rafmacalaba/datause-encoder-data huggingface dataset:
heterogeneity in wealth and liquidity constraints.<sup>18</sup> The difference is that the consumption data capture short- and medium-term deprivation, whereas assets (as also emphasized in the conceptual framework) represent a better indicator of long-term wealth (Aiken et al., 2023). Indeed, the correlation between these two variables is relatively low in our sample (0.37). The lagged values of the reversed GS-SPEI are included to capture lagged responses and cumulative or nonlinear effects due to repeated shock exposure over time.<sup>19</sup> Previous survey literature has made a strong case for investigating the way populations respond to the risk of cumulative shocks (Cattaneo et al., 2019), especially since climate change is the reason for the increased frequency and intensity of extreme weather events (Stott, 2016). The adaptive capacity score is included to capture heterogeneity due to a household's pre-shock ability to adapt _in situ_ . This variable is of particular relevance since the dynamics of the relationship between on-farm adaptation and migration remains under-explored (Cattaneo et al., 2019), and is built by leveraging information on farmers’ adoption of agricultural inputs and practices that are relevant in adapting to climate risk.<sup>20</sup> The education variable captures the role of human capital of household members in determining the household-level decision of dealing with climate risk by sending out individual migrants, another key mediating channel emphasized in the literature on climate migration ( <mark>Burzyński et al., 2022)</mark> and a main determinant of migration in the NELM framework (Stark & Bloom, 1985).

The choice of including only a limited number of conditioning variables for the estimation of CATEs is consistent with the adoption of the double orthogonalization approach discussed above 18 Monetary consumption values have been temporally and spatially deflated.

> 19 The SPEI variable we built captures growing-season weather anomalies occurring during the entire between-wave interval across different survey rounds. Including a lagged value of this variable means capturing weather conditions over a timespan of several years. For instance, by focusing on wave 4 (2018-2019) and using both current and lagged values of the reversed GS-SPEI, we can capture weather conditions in the period between wave 3 (2015-2016) and wave 4, as well as weather conditions in the period between wave 2 (2012-2013) and wave 3.

20 For instance, we include the use of pesticides in the score as the agronomic literature indicates that higher temperature and precipitation increase pest pressure, which may in turn lead farmers to use more pesticides (Bareille & Chakir, 2023). This results in a high degree correlation between different adaptive indicators, which is important in our case since we are not able to include all the possible adaptation variables in the construction of the index (e.g., use of drought-tolerant seeds, intercropping practices, etc.) due to missing data.

19
## 5.5 Foreign firms export higher quality varieties than domestic ones

A comparison of unit values of export varieties introduced by foreign and domestic firms indicates that foreign companies tend export higher quality varieties.

In Figure 8, we graph the density distribution of the prices of newly introduced exports normalized by the mean price of already exported goods within the same product category. We distinguish between products introduced by domestic firms (dashed line) and by foreign firms (continuous line).<sup>8</sup> We find that the distribution pertaining to foreign firms is shifted to the right, suggesting that when a foreign firm introduces a new export product it is more likely to be of higher quality (i.e., higher unit value) than when a domestic firm does so. The intuition emerging from the Figure 8 is confirmed by the Table 13.

Table 13: Export prices of new export products normalised to the sectoral mean of reference group: Foreign firms vs Domestic firms

|||p|ercentil|es|||
|---|---|---|---|---|---|---|
||10th|25th|50th|75th|90th|mean|
|Domestic Firms|0.29|0.55|0.90|1.30|2.02|1.07|
|Foreign Firms|0.21|0.55|0.94|1.63|2.68|1.25|

Notes: Baseline reference group is experienced exporters. Calculated at firm-product-level.

# 6 Export discovery

## 6.1 There is a lot of export discovery

We find evidence of frequent export discoveries taking place in the aftermath of NAFTA and the Peso crisis.<sup>9</sup> In fact, 1,587 of 5,607 (or 28%) newly introduced export varieties are products that were not exported by any firm in the dataset in the previous period. An increase in the number of products exported by Mexico in the postNAFTA period is also detectable in aggregate trade statistics and has been documented

> 8We classify as foreign firms that had some foreign participation in 1994, which is the first year of our sample. Unfortunately, we do not have such data for later years, so most likely we are underestimating the number of foreign firms. Thus some scepticism about this conclusion is warranted, even if the advantage is that focusing only on the first year of the sample makes the “foreign ownership” attribute exogenous.

> 9Recall that NAFTA came into effect on January 1, 1994 and the Peso crisis took place in December 1994. The period under analysis in this study is 1995-2003.

22
survey round remains balanced in terms of observable baseline differences. Given the lack of significant differences in attrition by treatment status, and that attrition does not appear to differentially select firms on observables, we maintain a missing-at-random assumption in our analysis for those attriting.

An important point of context is that the period of our study coincided with a period of rapid general economic growth in Sri Lanka. When we began our study in 2008, per-capita GNI (in constant 2011 PPP international dollars) was 7,598.<sup>7</sup> In May 2009, just before our wage subsidy intervention period began, the 25-year civil war ended, and the Sri Lankan economy grew at 8 to 9 percent per year over the 2010 to 2012 period, with per-capita GNI reaching 10,396 in 2014, the year of our last survey. We are therefore testing the return to additional labor in a growing economy, where firms may be expected to have opportunities to potentially grow.

# **4 Take-up and Who Did They Hire?**

# **4.1 Take-up**

During the eight months the incentive program was active, 60 of the 250 firms offered only the wage subsidy took it up (24 percent). The take-up rates were not statistically different (p=0.622) in the wage subsidy plus savings (24.2%), and for the wage subsidy plus training (21.2%) treatment groups, giving a total of 196 firms that used the subsidy for at least one month. Conditional on using the subsidy, the median firm used it for seven out of the eight possible months and received a total of 24,000 Rs. in subsidy. Only 17 percent of those using the subsidy used it for 4 or fewer months, and 68 percent used it for 6 months or more.

Table 3 examines the correlates of the take-up decision, building on early analysis presented in de Mel et al. (2010) which only had take-up data through to November 2009. We conduct probits of the probability of using the wage subsidy voucher for the wage subsidy only treatment group, and for all treatment groups offered the wage subsidy. The first column examines firm characteristics, the second owner characteristics, and the third both together. We see that take-up rates are lower in Colombo than in the southern cities of Galle and Matara, with Kandy in between. One possible reason is that wage rates are higher in Colombo, so the flat-rate wage subsidy may cover a lower proportion of the worker’s wage there. We find that firm characteristics have very little predictive

> 7 Source: World Development Indicators, World Bank.

13
|**Table 6: Impact on Profits and Sales**|Bf|Di||f Sbi||l|l|
|---|---|---|---|---|---|---|---|
||eore|urng||ter us|y|p‐vaue|p‐vaue|
|Sample size|<br>Subsidy|Subsidy|Year1|Year2|Year3+|equality|all zero|
|**Panel A: Unconditional Profits (Truncated at**|**99th Percen**|**tile)**||||||
|Assigned to Treatment<br>4638|897|676|1863|1861|1483|0.798|0.523|
||(1487)|(1246)|(1159)|(1454)|(1179)|||
|Control Mean|14572|16603|16557|18710|17854|||
|**Panel B: Inverse Hyperbolic Sine of Profits**||||||||
|Assigned to Treatment<br>4638|‐0.022|0.203|0.590**|0.754***|0.635**|0.265|0.086|
||(0.284)|(0.217)|(0.242)|(0.284)|(0.296)|||
|Control Mean|9.21|9.38|8.97|8.86|8.56|||
|**Panel C: Profits Conditional on Business Oper**|**ating (Trun**|**cated at 99**|**th Percen**|**tile)**||||
|Assigned to Treatment<br>4159|1124|871|1017|633|530|0.987|0.931|
||(1539)|(1269)|(1174)|(1499)|(1219)|||
|Control Mean|15954|17356|18658|21975|21588|||
|**Panel D: Log Profits Conditional on Business O**|**perating**|||||||
|Assigned to Treatment<br>4120|0.076|0.045|0.061|0.011|0.005|0.812|0.835|
||(0.070)|(0.058)|(0.059)|(0.061)|(0.056)|||
|Control Mean|9.39|9.45|9.55|9.71|9.72|||
|**Panel E: Unconditional Sales (Truncated at 99**|**th Percenti**|**le)**||||||
|Assigned to Treatment<br>4731|‐4486|5438|6950|16652*|7256|0.457|0.417|
||(7326)|(7124)|(7763)|(8846)|(9410)|||
|Control Mean|51783|60638|61625|69673|72910|||
|**Panel F: Inverse Hyperbolic Sine of Sales**||||||||
|Assigned to Treatment<br>4731|‐0.046|0.196|0.740***|0.882***|0.677**|0.128|0.032|
||(0.299)|(0.237)|(0.252)|(0.307)|(0.319)|||
|Control Mean|1016|1033|991|972|947|||
|<br>**Panel G: Sales Conditional on Business Opera**|.<br>**ting (Trunca**|.<br>**ted at 99t**|.<br>**h Percentil**|.<br>**e)**|.|||
|Assigned to Treatment<br>4247|‐4757|6636|4355|13817|4904|0.584|0.573|
||(7811)|(7504)|(8325)|(9574)|(10801)|||
|Control Mean|56577|63361|69061|81830|88142|||
|**Panel H: Log Sales Conditional on Business Op**|**erating**|||||||
|Assigned to Treatment<br>4226|‐0.009|0.103|0.140*|0.091|0.072|0.834|0.479|
||(0.087)|(0.074)|(0.079)|(0.078)|(0.081)|||
|Control Mean<br>Notes: robust standard errors in parentheses,<br>strata, variables used for re‐randomization, th<br>survey round dummies. *, **, *** indicate sig<br>p‐values are for test that the treatment effect<br>and that the treatment effect is zero in all fou|10.41<br>clustered a<br>e baseline<br>nificance at<br>is equal in<br>r periods.|10.44<br>t the firm<br>value (exc<br>the 10, 5,<br>the during|10.49<br>level. All re<br>ept for pan<br>and 1 perc<br>, year 1, ye|10.73<br>gressions c<br>els C and D<br>ent levels r<br>ar 2, and ye|10.73<br>ontrol for<br>on churn<br>espectivel<br>ars 3 to 4|randomiz<br>), and con<br>y.<br>periods;|ation<br>tain|

42
**Table A.2. Social Insurance Coverage Rates of Rural Migrant Workers**

|Year|Pension<br>Insurance|Medical<br>Insurance|Unemployment<br>Insurance|Work Injure<br>Insurance|
|---|---|---|---|---|
|||Participants (M|illions)||
|2006|14.2|23.7||25.4|
|2007|18.5|31.3|11.5|39.8|
|2008|24.2|42.7|15.5|49.4|
|2009|26.5|43.4|16.4|55.9|
|2010|32.8|45.8|19.9|63.0|
|||Coverage Rat|e (%)||
|2006|15.0|25.0||26.8|
|2007|17.6|29.8|11.0|37.9|
|2008|18.0|31.8|11.6|36.9|
|2009|18.7|30.6|11.6|39.4|
|2010|21.4|29.9|13.0|41.1|

Source: MOHRSS, Statistical Bulletin of Social Security Development (20062010), MOHRSS website. Note: The denominator for calculating coverage rate is the number of rural migrants outside their townships.

**Table A.3 Trends in the Evasion of Mandated Contribtions to Urban Social Insurance Programs**

|Year|Aggregated<br>Wage (Billion<br>Yuan)|Expected<br>Revenue Level<br>(Billion Yuan)|Actual Revenue<br>(Billion Yuan)|Total Evasion<br>(Billion Yuan)|Evasion Rate<br>(%)|
|---|---|---|---|---|---|
|2000|1095|449|264|185|41.1|
|2001|1221|500|310|190|38.0|
|2002|1364|559|405|154|27.6|
|2003|1533|629|488|140|22.3|
|2004|1762|722|578|144|20.0|
|2005|2063|846|698|148|17.5|
|2006|2426|995|864|130|13.1|
|2007|2947|1208|1081|127|10.5|
|2008|3529|1447|1370|77|5.3|
|2009|4029|1652|1612|40|2.4|
|2010|4727|1938|1882|56|2.9|

Note: (1) Aggregated wage is from the registered enterprises, excluding those private firms, self-employed and rural migrant workers; (2) expected revenue is calculated based on the assumption of 41 percent contribution of aggregated wage. Source: NBS, China Statistical Yearbook (2011), China Statistics Press, Beijing.

**Table A.4**

42
11

**Urban Poverty in Brazil**

Using a poverty line of about $US300 per capita per annum in 1985 (roughly 13 percent of Brazil's per capita GDP), about one-fourth of Brazil's population is in poverty."<sup>3</sup> Slightly under half of those living in poverty reside in urban areas. Poverty in Brazil is correlated with demographic factors as well as the income-earning characteristics of the household head. Regionally, 47 percent of urban poverty (and 55 percent of total poverty) is in the northeastern region, where the per capita income is about 60 percent of the country as a whole. Poor urban households tend to have fewer earners, more children, and a higher dependency ratio than the average for all urban households. Heads of poor urban households are as likely to be economically active as the average for urban Brazil, but their education level is, on average, 40 percent lower, their average hours worked and unemployment rates are higher, and thus the average income of the head and the household is lower. Twenty-two percent of poor urban households are headed by women, indicating that these households have a roughly 20 percent greater chance of being in poverty than the average Brazilian household.

Earlier, we saw that female-headed households exhibit different economic characteristics from other households. Are female-headed households who are poor also different from other poor households? To answer this question, we compare the characteristics of poor urban households with those of poor urban female-headed households, and poor female-headed households with children (Table 6). We disaggr'egated this analysis by region in order not to confound the analysis with the strong regional effect on poverty of the northeast, the area with the highest incidence of female-headedness as well.

In all areas, female-headed households have a somewhat higher incidence of poverty. What is striking about Table 6, however, is the poverty rate for female-headed households 13 The analysis of the correlates of poverty is taken from Fox (1990). The poverty line used and the analytical issues associated with this line are described on pages 2-6 of that paper.
development. To understand and counter this threat, one must examine the factors that dispose a country to adopt deposit insurance and influence its design. Demirgüç-Kunt, Kane and Laeven (2006) investigate this question using 1960-2003 data covering 170 countries.

Their goal is to assemble a comprehensive dataset with which to determine whether and how outside influences, economic circumstances, crisis pressures, and political institutions affect deposit-insurance adoption and design. To study this sample robustly, the authors use three complementary regression strategies: limited dependentvariable regression, hazard analysis, and Heckman selection models. They estimate adoption and design decisions simultaneously and control for the influence of: economic and political characteristics; disruptive events (such as macroeconomic shocks); occurrence and severity of crises; and the nature of the contracting environment. They find that outside pressure to emulate developed-country regulatory frameworks and political arrangements that facilitate intersectoral deal-making dispose a country toward adopting a DI scheme. Another strong and robust conclusion is that countries design their schemes especially poorly when they install DI in response to a financial crisis.

<u>Summary</u> **<u>.</u>** Research on the first four questions suggests that, to install DI successfully, weaknesses in a country’s contracting environment must be identified so that design features can be adapted to them. Decisions to install DI during and after a crisis must not proceed hastily. Policymakers must make a concerted effort to appreciate that preexisting weaknesses in transparency, government accountability, and private contract enforcement limit the kinds of reforms they may advantageously pursue.

13
**Table A4.  Regression analysis: linear probability model on correlates of upward mobility during the pre- crisis period 2006 to 2009 and crisis period 2009 and 2011**

|||**Period 2006-20**|**09**||**Period 2009-2 **|**011**|
|---|---|---|---|---|---|---|
||**Exiting**<br>**$2.5/day**|**Exiting**<br>**$5/day**|**Upward**<br>**mobility**|**Exiting**<br>**$2.5/day**|**Exiting**<br>**$5/day**|**Upward**<br>**mobility**|
|Female headed households|-0.0592|-0.0114|0.0112|-0.231***|-0.0406*|-0.0463***|
||(-1.29)|(-0.50)|(0.48)|(-4.95)|(-2.50)|(-3.57)|
|Age 25-29 (base)|||||||
|Age 30-34|-0.0758|-0.0298|-0.0552|0.0900|0.0191|0.0152|
||(-1.17)|(-0.67)|(-1.21)|(1.14)|(0.59)|(0.69)|
|Age 35-39|-0.0526|-0.0143|-0.0301|0.296***|0.0274|0.0828***|
||(-0.77)|(-0.33)|(-0.68)|(3.48)|(0.85)|(3.34)|
|Age 40-44|-0.0608|0.0391|-0.0245|0.223*|0.0546|0.0757**|
||(-0.87)|(0.84)|(-0.55)|(2.44)|(1.49)|(2.98)|
|Age 45-49|-0.0561|0.0263|-0.0308|0.223*|0.0511|0.0923***|
||(-0.84)|(0.56)|(-0.70)|(2.46)|(1.36)|(3.34)|
|Age 50-54|-0.234***|-0.0661|-0.147***|0.235*|0.0276|0.0562*|
||(-3.34)|(-1.48)|(-3.39)|(2.49)|(0.77)|(2.28)|
|Age 55+|-0.298***|-0.0397|-0.141**|0.240|-0.0294|0.0386|
||(-3.82)|(-0.69)|(-2.68)|(1.76)|(-0.75)|(1.01)|
|Rural|-0.245***|-0.114***|-0.177***|0.0903|0.0347|0.0756***|
||(-4.47)|(-5.54)|(-8.25)|(1.17)|(1.60)|(3.96)|
|Household size|-0.0115|-0.0205***|-0.0109|-0.0194|-0.0168**|-0.00479|
||(-0.96)|(-3.58)|(-1.58)|(-1.31)|(-2.88)|(-0.83)|
|Household dependency rate|||||||
|(children under 14)|-0.0540|-0.0153|-0.0178|-0.0643|0.000705|-0.00367|
||(-1.69)|(-0.69)|(-0.86)|(-1.54)|(0.03)|(-0.19)|
|Household dependency rate (adults|||||||
|over 65)|-0.536***|-0.175**|-0.162***|-0.151|-0.0746*|-0.0696|
||(-3.43)|(-3.13)|(-3.59)|(-1.01)|(-2.02)|(-1.83)|
|_Secondary education (base)_|||||||
|Primary education|-0.0967*|0.0206|0.0109|-0.0579|-0.0232|-0.00132|
||(-2.42)|(1.11)|(0.50)|(-1.06)|(-1.17)|(-0.06)|
|Tertiary education|0.0491|0.0374|-0.0952**|0.293|0.361***|0.144***|
||(0.45)|(0.81)|(-2.68)|(1.23)|(6.23)|(5.53)|
|_Household employment rate in:_|||||||
|Manufacturing|0.102|0.146*|0.0868|0.142|0.0771|0.0895|

36
markets are a range of related markets, differentiated by quality or by risks associated impediments to their delivery.

Prices for project-based offsets have increased regularly in recent years and the pricing process has become more transparent and market-driven, largely because the rules governing how they can be used in Annex B countries has become more clear and because of the development of formal markets for European allowances. The largest class of CDM transactions involves the direct purchase of CERs from registered projects. According to Capoor and Ambrosi (2007), weighted average prices for these primary CERs reached about US$10.90 in 2006, representing a 52% increase over 2005 levels.<sup>79</sup> Still, these average prices mask a range (US$6.80-US$24.75) related to the heterogeneity of the underlying projects and contracts.

Transactions on the primary market involve forward streams of credits and therefore the buyer faces a number of risks, linked to project performance and to the eligibility of the generated credits for its compliance purposes. Some risks are project specific – for example, risks related to the variability of rainfall feeding a small scale hydro-power project—while others may be country specific – for example, risks related to the performance of the national Designated National Authority. And, since most projects are related to an underlying business – for example, the production of electricity – vagaries associated with that side of project can affect emission performance as well. In addition, uncertainties about policy can affect how useful the credit is for meeting regulatory or even contractual obligations. By way of example, credits from land-use CDM projects are ineligible for delivery against CER future contracts sold on the EEX and the ECX.

CERs that have already been issued sell at a significant premium, since they are without project performance risk. To date, nearly xxx CERs have been issued by the CDM board, but many of these were sold under existing contracts so pricing information is scarce. Still, Capoor and Ambrosi note that issued CERs can trade at nearly double the prevailing price for primary CERs. Still, even issued CERs are not without risk, in part because the International Transaction Log – a system for tracking and affecting the transfer of tradable Kyoto units – including CERs – is not yet fully implemented. Consequently, since title of the CER cannot be transferred, some element of counter-party risk remains. Additionally, there may be an eventual requirement to become a project participant to enter a primary transaction, a time-consuming process that does not come without risks of legal or public exposure.

> 79 All prices in US$ per tCO2e, unless otherwise indicated.

49
**Table 5:** Living below the USD 1.90 per day poverty line.

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|IDP|0.228***||0.228***|0.223***|
||(0.000)||(0.000)|(0.000)|
|Female||-0.012*|-0.010*|-0.017|
|||(0.054)|(0.072)|(0.144)|
|IDP x Female||||0.010<br>(0.424)|
|Age|Yes|Yes|Yes|Yes|
|HH size|Yes|Yes|Yes|Yes|
|N|10608|10608|10608|10608|
|_R_<sup>2</sup>|0.192|0.139|0.192|0.192|

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. HC2 standard errors in parentheses.

25
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15