rafmacalaba/data-use-mentions-extended huggingface dataset:
the border. Countries may thus be more likely to import arms after discoveries closer to the border if the arms imports are linked to potential interstate conflict. Here I interact the discovery dummy with a measure of the country’s discovery’s minimum distance to the border. Since this distance does not exist for countries with no discoveries, I focus only on discovery countries here. Furthermore, since this distance exists only for years with a discovery, I aggregate it at the country level by taking the minimum of the distances of the country’s discoveries. The coefficient on the discovery dummy’s interaction with _MinDist_ therefore captures whether discoveries have a larger effect on arms imports in countries where discoveries are closer to the border. I take the minimum rather than the average distances as only one discovery near the border in a multi-discovery country may have as much effect as one in a single-discovery country. Results suggest no significant interaction however, and COMTRADE data suggest that discoveries far away from borders might have more of an effect, if any at all. Overall there does not seem to be any clear pattern suggesting that certain types of discoveries matter more than others.

# **4 CONCLUSION**

In this paper I have shown that arms imports increase by about 25% in the 5 years after a giant oil discovery. This effect is even larger, at around 51%, if the oil price is high. As a share of GDP, arms imports increase by around 30% in the 5 years after a giant discovery. I suggest that this relationship may be due to newly oil-rich countries wanting to protect their resources from rebels or foreign invasion, or to arms exporters using arms to _offset_ pricey oil, or to oil-dependent countries seeking to secure future oil supply and contain the risk of instabilities in a future oil supplier. I also suggested, based on the anecdotes of Mozambique and Angola, that an alternative mechanism is that shady businessperson exploit these opportunities to siphon debt money made available by the new discoveries.

This paper
price shocks, among others, threaten to dampen already-low rates of fertilizer use with potentially significant impacts on agricultural production and productivity.

In this paper, we leverage high-frequency phone survey data to shed light on dynamics in African smallholder agriculture against the backdrop of these crises, particularly as they relate to the availability, use, and price of inorganic fertilizer. Our data come from the longitudinal highfrequency phone surveys (HFPS) implemented since April 2020. The LSMS-HFPS regularly fielded a rotating set of questions on agricultural practices and outcomes, which form the basis for this paper. This phone survey data is also complemented with data from the face-to-face longitudinal household surveys that have been conducted under the LSMS-Integrated Surveys on Agriculture (LSMS-ISA) initiative and that have served as the sampling frames for the LSMSHFPS. Together, the data span the period from 2018 to 2024. In total, we rely on 34 waves of survey data across six countries, Burkina Faso, Ethiopia, Malawi, Nigeria, Tanzania and Uganda.

Using these data, we unpack the farmer experience with inorganic fertilizer over time, including not only incidence of use, but also the strategies employed by households in the face of price shocks and other constraints. We find that while the incidence of application remains relatively stable over most of the crisis period, a significant share of households reported using less than the amount that they needed, driving many households to apply fertilizer to a smaller share of their cultivated area or apply at a lower intensity. Some farmers tried to minimize further reduction by borrowing money or selling assets to buy the input, increasing their vulnerability to future shocks.

The remainder of the paper is organized as follows. Section 2 discusses the data utilized and the methodology deployed. Section 3 presents the results, and Section 4 discusses the importance and policy implications of these results. Section 5 concludes.

# **2. Data and Methods**

# _2.1. High-Frequency Phone Surveys_

This paper uses data from a series of high-frequency phone surveys (HFPS) conducted in six
inspection encourage trade? Private surveillance companies typically bring with them best practices in terms of customs and transit procedures, potentially resulting in shorter and less variable clearance times. This could encourage trade, partly or totally offsetting the slowing-down effect discussed above.

In their study analyzing the relationship between trade facilitation, trade flows and GDP per capita in the Asia-Pacific region (for the Asia-Pacific Economic Cooperation’s members), Wilson et al,(2003) define and measure trade facilitation using four different indicators for port efficiency, customs environment, regulatory environment, and electronic-business usage. They further introduce these indicators in an augmented gravity equation specification. They estimate their model solely in a cross-section context though, for the Asia-Pacific countries. In the absence of specific empirical data, the authors rely on survey information in order to construct the four indicators. Their sources are as diverse as to include the World Economic

> 6 Ramirez (1992) and Byrne (1995) also provide brief discussions as to the pros and cons of PSI. Page | 5
the US at the 10-digit HTS level are available from the US International Trade Commission's DataWeb data base for the years 1989-2003.<sup>24</sup> Given the need to instrument with ln(miht-2 ) , this allows us to estimate equation (2) on a dynamic panel of yearly, bilateral, product-level US import data from 19912003. As stated earlier, we have an unbalanced panel of 1471 different 10-digit HTS products in chapters 72 or 73 over the life of the sample.

# **3.4.2  Policy data**

This paper investigates the potential for differential effects of various instruments of import protection on steel US imports during the 1989-2003 period. We have collected detailed product-level changes to trade policies that were associated with antidumping and countervailing duty investigations, the removal of AD and CVD after revocation orders or sunset reviews, and the imposition and removal of acts of safeguard protection. All of these basic trade policy data are now available electronically through the World Bank’s _Temporary Trade Barriers Database_ (Bown, 2012). The information regarding the implementation and removal of AD or CVD is directly from the US _Federal Register_ , as well as from public documents that are available at either the USITC or the Department of Commerce's official websites.

Much of the data for the safeguard cases are also from either the _Federal Register_ or the ITC's official website. The US President imposed safeguard protection over various 10-digit HTS products in the steel industry on three occasions during the 1989-2003 period: circular welded pipe and steel wire rod in 2000 (five different 10-digit HTS products), as well as the 272 different HTS-products that were targeted by the March 2002 policy application that we have referred to as the “2002 steel safeguard.” In each case, the 10-digit HTS products as well as any excluded countries were made publicly available in the Presidential Proclamation that announced the safeguard policy.

The one important piece of policy information that was not readily available from electronic sources for the 2002 steel safeguard and
42, 451–471. https://doi.org/10.1146/annurev-soc-081715-074435

- Liang, Z., 1999. Foreign Investment, Economic Growth, and Temporary Migration: The Case of Shenzhen Special Economic Zone, China. Development and Society 28, 115–137.

- Liang, Z., Li, Z., Ma, Z., 2014. Changing Patterns of the Floating Population in China, 2000–2010. Population and Development Review 40, 695–716. https://doi.org/10.1111/j.17284457.2014.00007.x

- Liang, Z., Ma, Z., 2004. China’s Floating Population: New Evidence from the 2000 Census. Population and Development Review 30, 467–488. https://doi.org/10.1111/j.1728-4457.2004.00024.x

- Liang, Z., Miao, C.D., 2013. Migration within China and from China to the USA: The effects of migration networks, selectivity, and the rural political economy in Fujian Province. Population Studies 67, 209–223. https://doi.org/10.1080/00324728.2012.756116

- Liang, Z., Song, Q., 2016. Migration in China, in: White, M.J. (Ed.), International Handbook of Migration and Population Distribution, International Handbooks of Population. Springer Netherlands, Dordrecht, pp. 285–309. https://doi.org/10.1007/978-94-017-7282-2_14

- Lin, J.Y., 1992. Rural Reforms and Agricultural Growth in China. The American Economic Review 82, 34– 51.

- Liu, T., Shi, Q., 2020. Acquiring a Beijing hukou: Who Is Eligible and Who Is Successful? The China Quarterly 243, 855–868. https://doi.org/10.1017/
with each other indicates that they are describing related aspects of the business models used by different institutions, and the environments in which they operate (as reflected in the significant coefficients for the control variables).

One difference between the operating costs and yields regressions is that average loan size and its square are not statistically significant for institutions in the omitted category (not-forprofit NBFIs) in model 5, though the signs and magnitudes for those variables are not far off from those in models 2-4, in which all institutions are grouped together. The coefficients on the interactions between loan size (and its square) and the ownership type variables and the t-tests at the bottom of the table indicate that the quadratic relationship between operating costs and loan size is especially pronounced for the not-for-profit NGOs. Those tests also indicate significant relationships for average loan size and its square for the for-profit NBFIs. The loan size variables are also marginally significant for commercial microfinance banks. In all, there is a strong correspondence between the portfolio yields and operating costs regressions across types of institutions.

# **_Profit: Financial self-sufficiency_**

We begin with the MIX Market’s measure of profitability, the financial self-sufficiency (FSS) ratio. The FSS captures the difference between revenues and expenses, with adjustments made to account for some implicit subsidies.

_Summary statistics._ Table 8 gives the MIX Market’s calculation (as noted above, the MIX Market uses the country’s deposit rate, taken from IMF statistics, as the alternative cost of capital). The MIX Market calculations show the FSS to be above 100 (indicating “financial selfsufficiency”) for roughly half the full sample, seen by the level of 100 at the median. But there is 16
standard of living for almost all Russians after adjustment to the WTO, government safety nets are very important to help with the transition and especially for the poorest members of society.

We briefly describe the model and data in the next section. We focus on the evidence for endogenous productivity effects from liberalization of barriers against imports and FDI in services. Results are presented in section 2. We offer brief conclusions in the final section.

# **1. The Model**

We employ a small open economy computable general equilibrium model of the Russian economy with 55,000 households. As noted previously, this paper builds on two earlier papers where the model and data are documented. In Jensen, Rutherford and Tarr (2004), we describe the structure of the single representative agent model, how we disaggregated the official Russian input-output table, and how we calculated the Russian tariff and export tax rates. In Rutherford, Tarr and Shepotylo (2004) (RT&S) we extend the model to incorporate the entire 55,098 households of the Russian Household Budget Survey.<sup>1</sup> We refer the reader to those papers for more detailed documentation of the model. Given its importance to the present paper, we briefly summarize here some evidence of the importance of liberalization of barriers against foreign direct investment in services, productivity impacts of greater variety of imported goods and our approach to estimating barriers to foreign direct investment in Russian business services sectors.

The key modeling features that distinguish this paper from previous applied general equilibrium modeling exercises linking trade and poverty are that we permit foreign direct investment in business services and additional varieties of business services that endogenously

> 1     Key to our capacity to solve a computable general equilibrium model with such a large number of householdagents in the model was the development of a new algorithm. In Rutherford, Tarr and Shepotylo we explain the intuition for that algorithm and provide a graphical interpretation. In the website version of this paper, we provide the GAMS code for that algorithm.

5
surveys, prior survey information may help in identifying households that are active in agriculture or reside in rural areas (with some limitations, as these features are not time invariant), whereas in the case of RDD no prior information is available to inform the selection prior to attempting to contact the respondent.

There are drawbacks and limitations to using contact information from previous surveys as sampling frames for agricultural phone surveys, however. The sample size of the previous in-person survey inevitably constrains the sample size of the phone survey, which may be problematic especially when contact phone numbers are available only for a smaller share of in-person households. Moreover, depending on the time passed since the previous in-person survey, the list of phone numbers may not be current. This could mean that many phone numbers have been disconnected in the meantime which could also mean the list of households is no longer representative of the target population. Another drawback of selecting a phone survey sample from previous in-person surveys is that there is some loss in sampling efficiency due to the multi-stage, clustered design of in-person survey samples. These design effects from clustering the in-person sample will be imported into the phone survey sample and therefore increase the variance of estimates obtained from the phone survey sample relative to a simple random sample of the same size. An additional consideration when using phone numbers from previous in-person surveys is the purpose and target population of the previous in-person survey. It is important to make sure that the population of interest of the phone survey aligns with the previous in-person survey (Glazerman et al., 2020) or contains the population of interest. For example, an in-person survey whose population of interest was farming households would not be a suitable frame for a phone survey targeting the general population.

A sampling strategy based on a list of phone numbers generated by random digit dialing (RDD) or obtained from a mobile network provider is an alternative (or the only feasible option) especially when no previous contact information is available. These strategies also eliminate the need for sample clustering,
35

# **Appendix 1: Pricing Policy Information by Country**

Table A1.1 provides detailed information on the downstream petroleum sector in each country, focusing especially on pricing policies. The table indicates whether a given country is a net oil exporter ( √ appears if it is under E in the second column) and whether it has at least one domestic refinery (R in the third column). Where refineries are described as being small, they are of uneconomic scale. In discussing refineries, topping refineries, which cannot make gasoline, are excluded. The table also describes the country’s pricing policy and strategy, supply conditions, and factors affecting fuel demand, such as power outages driving demand for diesel for emergency electricity generation, and supply conditions, such as unscheduled refinery shutdowns. The subsidy figures are based on the country staff reports by the International Monetary Fund (IMF) or government sources—including annual budgets, central banks, and governments’ own news reports. Government and central bank reports are likely to be accurate, but IMF staff reports sometimes report energy subsidies (that is, fuel and electricity) as fuel subsidies, as in Table III.1 in IMF (2012l). Because of the uncertainty regarding the coverage of what is reported as “fuel subsidies” in IMF staff reports, the source of the subsidy data (namely IMF or government) is indicated.

Graphs of domestic and benchmark FOB diesel prices for the last five to six years in local currency are shown for 11 countries to illustrate the workings of different pricing policies and what consumers have faced. FOB benchmark prices in the relevant regional refining centers are also plotted to show how world diesel prices were moving during the same period. The sources of the data are described in Table A2.1 in Kojima (2012). Of all the petroleum products, gasoline and diesel are widely consumed in every country, while the consumption of other petroleum products depends on country circumstances. Of the two most widely consumed fuels, governments
this paper, an extra cm increases labor productivity by 3 to 7 percent<sup>32</sup> . Multiplying this coefficient with our measure of cm loss yields therefore a measure of productivity loss in percent.

These estimates are however obtained holding constant factor prices and technology in a partial equilibrium framework. General equilibrium interactions can offset or even reverse partial equilibrium conclusions. As argued in Acemoglu (2010), one potential reason for such effect is the imperfect substitution between factors and diminishing returns leading to a change in factor productivities and prices. It is therefore not clear whether these findings would aggregate up after a massive loss of productivity in the population.

The first two graphs in Figure 9 depict our productivity loss measure for 2 specific cases of countries using 7 percent per cm as the marginal effect of height on productivity.

> 32Based on estimates of the impact of fetal growth on height, schooling and wages derived from cohorts of twins in Behrman and Rosenzweig (2004) and Black et al. (2007), Well (2007) finds a coefficient of 0.034 for the return to an extra cm of height on log wages. Average wage represents labor productivity according to classic production theory. Well (2007) also found a coefficient of 0.07 when using estimates based on UK long run historical data.

35
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C12.3. SOSD: Social Sustainability Index C12.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 1*** 0.92***<br>H<br>Kendall tau-b 0.89*** 1*** 0.78***<br>Spearman 0.92*** 0.87*** 0.88***<br>A<br>Kendall tau-b 0.83*** 0.78*** 0.72***<br>Spearman 0.98*** 1*** 0.93***<br>M0<br>Kendall tau-b 0.94*** 1*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.2<br>1<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference<br>0<br>.2<br>0 -.1<br>.4<br>
*** _p <_ 0 _._ 01

# **3.2 PTAs and Vertical Integration Effects on Ownership in GVCs**

In this subsection, we employ the variation in input and output coefficients for each country pair and sector. Hence, in contrast to the country-sector-variant measures of upstreamness and downstreamness above, we utilize country-sector-to-sector data on the relative input and output dependence in this subsection. Recall that a higher input coefficient indicates a larger degree of backward integration (as the affiliate is a bigger supplier to the parent), while a higher output coefficient indicates a larger degree of forward integration (the affiliate is a bigger user of the parent). We can identify the main effect on the respective input and output coefficients apart from their interaction effects with PTA-Measures _ij,t_ .

Again, we will present effects on the positive counts and the propensity of any firm-to-firm ownership in separate tables.

12
no income. Importantly, all of these impacts only began in the month that the regions were captured.

However, columns (4)-(9) demonstrate that the other two shocks- the partial ceasefire and the capture of Al Jawf- did not have similar impacts on households. In the regions that were affected by these shocks, there was little evidence that there was any change to households’ primary income source or subjective well-being. Only in one of the specifications can one reject the hypothesis that there was no change in the months leading up to and following the shocks (change in share reporting wage labor was primary income source in Al Jawf).

# **Section 5b. Teacher Strike in IRG-Held Territory**

Estimates of specification (2) following the teacher strike in regions controlled by the IRG are presented in table 5.37 Despite the lack of change in any measure of violence described in figure 2, the teacher strike in response to conflict-related issues had a large impact on school attendance. Column (1) demonstrates that there was a 19.5 percentage point increase in the share of households in which not all children attended school prior to before the strike. This change is a more than doubling of the share of households with poor school attendance prior

> 36Given the non-schooling outcomes analyzed, the specifications also include summer months when children were not attending school.

> 37The sample for estimating the impact of the teacher strike includes all surveys completed during the school year between September 2019 and February 2020.

17
in the laboratory. The null results are just as consistent with laboratory experiments being generally wrong as they are with Guatemalan taxpayers being an exception from the rule.

In our view, this is an illustration of why there is value to a more large-scale and cross-country experimental study of tax morale. As described below, we implement our study on a uniquely broad set of respondents from countries spanning all continents, and exhibiting a wide variety of political institutions and tax systems. We believe that seeing how one and the same experiment replicates across a vast set of contexts is informative both for researchers interested in explanations that are as general as possible and for policy makers interested in finding solutions that are as specific as necessary.

## **Retributive justice and tax morale**

Finally, beyond specifically political institutions, specific arrangements related to the proper administration of government funds may also matter for citizens’ willingness to contribute to the tax system. In particular, as Tsai (2017) and Tsai et al. (2019) argue, high-level institutions that punish malfeasance in the public sector can help signal to ordinary citizens that their government cares about the public interest, and that corruption is not tolerated. As she argues, such institutions, besides deterring malfeasance ex ante, also help uphold the fundamental values of the political community, and allow elites to show themselves to be moral actors and leaders.

In this vein, Xiaobo et al. (2019) show in an empirical analysis of property taxes in China that the effectiveness of citizen input in generating citizens’ willingness to comply with taxation is dependent upon the existence of top-down sanctioning institutions. Below, we use a new prime about the existence of such institutions of “retributive justice”" as a treatment to investigate whether these affect tax morale.

# **Research Design**

In order to contribute to a better understanding of tax morale, we conduct a global online survey experiment of tax morale attitudes. At the empirical core of the study, we use an experiment to estimate the