rafmacalaba/datause-encoder-data huggingface dataset:
Promoting Democracy in Fragile States Table 2: Outcomes from Hypotheses and Associated Measures

|**Outcome**|**Measure**|**Source**|
|---|---|---|
|Enthusiasm for<br>participation|Attended rallies<br>Registered to vote<br>Turned out to vote<br>Able to associate slogans to parties<br>Purchased news clipping<br>Signed petition<br>Submitted issuespost card|Survey<br>Survey<br>Survey<br>Survey<br>Behavioral activities<br>Behavioral activities<br>Behavioral activities|
|Parochial voting|Diversity of vote choice (Herfindahl index)<br>Actual vote for national versus local candidates<br>Vignette vote for national versusparochial candidates|Polling place results<br>Polling place results<br>Survey|
|Manipulation and<br>surrendering of<br>rights|Perception that process was unfair<br>Exposure to intimidation<br>Fear of being in trouble if vote "the wrong way"<br>Exposure to threats<br>Witnessed calls to violence<br>Exposed to offers of gifts<br>Exposed to offers of ballot "help"<br>Passed ballot test<br>Valid voteproportions|Survey<br>Survey<br>Survey<br>Survey<br>Survey<br>Survey<br>Survey<br>Behavioral activities<br>Polling place results|

appeals that emphasized either regional, ethnic, national, materialist, or religious concerns. The behavioral activities include a suite of activities meant to gauge enthusiasm for participation as well as empowerment against intimidation. Respondents had the opportunity to purchase news clippings, sign petitions for clean elections, and submit postcards (to be submitted after enumerators left the village), providing a set of costly signals of interest in politics. Respondents were also issued a test on filling out a ballot, to examine effects on vulnerability to manipulation through lack of voter knowledge. Section G in the supporting information shows sample summary statistics for all of the outcomes.

# **5.4 Estimating Effects**

cation of which is derived from our study design: 22
the US distribution.<sup>11</sup> For two of the three countries in the original Bloom and Van Reenen (2007) sample, the UK and France, equivalence is emphatically rejected. To quantify these patterns more robustly, Table 4 presents quantile regressions of each country against the US at the 10<sup>_th_</sup> , 50<sup>_th_</sup> and 90<sup>_th_</sup> quantiles. Rendering the previous discussion of ranking of mean quality somewhat moot, Sweden, Germany and Japan show no significant difference with the US at the median (50<sup>_th_</sup> ) while all other countries do. What is striking, however, is that the three frontier countries lag the US importantly (and Germany and Japan statistically significantly so) at the 90<sup>_th_</sup> . That is, while the US does not have significantly better median firms than its close competitors, its best firms are better and by a substantial amount (.2). Japan more than offsets its weaker performance here by having significantly better firms at the lower tail to give it the highest mean value. But overall, the superior performance of the US at the upper tail suggests that Bloom and Van Reenen’s treatment of the US as the frontier– the best of the best– is justified and we follow their lead for the rest of the paper.

# **3.3 What Distributional Changes Characterize the Process of Convergence?**

Figure 4 pushes this simple quantile exercise further to allow us to graphically identify where the distributions most diverge. Every country in the sample is paired with the US data and a quantile regression is run of management quality on a constant, capturing the US, and dummy corresponding to the individual country as described earlier. The estimation is run for each viniventile (1/20<sup>_th_</sup> ) of the distribution and the coefficient on the dummy, capturing the difference with the US score, and the standard errors are plotted (see Koenker and Hallock, 2001). This allows us to graphically detect whether the coefficient of one quantile lies outside the confidence interval of another. The upper left-most panel in figure confirms the obvious. For the US, the higher quantiles have higher scores and significantly so. The standard errors are relatively small and the coefficients drift out of the confidence interval confirming that, indeed, the 50<sup>_th_</sup> quantile is significantly better than the 25<sup>_th_</sup> .

> 11In each case, and in particular, Japan, there is more mass at the center of the distribution and less in the tails, perhaps reflecting the smaller sample sizes: US (256), Sweden (208), Japan (53), Germany (116)which would, _ceteris paribus_ , generate fewer extreme observations.

9
policy actions in one market will eventually be transmitted to other markets as well. Thus, assessing how the prices of various food commodities move with re‐ spect to each other is paramount in understanding the way and the degree to which market conditions and policies affect prices. Examining such relationships ultimately comes down to estimating the degree of price co‐movement among various commodities.

While the general subject of price co‐movement has been extensively stu‐ died in the literature, analysis of the co‐movement among prices of different commodities is scarce. (For a brief literature review of price co‐movement and the reasons why the issue has not been adequately researched see the Appendix B.) Here we analyze the co‐movement of prices using a simple econometric model. The degree of co‐movement was analyzed among six food commodity prices, using ordinary least squares with annual data from 1960 to 2008: _Pt_<sup>_i_</sup> _= μ + β1Pt_<sup>_j_</sup> _+ β2MUVt + β3t + εt,_ where _Pt_<sup>_i_</sup> and _Pt_<sup>_j_</sup> denote the logarithm of commodity price _i_ and _j_ in year _t_ (expressed in nominal dollar terms), _MUVt_ denotes the deflator, _t_ is the time trend, and _εt_ denotes the error term; _μ_ , _β1_ , _β2_ , and _β3_ are parameters to be estimated.

<mark>The results are reported in Table 5. Because prices are non‐stationary (see previous</mark> section) examining the stationarity properties of the error term is a cru‐ cial step in establishing the validity of the model. All the regressions show strong performance, with an average _R_<sup>_2_</sup> of 0.84 and with unit root statistics that strongly confirm the stationarity of the error term. Moreover, in all cases the slope esti‐ mate of the price variable is significant at the 1 percent level.

The results imply that it is important not to analyze commodity markets in isolation from one another, because the impact of events that seemingly affect one market will eventually be equalized among most commodity sectors. Con‐ sider, for example, the palm oil/soybean oil parameter estimate of 0.97 and an _R_<sup>_2_</sup> of 0.93 (Table 5, bottom row). This suggests an almost synchronous movement of palm and soybean oil prices, despite the fact that soybean oil is an annual crop produced chiefly in North and South America and palm oil is a tree crop pro‐ duced almost exclusively in East Asia. The implication is that, whether biofuel mandates are applied to one or the other edible oil market, the effect will be eventually diffused among all edible oil markets. Not surprisingly, policies fa‐ voring biofuel production in the name of environmental benefits may in fact lead to less desirable outcomes. That is, the environmental benefits from switching from fossil fuel use to, say, rapeseed‐based biodiesel in Europe or soybean oil‐ based biodiesel in the US may be less than the environmental costs of expanding palm oil production in East Asia.<sup>**12**</sup> Similarly, prices of wheat, maize, and soy‐ beans—key food crops, produced primarily in the US, EU, and South America—

**_Page 15_**
<!-- Start of picture text --> A - By household size B - By number of children in the household<br>12 4 at<br>= 104 * ——— _ Per capita approach = ae 7 +<br>Fy 4 ——=—- FAO/WHO equivalence scale = cea pagans” 2<br>giy;28 .id‘ia ‘ome Square-root equivalence scale 8e2Ea 2eee L,Ke<br>£‘£<br>2a :. 3 oe<br>c 6 t4 a a *<br>o % ee a ie a on<br>c 4 Hl Hak 2 Pd<br>2 ‘ aoe a 4 ge ad ——— Per capita approach<br>Fa 4 ‘ pom “ a = ——+—- FAOMWHO equivalence scale<br>\ wootad i a6 The aif cosa Sguare-root equivalence scale<br>\ 3 yh<br>‘ f S é<br>2Miter NZ~2<br>23 4 EB] 6 7 & LE] 10 1 2 3 4 5 6<br>Household size Number of children<br><!-- End of picture text -->
changes are estimated. Fourth, by assembling detailed tariff data varying by country-product-year for the period 1997-2017, we consider the trade policy shocks not only in terms of country-product eligibility for duty-free treatment but also in terms of the magnitude and dynamics of the preference margin offered to beneficiary and non-beneficiary countries.

Our study also complements the handful of studies that assessed the impact of the special provisions in AGOA for textile and apparel articles, but, in contrast to our study, they focused only on early impacts, up to 2008 at most. Two studies anticipate some of the concerns we raise.<sup>10</sup> Edwards and Lawrence (2010) assess whether AGOA’s apparel provisions led to a sustained effect using a theoretical model. They find that MFA quotas on China helped AGOA countries, Lesotho in particular, to increase exports of low value-added apparel products to the US. But this increase was not associated with sustained benefits since the ROO - which allow countries to import fabric from any origin - did not encourage significant local value-addition.<sup>11</sup> Rotunno et al. (2013) also emphasize the role of simplified ROO (single transformation) under AGOA in fostering rapid growth of African LDCs’ apparel exports to the US in the first preference years. Importantly, they show this rapid growth did not represent true export success but instead reflected those countries’ imports of apparel from China being transshipped duty-free to the US without any value addition, a pattern which stopped after the MFA ended.<sup>12</sup> The restrictiveness of ROO may affect not only the immediate impact of trade preferences but also for its sustainability when preferences decline. Our firm-level analysis for Mauritius in Section 6 will demonstrate that the ROO liberalization led to a significant increase in its apparel exports to the US. In the long-run, restrictive ROO may limit preference utilization but, by

> 10 Mattoo et al. (2003) also anticipated difficulties for African countries’ apparel exports under AGOA after the MFA end. 11 AGOA’s simplified ROO for apparel will be described in Section 4.

> 12 The role of ROO for apparel exports under preferential access is studied by Collier and Venables (2007) and De Melo and Portugal-Perez (2013) who show AGOA with its simplified ROO had a significant positive effect on African apparel exports to the US before 2005 in contrast to weaker effects of Europe’s Everything But Arms with its more stringent ROO on exports to the EU.

10
formal financial services, either voluntarily due to cultural or religious reasons or involuntarily due to economic or social reasons (Mohieldin et. al. 2011).

The religious prohibition against charging interest is an important reason why many poor people in Muslim countries do not take advantage of financial services. Karim et al (2008) estimate that 72% of people living in the Muslim world do not use formal financial services, and that from 20% to more than 40% would not use conventional microfinance because it involves paying interest. Thus, increasing access to financial services in all segments of the population in these countries would require _Shari’ah_ compliant financing.

In addition, the poor are often excluded from lending by formal financial institutions because they are high risk, because they cannot afford the cost involved in servicing loans, or because lenders find it too costly to collect sufficient information on their creditworthiness (World Bank 2008). Since delivering micro-financial services entails high risks and costs, sustainability becomes an important concern in providing microfinance. Organizations providing microfinance to the poor thus potentially face a tradeoff between depth of outreach and sustainability.<sup>15</sup> To cite one study, Hermes et.al (2011) find a tradeoff between outreach and sustainability among 435 microfinance institutions over 11 years.

Given the tension between sustainability and outreach, two broad approaches to microfinance can be identified. The first is the poverty approach, in which the financial institutions operate as nonprofits with the objective of providing finance to the poor and core-poor (Schreiner 2002). Under this approach, financial services are provided by NGOs, government agencies, cooperatives and development finance institutions (Bennett 1998). The second approach is commercial, wherein the goal of providing services is profit maximization (Schreiner 2002). The operations of financial institutions are self-sufficient and sustained by providing larger loans to the relatively less poor at higher interest rates. Though the commercial approach can help the growth of micro and small enterprises, it may fail as a tool to eliminate core poverty (Weiss and Montgomery 2005).

Islamic microfinance is provided by a small number of providers, covering less than 1% of total microfinance outreach (El-Zoghbi and Tarazi 2013). In a survey of 255 institutions globally, ElZoghbi and Tarazi (2013) find that a large percentage of _Shari’ah_ compliant microfinance institutions appear to be commercial (in terms of the two kinds of institutions described above) and include banks, non-bank financial institutions (NBFIs), and village/rural banks, though some

> 15 Navajas et al (2000) and Schreiner (2002) identify six dimensions of outreach. Of these, only two are relevant here. Whereas _breadth_ of outreach is the number of clients served or the scale of operations, _depth_ relates number of poor covered by any microfinance program.

18
simulate the future spatial distribution of urban land. The final projection outputs, which have a spatial resolution of 1 km, are presented at 10-year intervals from 2020 to 2100, with 2015 being the reference year for baseline urban extent.

To overcome the uncertainty associated with climate change and illustrate the range of possible outcomes, Chen et al. (2020) adopt the SSP framework in producing the urban expansion projections. The five SSP scenarios, which are central to the Intergovernmental Panel on Climate Change (IPCC) reports, describe socioeconomic changes that affect demographic, economic, and climate change dynamics. The SSPs are part of a new scenario framework established by the climate change research community to facilitate integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation (Riahi et al. 2017). The three main sets of scenario drivers are (1) population and education; (2) urbanization, defined as the urban share of the population (Jiang and O’Neill 2017); and (3) GDP per capita and interpersonal income inequality.<sup>5</sup> The scenarios are each associated with certain levels of assumed mitigation and adaptation challenges: the sustainability scenario (SSP1), for example, is marked by low mitigation and adaptation challenges, while the regional rivalry scenario (SSP3) depicts a future with high challenges to mitigation and adaptation. It is critical to note that the SSP scenarios underpinning Chen et al.’s (2020) urban expansion projections frame our results on potential impact on crop and livestock production.

# **2.2 Crop and livestock data**

The data on crop production are drawn from MapSPAM (Spatial Production Allocation Model) (IFPRI 2019). MapSPAM provides global gridded data on the production of 42 crops, which are aggregated into eight groups in our analysis: cereals, fruits, oils, pulses, roots, sugar, vegetables, and non-food crops (see Table 1). We focus our analysis on food crops.<sup>6</sup> _Table 1. Crop group classification_

|_Crop group_|_Corresponding crop in MapSPAM_|
|---|---|
|_Cereals_|Wheat, rice, maize, barley, pearl millet, small millet, sorghum, other cereals|
|_Fruits_|Banana, plantain, tropical fruit, temperate fruit|
|_Oils_|Soybean, groundnut, coconut, oil palm, sunflower, rapeseed, sesame seed, other<br>oil crops|
|_Pulses_|Bean, chickpea, cowpea, pigeon pea, lentil, other pulses|
|_Roots_|Potato, sweet potato, yams, cassava, other roots|
|_Sugar_|Sugar cane, sugar beet|
|_Vegetables_|Vegetables|
|_Non-food_|Cotton, other fibre crops, arabica coffee, robusta coffee, cocoa, tea, tobacco, rest<br>of crops|

> 5 The urbanization projections developed for the SSPs are on the country level and non-spatialized (Jiang and O’Neill 2017).

> 6 Extending the analysis to non-food crops, such as those used for biofuels or fiber production, could be an interesting area of future research.

6
stable political institutions than South Africa. Some studies have shown tendencies exists for ethnic fractionalization to negatively influence economic success and the quality of institutions (see, e.g., Easterly and Levine, 1997; La Porta et al., 1999; Alesina et al., 2003). Easterly and Levine (1997) found that for Africa, greater ethnic diversity goes in line with low schooling, underdeveloped financial systems, distorted foreign exchange markets, and insufficient infrastructure.

In general, these results were confirmed using a survey data set from the Afrobarometer, a relatively new survey measuring the social, political and economic atmosphere in more than ten countries in Africa. Examining these results, we observe that individuals in Botswana are more compliant than those in South Africa. The marginal effects indicate that being a resident of Botswana rather than of South Africa increases the probability of reporting the highest tax honesty by around 6 percentage points. This result is robust across various specifications. _Figure A1_ in the Appendix reports the results of the experiment. It shows a clear difference in behavior between subjects from Botswana and South Africa throughout most of the different experimental treatments. Moreover, the results obtained also indicate that tax culture effect overwhelms the enforcement effort. Thus, increasing audit probability and/or the penalty for South African subjects leads to lower compliance.

# **IV. HOW STABLE IS TAX MORALE AND HOW IMPORTANT ARE SOCIAL INTERACTIONS?**

We have seen in the previous section that values and preferences are affected by policy or institutional changes. Thus, these results support Bowles‘ (1998) argument that ―we can neither accurately predict nor coherently evaluate the likely consequences of new policies or institutions without taking account of preference endogeneity‖ (p. 75). This can be seen as an indicator that tax morale does not remain stable over time. Changes in the political system have a substantial impact on tax morale. In the fields of behavioral law and economics, the importance of social norms in studying compliance with the law has been emphasized for several years (Ellickson, 1991; Tyler, 1990b; Sunstein, 1996; Cooter, 1998). Accordingly, social norms are shaped by law and by social interactions. In 29
# Table 6a: District-industry-year regressions for log total loans

|||<br>P|anel estimatio|<br>n with district-in|<br>dustry,state-yea|r and industry-ye|<br>ar FE||
|---|---|---|---|---|---|---|---|---|
|||Using district||Using state-|Adding||Misallocation|Misallocation|
||Baseline<br>estimation|<br>covariates<br>instead of FE|Dropping<br>weights|<br>industry-year<br>FE|<br>employment<br>control|Using<br>balanced panel|without industry<br>aggregation step|using OLS TFP<br>metric|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||||A. Using|land and labor m|isallocation in|organized sector|||
|Land misallocation index|-0.083|-0.063|-0.081+|-0.081|-0.053+|-0.066|-0.062|-0.071|
||(0.053)|(0.045)|(0.049)|(0.057)|(0.028)|(0.073)|(0.052)|(0.049)|
|Labor misallocation index|0.113+|0.051|0.109+|0.108|0.047+|0.076|0.161++|0.189+++|
||(0.067)|(0.053)|(0.058)|(0.077)|(0.026)|(0.104)|(0.064)|(0.062)|
|Log employment in|||||1.055+++||||
|district-industry-year|||||(0.020)||||
|Observations|14053|14053|14053|14053|14053|6439|14053|14053|
|Adjusted R-squared|0.629|0.314|0.631|0.629|0.817|0.611|0.629|0.630|
|<br>|||B. Using land|and labor misall|ocation in total|manufacturing se|ctor||
|Land misallocation index|0.013|-0.041|0.007|0.005|-0.058+|-0.041|-0.051|-0.013|
||(0.056)|(0.054)|(0.055)|(0.063)|(0.033)|(0.070)|(0.054)|(0.058)|
|Labor misallocation index|-0.045|-0.069|-0.054|-0.039|0.014|0.032|0.240+++|0.244+++|
||(0.070)|(0.059)|(0.067)|(0.078)|(0.031)|(0.081)|(0.070)|(0.063)|
|Log employment in|||||1.057+++||||
|district-industry-year|||||(0.020)||||
|Observations|14053|14053|14053|14053|14053|6439|14053|14053|
|Adjusted R-squared|0.628|0.314|0.630|0.628|0.817|0.610|0.631|0.632|

Notes: Estimations consider the log amount of financial loans in district-industry-year observations taken from the ASI as a function of local land and labor misallocation. In Panel A, land and labor misallocation are calculated using ASI plants only. In Panel B, misallocation is calculated using ASI and NSS data, to combine the organized and unorganized sectors. Estimations include district-industry, state-year, and industry-year FE, excepting column 2 that drops district-industry fixed effects and instead controls for local district traits. Observations are weighted by an interaction of log district size and log industry size measured in terms of employment in 2000. Estimations cluster standard errors by district. +, ++, and +++ indicate statistical significance at 10%, 5%, and 1% levels, respectively.