rafmacalaba/data-use-mentions-extended huggingface dataset:
received in these two markets.

Factor markets reach balance between demands and supplies via wage (or rent) adjustments. Across all factors, the factor demand curves are downward-sloping reflecting the responses of production activities to changes in factor wages. On the supply side of the labor markets, unemployment is endogenous – for each labor type, the model includes a wage curve (a supply curve) that is upwardsloping until full employment is reached, at which point it becomes vertical (see Figure 7; its supply curve assumes a minimum unemployment rate of 5 percent). Unemployment is defined more broadly than in official statistics to include un- and under-employment.

In the simulations, a broad definition of unemployment increases the scope for the existing labor force to generate a larger (smaller) amount of effective labor if the incentives to work were to improve (deteriorate); typically, this seems realistic. For non-labor factors, the supply curves and total employment are vertical in any single year.

The above discussion refers to the functioning of model economy in a single year. In MAMS, growth over time is endogenous. The economy grows due to accumulation of capital (determined by investment and depreciation), labor (determined by share of the population in labor force age, the labor force participation rate, and the educational system), other factors (on the basis of exogenous growth data), as well as because of improvements in total factor productivity (TFP). The educational system is important as it drives the evolution of the educational attainments of the labor force, influencing their productivity, but keeps the population who is enrolled in schools away from the labor force. Apart from an exogenous component, TFP depends on the levels of government capital stocks.

13
of Vietnam. The simple composite algorithm corrects for the disturbance by cloud-coverage that is perennial in tropical regions and stitches together cloud-free images collected at different times. This comes at the loss of temporal accuracy as the data is not sourced from a single snapshot in time. In specific, a standard Top-of-Atmosphere (TOA) calibration is employed on all USGS Landsat 7 Raw Scenes in one year with less than 10% cloud coverage and then use the median of each pixel that satisfies this restriction. Several characteristics of the band distribution within each commune are extracted. For the empirical analysis, the mean, median, and standard deviation, as well as the 25th and 75th percentile of all bands listed in Table 1 are calculated.

**Nightlights Data** For nighttime lights, the stable light band of the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) is used. In contrast to daytime Landsat imagery, these satellites measure light emitted from the globe at night. The dataset has a spatial resolution of 30 arc-seconds which, depending on the distance to the equator, is about 1 kilometer. Each pixel is coded with an integer value between zero (no light) and 63 (maximum light). This top coding due to saturation is an issue in bright city areas that easily hit the maximum sensitivity of the satellite sensor. While nightlights are in principle measured every 24 hours, the raw data requires elaborate ex-post processing and datasets are typically released as annual composites.<sup>6</sup> The paper uses the stable lights product that removes unstable light sources such as moonlight, clouds, fires, and gas flares that create large outliers in the data (Baugh et al., 2010). Annual images are extracted to calculate the sum of light (SOL) of each commune. This commonly used measure simply adds up all pixel values within a commune.

> 6The Visible Infrared Imaging Radiometer Suite (VIIRS) data product released by NASA now provides monthly releases from 2012 onward.

9
tional choices, labor supply, income, social and economic networks of the household. The main survey modules were directed towards the main female in the household, who is the intended beneficiary in ultra-poor households. In cases where the main female was different from the household head, the head of the household was also surveyed for the business activities and land modules. To capture the social and economic networks of the household, respondents were asked to list households they interacted with for each of the surveyed activities. For example, in the business activities module, the respondent reported her main employer(s) for all income-generating activities she was involved in. If they reported a household within the same community as their own, the identity of this household was recorded (using the census listing).

To capture transfers exchanged between households, respondents were asked to report any transfers their household received and any transfers their household gave to others during the 12 months preceding each survey wave. For each transaction, they were asked the value of the transfer, whether it was in cash or in kind, the location of the sender/recipient,<sup>14</sup> and the identity of the sender/recipient. If the transfer source/recipient was a household residing in the same village as the respondent, their identity was recorded using the census listing. During the piloting of the baseline survey, it was observed that many respondents reported major transfer transactions, but left out smaller, in-kind transfers (which would only come up upon prompting by enumerators) that typically consisted of food items. In order to capture such transactions, respondents were asked if their household ever received food from other households and if so who the main sources/recipients of such transfers were. For every respondent, up to three sources and three recipients of food transfers were recorded.<sup>15</sup>

**Attrition:** Over the four years from baseline to endline, 13% of ultra-poor households and 15% of non-ultra-poor
beyond observed exchange rates. In this paper, for ease of comparison with the previous section, we stick to verifiability just using exchange rate data.

For the verification of floating regimes, we focus on Brazil, Mexico, Peru, South Korea, and Thailand during specific periods in the 1990s. These countries provide a good opportunity to compare periods of intervention with periods of free-floating. Brazil, Mexico, South Korea, and Thailand suffered exchange rate crises in the 1990s, which forced them to abandon previous exchange rate arrangements and adopt systems officially described as free-floating **by** their respective authorities. Therefore, for these countries we perform the same statistical tests for periods labeled as free floating and for periods during which other regimes were in operation **-** namely periods of managed floating, bands, crawling pegs, and basket pegs. Peru, on the other hand, is an interesting case because despite declaring a free-floating regime for the entire decade, several papers have noted that the Peruvian exchange rate has remained surprisingly steady. (See Calvo and Reinhart, 2000, Hausmann, Panizza, and Stein, 2000, and Edwards and Savastano, **1999,** for characterizations of floating regimes.) As in other cases, the observed pattern calls into question whether the government is in truth following the regime that it says.

Following the methodology used in the previous section, we estimate our basic equation **(1)** for each of these countries over the periods noted in Table 4 and dictated **by** data availability over the 1990s. We allow for a constant rate of crawl, include as regressors the five major currencies **(US** dollar, yen, DM, British pound and French franc), and as before estimate the model **by OLS** in first differences.<sup>3'</sup>

> **3t** As we are only testing the hypothesis of all the weights being equal to zero (
Policy Research Working Paper 10724

# **Abstract**

The complex relationship between climate shocks, migration, and adaptation hampers a rigorous understanding of the heterogeneous mobility outcomes of farm households exposed to climate risk. To unpack this heterogeneity, the analysis combines longitudinal multi-topic household survey data from Nigeria with a causal machine learning approach, tailored to a conceptual framework bridging economic migration theory and the poverty traps literature.

The results show that pre-shock asset levels, in situ adaptive capacity, and cumulative shock exposure drive not just the magnitude but also the sign of the impact of agriculture-relevant weather anomalies on the mobility outcomes of farming households. While local adaptation acts as a substitute for migration, the roles played by wealth constraints and repeated shock exposure suggest the presence of climate-induced immobility traps.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at apaolantonio@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of
Figure 5: Shapley decomposition employment adjustments <!-- Start of picture text --> (a) Extensive margin (b) Intensive margin<br>59.8 72.4<br>22<br>20<br>11<br>5.6<br>1.6 1 2.3 4.3<br>Informality External attributes Informality External attributes<br>Severity of the lockdown (mobility) Severity of demand shock Severity of the lockdown (mobility) Severity of demand shock<br>Constraints Constraints<br>Notes: Firm attributes include sector, age, gender of the CEO, country, and panel firms.<br>Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).<br>60<br>60<br>40<br>40<br>Pseudo-R2 explained by group 20 Pseudo-R2 explained by group<br>20<br>0 0<br>Relative Shapley Value (%) - Probability of adjusting employment extensive margin Relative Shapley Value (%) - Probability of adjusting employment intensive margin<br><!-- End of picture text --> Figure 6: Employment adjustments in crisis and recovery phases (a) Employment adjustments by formal and informal firms (b) Employment adjustments by types of informal firms <!-- Start of picture text --> 0<br>-10<br>-20<br>-30<br>-100 -50 0 50 100 -100 -50 0 50 100<br>Change in sales over waves (p.p) Change in sales over waves (p.p)<br>Formal Informal Necessity De Soto Parasite Fully formal<br>0<br>-10<br>-20<br>Change in employment (%) Change in employment (%)<br>-30<br>-40<br><!-
Total**||**58,959**|**100**|**837**|**100**|

## B. Descriptive results on the scale and type of disaster losses

## b.1. Firms perceive high levels of flood risk

Flood risks are high throughout most of Tanzania, and the survey data confirm that firms are aware of these risks. Indeed, 47.7 percent of firms stated that they perceive at least moderate risk of on-site flooding, and 26.8 percent perceive the risk to be high or very high. Firms are also aware of the potential indirect effects on their operations—for example, due to supply chain or infrastructure disruptions. Indeed, 64.2 percent of firms stated that they perceive at least moderate risk of indirect risks; 33.6 percent perceive the risk to be high or very high. The perception of disaster risks differs significantly across regions (figure B.1 and B.2), with firms in Dodoma exhibiting consistently higher levels of perceived risk. However, 25
calorie intake is below even this basic threshold. Following the strategy described above, we run an OLS regression model in which per-capita consumption expenditure is regressed against poverty correlates. A clear pattern of error terms (not shown) becomes apparent: observations with a calorie intake of below 1000 tend to show high a concentration of negative residuals. This suggests that for households reporting average calorie intakes below 1000 per capita per day, the model predicts higher fitted expenditures, based on characteristics highly correlated to consumption, than do the original expenditure figures. In other words, the official food-consumption statistics for certain populations are conspicuously low and imply that poverty among these groups is both more prevalent and more severe than other relevant indicators would suggest.

To further assess these apparent incongruities in the data, dietary diversity is analyzed. Unexpectedly, the share of “cereals”, a food category that is typically a mainstay in the diets of the poor, in fact increases with expenditures, whereas consumption of nutritionally superior (and usually much more expensive) items such as meat, fish, and dairy products does not increase with expenditures.

3
# <u>Returns to Schooling and Sample Selection</u>

The earning functions were estimated also taking into account sample selection bias for women using Heckman’s (1979) two-step procedure, given that there are few male labor market non-participants, and deriving Heckman correction estimates for men yields results that are highly unreliable. We follow the convention of using Heckman’s two-step procedure since our goal is to analyze an underlying regression model. That is, we wish to predict the value of the dependant variable that would be observed in the absence of selection. In this case Heckman’s approach is more appropriate than other methods since the goal is to predict an actual response.<sup>2</sup> The results are presented in Table 3. Correcting for selection produces somewhat higher returns for females, but still lower than returns to males. This was also the case in a previous analysis for Argentina using 1985 data (Ng 1992).

# <u>Reasons for Increasing Returns</u>

Clearly the returns to schooling increased in Argentina over the decade of the 1990s and up to 2002. The returns to schooling increased while the economy grew (during the early 1990s), when the economy contracted (mid-1990s), and during the severe economic crisis (19992002) (Figure 4).

> 2 The estimation was done using H-TwoStep technique instead of MV following Nawata and Nagase (1996) who showed that MV estimation could be uncertain because iterations could be stopped at a local maximum and not at a global one.  Confidence intervals for the coefficient that summarizes the selectivity effect (lambda) is available upon request.

15
differences in the mean outcome via difference-in-differences is therefore over-correcting for differences which don’t have much predictive power, whereas the ANCOVA estimation adjusts the degree of correction for baseline differences in means according to the degree of correlation between past and future outcomes actually observed in the data.

The ratio of the difference-in-differences variance to the ANCOVA variance is 2/(1+ ρ). So when ρ=0, with a single baseline and follow-up, one would need twice the sample size when using difference-in-differences to get the same power as obtained with ANCOVA. When ρ=0.25, which we will see to be a reasonable estimate for several economic outcomes, the sample size needed is still 60% higher with difference-in-differences than with ANCOVA to get the same power. There are therefore important gains in power to be had from not using difference-indifferences to estimate treatment effects with standard economic variables.

This comparison of the ANCOVA and difference-in-difference estimators in terms of efficiency is appropriate for experimental estimation, where both estimate the same average treatment effect. However, in non-experimental estimation, the estimators also require different assumptions for efficiency. Imbens and Wooldridge (2009, p. 70) discuss this issue.

# **2.2. Multiple rounds pre- or post-treatment**

Now consider the general case of _m_ pre-treatment survey rounds and _r_ post-treatment survey rounds. The difference-in-differences estimator is obtained by estimation of equation (1). This can be written as: Where for is the mean of the outcome for treatment or control group _g_ in the post-treatment period, and _n_ is the number of cross-sectional observations assigned to treatment and to control. The pretreatment means are defined analogously.

7
|3.797|4.440|
|Head of Household is Male|0.694|0.791|0.852|0.775|0.831|
|Past client of formal inst.|0.457|0.802|0.787|0.523|0.712|
|Past client of informal inst.|0.155|0.203|0.226|0.229|0.215|
|Formal inst. present in village|0.278|0.783|0.759|0.443|0.762|
|Number of formal inst. in village|0.540|1.534|1.516|0.888|1.583|
|Savings in formal institution|0.510|0.814|0.834|0.329|0.800|
|Member of a village committee|0.056|0.121|0.140|0.074|0.119|
|Observations|880|446|494|447|345|

Note: The category “Own” includes households who do not have outstanding loans. “Formal C” includes institutions that require collateral: Commercial Banks, Finance and Insurance Companies and BAAC individual loans. “Formal NC” includes BAAC group loans, loans from Agricultural Cooperatives and loans from village-level institutions. “Informal P” and “Informal R” are merged into Informal. “Both” include households who actively borrowed from both formal and informal sources. Capital requirements, wealth and income figures are in 1,000 Baht.

Source: Townsend-Thai Data.

wealthier than informal borrowers but the high standard deviation suggests that there is more dispersion. The model can also rationalize these facts. Holding the ratio _η_ and ability _z_ constant, wealthier households will rely on formal institutions for additional 17
the Internet on trade either take a macro or a marketing-business approach which are usually based on small and localized business surveys.

Among the macro studies of the impact of the Internet on international trade, Freund and Weinhold (2004), for example, find that the use of the Internet stimulates trade. Evidence from time-series and cross-section regressions shows a significant effect of the Internet on trade in recent years. The results suggest that a 10 % increase in the growth of web hosts in a country leads to about a 0.2 % increase in export growth. For the average country in their sample, the Internet contributed to about a 1% increase in annual export growth from 1997 to 1999. Mann et al. (2004) quantify the effects of trade facilitation by considering four aspects of trade facilitation effort: ports, customs, regulations, and e-business. Based on a specific simulation design, the authors find that an improvement in their measure of e-business of the “below-average” countries “halfway” to the global average yields an increase in global trade of $154 billion. Ferro (2008) finds that a reduction in costs related to creating a website results in an increase in the proportion of firms that are able to export which in turn results in greater trade flows.

Marketing studies have also found positive effects of the Internet on trade. For example, Prasad et al. (2001) using survey data from a midwestern state in the U.S. find that firms' integration of Internet technology into marketing activities generally leverages the influence of market orientation on the firms' marketing competencies (compared with competitors), which in turn have a positive impact on their export performance. Daly and Miller (1998) present evidence from a 1998 survey of enterprises in 15 low and middleincome countries that suggests that firms in these countries use search engines to research 7
affected. We capture this effect using a dummy variable equal to 1 if the share of women workers in the firm prior to the pandemic was more than 25 percent and 0 otherwise (High share of women workers). The data source is ES.

We also consider the financial condition of the firm. Studies find that a stronger financial condition (more cash, less debt, and better access to finance) prior to the pandemic helped firms perform better during the pandemic (Amin and Viganola 2023, Ding et al. 2021). Since many SMEs may obtain finance from non-bank financial institutions, our main measure of a firm’s financial constraints is a broad one. This equals the pre-pandemic severity level (on a 0–4 scale) reported by the firms of access to finance as an obstacle to their operations (Finance obstacle). The second measure focuses on bank finance. It is equal to 1 if the firm had an overdraft facility prior to the pandemic and 0 otherwise (Overdraft). The data source for the two finance variables is ES. The age of the firm and location (urban vs. rural) have been found to impact the firm’s performance during the pandemic. Using data on over 4,000 firms in Spain, Fernández-Cerezo et al. (2022) report a much larger decline in sales due to the pandemic among younger firms and firms located in urban vs. rural areas. Age may matter because younger firms are less experienced and have fewer resources to deal with the pandemic. Younger firms are also disadvantaged because they have a less developed supplier network and a less dedicated customer base. Urban areas may be more susceptible to the virus because of greater contact between people due to higher population density, more crowded housing, and more interconnected public transport. Thus, we account for a dummy variable equal to 1 if the firm is 10 years old or younger (in 2020) and 0 otherwise (Young firm), and a dummy variable equal to 1 if the firm
# <u>pment%20Prospects%20in%20Rwanda.pdf.</u>

- McCall, James, Jordan Macknick, and Jordan Macknick. 2016. “Water-Related Power Plant Curtailments: An Overview of Incidents and Contributing Factors.” NREL/TP--6A20-67084, 1338176. Golden, CO (United States): National Renewable Energy Lab. (NREL). https://doi.org/10.2172/1338176.

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- Meyer, V., N. Becker, V. Markantonis, R. Schwarze, J. C. J. M. van den Bergh, L. M. Bouwer, P. Bubeck, et al. 2013. “Review Article: Assessing the Costs of Natural Hazards – State of the Art and Knowledge Gaps.” Natural Hazards and Earth System Sciences 13 (5): 1351–73. https://doi.org/10.5194/nhess-13-1351-2013.

- Mukherjee, Sayanti, Roshanak Nateghi, and Makarand Hastak. “A Multi-Hazard Approach to Assess Severe Weather-Induced Major Power Outage Risks in the U.S.” Reliability Engineering & System Safety 175 (July 2018): 283–305. https://doi.org/10.1016/j.ress.2018.03.015.

- Mukherjee, Sayanti, Roshanak Nateghi, and Makarand Hastak. “Data on Major Power Outage Events in the Continental U.