of picture text --> 3<br>2<br>1<br>0<br>1 Kordufan 5 1 Darfur 5 1 Sudan 5<br>(c) Groundnut and Sesame<br>5<br>4<br>3<br>2<br>1<br>0<br>1 Groundnut 5 1 Seasam 5<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

# Determinants of Crop Yield: An Empirical Investigation

To gain a better understanding of determinants of productivity, it is useful to empirically analyze the significant factors causing variation in yields across households. This can be done by regressing yields on potential determinants while controlling for certain household characteristics. An advantage of this approach, as opposed to looking at potential determinants individually, as done in Section IV.C, is that such analysis can account for the different determinants simultaneously and identify which among them is most accurate in explaining variations in productivity. Due to limitations of the data, this is not a causal analysis. Due to the likelihood for correlation between the determinant factors and the potential for omitted variables, it is impossible to use this analysis to conclude which factors, if changed at the household level, would lead to changes in productivity. However, it gives us a good starting point for 43
The change in the average labor income and aggregate expenditure is: where _λ_<sup>_Y_</sup> _nis_<sup>_≡_</sup> _λnis|nsy_ ̄ _λnns|nwis_ . Then, the change in _Xn_ is:

where _ωnL ≡ y_ ̄ _nLn_ + _qy_ ̄ _nnZLnn_ + _rnHn_<sup>,</sup><sup>_ωnZ≡_</sup> _y_ ̄ _nLn_ + _qqnnZZnn_ + _rnHn_<sup>,and</sup><sup>_ωnH≡_</sup> _y_ ̄ _nLn_ + _<u>rqnnHZnn</u>_ + _rnHn_<sup>.Then,thegoodsmarket</sup> clearing condition using exact hat algebra for each location _i_ and sector _s_ is: where _λ_<sup> ̃</sup> _ni_ = <u>�</u> _nλ_<sup>_′_</sup> _<u>nis</u>_<sup>_λ_</sup> _n_<sup>_′_</sup> _is_<sup>,and</sup><sup>_π_</sup> _nis_<sup>_X_=</sup> <u>�</u> _nπ_<sup>_′_</sup> _ns_<sup>_π_</sup> _nπ_<sup>_′_</sup> _snis_<sup>_π_</sup> _n|_<sup>_′_</sup> _sisX|sn_<sup>_X_</sup> _n_<sup>_′_.Icomputethecounterfactualssolvingtheprevioussystem</sup> of equations D.23.

# **D.4 Model with ex-ante decision**

In this section, I present a version of the model in which firms decide whether to operate in the formal or in the informal sector. The model is based on Ulyssea (2018) and Dix Carneiro et al. (2018) in which firms that operate in the informal sector face a distortion that increases with size. There is a infinite mass of potential entrants that exit at an exogenous rate _δs_ . The labor supply function takes the same form as in the main text. On the other hand, firms make two decisions. First, they decide whether to enter in the labor market and conditional on entry whether to operate in the formal or informal sector based on a pre-entry signal and a entry fixed cost. Second, firms decide the location in the city in which they are going to operate based on an extreme value type II shock. There is no production fixed cost.

The total operational profits of firm _ω_ that operate in location _i_ and sector _s_ , and sells to _n_ is given by: where _z_ ( _ω_ ) is the pre-entry signal, _εis_ ( _ω_ ) is an idiosyncratic productivity shock of firm _ω_ that varies across locations, and _υis_ ( _ris_ ( _ω_ )) is a distortion that captures the probability of getting caught if firm _ω_ operates in the informal sector. This probability increases with the size and revenue _r_ ( _ω_ ) of firm _ω_ . I assume that the idiosyncratic shocks are drawn from a Frechét distribution with shape parameter _ψ_ and scale parameters _Ais_ . Then, the share of firms with pre-entry signal _z_ from sector _s_ that operate in location _i_ is: With these assumptions, the expected value of entry for a firm with pre-entry signal _z_ that operates in sector 29
# **Surviving Firms of the Syrian Arab Republic: A Rapid Assessment**

Kinley Salmon, _Finance Competitiveness and Innovation, World Bank_ Nabila Assaf, _Finance Competitiveness and Innovation, World Bank_ David Francis, _Development Research Group, World Bank_ _Keywords:_ firm death; survival; enterprise survey; conflict; Syria _JEL Codes:_ O12, O14, O53, D74,
**page** 20

# Table **5.** The tstimated EffectLveness of the Javanese VIHW on the Twelve Disease Problems of Table 2 of the Text.

|Ghanaian<br> <br>**Di"as**<br> <br>Category<br>t<br>(1)|Ghanaia<br>Effec-<br>dvuess<br>(2)|n<br>Ghanaian<br>Incidence<br>(3)|Javanese<br>A<br>Diseas<br>Ja<br>Category<br>A<br>(4)|ggregated<br>vanese<br>geGroup<br>(5)|Percentage<br>Improveme<br>in Case<br>Fatality<br>Rate<br>(6)|nt<br>Javanese<br>Incidence<br>(7)|
|---|---|---|---|---|---|---|
|1.C old|102|1000|2. URI|0-15<br>15+|02<br>02|2000<br>1000|
|2. Skin Inf.|102|470|4. Sidn Dii.|0-15<br>15+|0Z<br>0Z|50<br>100|
|3.M alria|262|40|8. Malaisi|0-15|962|20|
|||||15+|1002|50|
|4. MalnutritLon|632|1.5|not included||||
|5.Gastroenteritis|382|70|5.MildDiarrhea|0-15<br>15+|02<br>0|2000<br>1000|
||||6.Severe Di&.|0-15|692|250|
|||||15+|40 2|80|
|6. Accidents|20Z|7.7|13. Burns|0-15<br>|542<br>|30|
|||||15+|0|10|
||||14. Fractures|0-15|442|1|
|||||15+|292|1|
||||15. C uts|0-15|632|15|
|||||15+|70 Z|15|
|7. Schistosomiasus|692|7|not included||||
|8. Pneumonia, Child|37X|2.4|1. LRI|0-15|792|50|
|9. Pneumonia, Adult|152|7|1. LRI|15+|792|10|
|10.&12. Prem. Brth|<br>10Z|9.6|21. Comp. Brth|0-1|85Z|90|
|&Birth Injury|21S|1.6|<br>&Pregnancy||||
|11. Comp. of Preg.|392|4.8|21. Comp. Brth|Wom. 15-4|4<br>212|24|

SOURCES: Grosse et **aL** (1979), Ghana Realth Assessment Team (1981) and Naciol Health Planning Unit (1978).

NOTES: Columns (2) and (3): From Table 2 in the text.

Column (3): Incidence per thousand in overall population from Table 1 of Ghana Health Assessment Team (1981).

Column (4): Appendix A of Grosse et al (1979). Column (5): Aggregates of the _ssx_ age-sex categories used in Appendices A and C of Grosse et al (1979).

Column (6): Calculated from the last two columns of Appendix C of Grows. et al (1979) by the formula (CFNRX-CFRX)/CFRX, where CFNRX is the case fataiicty rate without treatment, CFRX is the case fatality rate with treatment by a VHW.

Column (7): Derived from the incidence rates by age-sex group in Appendix A of Grosse et al by choosing a value in the mi4dle of the range of incidence races given in that source.
3 FOREIGN DIRECT INVESTMENT IN KENYA <!-- Start of picture text --> 800<br>600<br>400<br>200<br>0<br>China France South Africa Switzerland United Kingdom United States<br>Country<br>FDI instock (USD Millions)<br><!-- End of picture text --> Figure 21: China and UK hold the most FDI stock in Kenya (2012) Source: UNCTAD FDI/TNC database 2015

**Chinese firms are well-represented in large-scale infrastructure projects** China constructed 905.5 km of road in 2006 and invested AC200 million (US $227.6 million) to rehabilitate the NairobiMombasa road (Fiott 2006). In 2012, three kilometeters of road at the KWS Gate/Bomas Junction have been built for KES 2.67 billion (US $27.1 million) by the China Wu Yi Co., and many Chinese firms have been pre-qualified by the government to develop 2,000 km of road in various counties (Kenya Urban Roads Authority 2015). For the mega-projects, the China Road and Bridge Corporation is building a 609 km section of the Standard Gauge Railway (SGR) linking Nairobi and Mombasa for US $3.6 billion, and the China Communication Construction company is building three berths at the port on Lamu island for US $467 million (Financial Times 2014). With the slowdown of the Chinese economy, China’s low cost infrastructure providers will seek to market their services outside of China, including in Africa, so Chinese infrastructure projects in Kenya will likely continue.

**But the bulk of Chinese work in infrastructure is not investment, but contracting** The SGR and the berths at Lamu are not investments, but projects financed by China’s EXIM bank: the bank will finance 90 percent of the SGR, making SGR a Kenyan investment. Kenya has taken on debt for many major construction projects, and Chinese companies typically have no equity stake in the particular building, road, or port. The EXIM bank is responsible for promoting exports and investments of Chinese firms abroad, offering international guarantees, export buyers credit, and export sellers credit (Center for Global Development 2006). The bank meets demand for infrastructure in Kenya and Sub-Saharan Africa, a sector that traditional donors and investors have neglected because of fears of corruption and high risk. The EXIM bank, for example, provided US $95.4 million for drilling of 26 wells in Olkaria, Nakuru for geothermal energy in 2014 (Geothermal development company 2014). Once one accounts for large, debt-financed projects, medium sized private Chinese firms are a sizeable share of investment, and they tend to operate in the manufacturing sectors, creating many low-skilled manufacturing jobs. Futhermore, Chinese firms face the same problems that any firm faces in Kenya: poor electricity, roads, security and corruption.

23