rafmacalaba/data-use-mentions-extended huggingface dataset:
<br>ci<sup>(t)) </sup>||||0.008<sup>**</sup>|-0.001|
|||||(0.004)|(0.001)|
|Constant|-0.002<sup>***</sup>|-0.002<sup>**</sup>|-0.001<sup>*</sup>|-0.006<sup>**</sup>|0.001<sup>**</sup>|
||(0.001)|(0.001)|(0.001)|(0.002)|(0.000)|
|Observations|652|652|652|652|652|
|R<sup>2</sup><br>|0.370|0.441|0.447|0.447|0.447|
|Adjusted R<sup>2</sup><br><br>|0.288|0.367|0.373|0.373|0.373|
|Country dummy (αc)<br>|Yes|Yes|Yes|Yes|Yes|
|Indicator dummy (βc)<br>_Note: Robust_Standard errors in parentheses.<sup>*</sup>p<|Yes<br>0.1,<sup>**</sup>p<0.05|Yes<br>,<sup>***</sup>p<0.0|Yes<br>1.|Yes|Yes|

To ascertain the robustness of our findings, we use two different approaches.

First, we utilize data for 15 countries that have an additional survey conducted between the initial and final years of the survey period.
Since most of the firms in our sample were unlikely to receive cross-border loans from the parents of multinational banks due to their small size, evidence on the local lending by the affiliates of foreign banks is likely to be more relevant for our study. For example, there is some evidence that foreign banks in ECA reduced their lending earlier and faster than domestic banks during the crisis (de Haas, Korniyenko, Loukoianova, and Pivovarsky, 2011; Mihaljek, 2010).<sup>4</sup> However, like foreign banks, domestic banks did eventually adjust their lending towards less risky loans by reducing loan growth to firms and households (Mihaljek, 2010) and the foreign banks that participated in the Vienna Initiative – a public-private partnership between European governments, multinational banks, and international financial institutions in which foreign banks were eligible for financial support in return for commitments that they would maintain their exposures and keep subsidiaries adequately capitalized in affected host countries – were more stable lenders than those that did not, and had loan growth rates on par with domestic banks by the end of 2009 (de Haas, et al. 2011). Supply-side evidence on credit provision by banks does not, however, provide information on the types of firms that received that credit and their likelihood of survival during the crisis, which is our focus.

To summarize, we offer firm-level (rather than bank- or country-level) evidence to study how firm (and country) characteristics affected reported financial constraints and the likelihood of survival during the recent financial crisis in the ECA region. Our data on reported financial constraints in 2008-9 and firm survival in 2009 are more reflective of the effects of the crisis than other studies that have analyzed pre-crisis access to credit in 2008 using the BEEPS data. Moreover, because the most recent crisis episode began in advanced economies rather than in the ECA countries themselves, we offer a fresh look at the role of foreign banks when their parents are under financial pressure.

The empirical results in this paper show that firms that
household contributions unchanged in the short run. These differences lead to a testable prediction: If household and school inputs are (technical) substitutes, unanticipated inputs will have a larger impact on test scores than anticipated inputs. We test this using data on educational spending for largely substitutable school inputs, such as books and writing materials in both Zambia and India.

Our data from Zambia allow us to distinguish between two different types of school spending: a predictable and fixed rule-based school block grant and an unpredictable districtlevel source of funds that varied widely across schools. The cross-sectional variation in the _perstudent_ rule-based grant comes from variation in school enrollment, which is instrumented for with the size of the catchment area (Case and Deaton 1999, and Urquiola 2006 use a similar instrumental variable strategy). We find that household spending substantially offsets variations in predicted per-student school grants. Evaluated at the mean, for each dollar spent on schools via the predictable grants, household spending on education reduces by a similar amount. In contrast, unpredictable grants have no impact on household spending. We also find that student test scores respond positively to the unanticipated sources of funds (test scores in schools receiving these funds are 0.10 standard deviations (SD) higher for both the English and mathematics tests for a mean transfer of just under $3 per pupil), but that they do not vary with variations in anticipated funds. This evidence is strongly suggestive that the two main predictions of the model are correct and is robust to several checks. However, we cannot fully rule out all identification concerns, and therefore test the model again using experimental variation induced by a randomly-assigned school grant program in the Indian state of Andhra Pradesh.

The Andhra Pradesh (AP) school block grant experiment was conducted across a representative sample of 200 government-run schools in rural AP with 100 schools selected by lottery to receive a school grant (also around $3 per pupil) over and above their regular allocation of teacher and non-teacher inputs. The conditions of the grant specified that the funds
. Related works include a similar paper applied to the Horn of Africa (Herrera Dappe and Lebrand, 2021) and Moneke (2020) whose focus on ‘all-weather’ (i.e. gravel, asphalt or bitumen surface) roads is closer to ours.

We used two methods to map access to the electricity network: nighttime lights and maps of power transmission grids. Nighttime light data are available for most years and locations but convey imperfect information on electrification. Historical maps of electricity grids are more difficult to find and use in a consistent way. We used satellite images of annualized nighttime lights (VIIRS for 2016, DMSP for 1992-2013) and population rasters from World Pop to calculate the percentage of the population that was electrified (lived in settlements that produce some light at night). We compared the results for two metrics: a dummy that is equal to one if at least 50 percent of the population has access to electricity and the share of the electrified population per district. Such methods have been used before to estimate electricity access in remote areas and guide grid extension programs.<sup>5</sup> This method assumes that locations that emit lights at night are in settlements that have electricity access and that their electricity is

> 4To "ground truth" and take advantage of first hand local knowledge, Ali et al. (2015) detail how government offices across Nigeria were surveyed about the conditions of specific road segments near them.

> 5An example of mapping rural electrification based on night-time lights can be found at http://india.nightlights.io/.

6
two-week period and between 2.2% and 4.2% of VCs having any battles with fatalities and an insignificant and negative correlation of this measure with imagery-based analysis, the two variables indeed seem to measure very different aspects of violence.

# **3. Assessing the effects of the conflict on area cultivated with agricultural crops**

Our outcome variable is the area cultivated with winter or summer crops in 2019–23 aggregated at the level of more than 10,000 VCs nationally. The regressions for cultivated area that adjust for time-varying factors such as weather point toward significant conflict effects that persist and even grow in size during the periods for which data are available.

# **3.1 Data on crop cover**

To analyze the impact of the conflict on cropped area nationwide, we use the area cultivated with the main winter or summer crops at the VC level in each year during 2019–23. These crops traditionally made up more than 95% of Ukraine’s agricultural output value (Deininger et al. 2018). Cultivated area is of first-order importance and, as the statistics in appendix table A1 illustrate, can be reliably predicted using existing data, in contrast to yield. Exploring the impact on winter and summer crops separately is justified as both differ in their vegetation periods; the temperatures most conducive to their growth (Fang et al. 2023); and as farmers may substitute summer for winter crops if the latter failed, were damaged by fighting, or faced less favorable terms of trade (for example, due to higher transport costs).

Crop maps for the entire territory of Ukraine in 2019–23 were obtained using on the methodology discussed by Kussul et al. (2017), Shelestov et al. (2017), and Shelestov et al. (2020). In each year except 2022, when in-situ data were collected only for summer crops as the conflict made collection in the spring impossible, training data were gathered in two rounds of in-situ data collection along main
sectors. The firms were surveyed between 2006 and 2018. The sample is a pure cross-section in that each country (and firm) is included only once. The most recent round of ES available for the country is used. This constitutes our baseline sample.

We complement the ES with several other data sources to control for country characteristics such as income level, gross primary school enrollment ratio, ethnic fractionalization, et cetera. Data sources for these variables include World Development Indicators (WDI, World Bank), Freedom House’s Economic Freedom of the World, Polity IV, Worldwide Governance Indicators (World Bank), Alesina et al. (2003), and La Porta et al. (1999). A formal definition of all the variables used in the regressions is provided in Table 1.

# 2.1 _Dependent variable_

Our dependent variable is a measure of corruption experienced by the private firms. The ES asked firms about their experience with overall corruption. Specifically, firms were asked the amount of bribe (as percentage of annual sales) firms like itself typically pay to public officials to “get things done”. The motivation for this question is that firms are most likely to report their own experience with paying bribes. Our main measure of corruption is this bribe amount reported by the firms and expressed as a percentage of the firms’ annual sales ( _Overall Corruption_ ). The variable ranges between 0 and 100 with a mean value of 1.19 and the standard deviation equals 5.50. Averaged at the country level, overall corruption is lowest in Eritrea (0 percent) and highest in Niger (10.3 percent).

The ES also contains information on petty corruption. That is, instances of corruption that firms experience in soliciting the following public services, licenses and permits: obtaining electricity connection, obtaining water connection, obtaining construction permit, obtaining import 8
# **1.   A Framework for Land Policy**

Around the world, in both developed and developing countries, policy makers use a variety of tools to manage and accommodate urban growth and redevelopment. Government officials have three main concerns in terms of land policy (i) accommodating urban expansion, (ii) providing infrastructure, and (iii) managing density. Together, the planning of infrastructure and urban expansion, land use and density policies combine to shape the spatial structure of cities. When effective, urban planning aligns land use, population and employment density and infrastructure to optimize urban form and economic productivity (Joshi and Kono, 2009). Density is in fact a key driver of the demand for urban infrastructure and largely determines the functional efficiency of cities. Land instruments for accommodating growth and financing infrastructure are therefore most useful when placed in the context of an overarching policy framework that helps in sequencing among policies.

At early stages of urbanization, policies that facilitate rural-urban land use conversion will be critical to support urban expansion. Creating strong institutions that facilitate this transformation and help minimize transaction costs and reduce information asymmetries will contribute to enhancing the fluidity of incipient land markets. Clear definition of property rights and valuation will set the basis for well functioning markets (see Table 1).

As urbanization advances and reaches intermediate levels, population pressures in cities will increase and policies that promote the coordination of land use management and infrastructure development, and manage densification gain importance. Well designed policies may allow cities to grow vertically with the same parcel of land accommodating higher value economic use, and supporting more people and infrastructure. The challenge here is to allow for rapid substitution between land and capital, where land shortages will be managed through higher levels of capital investment. While the core land institutions mentioned above remain important, city and neighborhood level zoning, building codes, and FSIs gain importance.

Finally, as demand for urban built space increases with incomes, cities spread out (sprawl), and individual cities become part of a larger
production at the plot-level, as in the case of Malawi IHS, but for the past 6 months. The R1 households received their first visit in February 2016 to collect plot-level cassava production information for the period of August 2015–January 2016. The R1 households were visited for a second time in July/August 2016 to collect the comparable harvest information for the period of February 2016-July 2016.

The fourth group of households (R2) were visited once, at the end of the 12-month fieldwork in each EA (i.e. in July/August 2016), and were asked to report fresh cassava production at the plotlevel for the past 12 months, in line with the prevailing approach to household and farm surveys collecting information on production of tree and root crops, as in the case of the Malawi IHS.

Similar to the cash/airtime incentives provided to the diary households, each R1 and R2 household was provided a cash incentive of approximately US$7. Half of the cash incentive was paid at the end of the first visit, while the remaining half was paid in the final visit to the recall households.

# **3.2. Crop Cutting**

Although a well-implemented diary is the gold standard for _household-level production_ measurement, crop cutting has been recognized as the gold standard for _plot-level yield_ measurement for seasonal crops since the 1950s by the Food and Agriculture Organization of the United Nations (FAO). Besides the cost- and supervision-intensive nature of the exercise, several concerns have been raised regarding the accuracy of the method. Even if one places only one random crop cutting sub-plot within the sampled plot, the resulting yield estimate may carry a sampling error if the yields exhibit within-plot heterogeneity. Further, Fermont and Benson (2011) mention the following additional possible sources of error: (1) More thorough-harvesting of crop cut sub-plots vis-à-vis the typical farmer harvesting practices, (2) possible rounding of crop cut production estimates obtained through scales; (3) using faulty or inappropriate scales; (4)
, the farmer attended the training by himself: attendance of the spouse was only permitted for the gender reflection portion.

# **2.2 Data**

We collected data on study participants through in-person interviews and self-administered assessments throughout the training. Specifically, the data used for this study consists of three primary sources: (i) a household baseline survey administered approximately a month before the training, (ii) data provided by the farmers themselves during the training, which we collected and transcribed and (iii) an endline survey administered approximately two years after the training.

Source (ii) includes farmers’ responses to questions on decision- and income-sharing, the content of the filled-out action plans, and a quiz capturing gender attitudes and agricultural knowledge. This quiz was administered to farmers during the training after the gender sensitization but before the action plan creation 6
- _Economics and Statistics_ , _98_ (2)arXiv arXiv:1011.1669v3, 268–284. https://doi.org/10.1162/REST

- Johnson, H. B. (1967). The location of Christian missions in Africa. _Geographical Review_ , _57_ (2), 168–202. https://doi.org/10.2307/213158

- Kiszewski, A., Mellinger, A. D., Spielman, A., Malaney, P., Sachs, S. E., & Sachs, J. D. (2004). A global index representing the stability of malaria transmission. _American Journal of Tropical Medicine and Hygiene_ , _70_ (5), 486–498. https://doi.org/70/5/486[pii]

- Kudo, Y. (2017). Missionary influence on marriage practices: Evidence from the Livingstonia mission in Malawi. _Journal of African Economies_ , _26_ (3), 372–431.

- Mantovanelli, F. (2014). _Christian missions, HIV and sexual behavior in subSaharan Africa_ [Unpublished manuscript]. Retrieved September 12, 2017, from https://sites.google.com/site/fedmantovanelli/home/ research

- Masaki, T. (2018). The political economy of aid allocation in Africa: Evidence from Zambia. _African Studies Review_ , _61_ (1), 55–82.

- Meier zu Selhausen, F. (2014). Missionaries and female empowerment in colonial Uganda: New evidence from Protestant marriage registers, 1880–1945. _Economic History of Developing Regions_ , _29_ (1), 74–112. https://doi.org/10.1080/20780389.2014.927110

- Michalopoulos, S., Naghavi, A., & Prarolo, G. (2018). Trade and geography in the spread of Islam. _The Economic Journal_ , _128_ (616), 3210–3241
outcomes. We define dummy variables equal to one if the age of the household head is above the median, if the head is literate, if the wealth index is above the median, if the household size is above the median, and if the predicted outcome is above the median.<sup>38</sup> For each outcome and each covariate, we report the BHM posteriors of the average effect of index insurance offers for the group with the covariate equal to zero as well as the additional effect for the group with the covariate equal to one (Figure 3). In addition, using the four studies designed as price experiments, we report the effect of receiving insurance offers at a price below vs. above average. The results show limited heterogeneity along the covariates we study. We find some evidence that treatment effects are larger for households with low levels of predicted outcomes. The effect on seeds is +0.12 SD higher for households who would have used less seeds in the absence of index insurance offers. The effect on the risk index is +0.18 SD higher for households who would have cultivated less risky crops in the absence of index insurance offers.<sup>39</sup> Treatment effects also seem larger among worse-off households (according to the wealth index), among households with a younger head, and among households offered a lower price for the index insurance product – but posterior distributions include zero comfortably for these covariates. Results for household head literacy and household size display little heterogeneity. Overall, we find limited evidence that the heterogeneity in treatment effects can be explained using basic household and product characteristics. This suggests that most of the heterogeneity in treatment effects stems from more complex household/product characteristics (e.g. preferences or trust levels) as well as from contextual variables. Exploring the role of these covariates is a fruitful – and challenging – area for future research.

# **5.4 The predicted effect of index insurance in a new study
sup>7</sup> Second, we model the occupation of “skilled agricultural workers” only in the agriculture sector and we assign all workers in that occupation to the agriculture sector. More information is provided in Online Appendix C.

**Construction of the Data Set.** First, we calculate employment shares of men and women in each occupation-sector and country-year. We allocate part-time workers proportionally between the reported occupation-sector and the home sector based on their hours worked. This adjustment is particularly important because women are more likely to work part time. Second, we compute hourly earnings for men and women in each occupation-sector for the limited sample of countries that provide data on hours worked and earnings. Further details on the data cleaning and dataset construction are available in Online Appendix C.

> 7We attribute the small percentage of workers who work in different occupations in the agriculture sector proportionally to the same occupation in the other two sectors.

8
from administrative centers, markets, and population centers.<sup>31</sup>

# **3.3 Estimation Strategy**

As noted above, our data comes from four separate GHS rounds, run approximately every two years, with an observation for both the post-planting and post-harvest periods (Figure 2). While these data allow us to observe many of the same individuals at different time periods, the irregularity of the timing of the GHS rounds presents a few challenges for analysis. First, we expect individual and household labor decisions to be different in post-planting and post-harvest seasons (the patterns in Table 1 suggest this expectation has merit). Secondly, since exposure to violent events (as measured by the ACLED data) can happen at any time relative to GHS data collection, we must decide on a reasonable time window for that exposure. However, any choice of a time window for such exposure will require an assumption about the expected duration and form of any effect on labor-related outcomes. To clarify this point: if the effects of violence exposure are relatively long-lasting—that is, if exposure to violence has effects on individual work several months afterward—then a time window that is too short will be contaminated by prior exposure. For instance, if we were to choose a time window for exposure equal to one month, when the true effect of exposure to violence had effects that lasted over six months, then an individual coded as _not_ being exposed within the last month (but who _was_ exposed in the last six months) would be coded as not treated, even though such an individual would be experiencing the effects of prior violence exposure. Just the same, if we were to choose a time window that was too long (e.g., a time window of six months when the true effect lasted one month), any employment effects would be attenuated.

To more formally discuss our approach, we first use the baseline specification given by: Where _yiet_ is one of the seven labor-related