rafmacalaba/datause-encoder-data huggingface dataset:
subsidy increases as supply elasticity increases, and (v) demand elasticities influence the subsidy for large importers and exporters.

Empirical evidence about agricultural protection using the framework of Anderson (1992), Anderson and Hayami (1986), and Swinnen (1994) is plentiful. Two studies that employ cross-country agricultural protection data are summarized here, leaving discussion of U.S. studies for the following section. Olper (1998) employs a reduced form econometric model to explain the structure of Common Agricultural Policy (CAP) transfers in eight countries in the European Union. He considers effective and nominal protection rates in these eight countries annually over 1975-1989. The explanatory variables are (loosely) motivated by (i) the pressure group model of Becker (1983, 1985) and Olson (1965), (ii) extensions of the model of voter-politician interactions by de Gorter and Tsur (1991) and Swinnen (1994) and de Gorter and Swinnen (1994), and (iii) the model of altruism by Bullock (1994). Olper’s panel regression estimates yield three findings. First, agricultural protection increases under adverse market conditions for the farming industry supporting the counter-cyclical hypotheses of Bullock. Second, countries with comparative disadvantage in agriculture enjoy greater protection in agriculture supporting the view that Stolper-Samuelson effects motivate losers from liberalization to organize politically. Third, a high budget share for food consumption reduces protection perhaps indicating government’s concern for welfare losses from protection. In sum, both, special interests as well as government concern for welfare determine the structure of CAP transfers.

Honma (1993) empirically investigates whether Japan’s agricultural protection is determined according to the Anderson-Hayami (1986) framework of endogenous protection. The dependent variable in his regression is a nominal protection coefficient (NPC) measured as the ratio of the value of agricultural output in domestic prices to its value in border prices. Its log represents a rate of difference between the output valued in domestic and border prices. Using panel data on 14 industrial countries between 1955-1987 Honma finds that (i) NPC declines with comparative advantage in agriculture (measured as the ratio of labor productivity in agriculture to labor productivity in industry), (ii) there is inverted-U shaped relationship between the NPC and the share of agriculture in output (or employment), with a threshold value of that share equal to 4.5%; that is, NPC rises as the share in agriculture increases to 4.5% and falls beyond that, (iii) NPC increases as the terms of trade (measured as 6
measures of governance: as we discuss below interpreting outcome-based indicators of governance can also involve significant ambiguities. However, for rules-based indicators in particular there has been less recognition of the extent to which they are also based on subjective judgment.

A second drawback of this type of indicator follows from the simple observation that the links from such indicators to outcomes of interest are complex, possibly subject to long lags, and often not well-understood. This complicates the interpretation of rulesbased indicators. And of course, as we discuss below, symmetric difficulties arise in the interpretation of outcome-based indicators of governance, which can be difficult to link back to specific legal policy levers.

In the case of rules-based measures, some of the most basic features of countries' constitutional arrangements have little normative content on their own; instead such indicators are for the most part descriptive. For example, it makes little sense to presuppose that presidential (as opposed to parliamentary) systems, or majoritarian (as opposed to proportional) representation in voting arrangements, are intrinsically "good" or "bad" on their own. Rather the interest in such variables as indicators of governance rests on the case that they may matter for outcomes, often in complex ways. In an influential recent book, for example, Persson and Tabellini (2005) document how these features of constitutional rules influence the political process and ultimately outcomes such as the level, composition, and cyclicality of public spending, although the robustness of these findings has been challenged by Acemoglu (2005). In such cases, the usefulness of rules-based indicators as measures of governance depends crucially on how strong are the empirical links between such rules and the ultimate outcomes of interest.

Perhaps more common is the less extreme case in which rules-based indicators of governance do have normative content on their own, but the relative importance of different rules for outcomes of interest is unclear. The Global Integrity Index for example provides information on the existence of dozens of rules, ranging from the legal right to freedom of speech, to the existence of an independent ombudsman, to the presence of legislation prohibiting the offering or acceptance of bribes. The Open Budget Index provides highly-detailed factual information on the budget processes, including the types 9
imports and domestic production) of food commodities in the domestic market. On the contrary, the total supply of agricultural products declines by 7.85 percent, having severe implications for food security.

|Table 4: Sectoral impacts of COVID-1<br>**Production**|9(percent c<br>2020|hange from<br>2021|baseline)<br>2022|2023|2024|2025|
|---|---|---|---|---|---|---|
|Agriculture|1.61|0.99|0.47|0.20|0.05|0.00|
|Manufacturing|-13.22|-1.53|-0.65|-0.31|-0.14|-0.08|
|Oil|-28.79|-5.08|-1.91|-0.83|-0.32|-0.16|
|Services|-3.22|-0.28|-0.24|-0.12|-0.08|-0.06|
|**Exports**|||||||
|Agriculture|25.84|5.51|2.42|1.09|0.36|0.11|
|Manufacturing|-34.96|-5.92|-2.18|-0.94|-0.36|-0.18|
|Oil|-35.32|-6.01|-2.21|-0.96|-0.37|-0.18|
|Services|18.63|-2.58|-0.96|-0.43|-0.21|-0.13|
|**Imports**|||||||
|Agriculture|-75.55|-0.78|-0.14|-0.16|-0.14|-0.14|
|Manufacturing|-16.60|-0.70|-0.38|-0.16|-0.09|-0.06|
|Services|-32.52|-3.69|-0.86|-0.39|-0.17|-0.10|
|**Total Supply (Production + import)**|||||||
|Agriculture|-7.85|0.10|0.08|0.01|-0.02|-0.03|
|Manufacturing|-9.86|-0.18|-0.18|-0.10|-0.06|-0.05|
|Services<br>Source: CGE model simulation results|-8.46|-0.76|-0.32|-0.16|-0.09|-0.06|

Within the agricultural sector, simulation results suggest that cash crop production is expected to drop for a longer term while food crop and livestock production increase due to COVID-19 impacts. On the one hand, the driving force behind this asymmetrical impact between food and cash crops is the slowing economic activity in urban areas (e.g., productivity losses and declining export demand), which results in a significant decline in the intermediate demand for the cash crops. On the other hand, food crop and livestock production increase in response to plummeting imports.

Female-owned livestock, fishing, and forestry activity expand by almost 13 percent during the crises, resulting in an increase in the total production of livestock, fishing, and forestry activity and the total production of female-owned agricultural activities. In other words, female-owned agriculture shifts to livestock production as their capital productivity is affected less by COVID-19 impacts. This may allow female farmers to expand production since they are less affected by the higher input prices and shortage of capital.

However, the higher labor intensity in female-owned food crop production leads to a decline in their food crop production, while aggregate food crop production increases driven primarily by male-owned farms. Female-owned food crop farms are more labor-intensive and less capital-intensive than male-owned crop farms. According to the phone survey data, 4.7 percent of rural households reported an increase in the price of farming inputs, and 3.7 percent reported lack of labor as the shock that has most negatively impacted them during the pandemic.

9
**Table 1: Wife's Response on Decision Making and Violence**

||(1)<br>Physical|(2)<br>Physical|(3)<br>Emotional|(4)<br>Emotional|(5)<br>Sexual|(6)<br>Sexual|
|---|---|---|---|---|---|---|
|Wife says DM is husband [A]|0.033***|0.033***|0.030***|0.036***|0.034***|0.038***|
||(0.005)|(0.005)|(0.005)|(0.006)|(0.004)|(0.004)|
|Wife says DM is wife [C]|0.100***|0.095***|0.120***|0.119***|0.067***|0.065***|
||(0.009)|(0.009)|(0.009)|(0.010)|(0.007)|(0.007)|
|Constant|0.112***|0.180***|0.105***|0.093***|-0.006|-0.010|
||(0.009)|(0.026)|(0.009)|(0.027)|(0.006)|(0.018)|
|Observations|31229|27657|31243|27669|31228|27656|
|R-squared|0.033|0.050|0.029|0.042|0.048|0.059|
|Mean of sample|.16|.16|.18|.18|.08|.08|
|p-value ([A]=[C])|0|0|0|0|0|0|
|Indiv. controls|No|Yes|No|Yes|No|Yes|
|HH controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|

(1) Wife says DM is joint [B] is the reference category.

(2) Indiv. controls include variables like attitude of husband and wife towards violence, knowledge and alignment of preferences of husband and wife for children, years of marriage, total number of children the couple has, whether the couple is in a polygamous marriage and measures of wife’s bargaining power like her education, age when she was married, difference in wife and husband’s education and age, whether husband and wife work, whether she earns more and relative ownership of assets.

(3) HH controls include whether household is in rural area, it has electricity and wealth quintile to which the household belongs.

(4) Robust standard errors in parentheses; * p<0.05, ** p<0.01, *** p<0.001 For instance, when the husband is the main decision maker, the wife is 3.3 percentage points (26.3 percent) more likely to have faced any physical violence in the last 12 months compared to when decision making was joint. However, when wife is the main decision maker, she is 10 percentage points (62.5 percent) more likely to have faced physical violence. These differences are statistically significant at a 1 percent level, and consistent for the incidence of emotional and sexual violence.

Interestingly, these patterns remain unchanged when we include the four theoretical dimensions as explanatory variables in columns (2), (4) and (6), even though these dimensions show a significant relationship with violence on their own.

15
significantly lower for social minorities and education by itself is not sufficient to dismantle the structural barriers faced by members of these communities to avail of remunerative employment opportunities.

# **4 Policy Recommendations**

On the basis of the empirical findings, we offer the following three major policy recommendations concerning education, financial infrastructure and structural barriers for minorities in society.

## **4.1 Education**

The empirical analysis showed that the completion of secondary education was a robust consistent predictor of non-farm employment for rural workers. Importantly, secondary education was positively correlated with employment in rural salaried work, and in service occupations. Salaried work, particularly in service occupations, are typically white collar positions which offer significantly higher returns to labor. This is confirmed in the analysis using the Consumer Pyramids database where we see that secondary education has a strong impact on the likelihood of rural workers being engaged in white-collar activities. Additionally, we see that households with a secondary educated worker have 14 percent higher monthly per capita consumption. If secondary education is reflective of skill endowments and serve as a necessary pre-condition for employment in non-farm salaried occupations offering higher wages, a plausible public intervention would be to invest in secondary and tertiary education of rural workers. This attains particular relevance when considering that in 2015, only a third of rural adults aged between 25 and 65 had completed secondary education.

## **4.2 Financial Infrastructure**

The empirical findings find a limited role of financial infrastructure in affecting rural nonfarm employment. This is despite the fact that public sector banks continue to account for two-thirds of formal credit disbursement in the Indian economy and banks are obligated to channel 40 percent of their aggregate loan portfolios towards “priority sectors”, which include credit extensions to agriculture, small and micro-enterprises, and marginalized populations. Despite this, we see limited predictive power of either bank branch density, or per capita bank credit for rural non-farm employment. While there is a positive correlation of per capita bank credit (although not branch density) with rural micro-enterprise revenues, this is possibly not through the credit channel as per capita bank credit in a district does not 31
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Spending requirements in the

**Figure 17. Transport spending needs compared to Central African peers**

transport sector would absorb 8 percent of the GDP, above the average requirement for low-income countries (4.8 percent assuming a similar scenario).<sup>22</sup> Compared to the needs of Central African peers, the CAR’s transport needs are the highest after the Democratic Republic of Congo (figure 17).

<!-- Start of picture text --> As percentage of GDP<br>SSA<br>Fragile states<br>Low income countries<br>Resource-Rich<br>Middle income countries<br>Congo, Dem. Rep<br>CAR<br>Chad<br>Burundi<br>Congo Republic<br>Gabon<br>Cameroon<br>Rwanda<br>Equatorial Guinea<br>0 2 4 6 8 10 12 14<br>% of GDP<br><!-- End of picture text --> Spending needs in the CAR’s ICT sector, at $86 million per year, are the second largest. Most are associated with voice services ($54 million), in particular O&M of the existing network. Spending needs related to broadband provision account for around $ 29 million per year. Extending the ICT backbone would cost around $3 million per year (table 17).

_Source:_ Carruthers, Krishnamani, and Murray 2009. _Note:_ O&M= operations and maintenance; CAPEX = capital expenditure; GDP = gross domestic product; SSA = Sub-Saharan Africa; CAR = Central African Republic.

The CAR’s power spending needs gross domestic product; SSA = Sub-Saharan Africa; CAR = Central African amount to $55 million per year, or 2.7 Republic. percent of GDP. The overall cost of developing the power system appears high, but not unattainable. Raising electrification levels to meet national electrification targets (34 percent of the population) would entail a commitment of $50 million per year, mainly for expanding the existing generation capacity by 143 MW (table 18); 18 MW needs to be refurbished.

**Table 17. ICT spending needs**

$ millions per year

||New investment|Rehabilitation|Capital<br>Operations and<br>maintenance|Total|
|---|---|---|---|---|
|Voice|14|1.5|15.5<br>39|54|
|Broadband|10|1.5|11.5<br>18|29|
|Backbone|2|1|3<br>1|1|
|Total|26|4|30<br>56|86|

_Source:_ Carruthers, Krishnamani, and Murray 2009.

> 22 Taking into account the burden of transport spending needs, a pragmatic scenario rather than a basic scenario is assumed. For more details on the differences among scenarios (Carruthers, Krishnamani, and Murray 2009).

38
EFTA as a partner, and all those in which the IPRs provisions are considered legally enforceable. The results of this estimation are in Tables 12 through 14, respectively.<sup>22</sup> All three regressions tell a similar story. For example, in Table 12 we find in column (1) that the direct coefficient of a U.S.-partnered IPA on sales is zero, suggesting no impact on sales of low-IP goods. However, the coefficient on high-IP sectors is significantly positive and economically large. The second column indicates that this impact is spread out over the current year and one-year prior, suggesting a smoother but nevertheless rapid adjustment of local sales to implementation of an IPA. Adding a two-year lag in column (3) reduces the statistical significance of the distributed lag, presumably because of collinearity, but the overall effect on sales remains positive. In Table 13 we find weaker impacts of the group of IPAs involving any of the three _demandeur_ partners, though the contemporaneous impact remains significantly positive. This outcome is unsurprising in that the sales of U.S. affiliates may be less affected by EU and EFTA agreements than by those organized by the United States. A notable difference, however, is that the direct coefficient on sales is significantly positive in these cases, while there is little additional effect on high-IP sales. Finally, Table 14 records impacts of legally enforceable IPAs on sales that follow another pattern. The direct coefficients are significant in each case, though the lagged effects continue through two years. Again, however, the computed effects on sales by affiliates of high-IP goods are significantly positive. In sum, there is evidence that IPAs with elevated IPRs expectations are correlated with rapid expansion of sales by U.S.owned affiliates. Because those sales include exports to third countries, this may suggest that domestic capacity growth underlies our earlier trade results to some degree. We emphasize, however, that this finding needs to be treated with caution, given the broadly aggregated nature of the BEA affiliate-sales data.

> 22Because the BEA data on affiliate sales are reported at highly aggregated levels, we pool all years in the sample to maintain a large sample, rather than consider five-year intervals as performed in the trade regressions.

33
points. For those that did not pass, there are significant effects on 2 of 4 outcomes. Surprisingly, the effects are, if anything, somewhat large for those that passed; nevertheless, we cannot reject equality for any outcome other than eating maize next week (Column 3). At the bottom of the panel, we show the effect of SI for the average respondent (i.e. a weighted average of _β_ and _γ_ ), and test for significance. Effects are highly significant for 3 of 4 outcomes, and economically large. The pattern is largely the same in Panel B, where 2 of 4 outcomes are significant for both those who passed and those who did not, and where equality is not rejected for any outcome.<sup>23</sup> Table A3 runs a version of these results with a 6th screening question: “Did you travel internationally in the past week?” This question was added to try to include a question for which the answer should be no, and so could address the concern that some of the people who pass screening simply answer yes to everything, and may in fact, not understand ACASI. However, we do not use it as primary measure because some people do travel across boundaries, especially in Nimba county, Liberia, which borders Guinea. Results are very similar with this screening definition.

Finally, in Table A4 and Table A5, we examine heterogeneity in placebo effects with background characteristics that might be correlated with being able to complete the module, including education, mobile phone access, literacy, and age. Our results from Malawi suggest that these background characteristics have no bearing on comprehension; while there is some suggestive evidence from Liberia these characteristics matter. While this evidence is not definitive, it is suggestive that ACASI might be more effective with educated younger women, who have had more experience with mobile phones,<sup>24</sup> at least in the Liberian context. However, given the contrast in findings from Liberia and Malawi, taken together, these results again underscore the near impossibility of making any _ex ante_ judgments about the suitability of ACASI for any given context.

# **3.3 Implications of Placebo Effects on Measured IPV Prevalence**

These results show clear placebo effects, even among those who pass screening, and strongly suggest that some of those who pass screening still do not understand the questions. These placebo effects

> 23In Table A2, we decompose the ACASI effect on the probability of choosing “don’t know” or “refuse to answer” and find small effects of farm work, market visit, and meat. However, in Malawi, we find a decline in the probability of responding in this manner to the maize question. The overall probability of choosing these options is low for the farm and market question, but 27-29% for the other questions in Malawi. For the main analysis, observations for such responses (“don’t know” or “refuse to answer”) are dropped.

> 24This is similar to the finding in Falb et al. (2016).

11
arriving to Peru have taken up jobs in the informal sector, directly competing with relatively low skilled native workers. To causally identify the relationship, we use a shift-share instrumental variable strategy that exploits local exposure to exogenous national-level export shocks (Jaeger et al., 2018). As informal employment and discrimination could both be related to other area characteristics, we also control for the local industrial structure, household expenditure, population size, distance from the capital and center of economic activity (Lima), and, importantly, the number of Venezuelans based in each location prior to the current immigration wave, which we show to be a significant pull factor for where Venezuelans settle.

Our results show that weaker informal labor markets lead to significant increase in the discrimination reported by Venezuelans in Peru. Overall, a 10% decrease in the informal employment rate increases discrimination by 2.3-3%. This effect is twice as large for men as for women. The data we use also collects information on where discrimination occurs. We find that weaker informal labor markets lead to more discrimination for men in public places, as well as on public transit and, for women, on public transit exclusively. We do not find evidence of an impact on workplace discrimination for either gender.

One interesting pattern is that We find that more educated Venezuelans are more likely to report being discriminated against. A potential explanation for this, consistent with the previous results, is that higher skilled Venezuelans are disappointed with their situation in Peru, especially when they settle in areas with strong labor markets, and this lack of opportunity is either caused by or perceived as discrimination (Guerrero-Ble et al., 2020).

In the second part of our analysis, we examine the impact of immigration in terms of changes in the number of Venezuelans as a share of the local population in a province on a wide-variety of outcomes. We rely on administrative data to measure the number of Venezuelans newly registered in each district in Peru on a monthly basis between January 2015 and December 2020. We aggregate this information at the province level, which roughly corresponds to a labor market. Having a time-varying measure of the presence of Venezuelans in each of the 198 provinces allows us to use repeated cross-sectional data on outcomes for Peruvians and control for location and time fixed effects, as well as, location-specific time-trends. Hence, we identify the impact of the presence of Venezuelans by examining how outcomes for Peruvians change when more Venezuelans arrive in a province, conditional on the trend in that outcome.

However, it is possible that local shocks impact both the destination choice of Venezuelans and outcomes for Peruvians, hence we also use an instrumental variable strategy where we instrument for the number of Venezuelans in a location with the presence of Venezuelans in that 3