rafmacalaba/data-use-mentions-extended huggingface dataset:
with the U-shaped pattern found in the previous figure for the medium and long term. The clear implication is that more financially-open economies are more vulnerable to global disturbances. Still, beyond the 1-year horizon (shown in the top panel), as financial openness rises the variance contribution of demand shocks follows a declining pattern, qualitatively similar to that in Figure 9, but much less pronounced. At long horizons, depicted in the middle and bottom panels of Figure 10, the rising profile of the total contribution of global shocks is primarily due to that of monetary shocks, although supply and commodity shocks also show a slightly increasing contribution.

Lastly, Figure 11 reports GDP forecast error variance decompositions at different percentiles of commodity intensity. Like in the previous figure, the total contribution of global shocks rises monotonically with commodity intensity at all time horizons, which agrees with the conventional view that commodity-intensive economies are more vulnerable to global shocks. Inspection of the graphs shows that this is mainly due to the rising profile of the variance contribution of global monetary shocks. However, at the 10-year horizon the contributions of the other three shocks also show a rising profile, suggesting that the added vulnerability of commodity-intensive economies applies to all types of shocks.

# **5. Conclusion**

This paper takes a fresh look at the impact of external shocks on output fluctuations in developing countries. Using a large annual dataset comprising 82 countries over four decades, the analysis focuses on the effects of a set of real and financial global structural shocks that admit a clear economic interpretation. We consider four types of global shocks – demand, supply, monetary, and commodity shocks. In contrast with standard practice, each of these 30
that often operates at scales of less than one meter, the medium resolution Landsat images do not allow for feasible object recognition. The empirical exercise explores the small-scale properties of Landsat imagery to predict the distribution of economic activity and compare its performance to nightlights in Vietnam. A finely-geocoded nationwide dataset on enterprise counts, employment, and expenditure, allows to test the potential and limits of both nightlight and Landsat imagery at small geographic scales. The results indicate that Landsat outperforms nighttime lights at predicting measures of economic activity and consumption in parsimoniously specified regression models in the aggregate. Using only two simple Landsat spectral indices that are designed to capture certain land use categories can explain 25-35% of the variation in formal employment and enterprise counts as well as expenditure in the cross-section. More flexible specifications making use of more characteristics of the distribution of the raw Landsat data can improve this fit to 40-50%. Employing LASSO regularization and machine-learning type cross-validation techniques suggests that these relationships are stable and not just fitting statistical noise. In contrast to the cross-sectional results, the remotely sensed data has virtually no prediction power for changes in the time series.

The study adds to the literature on remote sensing by showing that parsimonious linear models of medium-resolution satellite data can capture the spatial variation of economic activity to a fair degree and are vastly superior to simple specifications of nightlight data in the context of a developing country. This provides a viable and inexpensive alternative for applied researchers to study areas with inferior data availability. The findings further substantiate concerns about the predictive power of nighttime lights at small geographies raised in previous studies. The negative result in the time-series cautions against the use of remotely sensed data in simple models to predict economic growth over time, as cross-section and time-series parameters are highly unstable in the setting of Vietnam.

4
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focuses on the material impact on receiving communities. Verme and Schuettler (2021) survey that literature and argue that it broadly shows that the impact on host communities’ employment and wages are not significant, but that when results are significant, they are often negative on employment and positive on wages, albeit in the short run. Hennig (2021) extends this to show that the presence of refugee shelters in Germany may result on downward pressure on rent prices, contributing to opposition to immigration. In contrast, Dempster et al. (2020) look at survey data to argue that there is no inevitable relationship between these types of material and economic effects and people’s attitudes to refugees. The literature assessing the impact of refugees in low- and middle-income countries shows that refugees can stimulate host economies (Alix-Garcia et al. 2018; Maystadt and Duranton 2018; Taylor et al. 2016) but also have negative impacts, for example by undermining health (Baez 2011; Montalvo and Reynal-Querol 2007) and competing for scarce resources and jobs (Depetris-Chauvin and Santos 2018; Tumen 2016; Ruiz and Vargas-Silva 2015). The total effect is mixed, with some benefiting and some losing from refugee presence (Fallah et al. 2019; Maystadt and Verwimp 2014; Alix-Garcia and Saah 2009).

The literature aiming to test the contact hypothesis with refugee and host populations mostly focuses on European countries. While most studies conclude that refugee-host contact has negative effects on host attitudes towards refugees, effects and mechanisms seem to vary with the context and the intensity of exposure. Exploiting a natural experiment in Greece, Dinas et al. (2019) show that exposure to the mass influx of asylum seekers during the European refugee crisis leads to an increase in right-wing support on the Greek islands. The mechanisms they identify are sociotropic and relate to the perceived negative impact of influx on culture. Exploiting the quasi-random settlement of refugees in Sweden between 1985 and 1994, Rozo and Urbina (2020) find migration shocks are associated with lower support for immigration. Here
POLicY REsEAkCH WORKINC. PAPER 1724

# Summary findings

Economic policymakers traditionally hold the view that, because of imperfectioms **in** capital markets, a shortage of long-terrn finance acts .-s a barrier to industrial performance and growth. ILong-term finance is thought to allow fhrmn to invest in more productive technologies, even when they do not produce immediate payoffs, without the fear oi premature liquidation. As a result, special state-supported term-lendirng institutions have been established, especially in developing countries.

But sonic believe that short-term finance may offer better incenrivcs because it allows suppliers of finance to monitor and montrol firms mnore effectively, thus improving the firms' performance.

Schiant,; -rh, **aind** Si ivis-lava en-pirically investigate the deterrlinaiits and Lonseqoences of the term structure of debt. Using a rich p.ncl of data on privately owned cornpanits in India, tl-ev also examine the influence of debt manu-ity strictures **oIn** those firms' performance, especially on productivity The results are not conclusive, but seem to support conventional beliefs about the importance of long-term finance to firm performance. Heavy leveraging, however, has a strong negative impact on productivity.

They base their econometric evidence on estimates of a maturity equation and of a production function augmented by financial variables.

The data on which these results are based have been generated by a financial system in which there is little competition, in which state-owned financial institutions are not guided by the profit motive and have no control over interest rates, so one cannot say whether short term finance would have been more beneficial in a less regulated system.

Moreover, by the end of the 1980s, the capital base of India's government-owned financial institutions had been severely eroded and they carried a heavy burden of nonperforming assets. This means that the benefits of long term finance must be weighed against the costs.

This paper -- a prod; ct of the Finance and Private Sector Development Division, Policy Research Department - was prepared for tlc conferernce "Firm Finance: Theory and
39 There seems to be little study of hov to mobilize the u eAoployed or underemployed physicians that abound in some parts of the Middle East and Latin America who are potentially valuable providers of family planning.

Information generated through the Contraceptive Prevalence Surveys and summarized in Tsui and Donaldson (1987) produced rural/urban breakdowns by source of contraception. The average percent across 22 of the more recent surveys of rural users obtaining supplies from private clinics is 13 percent vhile for urban contraceptive users the figure was 18 percent. The three CPS countries where the private clinics seem to be providing services for the largest proportion of users vere Korea, The Dominican Republic, and Bolivia. Private clinics were serving over thirty five percent of users in both rural and urban areas in those countries.

The issue of vhether contraceptive users in a particular country shift from the public sector to the private sector over time or whether shifts occur vithin the private sector between private clinics and other private sources of supply as not been addressed in any depth. Tsui and Donaldson (1987) indicate that there is evidence from Mexico that vhile private physicians and public clinics are primary initial providers of contraceptive services, pharmacies become the primary points of resupply. Further, the 1983 CPS in Tunisia asked current contracepting women where they originally obtained their current method and vhere they obtained their most recent supply. Private Doctors provided the original supply of the pill, condom and vaginal method to eight, seven and fourteen percent of women using that method respectively, while they provided the most recent supply to only four, four and seven percent respectively. The net gainers seem to be the pharmacists vho were much more likely to have provided the most recent supply of these methods than to have provided the initial supply.

**ml.fp:Faily** Plonniy Paper (wbole)
**SENA: A Threat to Identification?** We have not included SENA students in our analysis because SENA captured a negligible share of higher education enrollment in the early 2000s, and SENA students are not included in the student-level higher education dataset, SPADIES. Students in SENA, therefore, have been classified as "not enrolled" for our estimation. In principle, this could affect the interpretation of our findings.

Figure A3 presents the evolution of higher education enrollment in Colombia by program type. Panel (a) excludes SENA enrollment and leads to the conclusion that higher education enrollment has grown mainly through bachelor’s programs. This is consistent with SC programs having a small expansion margin. However, the story changes when we include SENA enrollment, which expands dramatically between 2005 and 2015 (panel b). Further, the distribution of SC students across cities changes over time and when SENA enrollment is included. The left side of Figure A4 shows that, before 2005, most SC enrollment outside SENA was concentrated in large cities; only later–and very slowly–did it rise in medium and small cities. SENA, however, was particularly aggressive in those markets (right side). A similar picture emerges when looking at number of SC programs rather than enrollment (Figure A5).

The fact that most SC programs were offered in large cities in the early 2000s helps explain our small expansion effects. Since those cities already had HEIs offering bachelor’s programs, those students might not have been persuaded to enter higher education just because of the opening of an SC HEI. In contrast, in smaller cities without other HEIs, such openings might have induced higher education entry. Data from later years (including more SC HEIs in smaller cities), especially if including SENA students, might have uncovered a larger expansion margin. Our results, therefore, illustrate what might happen when SC programs expand in markets in which other, more widely preferred higher education options are already available.

**Fully saturated model.
using customers are proxy for output,<br>annual X to be used in tariff revision should be around<br>2.4%.|
|Argentina|Panel of 8 gas Argentinean<br>distribution companies<br>1993-1997|Rossi (2001)|SPF|Major catching up and frontier shift during the period<br>for all operators.|
|Brazil|Cross-section of 24 distribution<br>operators<br>1997/1998 data|Resende (2002)|DEA|Argues for relative efficiency scores in the design of a<br>national yardstick competition regulatory regime in<br>Brazil.|

9
above travel times according to the road surface and condition. Walking paths are less affected by monsoon rains and are modified accordingly. Where surface conditions are not specified a uniform modifier of 50% has been applied to all vehicular roads and 75% to all paths.

**_Table 5: Monsoon speed modifications_**

|**Reported road**<br>|**_% Chan_**<br>**Asphalt & Surface**<br>|**_ge in Travel Time(_**<br>|**_km/h), Monsoon Sea_**<br>|**_son_**<br>|
|---|---|---|---|---|
|**condition**|**Treatment**|**Gravel Road**|**Earthen Road**|**Walking Path**|
|VeryGood|100%|90%|50%|90%|
|Good|100%|70%|40%|85%|
|Fair|75%|50%|30%|75%|
|Poor|50%|40%|20%|75%|
|VeryPoor|40%|30%|15%|75%|

Because Nepal’s poorly built roads erode quickly, maintaining accurate and up to date road condition data is a Sisyphean task. Road conditions change from year to year and the Department of Roads data collection apparatuses cannot keep up, presenting an unavoidable source of error for the monsoon model. Despite this, we elected to continue using the surface ratings provided by the Measuring Rural Access team because the road network data they collected is reasonably recent (2015) and by far the most complete data set available within Nepal. Moreover, IRI ratings may not capture the precise state of the road in a given moment, but they do suggest the priority given by local
Figure 2: NAIS Historical Farmer Premium Income and Insurance Claims by State, 2000-2008 <!-- Start of picture text --> 80 Gujarat<br>70 Andhra Pradesh<br>60<br>50<br>40 Karnataka<br>Bihar Maharashtra<br>30 Rajasthan<br>West Bengal<br>20 Madhya Pradesh<br>Uttar Pradesh<br>Orissa<br>10<br>Jharkhand<br>-<br>- 5 10 15 20 25<br>Average Premium Income 2000-2008 (USD millions)<br>(USD millions)<br>Average Claim Outgo 2000-2008<br><!-- End of picture text --> Source: Data from AICI Following Mahul and Stutley (2010) one measure of the value of the NAIS to participating farmers is the producer loss ratio, taken to be total claim payments to farmers divided by total farmer premiums. Since its inception, the producer loss ratio has been always higher than 100 percent, i.e., the total claim payments paid to farmers exceed the premiums received (including premium subsidies). This is a direct consequence of the caps imposed on the premium rates of oilseeds and food crops, described in the previous subsection. Between 2000 and 2008 the producer loss ratio was 3.5, but this hides a large disparity between non-borrowing farmers with producer loss ratio of 6.4 and borrowing farmers with producer loss ratio of 3.0 (see Figure 3). This disparity illustrates the impact of adverse selection: non-borrowing farmers choose to insure their riskier crops (James and Nair 2009). It should also be noted that the producer loss ratio of small and marginal farmers has tended to be less than the producer loss ratio of all farmers, perhaps suggesting that small and marginal farmers have been less adept at selecting against the insurer.

Figure 3: NAIS producer loss ratio (claims/farmer premiums), 2000-2008 <!-- Start of picture text -
<!-- Start of picture text --> Eligible communities (n=218):<br>Samplin * Cabo Delgado Province: Chiure (n=59)<br>ping * Nampula Province: Erati (n=40), Memba (n=40)<br>* Tete Province: Angonia (n=39), Changara (n=40)<br>Treatment communities (n=110) Control communities (n=108)<br>Communities surveyed (n=108) Communities surveyed (n=108)<br>Communities not surveyed due to Communities not surveyed due to<br>problem of identification on the field problem of identification on the<br>(n=2) field (n=0)<br>2414 householdswith target child 3-5 2273 householdswith target child<br>years old assessed 3-5 years old assessed<br>Communities surveyed (n=108) 3-year Communities surveyed (n=108)<br>Follow-up<br>112 pre-schools surveyed<br>1941 target households surveyed 1824 target households surveyed<br>1903 target children assessed 1779 target children assessed<br>Households lost to follow-up (n=473) Households lost to follow-up (n=449) ;<br>* HHs/Caregiver absent (n=188) * HHs/Caregiver absent (n=176)<br>* Moved away (n=109) * Moved away (n=95)<br>* Not identified/Not localized (n=102) * Not identified/Not localized (n=77)<br>* Not surveyed because of cholera (n=33) * Not surveyed because of cholera (n=71)<br>* Target child passed away (n=34) * Target child passed away (n=24)<br>* Refused to answer (n=6) * Refused to answer (n=5)<br>*
is assumed that the FSD implementation process did not provide an incentive for mothers to choose schools that implemented the reform earlier.<sup>12</sup> The data are drawn from the Chilean Socio-Economic Characterization Survey (CASEN) and administrative information from the Ministry of Education from 1990 and 2011. The results indicate that the introduction of FSD had a significant and positive impact on labor participation for single mothers without younger children. We estimate that 5 percent of single mothers without younger children entered the work force because of the implementation of FSD. This relatively small effect may be explained by the fact the new school day is not totally compatible with working hours. No significant labor supply responses are detected among other mothers with eligible children.

The implementation of the reform was gradual and heterogeneous in terms of timing and areas affected. Different regions and municipalities were integrated at different points in time and with varying levels of implementation intensity. Given that the resources assigned in the implementation of FSD mainly benefited at-risk municipalities and those with lower levels of female labor participation, our results may underestimate the true impact of FSD on female labor supply. On the other hand, the selection of school may be related to unobserved correlates of maternal labor supply-demand of childcare.<sup>13</sup> Even though the reform was to be implemented based on educational considerations (exogenous to job market considerations), its introduction was not random. Following Kruger and Berthelon (2011), we estimate the determinants of the implementation of FSD.<sup>14</sup> The results

> 12. See section IV for more details.

> 13. However, Elacqua, Schneider, and Buckley (2006) and Gallego and Hernando (2008) show that there is not any evidence suggesting a greater demand for schools with FSD.

> 14. The authors study the impact of full school days on adolescent pregnancy rates in Chile.

7
registration (Figure 17). LE-SE either do not see the benefit from registering (52 percent) or do not have information about how to register (43 percent). HE-SE also identify a lack of information (37 percent) and seem to focus on the costs and benefits, citing that they do not see the benefit of registering (35 percent). A smaller share (12 to 22 percent) of respondents cite inspections and the need to pay bribes as a reason for not registering. The findings are aligned with the idea that formality can be considered a factor of production that business owners will “purchase” when the benefits outweigh the costs.<sup>59</sup>

**Figure 17: Reason for not Registering with Authorities, by Informal Firm Type**

<!-- Start of picture text --> 48%<br>Lack of information about the process of registration 37%<br>43%<br>44%<br>No benefit from being registered 35%<br>52%<br>22%<br>Inspections and meetings with government officials 13%<br>19%<br>21%<br>Bribes registered businesses need to pay 12%<br>16%<br>35%<br>Taxes registered businesses need to pay 23%<br>33%<br>0% 10% 20% 30% 40% 50% 60%<br>employer self-employed (high education) self-employed (low education)<br><!-- End of picture text --> Source: Own elaboration based on IES. Notes: Weighted average including Mozambique, Zambia and Zimbabwe.

> 59 For example, Levenson and Maloney (1998) find that firms become formal when the perceived benefits that registration buys (access to finance, lower risk of fines, etc) exceeds the monetary and transaction costs of registering. De Mel et al. (2010) finds that it
Figure A3: Herfindahl-Hirschman index <!-- Start of picture text --> .2<br>.15<br>.1<br>.05<br>2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br>High Income Developing<br>HHI<br>3-year Moving Average<br><!-- End of picture text --> Note: The analysis relies on greenfield FDI investment values. The green line denotes the Herfindahl-Hirschman index (HHI) of high-income countries calculated as the weighted average of each country’s HHI for each year. The red line denotes the HHI of low-income countries calculated as the weighted average of each country’s HHI for each year. Countries’ shares in total greenfield FDI investment values are used as weights. Countries with on average less than 10 investing MNEs per year are excluded from this analysis. Countries are considered as low-income if low, lower middle income or upper middle income in 2003 based on the World Bank’s income classification.

Figure A4: FDI Concentration Ratio, M&A <!-- Start of picture text --> 80<br>75<br>70<br>65<br>60<br>55<br>2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br>High Income Developing<br>Concentration Ratio<br>5 largest investing MNEs 3-year Moving Average<br><!-- End of picture text --> Source: Authors’ analysis on Refinitiv M&A.

Note: The analysis relies on M&A investment project capital expenditure. The concentration ratio of country groups is calculated as the weighted average of each country’s concentration ratio. Countries are considered developing if classified as low income, lower-middle income or upper-middle income in 2003 based on the World Bank’s income classification.

19