rafmacalaba/datause-encoder-data huggingface dataset:
Reardon, T., A. Mishra, C. S. R. Nuthalapati, M. F. Bellamare, and D. Zilberman. 2020. COVID19's disruption of India's transformed food supply chains. _Economic and Political Weekly_ , 55(18).

Swinnen, J. 2020. Will COVID-19 cause another food crisis? An early review, Issue post, April 10, 2020. International Food Policy Research Institute, Washington, DC.

Torero, M. 2020. Without food, there can be no exit from the pandemic: Countries must join forces to avert a global food crisis from COVID-19. _Nature_ , 580, 588–589.

WFP (World Food Program). 2020. COVID-19 Will double number of people facing food crises unless swift action is taken. Press Release, April 21, 2020 (Rome: World Food Program).

WHO (World Health Organization). 2010. Indicators for assessing infant and young child feeding practices: Part 2 Measurement).

Wooldridge, J. M. 2007. Inverse probability weighted estimation for general missing data problems. _Journal of Econometrics_ , 141(2): 1281-1301.

World Bank. 2020. Poverty and Shared Prosperity 2020: Reversals of Fortune. Washington, DC: World Bank. doi:10.1596/978-1-4648-1602-4. License: Creative Commons Attribution CC BY 3.0 IGO.

21
9 In light of the motivation and goals described above, we devised a behavioral intervention that could be tested in the two states: social recognition for good performance. From January to March, 2016, we tested the intervention in a Randomized Controlled Trial (RCT). The study leveraged the ongoing resource tracking study of 140 facilities across the two states. Accordingly, we randomly assigned the 140 facilities to two arms per state for the purposes of this RCT, to find out whether the program could improve record-keeping. To maximize the study’s statistical power, we assigned half of the facilities to each arm in each state. To ensure that geography would not confound analysis, we stratified this randomization by local government authorities (LGA), meaning that we randomized the facilities to arms within LGA.

In Table 2, we show tests of balance for the observables that, _ex ante_ , we hypothesized might be responsible for heterogeneity in treatment effects. We do this for the entire sample, and separately by state. Most covariates are balanced at conventional statistical levels, but the logarithm of monthly outpatients is imbalanced in Ekiti, and the presence of designated staff for financial accounting is imbalanced in Niger.

The treatment “arm” provides social recognition as an incentive based on scoring the facilities’ record-keeping qualities. The record in question was the cash book form, a simple excel balance sheet that tracked the cash receipts from user charges as well as expenditures, by category, on a daily basis. The quality of those records could be assessed by enumerators who visited the facilities on a weekly basis. The precise design of the evaluation was as follows.

For four weeks of the incentives study, the two study arms being compared were

- A “Control”: Records were scored weekly by enumerators; however, scores were not shared.

- B “Social Recognition”: Records were scored weekly by enumerators; scores were then converted to a number of stars between 0 and 5. The number of stars was displayed in a public place on a Certificate of Excellence (for a picture of the certificate, see Appendix Figure A1) for anyone visiting the facility to see. Furthermore, at the end of the experimental period the best-performing facility and its accounting staff (i.e. all staff members that participated in completing the
**Table C2. Food items and budget shares in CPI price database (continued)**

|Consumption Survey Group|Budget Share|CPI Item|
|---|---|---|
|Other spices|0.000|Cube spices|
|||Benny|
|||Maggi cube (crevette)|
|Other condiments|0.013|(m)|
|Salt|0.010|Salt - iodized|
|Coffee (Nescafe)|0.000|Coffee<br>Pure cocoa powder (for example,|
|Pure cocoa powder|0.000|Brown Gold)|
|Tea|0.002|Tea bag|
|Cocoa with milk powder|0.012|Cocoa with milk powder|
|Water|0.038|Mineral water (bottled)<br>Bottle water|
|||Sachet water|
|Soft drinks|0.006|Soft drink|
|||Fanta (can)|
|||Sprite (bottle)|
|||Coca Cola (can)|
|Malt|0.012|Malt drinks (bottle)|
|||Malt drinks (can)|
|Fruit juice|0.005|Fruit juice|

_Note:_ Average budget shares is each item’s household expenditure share on average. For items with multiple prices per consumption survey group, the price relativities are averaged before mapping to the budget shares. Items with () use prices of similar items as follows: (a) guinea fowl - live; (b) evaporated milk; (c) groundnuts; (d) average of the fruit group; (e) half of the average price of the fruit group; (f) half of the price of groundnuts (shelled); (g) yam; (h) cassava; (i) average of the tuber group; (j) white beans (cowpea); (k) powder pepper; (l) powder pepper; and (m) cube spices, benny, and maggi cubes.

45
# **Appendix 4. Girls’ and boys’ outcomes**

||||**Hea**|**lth**||||**Educ**<br>|**ation**<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|||**Girls**|||**Boys**||**G**|**irls**|**B**|**oys**|
||(1)|(2)|(3)|(5)|(6)|(7)|(9)|(11)|(12)|(13)|
||Stunted|Wasted|Received all<br>four<br>vaccinations|Stunted|Wasted|Received all<br>four<br>vaccinations|Max<br>years of<br>schooling<br>among<br>girls (6-<br>15years)|Proportion<br>of girls<br>enroll in<br>school (6-<br>15 years)|Max<br>years of<br>schooling<br>among<br>boys (6-<br>15)|<br>Proportion<br>of boys<br>enroll in<br>school<br>among (6-<br>15)|
|Wife gives herself more power than|-0.043***|-0.001|0.041***|-0.028***|-0.010*|0.055***|0.237***|0.038***|0.159***|0.058***|
|husband [A]|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.04)|(0.01)|(0.04)|(0.01)|
|Husband gives herself more power than|-0.021**|0.004|0.026***|-0.027***|-0.008|0.033***|0.260***|0.036***|0.185***|0.054***|
|<br>herself [B]|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.04)|(0.01)|(0.04)|(0.01)|
|Wife and husband agree she is main dm or|-0.049***|-0.004|0.056***|-0.039***|-0.018***|0.060***|0.336***|0.052***|0.266***|0.068***|
|<br>dm is joint [C]|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.04)|(0.01)|(0.04)|(0.01)|
|Constant|0.395***|0.132***|0.610***|0.431***|0.121***|0.606***|2.678***|0.644***|2.935***|0.872***|
||(0.02)|(0.01)|(0.02)|(0.02)|(0.02)|(0.02)|(0.11)|(0.01)|(0.10)|(0.02)|
|_Controls_|||||||||||
|Women's status and empowerment proxies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Individual/Household-level characteristics|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|28,203|28,203|28,203|28,495|28,495|28,495|28,961|32,366|28,539|29,866|
|Adjusted R-squared|0.06|0.03|0.16|0.07|0.03|0.16|0.22|0.22|0.21|0.21|
|Mean Control|0.39|0.12|0.55|0.43|0.15|0.54|2.22|0.37|2.47|0.64|
|P-value A=B|0.00|0.38|0.07|0.90|0.68|0.01|0.55|0.68|0.49|0.56|
|P-value A=C|0.49|0.49|0.06|0.17|0.11|0.49|0.01|0.01|0.00|0.05|
|P-value B=C|0.00|0.11|0.00|0.14|0.05|0.00|0.04|0.00|0.03|0.01|

43
a panel. Note, however, that market entrance and exit can only be approximated; a firm may no longer be observed if its sales have fallen below the selection threshold or if it has actually left the market. Similarly, market size can only be approximated because the panel is drawn from a selection from the universe of firms, rather than from the whole market. We look specifically at the ICT sector, which we separate into hardware, software, telecommunications, and infrastructure subsectors.<sup>4</sup> The survey collected 2,955 observations from ICT firms over the 11-year period. Of these firms, about 34% were active in markets where competition was enforced. In other words, of 940 distinct firms from all ICT sectors, 57% were subject to competition enforcement in their sub-sector. The largest number of firms produced hardware, which accounted for 48% of total ICT sales in the 2007–17 period; telecommunications accounted for 22% of sales, software for 17%, and infrastructure for 14%. Apart from Hardware, the variations in markup were fairly constant over the sample period (Figure 11).

Most firms surveyed were established in the 2000s because of the inclusion criterion of large firms. Except for telecommunications, ICT employs a large share of labor in valueadded. Telecommunications and infrastructure have the highest shares in concentration (proxied by the four-firm ratio) and markups, calculated from variable profits. 5 This is to be expected given the large entry costs, economies of scale and scope.

ICT regulation and enforcement data are drawn from the Peruvian regulatory authorities: the Ministry of Transport and Communications (MTC), Osiptel for telecommunications and infrastructure, and Indecopi for other regulations pertaining to software and hardware.

The MTC is the technical authority for defining and enforcing policy; one of its goals is to connect economic agents nationwide. It has multiple functions, but we focus on its role in the design, coordination, and evaluation of regulations for infrastructure and telecommunications; the evaluation and granting of licenses; and the enforcement and supervision of spectrum assignment.

Osiptel is a specialized, autonomous, and decentralized public body tasked with promoting market competition. It regulates tariffs, regulates access, supervises, and enforces competition and technical norms for telecommunications. It therefore evaluates markets and carries out investigations both on its own initiative or in response to third-party complaints, resolving disputes as well as addressing anti-competitive behavior by imposing corrective measures or sanctions.

Indecopi is charged with promoting and defending competition in all other sectors. It can evaluate and approve corporate mergers in any sector and adjudicate cases that might

> 4For infrastructure we use ISIC 4321 and 4220.

> 5 <u>Π</u> _<u>t</u>_ Markups ( _μ_ ) can be calculated using profits (Π), sales ( _PQ_ ) and costs ( _CQ_ ): _PtQ_<sup>=</sup><sup><u>(</u></sup><sup>_Pt_</sup> _P_<sup>_−_</sup> _t_<sup>_C_</sup> _Q_<sup>_t_</sup> _t_<sup><u>)</u></sup><sup>_<u>Qt</u>_</sup> = 1 _− μ_<sup><u>1</u>.</sup>

13
the latter countries, microeconomic data from China are not available for the analysis of the paper. It is therefore not possible to incorporate them fully in the comparisons, necessitating reliance on the specification provided by authors of the cited papers in any assessment of factors driving inequality in China.

**Table 9.  China: Increases in Gini coefficients for per capita incomes from various studies**

||||**Rural**<br>|||**Urban**<br>|<br>|
|---|---|---|---|---|---|---|---|
||**Data**|**1988**|**1995**|**2001-2**|**1988**|**1995**|**2001-2**|
|Ravallion and Chen (2004)|SSB|0.297|0.334|0.365|0.211|0.283|0.323|
|Wu and Perloff (2004)|SSB|0.300|0.338|0.343|0.201|0.221|0.269|
|Li (2000)|SSB|0.301|0.323||0.230|0.280||
|Khan and Riskin (1998, 2004)|CASS|0.338|0.416|0.375|0.233|0.332|0.318|
|Gustaffson and Li (1999)|CASS||||0.228|0.276||
|Wagstaff (2005)|CHNS|0.395*|0.419*|||||
|Meng (2003)|CASS||||0.234|0.282||

Note : SSB –State statisitical bureau based on household budget survey, CASS- Economics Institute of the Chinese Academy of Social Sciences Survey * All China, 1989 to 1997.

The key determinants of inequality in China are very different from what has been observed in Eastern Europe and the former Soviet Union. As Figure 8 clearly shows China stands out as a country with an extremely large rural-urban gap. Indeed, Shorrocks and Wan (2004) report that, at an estimated 37 percent in 2000, China has the highest “between” urban-rural component of inequality in the world. A significant determinant of China’s inequality derives from the rural-urban divide, viz., migration from the former to the latter and rapid changes in the sectoral composition of output, a classic development phenomenon. In contrast, the turbulent early years of transition in some countries of Eastern Europe and the former Soviet Union witnessed a reversal of this gap as availability of some sources of livelihood became available in rural areas at a time when unviable enterprises were being restructured or closed in industrial cities.

permits (hukous) registered in urban areas. The estimates of unregistered migrants differ and are as large as 150 million. They are believed to earn significantly lower salaries and their omission from the sample definitely underestimates urban inequality in China.

34
Table A4: Madagascar - Educational attainment of children and their parents by five-years birth cohort

||Average years|||||
|---|---|---|---|---|---|
||of schooling|No schooling|Primary|Secondary|Tertiary|
|Son||||||
|1936-1940|1.5|78.2|14.5|5.5|1.9|
|1941-1945|2.1|74.2|12.0|11.2|2.6|
|1946-1950|2.6|65.8|18.6|12.6|3.1|
|1951-1955|2.6|67.6|16.3|11.8|4.3|
|1956-1960|3.0|63.5|18.5|13.3|4.6|
|1961-1965|3.3|58.3|19.9|17.9|3.8|
|1966-1970|3.1|58.9|21.3|17.1|2.7|
|1971-1975|2.8|61.1|22.6|14.1|2.2|
|1976-1980|2.5|65.0|21.9|11.0|2.2|
|1981-1985|2.6|63.5|20.8|14.6|1.1|
|Daughter||||||
|1936-1940|0.5|91.9|5.9|1.5|0.7|
|1941-1945|0.8|88.5|7.5|3.8|0.3|
|1946-1950|1.4|80.1|13.3|5.5|1.1|
|1951-1955|1.6|78.2|13.1|7.5|1.2|
|1956-1960|2.1|72.7|14.1|10.7|2.5|
|1961-1965|2.3|68.3|17.2|12.8|1.6|
|1966-1970|2.6|61.8|24.1|12.7|1.4|
|1971-1975|2.2|67.0|22.2|9.6|1.2|
|1976-1980|2.1|68.9|20.5|8.9|1.7|
|1981-1985|2.3|66.8|20.2|11.9|1.1|
|Mother||||||
|1936-1940|0.9|70.8|27.6|1.2|0.4|
|1941-1945|0.9|70.3|28.4|1.1|0.2|
|1946-1950|1.3|61.5|35.3|2.9|0.2|
|1951-1955|1.3|60.3|37.1|2.4|0.3|
|1956-1960|1.6|56.5|39.3|3.8|0.4|
|1961-1965|1.7|53.8|41.4|4.5|0.3|
|1966-1970|1.9|49.5|44.6|5.4|0.5|
|1971-1975|1.9|51.1|42.4|6.3|0.2|
|1976-1980|2.0|52.3|40.6|6.3|0.7|
|1981-1985|2.1|57.9|33.0|8.5|0.6|
|Father||||||
|1936-1940|1.5|61.5|34.2|3.9|0.4|
|1941-1945|1.3|62.5|34.1|3.2|0.2|
|1946-1950|1.9|52.4|41.9|5.1|0.7|
|1951-1955|1.9|50.3|43.6|5.7|0.5|
|1956-1960|2.1|46.8|45.6|6.9|0.6|
|1961-1965|2.3|43.7|48.3|7.2|0.8|
|1966-1970|2.7|38.9|49.8|10.4|0.9|
|1971-1975|2.7|40.3|48.4|10.5|0.8|
|1976-1980|2.7|41.9|46.1|10.4|1.6|
|1981-1985|2.8|47.6|37.8|12.6|2.0|

36
secondary education (12-14, 15-17, 18-20 and 21-24). As in the case of employment rates, we do not find any significant effect of homicide rates (including drug-related homicides) on NEET rates. Thus, all together, the outcomes of violence on employment and youth NEET rates (presented in Tables 7 and 8) further validate our results that education decisions are not the channel that explain the small negative effect of violence on the number of enrolled students.

Table 7: The impact of homicide rates on employment rates using the ENOE

|**Panel A. Men**<br>**Dependent variable: Rates fo**<br>Timeperiod: 2006-2010|**r Employment**|||Employm|ent Rate||||
|---|---|---|---|---|---|---|---|---|
||Ages 12-14<br>(1)|Ages 15-17<br>(2)|Ages 18-20<br>(3)|Ages 21-24<br>(4)|Ages 12-14<br>(5)|Ages 15-17<br>(6)|Ages 18-20<br>(7)|Ages 21-24<br>(8)|
|Homicide Rate|0.000464|0.000189|0.00238|0.000171|||||
||(0.00176)|(0.00360)|(0.00261)|(0.00334)|||||
|Drug Related Homicide Rate|||||-0.000110|0.000819|0.00231**|0.000194|
||||||(0.000527)|(0.00132)|(0.00101)|(0.00118)|
|Constant|0.149***|0.386***|0.625***|0.787***|0.151***|0.384***|0.623***|0.786***|
||(0.00808)|(0.0112)|(0.0112)|(0.0108)|(0.00722)|(0.00945)|(0.0103)|(0.00919)|
|Observations|5,018|4,949|4,731|4,743|5,018|4,949|4,731|4,743|
|R-squared|0.395|0.435|0.381|0.349|0.395|0.435|0.382|0.349|
|**Panel A. Women**||||Employm|ent Rate||||
||Ages 12-14|Ages 15-17|Ages 18-20|Ages 21-24|Ages 12-14|Ages 15-17|Ages 18-20|Ages 21-24|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Homicide Rate|0.000637|0.000200|-0.00159|0.00208|||||
||(0.00143)|(0.00309)|(0.00307)|(0.00274)|||||
|Drug Related Homicide Rate|||||-0.000309|-0.000706|-0.000729|0.000348|
||||||(0.000468)|(0.000731)|(0.000809)|(0.000883)|
|Constant|0.0653***|0.170***|0.292***|0.381***|0.0675***|0.172***|0.291***|0.384***|
||(0.00602)|(0.0100)|(0.0115)|(0.0103)|(0.00543)|(0.00821)|(0.00979)|(0.00906)|
|Observations|4,989|4,965|4,840|4,970|4,989|4,965|4,840|4,970|
|R-squared<br>**Notes.**Dependent variable is the the<br>to the national homicide rate per 100,0<br>Security (Consejo Nacional de Segurid<br>available from 2006 to 2010. Standard|0.305<br>rates of NiNi's (indivi<br>00 inhabitants in 200<br>ad Pública) of the Fe<br>errors clustered at t|0.334<br>duals that do not w o<br>7 (general homicide r<br>deral Government a<br>he municipality level i|0.352<br>rk or study) and emp<br>ate of 8.3 and drug<br>nd include all deaths<br>n parentheses. *** p|0.371<br>loyment by age grou<br>related homicide rate<br>by "presumed delinq<br><0.01, ** p<0.05, * p|0.305<br>p, calculated from E<br>of 2.6). Drug-relate<br>uent rivalry". All reg<br><0.1|0.334<br>NOE survey data. Ho<br>d homicides are colle<br>ressions include mun|0.352<br>micide rates are nor<br>cted by the National<br>icipality and year fi|0.371<br>malized according<br>Council of Public<br>xed effects. Data|

29
- 13 -

**<u>Table 4. OLS regression results</u>**

||_t_=2005<br>Coef.|Std.Err.||_t_=2010<br>Coef.|Std.Err.||
|---|---|---|---|---|---|---|
|ln_L_|0.244|(0.027)|***|0.364|(0.010)|***|
|ln_R_|0.471|(0.005)|***|0.453|(0.008)|***|
|ln_I_|-0.011|(0.012)||0.042|(0.004)|***|
|ln_F_|0.030|(0.011)|***|0.066|(0.004)|***|
|ln_TC1_|0.130|(0.026)|***|0.131|(0.031)|***|
|ln_TC2_|-0.259|(0.038)|***|0.090|(0.083)||
|constant|-5.233|(0.347)|***|-7.733|(0.399)|***|
|Obs|4624|||4624|||
|F statistic|3752.5|||1634.8|||
|R-squared|0.829|||0.800|||

Note: The dependent variable is ln _Y_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

To mitigate the endogeneity problem, the OLS model with the difference variables is estimated, which is empirically the same as the fixed-effects model with panel data ( **Table 5** ). Thus, the unobserved location-specific effects are controlled for. Still, autocorrelation remains unsolved. But all the coefficients turned out consistent with our expectation, but one, the effect of domestic market access ( _TC1_ ). All the crop production inputs are productive. The port access ( _TC2_ ) has a negative coefficient as expected: Agricultural production would be greater where transport costs to the port are lower or port accessibility is high.

With not only the location-specific fixed effects but also autocorrelation taken into account, the spatial autocorrelation regression model is estimated. The results are broadly consistent with _a priori_ expectations. The estimated autocorrelation parameters, i.e., _λ_ and _ρ_ , indicate that both cross-sectional OLS and fixed-effect models are likely biased. The hypothesis that the spatial autocorrelation parameters are zero can easily be rejected.

Moreover, the estimated spatial autoregressive term _λ_ , which is small but significantly different from zero, shows that spatial concentration is positive. It means that if agricultural production takes place in one location, its neighboring places are also likely to grow a similar amount of agricultural produce. This is a natural and consistent result with **Figure 2** . The
- 52 -

# 5.4.1. Ethnoecology

'Preliminary results show that indigenous agricultural soils actually improve in pH and major nutrients over the 20-year management cycle for fields. This is a major discovery and is the first time that such exhaustive soils data have been collected from indigenous fields. When fertility data from Kayapo fields are compared with those in the surrounding area, the success of indigenous soils management is shockingly impressive."

# Posey (1987)

Over five years, a team of 23 scientists have been studying Kayapo methodologies for resource utilisation. Their results confirm in fascinating detail the reach and complexity of indigenous management systems and suggest that western scientists may have to reassess their views about the extent to which "virgin" rain forest is really virginal.

Posey suggests opposing tendencies that prevent western scientists from taking folk science seriously. One is an idealization of tribal peoples as "natural conservationists" that ignores the trial and error experimentation that is the foundation of indigenous practices. Another, a dismissal of knowledge systems that are expressed in symbolic or mythical terms, also ignores their empirical merits.

As an example, Posey refers to the Amazon practice of felling large trees, apparently to obtain honey from arboreal hives. This is often cited as evidence cf ecologically unsound practice. But Posey's investigation showed that the Kayapo do this deliberately, with the intention of creating an open space which will be colonised by useful edible and medicinal plants. The Kayapo even plant species that will attract birds a,d animals and produce a concentrated hunting and gathering site.

The team discovered in addition a sophisticated methodolgy for ecological manipulation, using companion plants and techniques for pest control which employ such techniques as trap species, alternative hosts and predatory insects. After four years of field work, the team has concluded that the results of such work have clear applications in reafforestation, agricultural and wildlife management.

Martin (1988) describes an ethnobotanical project, conducted by the Society for the Study of the Biotic Resources of Oaxaca, Mexico, which demonstrates the nutritional and economic importance of wild plants.The continuing economic significance of collection and trade was indicated by the more than 400 species of food, medicinal and ornamental plants were collected from markets in the Oa::aca region. The project was organized so as to actively involve local indigenous villagers. Plants were collected and recorded by teams of old and young: older people who knew the plants; younger people who were more adept at translation and recording. The collectors' responsibilities went beyond field work to include the explanations of the project and its general relevance to other villagers.
23 the extent of party control of candidates. Systems that use “smoke-filled back rooms” to identify a party’s candidates offer party leaders greater influence than those that use party primaries. Where possible, the data set therefore includes a variable that reflects whether selection is of one of the following three types:

- 1 on a national level, i.e. by national executive, party leader, interest groups or party factions;

- 2 on a sub-national level, i.e. by subset of constituency party members e.g. on conventions;

- 3 by primary election, including party primary and primaries using all the votes of a constituency.

The DPI, finally, records the month when presidential/parliamentary elections were held.

# 6. Checks and Balances

A key element in the description of any political system is the number of decision makers whose agreement is necessary before policies can be changed. Numerous decision makers with different policy preferences are likely to respond differently (more incrementally, for example) to reform opportunities than single decision makers. On the other hand, countries with multiple decision makers may offer greater protection from arbitrary government action to individuals and minorities. Interests that are unrepresented by government decision makers when there are few key decision makers may gain representation when the number of decision makers rises.

The DPI significantly extends one existing measure of checks and balances, and introduces new measures and the possibility of constructing others. One existing measure is the Index of Political Cohesion, originally devised and coded by Roubini and Sachs (1989) for a set of 14 OECD countries to study the political and economic origins of high-debt countries.