rafmacalaba/datause-encoder-data huggingface dataset:
20 not fully eliminated.<sup>11</sup> On the expenditure side. the high real interest rates were the main problem, as they were rapidly leading to a build up of domestic debt. T:is issue was particularly acute in the Plan Primavera in Argentina and the Summer plan in Brazil.

The increase in real interest rates and the related build up of domestic debt during the period of the freeze were critical factors in some of the cycles for the abandonment of the freeze. As can be seen from figures 7. and 8. a and b, there was a noticeable cyclical pattern, high real interest rates and debt accumulation are characteristic of the early stage, with the pattern reversed at the end of the cycle. These cycles were marked in Brazil while they were milder in Argentina. It can be seen that the accelerations of inflation in Brazil prior to the Bresser and Summer plans generated negative real interest rates thus eroding the build up of domestic debt during the previous part of the cycle. However, this was a temporary phenomenon as the stock of debt increased rapidly during the first stage of the HS programs, quickly exceeding previous stocks, and real interest rates turned stronglv positive. A similar story is observed in the Argentine data. The debt cycle is more apparent for central bank debt, with a small build up after the Austral II and a large one during the first phase of the Plan Primavera. One remarkable feature of these episodes is that neither in 11 It might be puzzling that even during the period of the freeze inflation was not fully eliminated. There are two reasons for this. First, due to the way the in which price indices are computed, current inflation rates usually reflect past inflation with one or two months lags. Second, and probably more important, in most cases controls were not applied as drastically as to avoid completely any increases in prices; exceptions would be made if justified by increases in costs of imported inputs or if prices were markedly out of line at the time of the freeze. In addition, prices of agricultural products subject to seasonal fluctuations were by and large not subject to controls.
**Table 6.11 Value Added Losses to ZMVM Industry during PM10 and Ozone Contingencies** (value added per day in thousands US$ 1995 values, 1 US$=9.28 Mex. Peso)

||_Percentgrowth_|_Percentgrowth_|||
|---|---|---|---|---|
|_Industry subsectors_|_inPM1O contin-_<br>_gency_<br>_(percent)_|_inozone_<br>_contingency_<br>_(percent)_|_VAlossin_<br>_PM10_<br>_contingency_|_VAlossin_<br>_Ozone_<br>_contingency_|
|Nonmetallicminerals extraction|-42|-42|17.45|17.92|
|Food,beverages,andtobacco|-41|-41|259.01|392.08|
|Textilesandleatherindustry|-43|-43|68.61|99.89|
|Woodandwood products|-45|-45|7.86|5.10|
|Paperindustryandprinting|-46|-47|40.24|123.16|
|Chemicalindustries|-43|-43|215.15|247.84|
|Nonmetallicminerals industries<br>(nooil)|-38|-39|282.05|173.06|
|BasicMetallicindustry|-57|-58|70.89|45.09|
|Metallicproducts|-48|-47|236.40|276.49|
|Othermanufacturing|-41|-43|16.20|0.99|
|Services|-|-39|-|2.36|
|Electricitygeneration|-22|-22|-7.26|-7.00|
|TOTAL|-39|-42|1,306.57|1,376.94|

_Source:_ d= datgen. Emissions Inventory 2000; i= INEGI . Instituto Nacional de Estadistica, Geografia e Informatica, Censo Industrial. Mexico (1997).

To calculate the total losses resulting from environmental alerts, the costs per day has been multiplied by the number of days that the contingency is expected to be in place for each of the scenarios described in section 4. Here, the number of days for the first phase contingencies are counted. The precontingencies do not have explicit economic costs, while the second phase is never attained. The costs per year for phase I contingencies are given in table 6.12 in millions of US$ per year.<sup>24</sup>

**Table 6.12 Industry Losses in Four Scenarios for PM10 and Ozone Phase I Contingencies** (value added per year in millions of US$, 2010 values in 1999 prices)

||_Base_<br>case|_Scenario_<br>_1(10%)_|_Scenario_<br>_II(20%)_|_Scenario_<br>_IlI_<br>_(AQSI)_|_Scenario_<br>_IV_<br>_(AQS2)_|
|---|---|---|---|---|---|
|DayswithPM1Ocontingency|1.0|0.0|0.0|0.0|0.0|
|ProductionlossesduetoPM10||||||
|PhaseIcontingency|4.8|0.0|0.0|0.0|0.0|
|Days withozonecontingency|10.0|2.0|0.0|0.0|0.0|
|Production losses dueto<br>ozonePhaseIcontingency|45.4|9.1|0.0|0.0|0.0|

> 24 An exchange rate of 4 Pesos to the US$ was taken for 1995. These numbers were converted to 1999 US$.

_43_
answers and non-response to 125 seconds. The results are reported in tables 5 and 6 for all children between age 6 and 12. In table 6, we report the time each child took to answer. Overall there was a 22 seconds gain for girls in school meals villages. From columns 6 and 9, it can be seen that these gains in time are confined to older girls in school meals villages (13 to 15 years old), as these girls took 54 seconds less than the control group to complete the exercise. We do not, however, see any significant impact on boys. It is important to remind that our estimation strategy yields average intent to treat (AIT) effects.

A potentially interesting question is why the effects for older boys in the school meals villages in table 5 are negative and statistically significant. At this point, we do not have an explanation. However, similar negative impacts on learning outcomes have been encountered in Northern Uganda by Adelman et al. (2008). Likewise Grantham-McGregor, Chang, and Walket (1998) have identified a negative impact of school breakfast on learning outcomes in Jamaica that they have associated with school level organization. In particular, they have remarked that feeding program was likely to have a negative impact in schools which were not so well organized.

# _5.3 Cognitive Development_

During the follow up survey children between 5 and 15 were administered the WISC and Raven’s progressive matrices tests, and a digital span test. The WISC and Raven tests were originally developed by Raven in 1936. The test asks candidates to identify the missing segment required to complete a larger pattern. The digit span test assesses the number of digits a child can retain and recall. Hence the forward digit span test can be seen as a measure of short term or working memory.

Since these tests were not administered during the baseline, we rely on the differences between treatment and control villages in the follow up assuming that the differences at the baseline would have been minimal. The estimations results are presented in tables 7 for the WISC and Raven’s and in table 8 for the digit span tests. In each case the outcome variable is the child’s total 13
their employers. She reports that this pattern was not seen in the higher-caste Vellalar women who worked as gem cutters in the rural synthetic gem-cutting industry.

Another strand of literature that provides insights into the jati-gender relationship is on marriage markets in India. Recent surveys find that 95 percent of Indians still marry within their broad caste group <mark>(Desai and Dubey 2012).</mark> This has implications for the status of women. Within broad caste groups, women are not permitted to marry a man of a lower caste/jati, and strong punishments are imposed for transgressing that norm (Das 1975). However, hypergamy is, in theory, permitted (i.e., a woman may marry upwards in the caste/jati ranking).

<mark>In summary, most large national sample surveys lack details about jatis.</mark> While anthropologists have gathered in-depth information on the jati-gender relationship, their work has typically focused on small samples in a single community. The enormous geographic, demographic and cultural variations within India make it difficult to draw any broad conclusions from these findings. Furthermore, much of the anthropological literature is dated, and caste and gender relations have changed rapidly in recent years (Sanyal et al. 2015). Thus, examining recent survey data to discern patterns in these relationships could help expand our understanding of caste and gender roles. In this paper, we use data from three states to contribute to this literature.

# **3. Data**

We use data from baseline surveys collected between 2011 and 2013 for the impact evaluations of State Rural Livelihoods Projects in three states in India: JEEViKA<sup>11</sup> in Bihar, TRIPTI<sup>12</sup> in Odisha and PVP<sup>13</sup> in Tamil Nadu. The three states have very different economic, social and demographic characteristics. Key indicators are summarized in Table 1. Bihar is a predominantly rural state whose agrarian structure has a strong caste and class base (Rodgers and Rodgers 2001). Odisha is a coastal state with one of the highest populations of Scheduled Tribes (STs) in the country. STs account for 22.1 percent of the total population of the state and 9.7 percent of the tribal population of India (Census of India 2001). Both Bihar and Odisha are considerably poorer than Tamil Nadu, which is the 8<sup>th</sup> wealthiest state in India and has made great strides in education as well as urbanization. The average person in Tamil Nadu is approximately four times richer than their counterpart in Bihar and twice as rich as their counterpart in Odisha. This significant regional imbalance has been noted by other scholars (Dreze and Sen 2002).

Our surveys include 9,000 households from seven districts in Bihar surveyed in 2011, 3,000 households from ten districts in Odisha also surveyed in 2011, and 3,900

> 11 Bihar Rural Livelihoods Project 12 Targeted Rural Initiatives for Poverty Termination and Infrastructure 13 Tamil Nadu Empowerment and Poverty Alleviation Project

9
regional trading arrangements, COMESA<sup>2</sup> and the East African Customs Union.<sup>3</sup> We shall argue that while there are barriers against foreign investment in business services, _in practice,_ the Kenyan regulatory regime does not discriminate heavily against multinationals. However, Kenyan commitments at the WTO or in its regional arrangements are considerably less open than its practice. Binding commitments made at the WTO or in regional agreements provide a signal to investors in the services sectors that they are welcome and that the regulatory regime will not be turned against them arbitrarily.

What would be the consequences for Kenya of responding to the requests of its trading partners by agreeing to further commitments? How much would Kenya gain from reform of its regulatory regime if reform could reduce the costs of providing business services by both its domestic firms as well as multinationals? What would be the impact on industry, agriculture, wages, returns to capital, exports and imports, as well as the services sectors themselves from reforms in the services sectors? .

In this paper we develop a 55 sector small open economy comparative static computable general equilibrium model of Kenya that we believe is appropriate to evaluate the impact of Kenyan liberalization of services barriers. Our key modeling assumptions are that: we allow foreign direct investment in business services (as well as cross border trade) since a substantial portion of business services require a domestic presence; and business services supplied with a domestic presence are supplied by imperfectly competitive firms who produce a unique variety of the service. We adopt the Dixit-Stiglitz-Ethier structure for business services (as well as for increasing returns to scale goods) that implies endogenous productivity gains from the net introduction of new varieties of service providers. We also allow for endogenous productivity effects from additional varieties of imperfectly competitive goods.<sup>4</sup>

> 2 The Common Market for Eastern and Southern Africa (COMESA) is a preferential trade area among Djibouti, Kenya, Madagascar, Malawi, Mauritius, Sudan, Zambia and Zimbabwe.

> 3 The East African Customs Union is a customs union among Kenya, Uganda and Tanzania and more recently Rwanda and Burundi.

> 4 Elasticities of substitution for product categories in the Dixit-Stiglitz framework have been estimated by Broda and Weinstein (2004). They estimate that, although there are variances within the groups, for agriculture, services and goods the Dixit-Stiglitz elasticitiy of substitution is close to three. We choose three as our central Dixit-Stiglitz elasticity of substitution.

3
to successful entrepreneurship (and business training and mentoring add little value), we might expect the impacts of the franchise treatment to be smaller than the impacts of the grant treatment (because much of the program spending paid for training that, by assumption, would not be the relevant barrier to entrepreneurship for these individuals). On the other hand, the training and mentoring provided through the franchise program might impact participants’ productivity, increasing the fraction of high productivity types. If this were the case, we would expect the impacts of the franchise treatment to be more persistent than those of the grant treatment — though they might initially be smaller in magnitude, depending on the initial mix of types in the population and the value of the capital transferred to franchise program participants.

We test these predictions using data from two rounds of surveys: midline surveys that were conducted between 7 and 10 months after the interventions and endline surveys that were conducted 14 to 22 months after the interventions. Both the midline and endline surveys contain detailed data on involvement in income-generating activities. The endline survey also includes a range of measures of consumption, expenditure, and well-being — which might be impacted by treatment if participants saved or consumed the value of the capital they received without launching a small business.

# **4.1 Estimation Strategy**

In our main analysis, we report intent-to-treat (ITT) estimates of the impacts of the franchise treatment and the cash grant treatment on women assigned to each treatment group. Treatment assignment was random within strata, so the impacts of the interventions on any outcome _Yi_ can be estimated via the OLS regression specification: where _Franchisei_ and _Granti_ are indicators for, respectively, random assignment to the franchise treatment or the grant treatment, _δstratum_ is a randomization stratum fixed effect, _φenumerator_ is a survey enumerator fixed effect, _ζmonth_ is a fixed effect for the month the survey was administered, _Xi_ is a vector of individual controls, and _εi_ is a conditionallymean-zero error term.<sup>14</sup><sup>_,_15</sup>

> 14In our main specifications, we include controls for baseline household size, education level, and indicators for having given birth, having received any vocational training, or having any paid work experience prior to the baseline survey. Results are similar in magnitude and significance when these controls are omitted.

> 15We do not correct for the false discovery rate in our analysis. In our analysis of medium-term impacts, we consider a relatively small set of labor market outcomes (because the midline survey did not collect data on a broader range of outcomes), none of which can be treated as statistically independent. As will become apparent in the subsequent discussion, most of these outcomes are impacted by the treatments over the medium-term; so the overall pattern of findings is unlikely to be explained by multiple testing. In our analysis of longer-term impacts, we look at a broad range of outcomes; however, as almost none are

16
and they become urban only after the state government converts them, following a well-specified legal process. Converted settlements are labelled statutory towns.

**Figure 7. Gap between India’s predicted and official urban populations by state**

<!-- Start of picture text --> 2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>-6.0<br>-7.0<br>Urban population difference (millions)<br>Tamil Nadu Madhya Pradesh Rajasthan Gujarat Orissa Assam Chhatisgarh Uttar Pradesh West Bengal Karnataka Maharashtra Punjab Manipur Tripura Mizoram Jharkhand Goa Himachal Pradesh Meghalaya Sikkim Lakshadweep Dadra and Nagar Haveli Haryana Nagaland Daman and Diu Chandigarh Puducherry Uttarakhand Delhi Andhra Pradesh Kerala Bihar<br><!-- End of picture text --> _Note_ : The figure presents the difference between the urban population by state based on our prediction and that based on the official census. The unit of measurement is million people.

There are guidelines for classifying a settlement as a statutory town, but they are vague and not binding. As a result, state governments exert large discretion in their choices, and the criteria to define a statutory town vary considerably across states. For example, in the southern coastal states the threshold population of statutory towns varies from around 2,000 in Tamil Nadu to over 20,000 in Kerala and above 30,000 in Andhra Pradesh (Aijaz 2018; Denis and Marius-Gnanou 2011). The first state has the least strict criteria in the country while these last two states have among the strictest, which is consistent with the sizeable gap between our predicted urbanization rates and the corresponding official figures.

# _A comparison with other estimates_

Our results are in contrast with those reported in several studies using traditional data. Measures based on different thresholds for population size yield urbanization rates ranging from 47.2 to 64.9 percent for 2011 and those based on different cutoffs for population density produce urbanization rates at 55.0 and 78.0 percent (IDFC Institute 2015; Ministry of Finance 2017). In a World Bank report, a measure based on an agglomeration index that combines population density, the population of a “large” center and the estimated travel time to that center yields an urbanization rate of 55.3 percent in 2011 (Ellis and Roberts 2016).

29
**Appendix I: Sources and quality of data and explanations regarding development of component indices**

_Political Freedom:_ Haq (1995). This index measures four factors that reflect an indlividual's ability to exercise political freedom: a country's political process, statutory freedoms, an individuals ability to exercise freedom of expression, and degree to which discrimination is tolerated.

_Political Stability, Judicial Efficiency, Red Tape:_ Mauro (1995). These indices, developed by Business International Corporation (BIC), are unavoidably subjective. These indices were developed with an investor's perspective in mind. This perspective may lead to different assessments of, say, judicial efficiency than an index with a less pecuniary perspective. These indices, which we have not been updated, are based on older data than the other indices -- 1980-83.

_Corruption:_ Transparency International/Gottingen University (1996). This index, frequently updated, is based on survey results from several risk assessment consulting groups, the Global Competitiveness Report, and the Institute for Management Development.

_Human Development Index:_ Human Development Report (1996). This index, published by the United Nations Development Program measures life expectancy, adult literacy, enrollments in primary, secondary and tertiary education institutions, and per capita GDP in purchasing power parity terms.

_Gini Coefficients:_ Deininger and Squire (1996). These estimates of income equality are based on household surveys which are presented by the authors as both a substantial improvement over previous work in both data quality and coverage.

_Outward Orientation:_ World Bank (1996a). This index uses an initial assessment of outward orientation made for 1980-83 and modifies it according to the speed of integration index calculated for 1990-93. The factors composing the index are: a population-adjusted trade ratio, country credit rating by Institutional Investor, Foreign Direct Investment as a share of GDP (in PPP terms), and the share of manufacturing that is exported.

_Central Bank Independence:_ Cukierman, Webb and Neyapti (1992). This index is compiled from examination of 16 statutory aspects of central bank operations including thce terms of office for the chief executive officier, the formal structure of policy foimulation, the bank's objectives (as stated in its charter), and limitations on lending to the government.

_Debt to GDP Index:_ Compiled from IMF (1995) and IFS (1995) using data from most recent year available.

21
# **4.2 Estimating the Effects on Labor and Capital Demand**

Recent technological innovations have revamped an old concern about productivity-driven displacement of jobs and shifts of labor demand towards skilled workers. New theoretical models developed to understand the potential effects of automation on jobs, as well as the channels through which it operates, depart from the standard factor-augmenting technological change approach. Instead, they propose a new framework, where robots compete against workers, and thus machines replace tasks previously performed by humans (Acemoglu and Restrepo 2018, 2019b, 2020a; Autor and Salomons 2018). However, the displacementinduced contraction in wages dynamically creates incentives for the introduction of new tasks, in general equilibrium, where labor has a comparative advantage relative to technology, the so-called reinstatement effect. This new setup thus enables researchers to think about new forces at work, which have opposing effects on labor demand in general equilibrium.

Our estimation framework with enterprise panel data is, by definition, partial equilibrium. But the estimation framework is flexible and allows for the estimation of the effects of the choice variables on both TFPR and factor demand. As mentioned, the latter effect has two channels, the factor-augmenting or saving effect as well as a scale effect. This allows for a direct test of labor- (and capital-) saving hypothesis. Since our TFPR measure confounds both prices and efficiency, our productivity-driven effect is not fully comparable to the displacement effect cited in the literature (Acemoglu and Restrepo 2018, 2019a). This is because the price-related component of this effect could be labor-augmenting, as efficiency gains are passed-through onto product prices. That is, efficiency gains resulting in price reductions can increase product demand. The efficiency-related component of this effect could be labor-saving, just like the displacement effect cited in the literature. The scale effect, however, is unambiguously labor-augmenting. It is associated with an expansion in firms’ profits due to a reduction in marginal costs or the scale-up of demand for a firm’s output as digitization allows firms to reach a larger potential customer base. Thus, to estimate the factor demand effects from digitization (as well as that of exporting and managerial 18