rafmacalaba/datause-encoder-data huggingface dataset:
temporal variation and the 2016 drought. Dummy variable for standard precipitation index below −1 is a common measure of drought that does not work well in our case. The likely reason is that the precipitation distribution has a long right tail driving the standard deviation upward. Accumulated excess rainfall is correlated to the total number of days with excess rainfall and gives similar results. Finally, we already explained why the NDVI may be endogenous, but we run the corresponding specification for completeness. It captures both floods and droughts as well as anything else that can affect greenness.

As a robustness check, we relax some of the assumptions behind the simulations. We present national statistics when drawing only the weather shock; the weather and idiosyncratic shocks jointly (as in the main results); residuals inside groups of households with similar characteristics; and residuals. In general, drawing residuals leads to noisier measures as they might exceed the effects of the shocks. This discrepancy worsens when drawing residuals inside groups. Our main simulation is directly conducted on consumption per capita to keep all variations. In the annex, we present the ones based on the prediction of percentage change in consumption per capita. Results are similar when drawing shocks but vary when adding residuals’ draw since the residuals from the log regression behave better. We also present simulations based on IHS3 and IHS4 only. Then, we relax the assumption of independence between drought and rainfall. We make them perfectly correlated by drawing them simultaneously. Results are very similar. Finally, we also present matrices of the number of vulnerable versus poor households for each IHS year in the annex.

# **5 Conclusion and Policy Implications**

This study examines the impact of droughts and excess rainfall on the welfare of Malawian households using a nationally representative household survey and exogenously measured weather shock indicators. After taking into account household characteristics as well as idiosyncratic shocks and carefully dealing with transfers and aid in response to drought, we quantify vulnerability to poverty during 2004–2016. The results indicate that weather shocks have severe impacts on the welfare of households and push non-poor households into poverty. In particular, drought during the growing season decreases consumption per capita without aid by 5–12 percent. Excess rainfall during the first two months also has an adverse effect on consumption per capita. Leveraging this link between consumption and weather shocks, we conducted 4,000 simulations to estimate vulnerability to poverty following extreme weather events. Households’ vulnerability to poverty is generally higher than static poverty. This is particularly true during a good weather year when the static poverty measures are lower than the share the population that has a more than 50 percent chance of falling into poverty. In years of extreme droughts such as 2016, recorded poverty rates tend to be higher than vulnerability, which indicates that the magnitude of drought these years is 27
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17
**Table A3. Source-country characteristics and import prices in cross section, 1997**

||d|ep. var.: frm-pr|oduct log import price||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|richer than Portugal|0.55***|0.27*|||
||(0.11)|(0.15)|||
|log GDP/cap.|||0.20***|0.10**|
||||(0.03)|(0.04)|
|log GDP|0.05**|0.03|0.04**|0.02|
||(0.02)|(0.03)|(0.02)|(0.03)|
|European Union|-0.34***|-0.07|-0.29***|-0.07|
||(0.08)|(0.16)|(0.07)|(0.15)|
|landlocked|0.18**|0.06|0.11|0.06|
||(0.08)|(0.12)|(0.10)|(0.13)|
|log distance|-0.08|0.04|-0.07*|0.05|
||(0.05)|(0.09)|(0.04)|(0.08)|
|product efects|Y|N|Y|N|
|frm-product efects|N|Y|N|Y|
|R2|0.75|0.97|0.75|0.97|
|N|21792|21792|21792|21792|

Notes: Sample is all firm-product-source observations for firms in estimation sample. Petroleum imports excluded. Robust standard errors, clustered by source country, in parentheses. *10% level, **5% level, ***1% level.
Has this come with higher average household living standards? Macro-level economic growth, as measured in the national accounts (NAS), does not automatically translate into similar growth in average household living standards, as measured by the mean for the distribution of household consumption or income on which poverty measures are based. There are invariably gaps between NAS aggregates and the grossed-up consumption or income aggregates from the household surveys used to measure poverty. There are a number of reasons for these gaps. In practice, the way that the NAS are constructed means that there is nothing exactly corresponding to household consumption as measured in surveys, so full agreement should not be expected. Measurement errors in both sources also play a role, as do differences in accounting periods and sampling problems. A likely source of discrepancies between the two data sources is underreporting of incomes or consumptions in sample surveys or selective compliance in the randomized assignments that are used in implementing the surveys. The rich will undoubtedly have a stronger incentive than the poor to underreport their incomes or consumption and will be less likely to be available for interviews. Indeed, in one assessment (using data for the United States), selective compliance—in which the rich are less likely to participate in surveys than the poor—resulted in a sizeable underestimation of inequality using the unadjusted sample survey data but made little difference to poverty measures (Korinek et al. 2006).

Compiling evidence on how average living standards have been evolving over time in the developing world requires a large number of household surveys, which come irregularly over time. Thus the calculations are slightly more complicated than when using annual NAS data. Drawing on some 900 household surveys for 125 countries, Chen and Ravallion (2012) provide estimates of poverty measures for “reference years” spanning 1981–2008 at three yearly intervals, which have been updated to include 2010 for this paper. Given the irregular and unsynchronized spacing of surveys, Chen and Ravallion (2010, 2012) use an interpolation method that employs growth rates in private consumption from the NAS for those dates when surveys are unavailable. The survey means are a mixture of consumption and income, depending on what is available in the surveys. For the Chen-Ravallion poverty measures, two-thirds of the surveys use consumption, which is preferred to income as a welfare metric. (Income is only used when consumption is unavailable.) On calculating the overall survey means for the Chen-Ravallion reference years one finds that the trend rate of growth over 1981–2010 was 1.9 percent per annum with a standard error of 5
<!-- Start of picture text --> Adit<br>Agdt<br>Age = VAG + Ut<br>Age = Agit + AGat<br>Y +COV[Agi¢,Agate) /V[Agit]<br><!-- End of picture text --> <!-- Start of picture text --> Ay; = BYAgGat + & + Puy<br>Ay, Adgit<br>y=1<br>By<br><!-- End of picture text --> <!-- Start of picture text --> Y<br><!-- End of picture text -->
account for scope economies (across destinations) and acts as a proxy for the firm’s export performance for a given product. It enters with the expected positive and significant sign.

In all of the specifications, the coefficient on the density indicator is positive and significant at the 1% level. This indicates that for a given firm, export performance is higher for products with denser connections to the local productive structure.<sup>15</sup> In column 3, we verify that similar findings are obtained when the numerator in Equation 2 excludes products _j_ for which the RCA of locality _l_ can be reversely influenced by the export performance of a firm of our final sample; that is, the RCA dummy for product _j_ turns from 1 to 0 when we exclude the export of product _j_ of one of the firms in our sample. As explained above, this strategy helps preclude the issue of reverse causality between the export performance of firms and our density indicator. In column 4, we check that our results remain similar when further controlling for the lead density (computed using RCAs in 2006). The results indicate some autocorrelation in density over time, which calls for caution in interpreting correlations. Nevertheless, findings of a positive and significant effect for the lagged density, but not for the lead density, suggest that the positive association between density in 2000 and that export performance does not simply reflect a simultaneity effect.

In columns 1 to 4, the density indicator is computed for each prefecture using the World Product Map; the numerator in Equation 2 only considers the products for which the prefecture has a comparative advantage with respect to the world. In column 5, we instead rely on the China Product Map, so prefecture-level comparative advantage is calculated using China as the reference. There is a strong and significant positive correlation (0.56) between the city-level RCA index measured with respect to the global export flows and the index measured with respect to the Chinese export flows in our sample of city-product pairs. Nev16
Lagos streets. In total 2,938 micro‐entrepreneurs were recorded with geo‐positioning and photographs to confirm identity in follow‐up interactions and verify intervention compliance (see Annex for an example of the invitation created from this information to verify identity at the event). The criterion used for selection into the sample was being the owner/operator of a business operating within the study area. These businesses were then randomized into one of 5 groups: (i) pure control [ **PC** ]; (ii) placebo screening [ **C** ]; (iii) Story of Gold Screening [ **MOVIE** ]; (iv) placebo screening plus presence of MFB [ **MFB** ]; and (v) Story of Gold plus presence of MFB [ **MOVIE/MFB** ].

The **PC** group was not invited to attend any screening. The other four groups were invited to attend one of 8 screenings (2 per group). Invitations were delivered one week before the screening and two screenings took place every Thursday during September 2012 for 4 weeks. Invitations to each screening were identical and events were held at the same time each week (8am – 11am), chosen because the cleaning of the market took place at this time, ensuring low opportunity costs to participation since businesses were not allowed to trade during this time. This uniformity of invitations and event dates was used to minimize the possibility of differential take up across screening events.

In **C** screenings, people were shown a Nollywood movie that had no financial messages associated with it, but were given a brief talk after the event about the importance of hygiene in markets to provide quality products and services. This was done explicitly to control for the “event effect” of having received a personalized invitation and participation in a big screening event possibly confounding results, and also to create a comparable group of compliers in both treatment and control groups to simplify the analysis. The standard Credit Awareness program (screening _The Story of Gold_ and interacting with an MFB) was split in order to differentiate the impact of the movie from the increased access of financial products coming from the MFB’s presence. As such, a 2x2 factorial design was implemented for the treatment arms in order to detect the differential impact of each component and the interaction effect relative to **C** .

In total, 1,261 people (60% of those invited) attended the movie screenings, where a short questionnaire was administered at the end of the event to measure perceptions and attitudes about savings, borrowing and MFBs. Administrative records were kept at the **MFB** and **MOVIE/MFB** events to record the people who (i) engaged with _Accion_ to open an account at a later stage and (ii) actually opened an account at the event.

8
- 50 -

# **Table 5a Parameters** of the Lorenz Curves 1984

||Urban|Rural|Total|
|---|---|---|---|
|lna|-.4906<br>(.0089)|-.6053<br>(.0056)|-.5038<br>(.0081)|
|a|.9336<br>(.0052)|.9184<br>(.0032)|.9397<br>(.0047)|
||.5230<br>(.0052)|.5490<br>(.0033)|.5131<br>(.0047)|
|R2|.9999|.9999|.9999|
|SEE|.0056|.0035|.0051|
|Mean dep. var.|-1.7727|-1.8968|-1.7825|
|St. dev. dep. var.|.4075|.3898|.4147|
|LM tests:||||
|Funct. form (X<sup>2</sup>(1))|2.444|2.2860|3.9873|
|Normality (X<sup>2</sup>(2))|0.5208|.9299|.6934|
|Heteroscedasticity<br>(X<sup>2</sup>(l))|0.5838|.1722|.4906|

Note: Standard errors in parentheses.
42 However, across African countries, the share of non-applicants stating no need as reason for not applying varies a lot, ranging from 15 percent in Cote d’Ivoire to 85 percent in Eritrea.

**Table 3: Demand and supply constraints prevent enterprises from loan applications**

|**_Doyou have a loan?_**||||
|---|---|---|---|
|yes|no|||
|**16.52**|**83.48**|||
|**28.64**|**71.36**|||
||**_Didyou apply for a l_**|**_oan?_**||
|Region|yes|no||
|Africa|**27.76**|**72.24**||
|Rest of the World|**30.56**|**69.44**||
|||**Why did**<br>**you not**<br>**apply?**||
||||**Africa Rest of the world**|
|||no need for a loan|**43.24**<br>**62.22**|
|||applicationprocedures are complex|**15.91**<br>**7.05**|
|||interest rates are not favorable|**14.03**<br>**10.27**|
|||collateral requirements are too high|**9.01**<br>**4.28**|
|||size of loan or maturityare insufficient|**2.05**<br>**1.15**|
|||necessaryto make informalpayments tog|**3.77**<br>**2.02**<br>|
|||did not think it would be approved|**7.76**<br>**7.10**|

Source: Enterprise Surveys, own calculations In summary, firm-level survey data provide insights into the constraints for SMEs’ access to external finance. Combining such data with bank-survey data on lending constraints (as done, e.g. by the EBRD across transition economies and for individual countries, such as Kenya and South Africa), loan portfolios and interest rates, and with institutional data from Doing Business and macroeconomic data can provide valuable insights into the location of the SME access possibility frontier relative to the actual provision of SME lending and the policy gaps that makes the two differ. Similar analyses could be 42
**Figure 13:** Relationship between childhood stunting and health expenditure (as a percent of GDP)

<!-- Start of picture text --> KOR USA<br>KAZ CHN JPN<br>TUV<br>GEO<br>NAM<br>VNM SWZ<br>ZAFZWE<br>LBR<br>LSO<br>ETH<br>TCD<br>NGA<br>TLS<br>0 5 10 15<br>Domestic government spending on health (% of GDP)<br>High income Upper middle income Lower middle income Low income<br>1<br>.8<br>.6<br>Fraction of children under 5 NOT stunted<br>.4<br><!-- End of picture text -->

**Figure 14:** Relationship between quality-adjusted years of school and education expenditure (as a percent of GDP)

<!-- Start of picture text --> SGP<br>KOR JPN<br>KAZ RUS SWE<br>USA<br>VNM<br>CHN<br>GEO<br>TUV<br>KIR<br>ZWE<br>TLS SWZNAM<br>LSO<br>ZAF<br>ETH<br>NGA<br>TCD<br>LBR<br>0 5 10 15<br>Domestic government spending on education (% of GDP)<br>High income Upper middle income Lower middle income Low income<br>12<br>10<br>8<br>6<br>4<br>Quality-adjusted years of school<br>2<br><!-- End of picture text --> 22
costs and benefits of informality (in terms of regulatory burden, enforcement, and access to public services) and the distribution of workers’ skills. In the long run informal employment varies with the trends in these variables, and in the short run it reacts to accommodate transient shocks and also to close the gap that separates it from its trend level.

The paper then examines empirically informality’s long- and short-run relationships. It does so in the context of an error-correction framework, using country-level data at annual frequency for a sample of developed and developing countries. Using the share of self-employment in the labor force as proxy for informal employment, the paper finds that in the long run informality is larger in countries that have lower GDP per capita and impose more costs to formal firms, in the form of more rigid business regulations, less valuable public services, and weaker monitoring of informality. The results are robust to the criteria used to obtain the sample and to the influence of outlier observations.

The short-run results indicate that informal employment follows a stable, trendreverting process. Moreover, informal employment is found to be counter-cyclical for the majority of countries. Informality’s counter-cyclicality decreases with the level of informal employment and, independently, decreases with the quality of policy and judicial services (and less significantly with GDP per capita, business regulatory flexibility, and strength of enforcement). These results are robust to changes in the sample and to the influence of outliers, even when only developing countries are considered in the analysis.

23
restrictions and the autocorrelation tests confirm the validity of the instruments, as in the case of agriculture, the System GMM is considered more efficient than the difference GMM.

For regressions including all countries, as shown in columns 1-3 of Table 1, the two-step system panel GMM estimation results indicate that electricity consumption has a significant positive impact on the value added in agriculture, while the fixed-effects and two-step difference GMM estimations yield insignificant coefficients. The two-step system GMM estimation shows that a one-percent increase in electricity consumption raises the sectoral value added by 0.024 percent. Not surprisingly, the coefficients become insignificant when regressions include only the sample of non-high-income countries, as shown in columns 4-6 of Table 1 across all estimation methods. An increase in electricity consumption in developing countries does not necessarily lead to a growth in value added because of the dominance of labor-intensive technologies or manual work, which do not rely on electricity and also face other obstacles for agriculture growth, such as lack of irrigation, fertilizers, or poor roads to markets, that would continue constraining sectoral growth even with a higher supply of power.

Estimation results for the manufacturing sector using all countries’ data indicate that the system GMM provides efficient and consistent coefficients, with a magnitude of 0.036 for the elasticity of the sectoral value added with respect to the electricity consumption, as shown in Table 2, column 3. Similar to the agriculture sector, the system GMM provides an efficient and consistent estimator, as opposed to fixedeffects and two-step difference GMM approaches in the manufacturing sector. Electricity consumption proves to be positive and significant across all approaches, with the coefficients varying from 0.036 to 0.096. As expected, the electricity-value added elasticity is higher in the manufacturing sector with the two-step system GMM as opposed to agriculture, given the nature of production technologies in manufacturing that somewhat rely on automation and, hence, electricity.

Table 2, columns 4-6 show the regression results for the manufacturing sector using the sample of nonhigh-income countries, with column 6 reporting the coefficients for the preferred two-step system GMM estimation method. In contrast to agriculture, the elasticity coefficient remains positive and significant, indicating that electricity is an important component for growth in manufacturing across all country groups. This is due to the fact that manufacturing is more homogenous across countries compared to agriculture and services because organizing manufacturing production is an independent process that is less constrained by other growth obstacles. Rodrik (2013) shows unconditional convergence at a rapid pace in manufacturing industries across countries compared to agriculture and services. Manufacturing industries produce tradable goods and can be rapidly integrated into the global markets, which leads to 15