rafmacalaba/datause-encoder-data huggingface dataset:
18

# **References**

Anderson, J.E. (2009a), “Consistent Trade Policy Aggregation”, _International Economic Review_ 50(3): 903–27.

Anderson, J.E. and J.P. Neary (2005), _Measuring the Restrictiveness of International Trade Policy_ , Cambridge MA: MIT Press.

Anderson, K. (2009b), ‘Five Decades of Distortions to Agricultural Incentives’, chapter 1 in K. Anderson (ed.), _Distortions to Agricultural Incentives: A Global Perspective, 1955-2007,_ London: Palgrave Macmillan and Washington DC: World Bank.

Anderson, K. and J.L. Croser (2009), _National and Global Agricultural Trade and Welfare Reduction Indexes, 1955 to 2007,_ database available at <u>www.worldbank.org/agdistortions</u> Anderson, K. and W. Masters (eds.) (2009), _Distortions to Agricultural Incentives in Africa_ , Washington DC: World Bank.

Anderson, K. and E. Valenzuela (2008), _Global Estimates of Distortions to Agricultural Incentives, 1955 to 2007_ , database available at <u>www.worldbank.org/agdistortions</u> Bach, C. and W. Martin, (2001), ‘Would the Right Tariff Aggregator for Policy Analysis Please Stand Up?’ _Journal of Policy Modeling_ 23: 62–35.

Chen, S, and M. Ravallion (2008), ‘The Developing World is Poorer Than We Thought, But No Less Successful in the Fight Against Poverty’, Policy Research Working Paper 4703, World Bank, Washington DC, August.

Croser, J. and K. Anderson (2010), ‘Changing Contributions of Different Agricultural Policy Instruments to Global Reductions in Trade and Welfare, Contributed Paper for the Annual Conference of the Australian Agricultural and Resource Economics Society, Adelaide, 10-12 February.

Croser, J.L., P.J. Lloyd and K. Anderson (2010), ‘How do Agricultural Policy Restrictions to Global Trade and Welfare Differ Across Commodities?’ _American Journal of Agricultural Economics_ Vol. 92 (forthcoming).

Feenstra, R. (1995). ‘Estimating the Effects of Trade Policy’, in G. Grossman and

- K. Rogoff (eds.), _Handbo_ o _k of International Economics, Vol. 3_ , Amsterdam: Elsevier.
**Table 15 : Simulations of the effects on employment and earnings of regional specific labor market policies by region**

||||_The la_<br>|_bor market structure of C_<br>|_entral Region as reference_<br>|% change in<br>|
|---|---|---|---|---|---|---|
||Estimated<br>|Estimated monthly<br>|simulated employment|% change in<br>|simulated monthly<br>|monthly<br>|
||employment rate<br>|earning<br>|rate<br>|employment rate<br>|earning<br>|earning<br>|
|Central Region<br>|60.89%<br>|2785<br>|60.89%<br>|0.00<br>|2785<br>|0.00<br>|
|Eastern Region|47.74%|2811|59.95%|20.36|2903|3.16|
|Zagreb Region|55.67%|3703|62.07%|10.31|3227|-14.75|
|Adriatic North Region<br>|58.39%<br>|3495<br>|61.63%<br>|5.27<br>|3076<br>|-13.62<br>|
|Adriatic South Region|48.81%|3524|61.58%|20.75|3180|-10.83|
||||_The la_|_bor market structure of E_|_astern Region as reference_|% change in|
||Estimated|Estimated monthly|simulated employment|% change in|simulated monthly|monthly|
||employment rate|earning|rate|employment rate|earning|earning|
|Central Region|60.89%|2785|48.57%|-25.37|2701|-3.12|
|Eastern Region|47.74%|2811|47.74%|0.00|2811|0.00|
|Zagreb Region|55.67%|3703|51.43%|-8.23|3088|-19.91|
|Adriatic North Region<br>|58.39%<br>|3495<br>|50.56%<br>|-15.47<br>|2959<br>|-18.09<br>|
|Adriatic South Region|48.81%|3524|50.37%|3.11|3056|-15.34|
||||_The la_<br>|_bor market structure of Z_<br>|_agreb Region as reference_<br>|% change in<br>|
||Estimated|Estimated monthly|simulated employment|% change in|simulated monthly|monthly|
||employment rate|earning|rate|employment rate|earning|earning|
|Central Region|60.89%|2785|52.84%|-15.25|3241|14.07|
|Eastern Region|47.74%|2811|51.74%|7.73|3367|16.50|
|Zagreb Region|55.67%|3703|55.67%|0.00|3703|0.00|
|<br>Adriatic North Region|58.39%|3495|54.57%|-6.98|3553|1.64|
|Adriatic South Region|48.81%|3524|54.45%|10.37|3659|3.68|
||||_The labor_|<br>_market structure of Adria_|<br>_tic North Region as referen_|<br>_ce_<br>% change in|
||Estimated|Estimated monthly|simulated employment|% change in|simulated monthly|monthly|
||employment rate|earning|rate|employment rate|earning|earning|
|Central Region|60.89%|2785|57.21%|-6.43|3237|13.97|
|Eastern Region|47.74%|2811|56.13%|14.94|3363|16.41|
|Zagreb Region<br>|55.67%<br>|3703<br>|59.12%<br>|5.84<br>|3623<br>|-2.21<br>|
|Adriatic North Region|58.39%|3495|58.39%|0.00|3495|0.00|
|Adriatic South Region|48.81%|3524|58.29%|16.27|3607|2.28|
||||_The labor_|<br>_market structure of Adria_|<br>_tic South Region as referen_|<br>_ce_<br>% change in|
||Estimated|Estimated monthly|simulated employment|% change in|simulated monthly|monthly|
||employment rate|earning|rate|employment rate|earning|earning|
|Central Region|60.89%|2785|47.20%|-29.02|3157|11.77|
|Eastern Region|47.74%|2811|46.42%|-2.83|3280|14.30|
|<br>Zagreb Region|55.67%|3703|50.12%|-11.06|3542|-4.55|
|Adriatic North Region|58.39%|3495|49.21%|-18.65|3417|-2.28|
|Adriatic South Region|48.81%|3524|48.81%|0.00|3524|0.00|

27
Table 1: University of Sana's Annual Budget Expenditure (YRIs Thousands: 1987 Constant PrIces) *

|ITEM|1987|<br>1988|<br>1989|<br>1990|Averag<br>Rate of|e<br>X Annual<br>Increase|
|---|---|---|---|---|---|---|
|Salaries|63,123|70,492<br>(11.7)|98,078<br>(39.1)|118,381<br>(20.7)||23.3|
|Goods and Services|42,934|59,<br>162<br>(37.8)|84,097<br>(42.1)|96,549<br>(14.8)||31.0|
|Capital<br>Expenditure|28,299|42.939<br>(51.7)|109,245<br>(154.4)|89,789<br>(-17.8)||46.9|
|Subsidy Expenditure|895|789<br>(-11.8)|683<br>(-13.4)|618<br>(-9.5)||-11.6|
|Totat|135.251|173,383<br>(28.2)|292.102<br>(68.5)|305.337<br>(4.5)||31.2|

Source: University of Sanala Figures in Parentheses are percentage increase on previous year

* Source of GOP Deflators Is World Bank Memorandum (1989) Note: 1989 and 90 figures are estimated using average deflator figures for the past 7 years

Table 2: University of Sanala Annual Internal Revenue (YRIs Thousands: 1987 Constant Prices) *

|ITEM|1987|<br>1988|<br>1989|1990|AverageXAnnuaL<br>rate of increase|
|---|---|---|---|---|---|
|Service<br>Revenues|7,294|6,679<br>(-8.4)|11,657<br>(74.5)|11,304<br>(-3.0)|15.7|
|Special<br>Revenues|110|<br>71<br>(-35.5)|<br>416<br>(487.7)|<br>754<br>(81.3)|<br>89.8|
|Total|7,404|6,750<br>(-8.8)|12,073<br>(78.9)|12,057<br> (-0.1)|<br>17.6|

Source: University of Sanala Figures in Parentheses are percentage increase on previous year

* Source of GDP Deflators is World Bank Memorandum (1989) Note: 1989 and 90 figur s are estimated using average deflator figures for the past 7 years

- 12 -
(27) _β(R) = λR – μR_<sup>_2_</sup> where _λ_ and _μ_ are positive constants, so that the first- and second-order derivatives of the _β_ function are given by (28) _β’(R) = λ – 2μR > 0_ The main point here is that when the curled bracket in (26) is large, the first derivative _β’(R)_ will be small, and _R_ correspondingly large. A large overall positive utility effect of a given shift in the retrofit function then leads to a large optimal R&D effort _R_ .

We have here assumed that the investment in question only affects costs for one particular infrastructure facility. In practical cases of individual R&D projects that affect future infrastructure costs, such R&D expenditures are likely to have most of their effects on costs for other projects.<sup>24</sup> In the context of our model, the marginal social benefit of R&D is much greater than the “private” benefit for the infrastructure project sponsor. Consider here a case where (27) correctly represents the overall social impact of _R_ on retrofit costs, while the private impact is only a fraction _h_ (< 1) of the social impact. In this case, the marginal change in _β_ when _R_ changes, as perceived privately, is also a fraction h of the social impact given by (28), and thus When _h_ is smaller, _R_ must be smaller to fulfil (26).<sup>25</sup> As a result, the R&D activity will be (perhaps much) lower than optimal when most of the overall returns to private R&D accrue to others. One then faces an obvious problem of policy coordination across countries, which in principle could be as serious as that for regular mitigation policy. High appropriability of rents to developers of new technology will tend to reduce this coordination problem.<sup>26</sup>

# **7. Simulations**

We will now illustrate some basic properties of this model, through simulations of energy and retrofit cost distributions, under some simplified assumptions. The simulations will focus on the period 2 cost structure, and how conditional energy, retrofit and total expected cost is affected by expectation and variance of the two cost items, given an optimal ex post strategy to minimize these costs.<sup>27</sup> Most of these simulations depart directly from the expected cost expressions (5) (expected energy cost in period 2) and (6) expected retrofit cost in period 2). We generally take _H1_ to be given (i e we do not study the optimal _H1_ decision) and equal to

> 24 Another way to express this effect is that there is likely to be a high degree of “technology spillovers” associated with R&D for development of new retrofit technology; see discussions of such spillovers e g by Golombek and Hoel (2005, 2006).

> 25 With this formulation, when _h_ is small, no such non-negative _R_ can be found. Then no R&D investments will be undertaken by private agents.

> 26 One way of securing this is strong patent laws. But this has other negative side effects, in particular, the markets in which the newly developed technologies are applied will not be competitive; see e g Greaker and Pade (2008).

> 27 Simulations have been done using Matlab.

18
level, we can estimate mining effects on socio-economic outcomes with greater local accuracy and specificity. Second, the precise identification of the location of mines allows us to conduct a thorough study of how far geographically the impacts of mining activity can be observed. This represents an improvement with respect to related studies, which have focused mainly on the aggregate impact over large regions of revenue windfalls derived from oil extraction. In contrast with mines, oil fields and oil wells tend to be spread over several local administrations, making it necessary to conduct impact analyses at higher levels of aggregation (Michaels, 2010). This runs the risk of missing some of the specific local effects and suffers from aggregation bias and greater measurement error (Caselli and Michaels, 2009).

Our identification strategy is based on comparing socioeconomic outcomes in districts where mines are located, with outcomes in neighboring or nearby districts of similar characteristics. Our premise is that, while economic and political factors may influence broad geographical patterns of mining activity, at lower administrative and geographic levels the location of a mine is primarily dictated by geological factors. By comparing neighboring or nearby districts we can therefore minimize omitted variables biases related to endogenous location decisions. Figure 1 reports the location of mining districts and provinces across the Peruvian territory. It shows that mining is concentrated in the Andean region and in the Amazon basin. To reduce potential omitted variable biases, we restrict therefore the analysis to departments that report mining activity and, given how much the capital differs from the rest of the country, we exclude the department of Lima from the sample. Our base sample consists, therefore, of 87 producing districts and 1,195 non-producing districts spread over 142 provinces and 16 departments, with an average of 9 districts per province.

Several findings emerge. In 2007, the year of our analysis, average per capita expenditure in producing districts was around 10 percent higher than in non-producing districts of the same province. Producing districts also faced around 2.5 percentage points lower rates of extreme poverty, and had 2.5 percentage points fewer households with basic needs uncovered. Illiteracy was also lower. When compared to non-producing districts in _other_ provinces, impacts are larger: per capita expenditure in producing districts is 14 percent higher, and poverty rate is between three and four percentage points lower. We concentrate our analysis on 2007 because this is when the most recent census was conducted. The previous census had been done in 1993, that is, prior to the mining boom. When we use data corresponding to 1993, we find that mining districts did not exhibit any statistically significant difference with respect to otherwise similar districts, which reinforces the causal interpretation of the impacts of mining activity.

3
45

# Table 17: Brazil: Outcomes For Children By Income Class and Metropolitan Area, 1984 (percent of children)

|||Recife||In<br>S|come Cl<br>bo Paul|ass<br>o|P|orto Ale|gre|
|---|---|---|---|---|---|---|---|---|---|
|Children's Age<br>and Status|<5|25-50|>50|<5|25-50|>50|<5|25-50|>50|
|Age 7-9<br>-not in school|22|12|4|14|6|2|17|6|3|
|Age 10-14<br>- notmschool|21|14|4|12|7|3|18|10|4|
|-inlabor force|10|6|2|11|8|4|13|8|3|
|Age 10-14 in||||||||||
|<br>the labor force<br>- not inschool|61|47|42|40|32|23|69|65|56|

Source: author's tabulations Note: Income class refers to percentile in the household per capita income distribution.
# Annex 1: Detailed data and studies for cost-of-illness estimation

## 1. Healthcare associated infections

Watson et al (2019) identify five main types of HAI: urinary tract infections, gastrointestinal infections, ventilator associated pneumonia / hospital associated pneumonia, bloodstream infections and surgical site infections. In addition to these, though accounting for fewer HAIs, are respiratory tract infection, skin and soft tissue infection, and ear, eye, nose, and throat infection.

Global reviews and national studies typically provide estimates of HAI numbers as a prevalence at a point in time. Some studies report HAI incidence in terms of patient days with HAI per 1,000 patient days. Only one study in Africa - Abubakar et al (2022) – provides a pooled estimate for HAIs, estimating 12.76% (95% confidence interval 10.30– 15.23), based on 15 eligible articles. A high degree of heterogeneity was observed between studies and between types of infection. Irek et al (2018) presents additional results for 35 studies in Africa, including 13 from Ethiopia, 2 Kenya, 3 Tanzania and 2 Uganda. These studies and further searches revealed several additional studies from the 9 countries. Other reviews that covered studies from Africa include Nejad et al (2011) and Allegranzi et al (2011). All gathered studies are presented in Table 5. Some studies examined only surgical site infections, while others only captured neonatal infections. Estimates that report culture-confirmed infections could be a significant underestimate of the actual HAI rates; hence studies that report these are noted in column 2. Almost all studies in Table 5 are single site studies, i.e., one hospital or health center, and hence are not nationally representative.

**Table 5. HAI rates collected from studies conducted in nine countries**

|**Country**|**Prevalence**|**Context**|**Reference**|
|---|---|---|---|
|Angola|-|-|-|
|Congo, Dem.<br>Rep.|64.9%|Teaching hospital in Congo|Ngolet et al, 2021|
||17.5%|Maternityhospitals in Mbujimayi|Bukasa et al,2018|
|Ethiopia|17%|Systematic review of 18 studies|Alemu et al(2020)|
||2% to 82% byinfection<sup>1</sup>|Rangeprovided from 13 studies|Irek et al,2018|
||19%|One tertiaryhospital|Ali et al,2018|
||15%|Two teachinghospitals|Yallew et al(2016,2017)|
||12.7%|Pediatricpatients|Sahiledengle et al(2020)|
||3.5%(SSI)|One teachinghospital|Amare et al,2011|
|Kenya|2.6% culture-confirmed|Oneprivate hospital|Patil et al,2022|
||7.0%(SSI)|One teachinghospital|Dinda et al,2013|
|Madagascar|19.6% (NN) of which<br>1.5% culture-confirmed|Neonates - births in 3 districts<br>2012-2018|Huynh et al, 2021|
|Mozambique|-|-|-|
|Sudan|25.2%(SSI)|One State|Ahmed,2012|
||9%(SSI)|One teachingHospital|Elbur et al,2012|
||14.2%<sup>2</sup>|Emergencyhospital setting|Spagnolello et al,2022|
|Tanzania|14.6%(SSI)|Two hospitals|Moremi et al,2017|
||35.6%(SSI)|National hospital|Akoko et al,2012|
||26%(SSI)|One medical center|Mawalla et al,2011|
||14.8%|One tertiaryhospital|Goslinget al,2003|
||10.9%(SSI)|One hospital|Bayadorjet al,2023|
|Uganda|28%|One large hospital|Greco & Magombe(2010)|
||10%(SSI)|National hospital|Seni et al,2013|

30
# _Organized Sector: Annual Survey of Industries_

The organized sector for Indian manufacturing comprises establishments with more than 10 workers if the establishment uses electricity. If the establishment does not use electricity, the threshold is 20 workers or more. These establishments are required to register under the India Factories Act of 1948. The unorganized manufacturing sector is, by default, comprised of establishments which fall outside the scope of the Factories Act. The organized sector accounts for over 80% of India’s manufacturing output, while the unorganized sector accounts for a high share of employment and over 99% of establishments (Ghani et al. 2013).

The organized manufacturing sector is surveyed by the Central Statistical Organization through the Annual Survey of Industries (ASI). These surveys are used for many published reports on the state of Indian businesses and government agency monitoring of the Indian economy. The typical survey collects data from over 150,000 Indian establishments. In this respect, the surveys are comparable to the Annual Survey of Manufacturing conducted in the United States, with the Indian sampling frame being about three times larger.

Establishments are surveyed with state and four-digit National Industry Classification (NIC) stratification. The survey provides sample weights that we use to construct populationlevel estimates of total establishments, employment, and output by district. Districts are administrative subdivisions of Indian states or territories that provide meaningful local economic conditions. The average district size is around 5,500 square kilometers—roughly twice the size of a U.S. county—and there is substantial variability in district size (standard deviation of ~5,500). Indian districts can be effectively considered as self-contained labor markets. As we discuss further below, we use district variation to provide more granular distances from the various highway networks.

ASI surveys record several economic characteristics of plants like employment, output, capital, raw materials, and land and building value. For measures of total manufacturing activity in locations, we aggregate the activity of plants up to the district or district-industry level. We also develop measures of labor productivity and TFP. Labor productivity is measured through output per employee at the plant level, with an average then taken across plants for a district. TFP is calculated through a residual regression approach. For every two-digit NIC industry and year, we regress log value-added (output minus raw materials) of plants on their log employment 6
**Table 37. Governance drivers of political risk: Inter-agency coordination**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0851|0.0803|||0.176|0.176|||
||(0.0551)|(0.0555)|||(0.109)|(0.113)|||
|Export % of sales|-<br>0.00208***|-0.00132*|-<br>0.00197***|-0.00123|-<br>0.00409***|-0.00258*|-<br>0.00395***|-0.00251|
||(0.000708)|(0.000756)|(0.000719)|(0.000755)|(0.00139)|(0.00153)|(0.00146)|(0.00157)|
|Share of sourcing via imports|0.00109|0.000610|0.00120|0.000651|0.00188|0.00109|0.00226|0.00124|
||(0.000778)|(0.000805)|(0.000808)|(0.000834)|(0.00154)|(0.00164)|(0.00167)|(0.00176)|
|High-income source country|-0.0193|0.0167|-0.0217|0.0202|-0.0308|0.0327|-0.0477|0.0253|
||(0.0814)|(0.0798)|(0.0817)|(0.0795)|(0.167)|(0.166)|(0.172)|(0.171)|
|>250 employees|-0.0403|-0.0450|-0.0507|-0.0550|-0.0980|-0.114|-0.118|-0.137|
||(0.0682)|(0.0682)|(0.0681)|(0.0686)|(0.133)|(0.136)|(0.138)|(0.141)|
|Cumulative inv. >US$10M|-0.00134|-0.0582|-0.00520|-0.0609|-0.00210|-0.115|-0.0182|-0.126|
||(0.0679)|(0.0689)|(0.0676)|(0.0686)|(0.134)|(0.140)|(0.138)|(0.144)|
|Years in country|0.000415|0.000824|0.000526|0.000844|0.00107|0.00210|0.00114|0.00201|
||(0.00182)|(0.00182)|(0.00180)|(0.00182)|(0.00360)|(0.00360)|(0.00360)|(0.00366)|
|Level of foreign ownership (%)|-0.00143|-0.000799|-0.00147|-0.000652|-0.00294|-0.00173|-0.00309|-0.00157|
||(0.00116)|(0.00128)|(0.00112)|(0.00122)|(0.00236)|(0.00271)|(0.00234)|(0.00264)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.015|0.047|0.038|0.069|||||

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Coordination between public agencies]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

54
_Table A 6_ : Malawi - Regressions of self-reported harvest realizations on expected harvest and a range of controls.

# **Malawi**

|**Panel A: Harvest value (MWK)**|(1)<br>IHP|(2)<br>S19|(3)<br>IHP|(4)<br>S16|(5)<br>IHP|(6)<br>S13|(7)<br>IHPS1|(8)<br>0|
|---|---|---|---|---|---|---|---|---|
|Expected harvest value (MWK)|0.514***|0.402***|0.523***|0.336***|0.725***|0.437***|0.354|0.257|
||(0.129)|(0.0927)|(0.102)|(0.109)|(0.102)|(0.110)|(0.225)|(0.193)|
|Planted area harvested (%)||181.2***<br>(38.06)|||||||
|Constant|13,434***|4,466|14,445***|-16,564|7,743**|66,365|7,808**|16,974|
||(4,447)|(24,474)|(4,991)|(28,693)|(3,442)|(51,522)|(2,970)|(16,379)|
|Observations|2,114|2,112|1,379|1,379|1,247|1,247|1,258|1,258|
|Adjusted R-squared|0.541|0.646|0.368|0.469|0.510|0.593|0.144|0.327|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|
|**Panel B: Harvest value(MWK/ha)**|(9)|(10)|(11)|(12)|(13)|(14)|(15)|(16)|
|Expected harvest value (MWK/ha)|0.551***|0.425***|0.530***|0.409***|0.678***|0.379***|0.0936***|0.0486**|
||(0.0343)|(0.0345)|(0.0513)|(0.0501)|(0.111)|(0.0820)|(0.0271)|(0.0210)|
|Planted area harvested (%)||877.4***<br>(139.8)|||||||
|Constant|52,167***|147,796|59,532***|-20,166|39,442***|378,749|38,228***|80,479**|
||(6,216)|(94,256)|(9,307)|(101,789)|(11,478)|(241,858)|(2,751)|(34,676)|
|Observations|2,114|2,112|1,379|1,379|1,247|1,247|1,258|1,258|
|Adjusted R-squared|0.392|0.445|0.294|0.350|0.364|0.489|0.085|0.298|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|
|**Panel C: Maizeproduction(kg)**|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|Expected maize harvest (kg)|0.783***|0.545***|0.654***|0.440***|0.580***|0.112|0.129***|0.0276|
||(0.0488)|(0.0730)|(0.0528)|(0.0765)|(0.105)|(0.114)|(0.0282)|(0.0209)|
|Planted maize area harvested (%)||1.828***<br>(0.510)|||||||
|Constant|47.25**|-278.1|71.41***|-482.3|312.0***|2,710*|428.3***|1,023***|
||(19.89)|(525.7)|(24.72)|(583.6)|(81.05)|(1,418)|(35.87)|(356.9)|
|Observations|568|544|493|490|442|442|842|842|
|Adjusted R-squared|0.666|0.718|0.460|0.557|0.249|0.386|0.026|0.398|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|

Note: Regression of self-reported realized harvest value (per hectare) (Panels A + B) or production (Panel C) on expected harvest value (per hectare) (Panels A + B) or production (Panel C), planted area loss (only IHPS19), and a number of plot, manager, and household controls as well as inputs and district fixed effects. Controls: Inputs - Plot area, total labor (p. ha), any hired labor dummy, seed quantity (p. ha), improved variety dummy (only IHPS19 and IHPS15), inorganic fertilizer quantity (p. ha), any organic fertilizer dummy; Household - dependency ratio, head employed for wage dummy, any HH member owns non-ag enterprise dummy, any HH member has primary education dummy, agricultural asset index; Manager - female dummy, age, primary education dummy; Plot - intercropped dummy, precipitation of wettest month, soil fertility index, cash crop dummy; Zone fixed effects. Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 56