rafmacalaba/datause-encoder-data huggingface dataset:
trades are rather artificial. For example, there are many firms that trade the same number of shares (which usually is a round number such as 100 or 1,000) for several years in a row and the shares have the same price (again, very often a round number, 100 and 1,000 are very common). The reason for such artificial trading patterns is obvious—the CMA has a rule that requires delisting any firm that has not been traded for more than six months. However, this requirement could be satisfied by trades by firm insiders, which may be regarded as “cosmetic” trading.

To evaluate the extent of such trading behavior an indicator of “cosmetic trading” was created.<sup>9</sup> The percentage of all firms that are either not traded at all or traded only in a “cosmetic” way is reported in Figure 1 (Panel A).<sup>10</sup> About 40 percent of the firms are traded normally (i.e., their prices are determined by the supply and demand factors), while the remaining firms are either not traded or “cosmetically” traded. The percentage of non-traded firms is declining over this period (from about 40 percent to about 20 percent), however, the percentage of “cosmetically” traded firms is correspondingly increasing. It appears that about 60 percent of listed firms are actually not being traded by market participants.

Table 9 reports total market capitalization and total value traded, together with percentage of “cosmetic” and “traded” firms (note that these numbers are calculated for the whole universe of the listed firms). The total market capitalization was increasing until 1999 (likely as a result of adding new listings) after which is started to decline reflecting overall declines in the market valuations. The value traded also reached a peak in 1999 and declined sharply in 2000 and 2001 (declined by about 50 percent in each year).<sup>11</sup> The

> 9 The indicator takes value of one if one of the following rules are satisfied: (a) the price is the same for at least two years in a row, (b) the volume traded is the same for at least two years in a row, (c) the price or volume is a round number such as 100 or 1000, (d) the firm is not traded in the previous year and in the following year (this rule isolates a few companies with occasional trades that occur once every several years and are most likely done for “cosmetic” purposes). Of course, this indicator is not perfect and some measurement error is expected as these rules cannot pick all the “cosmetic” trades or, alternatively, could misclassify some “real” trades as “cosmetic” trades but this error is thought to be small.

10 We only perform this exercise for the firms for which we have both financial and market data to make sure we are not misclassifying any firms that were not listed as “not-traded.”

> 11 Note that these numbers do not match numbers reported by CMA—the order of magnitude is right but there is about a 40 percent difference for 2000 (the CMA numbers are lower) and about a 40 percent difference for (continued)

12
<!-- Start of picture text --> N<br>N<br>% eo4* Hi<br>bs n ra . id ves<br>IN 6% ay wv<br>S mw wy<br>a0<br>So<br>a<br>o. o<br>Bo<br>=<br>+<br>©<br>nN<br>i<e)<br>1940 1960 1980 2000 2020<br>== === GDPpc in Kenya === GDPpc in Uganda<br>ME civil war in Uganda<br><!-- End of picture text -->
and spatial price variabilities. Resident enumerators (stationed in the EAs) were used to implement this arm, thus making diary implementation and supervision more feasible.

# **3.2.2. Treatment 1: 7-day recall with short-list of food items**

The food consumption module in this arm (henceforth, recall short list or T1) was implemented as part of a multi-topic questionnaire in a “single visit” to the household. During the interview, households were asked to recall their food consumption over the 7 days preceding the day of the interview (excluding the day of interview). A total of 75 food items were included in this module. The respondent of the food consumption module is generally an adult female household member responsible for food purchases and preparation. The respondent is first presented with a YES/NO filter question for all 75 food items included in the module. For those items that the respondent indicated their household consumed within the last 7 days (YES response in the first filter question), additional questions on the quantity consumed, sources of consumption (purchased, own production, and gifts), and total value of purchases if any of the household’s food consumption came from purchases.

Information on quantity consumed was collected only in standard units (except for eggs where “piece” was allowed). In addition to food consumed within the household, information on FAFH at the individual level was also collected. Food consumption aggregates were then generated using the quantity consumed and price information collected in this module, while adjusting for temporal (within survey months) and spatial price variabilities. Mobile teams that operate in a roving manner were used for data collection in this study arm.

9
Philippines and Thailand would experience some rise in production under AFTA, but not nearly as much as under MFN liberalization.

The simulation also shows that the expansion of production and exports in various economic sectors is about the same under the AFTA and MFN liberalization scenarios. However, improvements to economic welfare (real expenditures on final demand, or real absorption) are much smaller for most countries under AFTA than under MFN liberalization. Only Singapore and (Malaysia to a lesser extent) show improved economic welfare under AFTA because of their relatively open economies, which would bring benefits to them from the creation of new trade and the diversion of trade away from the rest of the world.

Overall, the DeRosa (1995) study (supported by a 1996 study by Lewis and Robinson) finds that an MFN liberalization scenario yields significantly larger gains in trade and economic welfare than does an AFTA scenario for ASEAN5.

A recent study by Fukase and Martin (2001) assesses the effect of AFTA on the new ASEAN countries (Cambodia, Lao PDR, Myanmar, and Vietnam) also using a CGE model.'<sup>79</sup> The authors look at benefits/costs on an AFTA-only basis and on an extended MFN liberalization basis. The authors decompose the sources of welfare change into (1) welfare gains from trade creation, (2) welfare losses from trade diversion, and (3) welfare gains from terms-of-trade improvements in a country's exports. Simultaneous changes in these three components determine the net effect of AFTA.<sup>80</sup> The findings of this study are summarized in Table 3.4.

Table 3.4

|**Impactof**<br>|**AFTAonASEAN NewMembe**<br>|**rCountries**<br>|
|---|---|---|
|**ASEANMemberCountry**|**OverallEffect**|**MajorSpecific Effects**|
|Cambodia|Somewhatbeneficial|Tradediversion offsetsbenefits<br>oftradecreation.Terms-of-trade<br>gains from improvedaccessto<br>partnermarketsarelimited.Entry<br>intoAFTAimportantin<br>stimulatingdevelopmentofnew<br>revenue sources,suchasVAT.|
|LaoPDR|Economically beneficial|**_Importside:_**Tradecreating,thus<br>reflectinghighshareofASEAN<br>countriesin LaoPDR'simports<br>andslightlyhigherinitialtariff<br>rateslevied onASEANgoods.|
|||**_Exportside:_**Positiveeffectson<br>mostimportant export<br>commodities (wood productsto<br>ASEANandgarmentstoEU).<br>AFTAalsoreduces vulnerability<br>to shocksrelatedtosmallnumber<br>ofexports,solikelytostimulate<br>exportsinprocessedfoods,<br>animalproductsand some labor-<br>intensiveindustries.|
|Myanmar|Economically beneficial|Gainsfromtradecreation(from<br>tariffreductions)onlypartially<br>offsetbycostsoftradediversion|

54
12 The table suggests that the efficiency gains have indeed often been significant but were generally not quickly passed on to users, during the 1990s. More specifically, while technical/productive efficiency improved, which means costs dropped, average tariffs did not drop commensurately. It suggests that a rent may have been created through efficiency gain improvements. The bottom part of the table reveals the beneficiary of the rent: the government. It may have hijacked the efficiency gains through the tax system. While there are obvious advantages to this for any country in fiscal crisis, it significantly reduces the signaling effect of the price system.

It turns out that, in Argentina, as in most of the other countries of the region, all three levels of government have contributed to minimize the redistribution of the efficiency gains to the users, through major increases in indirect taxes. For Argentina, the infrastructure sectors are such an effective tax handle that they generate over 1% of GDP in tax revenue across government levels. Total indirect taxes add up to 40-50% on telecoms services, 20-57% in energy and over 20% on water services. This is 4 to _5_ times the revenue it used to generate when these services were public.

The upshot of this review of the distribution of the efficiency gains is a-rationaldiscontent by the users. Users grew unhappy as average tariffs slowly increased. In water, this was due to a major catching up of tariffs with costs in a sector in which average costs recovery rates have long been around 25%. **8** As for passenger transport, the effective increase in tariff was infinite in many cases, since prior to the reforms, roads were free and the effective fares in buses and rails were quite low since many users evaded the payment obligation under public operation. Even for those users, including non residential users, who were used to pay for the services, over time, the improvements in service quality were insufficient to compensate the fact that tariff appeared to be creeping up-and no one seemed to really notice the role of taxes in this creeping up.

In sum, it seems that the successive public administrations have benefited from the efficiency gains. In addition to the lump-sum payments at the signature of the contract or the annual payments due by the operator, the governments are now able to cash in more from reforms through the tax system. The problem is that in water and transport, the reforms have

> **18** See World Development Report (1994).
that females are more likely to work while in school in the younger cohort (note that schooling is controlled).

The results also suggest that the probability of the wage being observed is lower if the individual is more schooled, female with TEEP, has an educated mother, and is part of a wealthier family. School and work are competing for our sample, so it is reasonable that more highly educated students were less likely to participate in the labor force. This inclination is stronger in the TEEP cohort. TEEP reduced females’ likelihood of labor force participation, which is consistent with the observation that female TEEP beneficiaries spent more time studying. Also, students from relatively wealthier households are less likely to work while schooling. Table A3 shows that the inclusion of inverse Mill’s ratio does not alter our main findings.

# **References**

Altonji, J.G., and R.M. Black, 1999, Gender and Race in the Labor Market, In _Handbook of Labor Economics_ , vol. 3, ed. O. Ashenfelter and D. Card, Amsterdam: North-Holland. Anderson, K.H., E.M. King, and Y. Wang, 2003 , Market Returns, Transfers and Demand for Schooling in Malaysia, 1976-89, _Journal of Development Studies_ , 39: 1–28.

Alderman, H., J. R. Behrman, V. Lavy, and R. Menon. 2001, Child Health and School Enrollment: A Longitudinal Analysis, _Journal of Human Resources_ 36: 185-205.

Alderman, H., J. Hoddinott, and B. Kinsey. 2006, Long Term Consequences of Early Childhood Malnutrition, _Oxford Economic Papers_ 58: 450-474.

Alderman, H. and E. King, 1998, Gender differences in parental investment in education, _Structural Change and Economic Dynamics_ , 9: 453–468.

Aslam, M., 2009, Education Gender Gaps in Pakistan: Is the Labor Market to Blame?, _Economic Development and Cultural Change_ , 57: 747-784.

Card, D., 1999, The Causal Effect of Education on Earnings, In _Handbook of Labor_ _Economics_ , vol. 3A, ed. O. Ashenfelter and D. Card, Amsterdam: NorthHolland.

---------, 2001, Estimating the Returns to Schooling: Progress in Some Persistent

Econometric Problems, _Econometrica_ , 69: 1127–60.

Case, Anne and Angus Deaton, 1999, School Inputs and Educational Outcomes in South Africa, _Quarterly Journal of Economics_ , 114: 1047-1084.

26
# **Welfare and poverty impacts of India's National Rural Employment Guarantee Scheme: Evidence from Andhra Pradesh**

## **1. Introduction**

Persistently high rates of extreme poverty and gender inequality, together with increased frequency of natural and man-made disasters, have increased policymakers’ interest in public work programs as a form of productive safety nets. The ability to set wages in a way that is self-targeting and fosters gender equality, combined with the opportunity to construct physical infrastructure that can enhance growth and wages in the long term, makes such programs very attractive compared to available alternatives. At the same time, however, there has been concern that implementing these programs successfully carries high administrative requirements and that where these controls are not in place, large amounts of resources may be wasted or end up lining the pockets of local officials.

With a budget of US$7.8 billion in 2011/12 alone, India’s Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) is one of the largest programs of this kind globally.<sup>1</sup> It guarantees employment for up to 100 days per fiscal year at wages that are equal for men and women, thus serving not only as an insurance substitute but also having the potential to enhance female empowerment. Implementation, at least in some states, also includes some innovative features, such as making all project-related data available on the internet, directly depositing payments into beneficiaries’ accounts, and regular _social audits_ to minimize corruption.<sup>2</sup> While this suggests that NREGS could herald a new generation of such programs, the implementation has not been uncontroversial.

Supporters point to awareness and high participation rates by females (greater than 50 percent) and the poor, that are significantly above those in earlier or comparable programs, as well as anecdotal evidence suggesting that the program has made clear contributions to decentralization, transparency of political

> 1 Since 2009, the program is referred to as Mahatma Gandhi National Rural Employment Guarantee Scheme. We use the earlier name throughout the paper

> 2 Social audits are audits conducted with active involvement by primary stakeholders. They resulted in discovery of frauds on a significant scale. Some officials embezzled money by “creating fake muster rolls, inflated bills, exaggerated measurements, and non-existent works, all through bribes and cuts from wage seekers” (http://125.22.8.66/SocialAudit/).

2
tailed survey information to provide direct evidence on the possible channels through which the API _Plus_ program enhance children’s outcomes.

# **3.3** **_Plus_ vs.** **_Original_ : Channels**

We start by evaluating the role of the remedial education sessions. The estimates displayed in Table B-10 suggest that there is no differential effect across the four children’s outcomes ( _p_ - values = 0.766, 0.675, 0.639, and 0.937) in the relative impact of the two training modalities between children who are more or less likely to be eligible for the remedial sessions (see also Figure B-2). Although the design of the second experiment does not allow us to directly isolate the direct effect of the remedial education sessions within each API modality, this evidence suggests that such mediating factor is unlikely to explain the differential impact between the _Plus_ and the _Original_ documented in Table 2.<sup>11</sup> We next consider the role of the pedagogical practices of the community instructors. Table B-11 reports estimates of the effect of the two API modalities using data at the instructorschool level (the sample average number of instructors per school is 1.2 in the school year prior to the start of the second experiment) on four summary measures of pedagogical practices based on GLS-weighted indices across an array of instructor-student interactions (for details, see Appendix A.2). The results show erratic patterns of positive and negative signs with no statistically significant effects of either API modality. The overall index of pedagogical practices reveals a non-negligible negative effect of 0.18 standard deviations for the _Plus_ modality, indicating, if anything, a crowding-out effect of the presence of the mentors on instructors’ job effort. The effects are quantitatively and statistically similar between API modalities across the different pedagogical practices, as shown in the third row of Table B-11.

Finally, we study the role of the mentor/parent interactions during the home visits as a potential mechanism behind the large and positive effect of the _Plus_ modality. Panel A in Table 4 displays the estimated differences across the two API modalities on selected survey variables when 591 parents were asked about the frequency and content of their interactions with the mentors over a period of two months prior to the survey. The number of observations varies across the columns in Panel A due to some of the parents not responding to the survey questions. Missing values for each outcome are balanced with respect to the assignment of

> 11The correlation between the school-level rankings as implied by the average diagnostic test and the math and reading scores is 0.51 and 0.52, respectively. In the absence of randomization across the different components of the intervention within each modality, the direct effect of the remedial education sessions cannot be separately identified from heterogeneous treatment effects by academic achievement.

16
<!-- Start of picture text --> oO~<br>N<br>a<br>2<br>no}oSSoe<br>av<br>Ss2<br>Be<br>for}<br>1 1.5 2 2.5 3 3.5 4 45 5<br>GBF- Intensive<br>'—— Female ——®— Male<br><!-- End of picture text --> <!-- Start of picture text --> =<br>2<br>2c<br>no}orS[J<br>a<br>Ss2<br>Be<br>ror)<br>1 1.5 2 2.5 3 3.5 4 45 5<br>SBF- Intensive<br>'—— Female ——®— Male<br><!-- End of picture text --> <!-- Start of picture text --> o~<br>at<br>2<br>rsS<br>no}[Jan<br>2oO<br>goD<br>foe}<br>1 1.5 2 2.5 3 3.5 4 45 5<br>GBF- Intensive<br>'—— Female ——®— Male<br><!-- End of picture text --> <!-- Start of picture text --> =<br>=><br>2oNS<br>no}a[J<br>2oO<br>gBe<br>foe}<br>1 1.5 2 2.5 3 3.5 4 45 5<br>SBF- Intensive<br>'—— Female ——®— Male<br><!-- End of picture text -->
average, the time preference rate of the Latin American countries would be .2 percent lower than that of the industrial countries.

**<u>Table 1. Crude death rates (%)</u>**

|Latin Am|erica|Devel|oped|
|---|---|---|---|
|Argentina|0.8|Australia|0.6|
|Bolivia|0.8|Canada|0.7|
|Brazil|0.7|France|0.9|
|Chile|0.5|Germany|1.0|
|Colombia|0.5|Italy|1.0|
|Honduras|0.6|Japan|0.9|
|Mexico|0.4|Spain|0.6|
|Nicaragua|0.5|UK|1.0|
|Peru|0.6|US|0.8|
|Average|0.6|Average|0.8|
|Source: WDI|(2007)|||

# _III.2 Per capita consumption growth rates_

The second element that is needed in the calibration of the STP is the expected growth rate of per capita consumption. A standard approach taken in much of the existing empirical literature (Pearce and Ulph, 1995; Kula, 2004; Evans and Sezer, 2004; Evans, 2004) relies on generating the expectations of the future rate on the basis of past per capita consumption growth rates. For example, in their studies, Kula (2004), Evans and Sezer (2004), and Evans (2004) proxy expected future growth with the average consumption growth rate observed over the previous three decades, whereas Pearce and Ulph (1995) rely on four decades of data.

While in principle this is a simple and transparent way to operate, in the Latin American context can be problematic for two reasons. First, if one views the lost decade of the 1980s as an outlier not likely to be repeated in the future, averages that include the 1980s can be way too low. For example, between 1981 and 1990 Nicaragua experienced a decline in per capita consumption of almost 60 percent. In Peru the decline was less marked but still above 25 percent. Similarly, during the 1980s consumption per capita fell by more than 10 percent in Brazil. It is worth noting that of the countries under analysis in this paper, only Chile and Colombia had in 1990 per capita income levels higher than in 1980.<sup>14</sup> The first column of Table 2 reports average per capita consumption growth rates for the countries under consideration over 1961-2006 whereas in the second column, we replicate those averages but now ignoring the growth outcomes of the period 1981-1990. Inspection of these two columns shows that the 1980s contributed to lowering long run

> 14 For Argentina we lack real per capita consumption data from 1980 to 1993. Yet, if we note that per capita GDP during the 1980s declined by 25 percent, it seems reasonable to assume that per capita consumption did not increase in this country either during the lost decade.

7
1.6 Recognizing the potential payoffs, World Bank FMIS projects have had highlevel objectives along the lines of improved service delivery, improved public sector effectiveness, better expenditure management, or improved transparency and accountability. Reviewing all objectives of the FMIS projects<sup>5</sup> shows that the majority (40.5 percent) were focused on accountability, fiduciary responsibility, and oversight, followed by transparency (39.7 percent), efficiency (35.7 percent), and effectiveness (20.6 percent).<sup>6</sup> This is summarized in table 2 below.

**Table 2 FMIS objectives by area of focus (FY1985-FY2017)**

|**Area of focus**|**Number of projects**|**Share**|
|---|---|---|
|Accountability, fiduciary responsibility and oversight|51|38.1%|
|Transparency|50|37.3%|
|Efficiency|45|33.6%|
|Effectiveness|26|19.4%|
|Capacity and coverage|20|14.9%|
|Public service delivery and access to services|12|9.0%|
|Credibility|9|6.7%|
|Allocation|7|5.2%|
|Quality and timeliness of reporting and information|5|3.7%|
|Utilization|5|3.7%|
|Equity in resource sharing and inclusiveness|3|2.2%|
|Reliability|3|2.2%|

_Source: A_ uthors’ review and classification.

# **Motivation and Scope**

1.7 The above sections have established that there is broad agreement on the potential of FMISs to contribute to larger PFM objectives and that significant time and resources have been dedicated in the pursuit of these. The nature of FMIS implementation is however complex (Dener et al 2011, Hendricks 2012, and Hashim 2014) and the academic literature remarkably thin on causal effects of FMIS investments on intermediate or final outcomes (see table 2 for examples). A Boolean search in Web of Science found five studies which were however of relatively low quality and none were of experimental or quasi-experimental nature. Combaz (2015, p.3) refers to this state of the FMIS literature as a “dearth of rigorous evidence”. There are however a

> 5 Project objectives were obtained from the IEG DataMart and coded following a grounded theory approach.

> 6 Categories per project are not mutually exclusive and thus add up to more than 100 percent.

**14**