rafmacalaba/data-use-mentions-extended huggingface dataset:
anecdotal evidence that some villages have difficulty recruiting committee members, and that some longerserving officers are experiencing “volunteerism fatigue”.

|**Table 3. Village Fund Membership a**|**nd Number of Borrowers, 2010**|
|---|---|
||Median<br>Mean|
|Number of VF members per village|113<br>132|
|_Of which:*_||
|_Men_|_50_<br>_57_|
|_Women_|_60_<br>_73_|
|Number of VF borrowers per village|77<br>84|
|_Of which:*_||
|_Men_|_30_<br>_35_|
|_Women_|_43_<br>_48_|
|**_Memo items:_**||
|Number of households|210<br>314|
|Population|850<br>1,170|

Note: * The subtotals do not sum up to the total because the sample sizes differ; not all villages provided a breakdown of VF membership by gender. Source: Village Fund Survey, 2010.

|**Table 4. Size of**|**Village Fund comm**|**ittees**|||||||
|---|---|---|---|---|---|---|---|---|
|Size|<9<br>9|10|11|12|13|14|15|>15|
|% breakdown|3.5<br>39.9|8.8|14.2|5.7|7.2|4.3|15.4|1.0|

Source: Village Fund Survey, 2010.

The VF committee is typically headed by a small group of officers – usually a President, Vice-President, Secretary, and Treasurer. These officers
<!-- Start of picture text --> 2014<br>2011<br>2008<br>2005<br>2002<br>1999 Year<br>1996<br>1993<br>1990<br>1987<br>1984<br>9 8 7 6 5 4<br>Employee Non-Wage Labor Costs (as% of Wage)<br><!-- End of picture text --> <!-- Start of picture text --> 2014<br>2011<br>2008<br>2005<br>2002<br>1999 Year<br>1996<br>1993<br>1990<br>1987<br>1984<br>56 54 52 50 48 46<br>Employer Non-Wage Labor Costs (as% of Wage)<br><!-- End of picture text --> <!-- Start of picture text --> 2014<br>Calculations Subsidized Calculations<br>the<br>2009 to<br>Author’s Author’s<br>access<br>costs. 2001.<br>2004 Health<br>have<br>these before<br>of who<br>1999 Year<br>surveys<br>Subsidized<br>Workers the<br>Surveys<br>of in<br>construction 1994<br>detailed Values Household<br>a 1989 availability<br>Evolution of Employee Non-Wage Labor Costs Evolution Informal-Sector<br>Wage)for of data<br>%of Table12 Statutoryon 1984 12: No Colombianon<br>Figure 10:(asSee based Figure Percentage Regime- based<br>Costs Calculations 2014q1 dataNo<br>Labor<br>Author’s 2009q1<br>Non-Tradables.<br>costs. to Bank<br>Non-Wage 2004q1 Prices<br>these<br>of relative Central<br>1999q1 Year
the ruling party and then by cooperative unions. The parastatal, Agricultural Input Supply Enterprise, continues to be a major importer and distributor of fertilizer. In addition, since 1994, about 90 percent of fertilizer has been delivered on credit at below-market interest rates and guaranteed by regional governments, displacing sales from the private sector, including a substantial share sold on a cash basis.

Fertilizer consumption per ha has increased only marginally over the past decade, and there is evidence that many farmers have dis-adopted seed-fertilizer technology packages over time. A study of Ethiopian smallholders found that half of farmers surveyed reported that fertilizer arrived after planting, and one-third reported underweight bags (Bonger et al., 2004). Loan recovery, using extension agents and local officials, was generally successful until the collapse of maize prices in 2002 forced rescheduling that incurred significant fiscal costs. Spielman et al. (2010) conclude that although state-led policies have generated some positive impacts in Ethiopia, they also reduce the quality and timeliness of inputs services, limit farmers‘ options, incur hidden costs, and entail the risk of large fiscal outlays.

Xu et al.‘s (2009) study in Zambia illustrates that, in the more remote areas, where farmers faced nitrogen-maize price ratios that were 20 percent higher than elsewhere, fertilizer use was profitable only for a minority of farmers. At the same time, fertilizer use was profitable for farmers in the more accessible areas only when its delivery was timely. Subsidized fertilizer under government programs in Zambia has often been distributed late. The authors report that government programs have also caused private traders to wait and see where subsidized fertilizer is being distributed before deciding where to distribute commercial fertilizer, exacerbating the problem of late delivery even for commercial fertilizer.

15
costs of subsequent registration brought about by policy reform on the likelihood of registering a transfer or mortgage on a property; and 3 denotes the combined effects of policy reform and elimination of first-time registration cost on the likelihood of registering a transfer or mortgage on a θθ property. The net impacts of reducing barriers to first time sporadic registration or of systematic registration can then be expressed as ( 2 1) and ( 3 2), respectively. **4. Econometric results** θθ −θθ θθ −θθ Estimates of titling impacts using the DID design show insignificant impacts on market participation or investment. By contrast, analysis of administrative data indicate significant positive effects, overall and for systematic and sporadic titling separately, on the gender of registered right holders as well as policy reform effects on the share of titled plots being subject to a registered land or mortgage. Restricting the sample to the areas chosen as treatment and control for the DID specification affects estimated coefficients’ magnitude but not significance and comparing registry to survey data for the same household points towards measurement error as a key factor that biases estimated program effects towards zero. We use this to assess distributional effects and discuss why, despite large impacts, urban land policy reform or titling more generally is rarely observed in practice.

> 15 Unit prices for registered transactions of US$15/m2 and US$12/m2 in the pre-and post-implementation period and US$17/m2 during implementation are difficult to interpret without additional information on property characteristics.

13
Tobacco UNMFD|304.089|81.8|
|Mauritius|36.1|1,543.916|Clothing not of fur|903.458|58.5|
|Mozambique|11.9|182.571|<br>Fish, fresh|89.032|48.8|
|Namibia||||||
|Rwanda|9.0|42.031|Coffee|33.102|78.8|
|Somalia|..|29.195|Fruit, nuts|14.139|48.4|
|South Africa|20.8|15,319.178|Silver, platinum,|1,990.128|13.0|
|Sudan|4.6|241.270|<br>Cotton|52.004|21.6|
|Tanzania|14.8|369.521|Coffee|112.765|30.5|
|Uganda|6.0|476.552|Coffee|375.522|78.8|
|Zambia|24.6|459.326|Copper|176.498|38.4|
|Zimbabwe|19.9|1,276.976|<br>Tobacco UNMFD|387.027|30.3|
|**_Sub-Saharan_**|**17.8**|**53,986.029**|**Crude petroleum**|**18,711.169**|**34.7**|

**_Africa_**

_Note:_ OECD import data are used to get a picture of Sub-Saharan African exports because of problems with missing or unreliable data when the reporting country is from Sub-Saharan Africa.

_Source:_ OECD; UN Comtrade; and World Bank 1999b,c.

5
The first wave of the survey, conducted between August and December of 2007, included 658 HIV households and 341 comparison households. In the second wave, one year later, HIV households that could not be found and interviewed were not replaced, but comparison households were. The panel consists of 896 households interviewed in both waves: 616 HIV households and 280 comparison households. At the individual level, there are 616 identified HIV positive patients, 2579 individuals living in HIV households but not identified as HIV positive, and 1431 individuals belonging to comparison households.

In addition to the living standards and health questionnaire, we collect biomedical records for HIV positive individuals at the health facility, allowing an objective measure of the health and treatment status of patients. These biomedical records include the date of treatment initiation as well as the dates, weights and CD4 counts at the last visits (up to 7) by the patient at the health facility. There are 498 observations in this biomedical record dataset, out of the 616 potential patients identified in the living standards and health survey.

# **3. Is there an impact of the food crisis on the welfare of households in Mozambique?**

The first step of our analysis is to assess whether the food crisis had an impact on the welfare of households in Mozambique, and, in particular, on their nutritional status. Indeed, if the crisis lowers the income of households, then access to food could be restricted which will have a negative impact on adherence to treatment or health outcomes.

As mentioned in the introduction, a food crisis, namely, the drastic increase in the price of food products, affects different types of households differently. An urban household is mainly a net consumer of food and is therefore affected negatively by a price increase, whereas a rural household can be affected positively if he is a net seller of food. In the data, health facilities in which HIV patients are found for the survey are identified as rural or urban. Using this definition of urban or
**When NTMs are included to the calculation of the overall trade restrictiveness index (OTRI),**<sup>**15**</sup> **the MENA region comes across as the most restrictive region in the world, driven by their high NTMs on agriculture goods** (Figure 7). According to the firms surveys mentioned above, transport-related infrastructure and real trade costs (i.e., trade facilitation) have become the most important constraint to trade in the region. This confirms recent analytical studies that conclude that the magnitude of Arab trade flows is significantly lower than it would otherwise be as a result of high real trade costs (Dennis 2006, Harb 2007, and Peridy 2007).

**_Figure 7.  Overall Trade Restrictiveness Index by region, 2008_**

<!-- Start of picture text --> OTRI<br>Percent<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>East Asia & Europe & Central Latin America & Middle East & South Asia Sub-Saharan<br>Pacific Asia Caribbean North Africa Africa<br>Total trade Agrigulture Manufacturing<br><!-- End of picture text --> _Source:_ World Bank and UNCTAD staff estimates.

**Limited access to Arab markets for world exporters tends to insulate the region.** The level of trade policy restrictions confronting exporters from a country in other markets can be measured by the Market Access Overall Trade Restrictiveness Index (MA-OTRI). This index measures the uniform tariff faced by exporters in all the external markets that is consistent with the current aggregate export of a country. Given trade preferences and the composition of goods exported, most countries have good market access in the market of high-income countries (Figure 8).  Market access in the MENA region is quite restrictive,
# **5 Conclusions**

Our analysis, covering 89 developed and developing countries from 2006 to 2019, offers a unique perspective on the effects of conflict on firm activity. By leveraging harmonized firm-level data, we document several empirical regularities that hold across different settings and contexts. The global scope of our analysis enables cross-country comparisons, providing novel insights and a more comprehensive understanding of how conflict affects firm performance across the world.

Our results show that higher exposure to conflict reduces both firm sales and production costs. Conflict disrupts access to domestic and imported intermediate inputs and raw materials, forcing firms to scale down production, which in turn lowers sales. Sales are further depressed by intensified competition from informal firms. While reduced output drives down production costs through lower input purchases, firms also respond to conflict by cutting labor costs—substituting skilled workers with unskilled ones. As a result of these adjustments in both sales and costs, the average effect of conflict exposure on profits is null. However, the estimated effect on profits is consistently negative and statistically significant in countries with specific characteristics.

A key advantage of our global analysis is that it allows us to provide novel evidence on how economic, social, and political country-level characteristics shape the effect of conflict on firm behavior. We find that the impact of conflict exposure is more severe for firms operating in countries with high trade openness, low economic complexity, weak bureaucratic quality, illicit drug production, and those initially at peace. In these contexts, conflict leads to substantial declines in both sales and profits. Finally, we show that the relative importance of the mechanisms through which conflict affects firm performance also varies systematically with country characteristics.

Three policy implications emerge from our analysis. First, the quality of government institutions plays a critical role in helping firms cope with the adverse effects of conflict. Our findings highlight the importance of investing in state capacity and bureaucratic capabilities, particularly in settings characterized by high conflict risk. Second, economies with low complexity and high trade openness—typical of
averages. Markers show median of GDP-weighted averages of the six EMDE regions. Orange whiskers show max-min range.

B. Estimates based on the production function approach. Sample includes 53 EMDEs, of which 3 economies are from SAR (Bangladesh, India, and Pakistan).

C.D. Sample includes three SAR economies (where potential growth estimate is available for both investment growth and TFP growth measures for the period 2000-21).

E. Period averages of simple annual averages. Percentage of population ages 25 and above that completed at least lower secondary education. Sample for SAR includes Bangladesh, Pakistan, and Sri Lanka.

F. Working-age population refers to population ages 15-64. Sample includes three SAR economies.
trust on tax morale, potential endogeneity issues must be addressed.

I tackle potential endogeneity issues using an instrumental variable approach. An appropriate instrument for trust is one that is correlated with trust but uncorrelated with tax morale. I employ a set of three variables that meet these requirements based on previous research on the slave trade and social capital. The first instrument relies on the pioneering work of Murdock (1959) on slavery at the tribe level. Murdock (1959) mapped the spatial distribution of ethnic groups and the number of slaves exported during the transatlantic and the Indian Ocean slave trade. The identification strategy consists first in matching the number of slaves exported and the initial land area to the corresponding ethnic group information collected in the WVS. I then generate a variable capturing the intensity of the slave trade at the tribe level as the ratio of the number of slaves exported during the transatlantic and the Indian Ocean slave trade over the initial land area of the ethnic group. I argue that this variable is a suitable instrument for several reasons. The trade of slaves caused a culture of mistrust, which may have persisted to this day and alter taxpayers’ trust both in public institutions and in the neighborhood. The literature supports the argument on the long-term legacy of slavery in Africa and the receiving countries.<sup>12</sup> The trust shocks caused by slavery, an event lasting for more than 400 years, may remain persistent and affect the actual level of trust (Nunn and Wantchecon, 2011). The slaves were captured through state-organized raids and warfare, and a ubiquitous environment of insecurity caused individuals to turn on each other (Piot, 1996). The slave trade results, therefore, in a general environment of mistrust in the ethnic groups affected (Nunn and Wantchecon, 2011). The historic nature of the data on slavery provides a solid basis of the exogeneity of the instrument as there is no apparent reason for which the slavery origin of the ethnic group could affect the actual tax morale directly, except
<!-- Start of picture text --> Formal and Informal Employees by Occupation<br>In European Countries, by Region. 2004-2009<br>PL EELVSKBGRUROUAHUCZ SI DKSE FI NO ESPT IT GRCY IL FRBECHDEAT NLUK IE<br>East North South West<br>PL EELVSKBGRUROUAHUCZ SI DKSE FI NO ESPT IT GRCY IL FRBECHDEAT NLUK IE<br>East North South West<br>Manual, low-skilled Manual, high-skilled<br>Non-manual, low-skilled Non-manual, high-skilled<br>100<br>75<br>50<br>25<br>0<br>100<br>75<br>50<br>25<br>0<br><!-- End of picture text --> Formal: Employee with a contract; Informal: Employee without a contract. Self-employed excluded Source: Calculation with ESS data

# **Figure 9 Occupational composition of formal and informal employees, by country**

This is true for all countries examined except for the Czech Republic and Slovenia. These three countries aside, the share of low-skilled manual (elementary) occupations among no-contract employees in Central and Eastern European countries except Poland, as well as in Denmark, Italy, Greece, Cyprus, and Western European countries except Ireland, varies between 14% and 24%, whilst in the formal sector it varies between 6% and 14%. The contrast is even sharper in Poland, Spain, Portugal and France, where the share of elementary occupations is less than 14% among formal employees but among their informal counterparts varies from 31% in France and Poland to 36% in Portugal to 56% in Spain. For the latter two countries this, plausibly, has to do with infamously strict EPL which makes it prohibitively expensive for employers to hire formally anyone but the most productive.

On the opposite end of the skill ladder, one finds that in every European country but Greece, Spain, and Portugal, 34% to 55% of formal employees hold
Bosa. In AMBA, the drop in trip generation was greater in the northern and coastal districts of CABA as well as in the coastal municipalities of Vicente López and San Isidro.

**_Figure 4:_** _Change in number of trips per person per day (trip generation rates) on weekdays in October 2020 vs. 2019_

<!-- Start of picture text --> a. Bogota b. AMBA<br>3 3.0<br>2019 2020<br>2.5 2.5<br>-15.4% -7.6% -16.7% -7.6% -25.3% -14.4% -30.5% -15.7%<br>2 -1.6% 2.0<br>-1.7% -25.1% -26.5%<br>1.5 1.5<br>1 1.0<br>0.5 0.5<br>0 0.0<br>Workday Saturday Sunday Workday Saturday Sunday Workday Saturday Sunday Workday Saturday Sunday<br>Residents of Bogota Residents of Bogota D.C. Residents of AMBA Residents of CABA<br><!-- End of picture text --> Source: Authors based on CDR data The drop in TGRs in 2020 was associated with a significant increase in the share of residents not traveling at all. In Bogota, the share of such residents on weekdays increased from 24 percent in 2019 to 35 percent in 2020. The pandemic reversed the geographic distribution of non-travelers, with peaks in 2020 in the northern localities and markedly increasing differences between localities. In Usaquén, Chapinero and Teusaquillo, the share non-travelers doubled with respect to 2019, likely due to having more possibilities to adopt teleworking and other virtual activities (given the higher socioeconomic level of these localities). In contrast, this share grew by only 25-30 percent in Bosa and Ciudad
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" of surveys from fifteen of these countries (Table 1).8 The pooled sample includes households on four continents in both low- and middle-income countries. The fifteen surveys were administered between 1988 and 1997.

This multi-country LSMS data set is unique in five important respects. First, it enables us to look at multiple infrastructure services for the same household. Second, because the LSMS surveys are primarily designed to measure households' economic well-being (i.e., living standard), the data set arguably contains the best information available on household expenditures, consumption, and income available anywhere for multiple developing countries. This enables us to clearly identify the poorest households in our sample and their use of infrastructure services. Third, the LSMS surveys generally utilize similar survey administration protocols, quality-control procedures, and survey questions across countries. Fourth, the LSMS surveys have been implemented in many developing countries; this enables us to construct a global perspective on infrastructure coverage and the poor that is not possible with a survey in a single country. It is important to emphasize, however, that the households in our sample from these fifteen countries are not in any sense a random sample of households in the developing world. Fifth, some LSMS households surveys were accompanied by community surveys that gathered information about the availability of infrastructure (and other) services in the areas where sample households live. The community surveys enable us to distinguish between (1) households that do not have infrastructure services and could not have such services because they do not have access in their neighborhoods; and (2) households that do not have infrastructure services, but do have access and could have chosen to have such services if they had the resources and desire to do so.

The fifteen LSMS surveys in the multi-country data set include roughly similar questions, but the answer categories and exact question wording are often different from country to country. For 8 These fifteen LSMS surveys were chosen
are generally better off than the non-program schools but not across the board: the program pilot schools tend to be larger, with more enrolled students, but at a higher student-

> 7 The study team was able to obtain student-level WASSCE 2018 data for the 32 schools in the sample. We then used the WASSCE ID collected during the survey to match students’ WASSCE scores with other information. The match rate was 99.81%.

12
After concluding, we provide an extensive Appendix with additional details on the data and the model.

# **2 Data and Summary Statistics**

Our empirical work is based on merging three large datasets at the firm level. First, we construct a novel dataset on Spanish public procurement contracts published by the official bulletin of the Spanish Central Government ( _Bolet ́ın Oficial del Estado_ , BOE) over the 2000-2013 period. We have information on the type of good or service provided, the institution awarding the contract, the initial bidding and final price of the contract, the type of procedure used to allocate the contract, and the firm(s) that won the contract. Second, we use more standard firm-level data on balance sheets and income statements of the quasi-universe of Spanish companies between 2000 and 2016, a dataset that is maintained by the Banco de Espa ̃na and taken from the Spanish Commercial Registry. And third, we use credit register data for Spain, which contain detailed information on all outstanding loans over 6,000 euros to non-financial firms granted by all banks operating in Spain (including whether or not a non-personal collateral was posted on a particular loan). Additionally, the credit register data set contains rich information on loan applications. Appendix A provides details about the different data sources and samples that we use.

**Main sample.** We merge the three data sets to build a panel of firms at the annual frequency (which is the frequency of observation of our firm-level data) with detailed information on credit and procurement activity. For our sample we only keep those firms that get at least one procurement project over the sample period.<sup>9</sup> This leaves 12,000 unique firms observed for an average of 12 years. Alternatively, in the Appendix, we also build a sample at a quarterly frequency (that delivers a tighter identification of the effects of procurement on credit growth but cannot be used to examine the effects of procurement on firm sales) and