rafmacalaba/datause-encoder-data huggingface dataset:
quintile; but raising maize yields has a dramatic impact upon the poor, raising their incomes by 5.66% compared to just 0.5% for the best off.

Applying the basic methodology in this way thus provides a valuable first estimate of the potential effects of a reform. However, the results of such simulations are contentious because they do not allow producers or consumers to respond to changed prices. Possibly more serioiisly they ignore the role of labour markets; the above simulation must, by construction, giVe the result that net consumers of a product whose price falls will gain. However, there is some evidence that households who are net consumers of a staple food do not necessarily gain when the price of that food falls if they obtain much of their income from working on farms producing that good, because the reduction in profits for the farm owners results in downward pressure on wages or employment. The next section shows various ways in which the methodology can be extended to address these and other concerns.

# 4. **Extensions of the basic methodology**

The above approach to the analysis of the impact of structural reforms on the poor is attractive because its data requirements are relatively low; most reasonable income and production surveys will contain data on quantities produced, inputs used and income received from a variety of different sources.<sup>14</sup> However, it has a number of important weaknesses.

> Firstly, the model does not allow for substitution in consumption or production. In reality

> quantities will adjust (indeed the reallocation of resources may be one of the objectives of the reforms). Resources will shift into the production of goods whose prices have increased, and away from the use of inputs and the consumption of goods whose prices have gone up.

Secondly, the model does not include any markets. Prices are completely exogenous and, unless changed for the purpose of the simulation, remain fixed. Yet in many cases prices will be determined by markets; in particular changes in the demand and supply of labour will determine the real wage of unskilled labour - a key variable for understanding the poverty impact of a reform.

Thirdly, the model does not allow for growth. Yet promoting growth - particularly growth which includes the poor - is often the central aim of such reforms. Allowing the model to incorporate assumptions about growth can be important for simulating medium to long-run impacts.

14 However, even here there may be difficulties: price information is often not well recorded (often one has to rely on unit prices rather than community price surveys); similarly wage information is often absent so that wages have to be inputed from earnings and hours worked; and transfers data often gives no indication of the source of the transfer (what sector was the person working in) or the determinants of the size of the transfer.

12
Small-scale interventions align with Banerjee and Duflo’s (2011) concept of “small radical thinking” which has proven effective in other areas, such as poverty reduction. However, our findings also reaffirm a longstanding principle: silver bullets do not work. The strong predictive path for participation, after accounting for societal, data, and algorithmic biases, highlights that strategies focused on a single factor, like education or political interest, may not succeed given the complex interplay of conditions necessary for participation. This reinforces the idea that integrated, rather than isolated, interventions are needed to meaningfully increase civic participation.

Finally, effective increases in participation require not only integrated policies, but also integrated knowledge. Future research should include the analysis of emerging forms of participation, such as e-participation—participating in online surveys, using government digital transparency platforms, or adding political banners to profile pictures—in addition to traditional face-to-face participation. Additionally, research should continue to examine multiple biases simultaneously, enhancing the flexibility of predictive models to capture nonlinearities and the complexities of these participation decisions.

# **References**

Ali, M., P. Sapiezynski, M. Bogen, A. Korolova, A. Mislove, and A. Rieke. 2019. "Discrimination through Optimization: How Facebook’s Ad Delivery Can Lead to Biased Outcomes." Proceedings of the ACM on Human-Computer Interaction 3: 1–30.

Baeza-Yates, R. 2018. "Bias on the Web." Communications of the ACM 61(6): 54–64.

Barnabó, G., A. Fazzone, S. Leonardi, and C. Schwiegelshohn. 2019. "Algorithms for Fair Team Formation in Online Labour Marketplaces." In Companion Proceedings of the 2019 World Wide Web Conference, 484–490. New York, NY: Association for Computing Machinery.

Beauvoir, S. de. 1949. The Second Sex. London: Jonathan Cape.

Bender, E. M., and B. Friedman. 2018. "Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science." Transactions of the Association for Computational Linguistics 6: 587–604.

Bem, S. L. 1993. The Lenses of Gender: Transforming the Debate on Sexual Inequality. Yale University Press Blodgett, S. L., S. Barocas, H. Daumé III, and H. Wallach. 2020. "Language (Technology) Is Power: A Critical Survey of 'Bias' in NLP." arXiv:2005.14050.

Booth, B. M., L. Hickman, S. K. Subburaj, L. Tay, S. E. Woo, and S. K. D’Mello. 2021. "Bias and Fairness in Multimodal Machine Learning: A Case Study of Automated Video Interviews." In Proceedings of the 2021 International Conference on Multimodal Interaction, 268–277.

Correll, S. , S. Benard, and I. Paik. 2007 **.** Getting a job: Is there a motherhood penalty? _American Journal of Sociology_ , 112(5), 1297–1338 19
we analyze the amount of effort put in by a group per period in each task, clustering standard errors at the group level.

Table 4: Number of groups and the total number of observations (over all periods, tasks and subjects), presented in panels A and B respectively, in each of the four treatments in the laboratory experiment.

|**Thresh. payments**|**Info**|**rmation**|**Thresh. **|**payments**|**Info**|**rmation**|
|---|---|---|---|---|---|---|
||Yes|No|||Yes|No|
|Yes|10|7|Yes||360|252|
|No|7|8|No||252|288|

(a) Group level (b) Subject - Period - Task level

# **5.2 Results of the laboratory experiment**

We first test whether there are any treatment differences between the threshold and linear group payment schemes in either the presence or the absence of information on the number of balls caught by one’s group in the previous period. The most conservative test compares the average number of times members of a group clicked to move her tray during a task, averaged over all tasks, between the two treatments. As shown in Table 5 the differences are small, and statistically insignificant. That means that overall, we find no evidence of the one treatment inviting more effort than the other.

Table 5: Amount of effort put into catching balls in each period of a task, averaged over all subjects and all tasks in a group, in the four treatments of the laboratory experiment.

|**Threshold**|**Inform**|**ation**|p-value|
|---|---|---|---|
||Yes|No||
|Yes|72.675|75.524|0.501|
||(2.574)|(3.459)||
|No|73.417|71.625|0.663|
||(3.360)|(2.465)||
|p-value|0.859|0.351||

_Notes_ : Standard errors, clustered at the group level, are presented in parentheses.

To uncover any differences in the underlying dynamics, we run panel regression analyses using as dependent variable the amount of effort put in by a group in each of the three periods of a task. These regressions are run using task fixed effects, and with standard 26
net exports could be weaker, and (iv) the portion of the rule-of-thumb consumers (nonRicardian households) could be higher, vis-à-vis the Slovak Republic. Similarly, we try to illustrate why in Macedonia, relative to the Slovak Republic, (v) the effect of increasing capacity utilization (demand pressure) on inflation could be higher, and (vi) the exchange rate pass-through could be lower.

# **_5.1. Microeconomic Narrative for the IS Curve Estimates_**

_(i) Why could consumption habit formation or the cost of capital stock adjustments be higher in Macedonia?_ Data on CPI’s weights, which are typically based on households’ consumption patterns, seem to confirm stronger habit formation in Macedonia. The CPI basket changed in both countries between 1999 and 2007; however, the overall change in the structure of the CPI (approximated by the standard deviation of the changes in the weights of the various COICOP categories) was greater in the Slovak Republic. Faster rising incomes in Slovakia compared to Macedonia meant that Slovaks increased spending on nonnecessities more than Macedonians. In addition, developments in prices differed considerably; for example, price-adjustment in energy and utilities to cost recovery levels in the Slovak Republic occurred much faster compared to Macedonia, meaning that Slovaks’ consumption habits had to adjust much earlier.

**<u>Table 2: Changes CPI weights in the Slovak Republic and Macedonia</u>**

||Slo<br>|vak Repu<br>|blic<br>|<br>|Macedon<br>|ia<br>|
|---|---|---|---|---|---|---|
||1999|2007|change|1999|2007|change|
|Food and non-alcoholic beverages|293.4|168.3|-125.1|488.2|373.0|-115.2|
|Alcoholic beverages, tobacco and narcotics|46.3|55.7|9.4|43.0|40.0|-3.0|
|Clothing and footwear|120.6|42.0|-78.6|79.0|76.0|-3.0|
|Housing, water, electricity, gas and other fuels|138.8|227.1|88.3|118.1|121.0|2.9|
|Furnishings, household equipment and routine<br>maintenance of the house|61.3|58.2|-3.1|38.2|50.0|11.8|
|Health|14.5|36.7|22.3|22.6|34.0|11.4|
|Transport|98.9|102.7|3.9|90.9|91.0|0.1|
|Communications|15.9|44.0|28.1|30.5|64.0|33.5|
|Recreation and culture|95.1|85.1|-10.0|33.0|43.0|10.0|
|Education|11.6|17.5|5.9|1.1|8.0|6.9|
|Restaurants and hotels|52.1|87.5|35.4|9.8|49.0|39.2|
|Miscellaneous goods and services|51.7|75.2|23.5|45.6|51.0|5.4|
|Standard deviation|||54.7|||38.6|

14
40 Table 6. Reduced Form Equations for Contraceptive Use and Fertility in the Last Five Years in Tunisia

|**Variables**|Proportion of Last<br>**Five Years wvith**<br>Modern Temporary|Proportion of Last<br>**Five Years with**<br>Traditional|Births Last Five<br>**Years**|
|---|---|---|---|
|Age 15-25|0.11 (1.64)|-0.42 (2.50)"O|1.26 (12.58)"'|
|Age 26130|0.09 (2.37)00|-0.17 (2.54)"|1.00 (17.75)..|
|Age 31-35c|0.11 (3.28)|-0.17 (2.64).|0.70 (14.62)..|
|Education spouse|-0.01 (1328)|0.06 (4.73)0*|-0.01 (0.87)|
|ERducation 4-|0.07 (1048)|-0.02 (0.19)|0.14 (1.78)|
|Education 7+|0.12 (1097)|-0.17 (1.05)|0.02 (0.21)|
|Rural 12|-0.08 (1.91)|-0.21 (2.55)|0.18 (2.32)|
|Sm31i city|0.01 (0.25)|-0.08 (0.81)|0.14 (1.57)|
|Urban|-0.02 (0.13)|0.44 (1.55)|0.10 (0.46)|
|Remote|-0.06 (0.78)|-0.10 (0.71)|0.13 (1.82)|
|Doctor < 5, 5 years ago|0.05 (0.97)|-0.01 (0.11)|-0.13 (1.48)|
|Clinic 2 <_5,_5 years ago|0.07 (1.15)|0.14 (1.24)|-. 03 (0.31)|
|Clinic 3<5, 5 years ago|0.17 (3.124)|-0.03 (0.39)|-0.12 (1.26)|
|Hospital<_5,_5 years ago|40.03 (0039)|0.21 (1.64)|0.01 (0.06)|
|Pharmacyq<br>5, 5 years ago|o0.00 (0.06)|0.07 (0.76)|-0.13 (1.21)|
|Doctor <_10,_5 years ago #-0.26|(1.63)|-10.42 (40.37)---|0.10 (0.58)|
|Clinic 2 <_10,_5 yeats ago $0.33|(4.79)...|0.41 (2.73)...|-0.27 (1-92)<br>l|
|Clinic 3 <_10,_5 years ago #0.05|(0.47)|0.16 (0.85)|-0.04 (0.43)<br>||
|Hospital <_10,_5 years ago #|4.Z9 (2.00)*'|40.12 (0.32j|0.18 (0.96)<br>l|
|Pharmacy. e 10, 5 years ago #0.15|(1.15)|0.17 (0.49)|o0.05 (0.37)|
|Community mortality|-2.44 (4.55)" '|1.63 (2.41)" '|2.65 (2.30)"|
|Education opportunities < 5|0.03 (0.58)|40.01 (0.19)|-0.03 (0.58)|
|Education opportunities<10 #|-0.02 (0.29)|-0.02 (0.12)|0.02 (0.26)|
|Constant|0.05 (0.37)|-1.44 (6.03)..|0.00 (0.02)|

**#** Rural areas

* Significant at 10% level ** Significant at 5% level Significant at 1% level
want to smoke?”). Negative correlates with CARES takeup were: wanting to quit smoking more than a year in the future (perhaps an indicator of procrastination) and smelling like cigarettes (likely an indicator of heavy smoking). Table 2 shows multivariate estimation of takeup correlates.<sup>18</sup> The main results here are that the full set of baseline characteristics are jointly significant but explain only about 10% of the variation in the takeup decision.

# _B CARES Usage_

Table 3 shows some summary statistics on CARES deposits.

Opening balances were 57 pesos on average: this is four times the monetary value of the number of cigarettes the client reported smoking per week. Ninety percent of clients opened with the minimum amount of 50 pesos. Eighty percent of clients then made additional contributions. On average CARES clients made a deposit every two weeks, and by six months the average balance grew to 553 pesos. Given self-reported smoking intensity and a per-cigarette cost of one peso, the average CARES client committed roughly six months worth of cigarette spending to the account.

Not surprisingly CARES clients who used the account more intensively were more likely to pass the urine tests. We show results for the 6-month test in Table 3 and the 12month test in Appendix Table 2. Successful clients made more deposits, were more likely to retain deposit collection services by making regular deposits, and had larger balances at contract maturity. These differences were more pronounced for 6-month test passage than for 12-month passage. Of course, since contract terms and deposit requirements were not randomized, we can not infer a causal relationship between deposit amount, deposit regularity and success.

However, the pattern of deposits is intuitive. Figures 1 and 2 show that those who succeed start off making higher deposits, and continue to make deposits throughout the entire life of the project. The trend is downward, in that deposits get smaller and less frequent towards the end. Once enough funds are in the account to be binding and modify one's behavior, further deposits are unnecessary to change behavior. On the other hand,

> 18 All takeup and impact regressions include indicator variables for the three marketing waves.

12
# **1. OVERVIEW**

Since the WTO’s Doha Agenda failed to deliver meaningful services liberalization, many countries are turning to regional fora in the hope of greater success. That raises the question of whether regionalism can do what multilateralism has failed to do, i.e. promote greater openness of services markets. While previous research has pointed to the wider and deeper legal commitments under regional agreements as evidence that it can (e.g. Marchetti and Roy (2008), Fink and Molinuevo (2008), Mattoo and Sauve (2011), no previous study has assessed the impact of such agreements on _applied policies_ . The ASEAN region is particularly interesting because it is widely believed to be at the frontier of what Baldwin (2011) has called “globalization’s second unbundling,” with regional integration of services markets linked to the thriving regional supply chains. Drawing upon surveys in 2008 and 2012 of applied policies in the key services sectors of ASEAN countries, we assess the impact of ASEAN Framework Agreement on Services (AFAS) and the ambitious ASEAN Economic Community Blueprint which envisages integrated services markets by 2015.

We address four questions: How open were ASEAN services markets? Did these markets become more open between 2008, when the first policy survey was conducted soon after they agreed on the far reaching ASEAN Economic Community Blueprint, and 2012? Was ASEAN integrating faster internally than with rest of the world? How far did the ASEAN countries implement their commitments under the ASEAN Framework Agreement on Services (AFAS) and how far were they from meeting the market integration goal set out in the Blueprint?

Our stark conclusion is that the recent commitments scheduled under AFAS may have created greater regional policy-certainty but did not, in general, produce significant liberalization (because they were not sufficiently ambitious), and the 2015 deadline stipulated in the Blueprint did not instill the intended sense of urgency. One consequence of these agreements – which cover primarily intra-ASEAN trade - could have been that ASEAN countries would have been more open vis-à-vis each other than vis-à-vis non-ASEAN countries. For the most part, they were not. For the seven broad sectors (and relevant modes) covered by the questionnaire, there was surprisingly little difference between policy treatment of intra-ASEAN and extra-ASEAN trade. That is, the supposedly preferential policies vis-à-vis other ASEAN countries were 2
**Labor vs. imported intermediate goods demand:** Each unit of the final-stage good produced in country _i_ also generates a demand _e_<sup>_γiFi_</sup> for the most upstream intermediate goods, i.e. intermediate goods imported from the next country in the chain. In terms of value rather than quantities, we obtain that the share of imported inputs in the total cost of production in country _i_ is:

Using this expression, we can retrieve the demand for local labor in country _i_ . The share of local demand in the production of country _i_ has a simple interpretation: it corresponds to the value-added content of exports for country _i_ in that chain. As with the price of the produced good, this expression mimics a CES cost function. The share of labor (one minus the above expression) depends positively on the range of tasks to be performed as well as the price of intermediate goods. The elasticity of substitution between imported inputs and local labor is in turn endogenously determined by diseconomies of scope at the firm level.

**Gross-output-to-value-added ratio:** We define gross output as: _GOi_ = �0 _Fi_<sup>_pfieγifdf_by</sup> integrating the value of all transactions along the chain, while total value added by country _i_ corresponds to: _V Ai_ = �0 _Fi_<sup>_ci_(</sup><sup>_sfi_)</sup><sup>_eγifdf_.Theratioofthesetwovariableshasausefulempirical</sup> counterpart since it is readily available in typical input-output tables provided by statistical agencies. Here, we find that the GO-VA ratio equals:

Strikingly, this result also holds at the firm level. To be more precise, the ratio of price to cost at each stage is constant and equal to: We can interpret this ratio as an index of fragmentation at the firm level. In particular, this ratio reflects the two key forces present in our model: stronger diseconomies of scope (coordination costs) _θi_ lead to more fragmentation while larger transaction costs _γi_ lead to less fragmentation. As seen in equations (15) and (18), this ratio also dictates the difference in scope between upstream and downstream firms.

The relationship between the structural parameters and summary measures of fragmentation are summarized in the following lemma: 16
of GDP, having declined from 18 percent of GDP during 2010-15, while in Georgia remittances have averaged 11 percent of GDP since 2010. Uzbekistan has also received substantial amounts of remittances, especially during 2005-15, although they have subsequently fallen back.

**Table 3 The Stock of Migrants from the FSU countries of the Caucasus and central Asia in 2017 by total and main countries of residence.**

||Armenia <br>|Azerbaijan <br>|Georgia K<br><br>C|azakhstan<br><br>ountry of o|KyrgyzRep <br><br>rigin of mi|Tajikistan T<br><br>grants|urkmenistan <br>|Uzbekistan<br>|
|---|---|---|---|---|---|---|---|---|
|Total 000s)|951|1,155|838|4,074|760|579|243|1,992|
|Country of residence|<br>%|%|%|%|%|%|%|%|
|Percent of total|||||||||
|Armenia||7.6|4.9|0.0|||0.4|0.1|
|Azerbaijan|15.3||6.0|0.1|0.3|0.0|0.7|0.8|
|Russia|55.5|66.5|53.8|62.9|77.9|80.7|76.4|57.6|
|Ukraine|5.0|7.2|7.8|5.5|3.6|5.1|9.4|11.1|
|Kazakhstan|1.0|4.3|0.4|0.0|0.9|2.8|0.4|14.5|
|Germany|1.3|1.7|2.7|25.0|11.0|5.0|2.7|2.1|
|Greece|1.5|0.0|9.8|0.7|0.1|0.0|0.0|0.4|
|USA|9.7|1.9|3.2|0.7|0.8|0.8|0.9||
|Others|10.6|10.9|11.5|5.1|5.5|5.5|9.0|13.3|
|Total|100|100|100|100|100|100|100|100|

Source: United Nations, Department of Economic and Social Affairs, Population Division, Trends in International Migrant Stock, 2017 Revision

**Table 4 Remittances as percent of GDP (2000-18)**

||2000‐04|2005‐09|2010‐14|2015‐18|
|---|---|---|---|---|
|Armenia|12.9|17.6|18.3|13.1|
|Azerbaijan|2.0|3.6|2.6|2.4|
|Georgia|6.7|8.5|11.2|11.4|
|Kazakhstan|0.3|0.3|0.3|0.3|
|Kyrgyz Republic|2.8|18.5|29.2|30.0|
|Tajikistan|9.3|37.2|40.1|29.0|
|Turkmenistan|na|0.2|0.1|0.0|
|Uzbekistan|na|7.3|9.4|3.4|

Source: World Development Indicators 5
# **5.1    Risk Sharing and Cash Transfers**

The premise underlying most of the standard tests in the risk sharing literature is to assess whether idiosyncratic shocks contain any information that could explain consumption growth. The perfect risk sharing hypothesis implies that such shocks will have no impact as their effects will be perfectly shared across the members of the community.

In Section 3.2, it has been highlighted how the literature has rejected this hypothesis in a variety of contexts. Still it is interesting to investigate it in the context of rural Malawi, as its rejection would provide support for the introduction of social protection mechanisms helping households to pool risk more effectively. The dataset allowed us to construct the consumption variables for both round 1 (i.e. baseline round) and round 3 (final round). We looked at two specifications of consumption: food consumption and total consumption. The food consumption variable has been constructed as the sum of the expenditures on more than 110 food items consumed at the household level over the week preceding the interview. In particular, the food items were grouped in the following categories: cereals and grains, roots and tubers, pulses, vegetables, meat, fish and animal products, fruits, cooked food from vendors, milk and milk products, sugar, fats and oil, beverages, spices and miscellaneous (Miller, Tsoka, Reichert 2010b). The non-food consumption variable is the result of the sum of expenditures on more than 30 non-food items divided on the basis of one week, one month or one year recall. The one week recall category includes items like charcoal, cigarettes, matches, newspapers and public transports; the one month recall category includes a range of items from bar soap and other personal care products to light bulbs, bicycle and vehicle repair services, electricity and telephone units; the one year recall category includes carpets, mosquito nets, building items, insurance, funeral costs, marriage costs and gifts<sup>7</sup> . The sample contains households of different sizes and compositions: so it is therefore important to adjust our consumption estimates for these differences as we would introduce an important distortion in the results otherwise (White and Masset, 2003). This leads to the concept of adult equivalence scales. Using OECD equivalence scales, we then calculated the expenditure per adult equivalent following the same approach used by White and Masset (2003), with the total number of adult equivalents per household _i_ given by:

> 7 All consumption variables have been harmonized as annual variables.

14