rafmacalaba/datause-encoder-data huggingface dataset:
offs for machinery were withdrawn. The dummy for the negative real interest rate covers 1973-1982 and 1985-87 the Pass Law dummy covers 1968-1985. Farm size is used in the model to represent transaction costs of land purchases and sales and of labour management, as in de Janvry et al. (1989).

# Order of Integration

Cointegration techniques are used to establish valid relationships between the variables. If the variables are cointegrated, then deviations from the long run equilibrium path should be bounded. In simple cases two conditions must be satisfied for variables to be cointegrated. First, the series for the individual variables should be integrated of the same order and second, a linear combination must exist that is integrated of an order one less than the original variables; that is, if the variables are integrated of order one (denoted I(1)), the error term from the cointegrating regression should be stationary (or 1(0)).

The time series properties of the variables, all in logarithms, are reported in Table 1'. The standard Dickey-Fuller (Dickey and Fuller 1981) (DF) test and the Sargan and Bargava (1983) CRDW test are used to determine the order of integration. Column two shows that all the variables are first order autoregressive (AR) processes, which indicates that the error terms in the DF tests are white noise, as required. This was confirmed using the Ljung and Box (1978) Q- statistic for serial correlation, which is appropriate for small samples.

The next column shows that the DF test statistics indicate that all the variables are non-stationary in the levels, except for mechanical patents (all except the -3.81 for mechanical patents are greater than the critical value of -2.90). However, the DF test values for the first differences are all less than the critical value, indicating that all the variables are 1(1), except for mechanical patents, which appears to be I(0). The CRDW tests in column four confirm the 1(1) results and suggest that mechanical patents is also borderline 1(1). The Dickey-Fuller 03 test ' The Pass Laws dummy is excluded at this stage and so is farmer education, as these two variables were not significant in any of the regressions.

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## **3.2 Post-ERP Developments**

## **3.2.1 The Public Sector**

In the years following the ERP there has been clearly an attempt to reverse the employment-real wage trade off implicit in the policies of the previous era. The government was persuaded to cut employment and increase the wage of the smaller work force in the interests of efficiency. The public sector employment was cut by 60 per cent between 1985 and 1991 ( see Table 3.1 above). But at the same time real wage in the public sector went up by 170 per cent over this period, including a major hike of 80 per cent in 1992 ( World Bank 1994, table 4.2, p. 165). This put the public sector wage back to its 1970 level, but it was still only 72 per cent of its peak in. the early sixties.

# **3.2.2 The Private Sector**

The recovery phase 1983-87 saw a significant increase of private sector employment, and a slower growth of public employment. The private share of formal employment (SOPSE) climbed to a level of 20 per cent. But the figures bear adequate testimony to the story, that whatever might have happened in the rural and the urban informal sectors, recovery in the formal wage sector has not been sustained. In the period 1987-91 private employment fell drastically. In fact in 1991 it was only 70 per cent of its level in 1.980. This time the public sector, under pressure from the adjustment program, offered only limited buffer for the decline in employment.

The setback after 1987 is evidently related to the trade liberalization measures which were strengthened in the second phase of the program. This disappointing trend in employment is related to the fortunes of the Ghanaian private manufacturing sector in the post adjustment period and will be discussed further at a later point in the paper.

The average real earnings in the private formal sector seem to have increased at the same rate as real wages in the public sector in the post-ERP period until 1988 ( World Bank, ibid.). But did private sector wages fall to as low levels as they did in the public sector in the years of accelerating inflation before the ERP? Unlike the public sector, the private employer could be expected not to let wages fall below a threshold which would actually lead to a proportionately greater fall in efficiency than wages per worker. Nor is the latter obliged to follow a policy which favors retention of employment more than falling wages. In fact, we have already seen that private formal employment fell sharply while the public sector work force was expanding in the years prior to the ERP. Aggregate earnings data do suggest that in 1988, private sector average earnings were higher than those in the public sector; the private-public differential was 1.46. This differential in favor of the private sector seems to have been whittled down in the years after 1988 as public sector wages increased further. The -over-all private-public earnings ratio stood at 1.07 in 1991 (ISSER, Table 8.14, p.153). The massive wage increase of 80 per cent in the public sector in 1992 must have pushed this ratio much further down although the data are not available for more recent years from this source.

10
administration, and “inside the border” elements, such as domestic Regulatory Environment and the infrastructure to enable e-business usage.

# **4. Data issues**

# **4.1 Rationale for Selecting Trade Facilitation Indicators**

We use four distinct areas of focus that meet policymakers’ needs for specificity on how to approach trade facilitation efforts. They are: (1) Port Efficiency, (2) Customs Environment, (3) own Regulatory Environment, and (4) Service Sector Infrastructure. Port efficiency is designed to measure the quality of infrastructure of maritime and air ports. Customs environment is designed to measure direct customs costs as well as administrative transparency of customs and border crossings. Regulatory environment is designed to measure the economy’s approach to regulations. Service sector infrastructure is designed to measure the extent to which an economy has the necessary domestic infrastructure (such as telecommunications, financial intermediaries, and logistics firms) and is using networked information to improve efficiency and to transform activities to enhance economic activity. Besides the observation that these categories match areas for policy-makers attention, these trade facilitation measures also match several GATT articles and appear in the list of Singapore issues in the Doha Development Agenda. Therefore, they have salience for WTO negotiations.

# **4.2 Constructing the Measures Used in This Study**

We use data from WMO (2003) that rely on three sources--World Economic Forum Global Competitiveness Report 2001-2002 (henceforth GCR), IMD Lausanne, World Competitiveness Yearbook 2002 (henceforth WCY), and Kaufmann, Kraay and Zoido-Lobaton (henceforth KKZ). See the Data Appendix for a more complete description of the sources and each of their methodologies for collecting and preparing data about a country.

Table 1 reports information about these indicators. It displays each input for the trade facilitation indicator, the mean, standard deviation, and minimum value along with countries of best and worst practice. For best practice, Singapore and Finland stand out. As the focus of this paper is the likely impact of changes in trade facilitation measures on Mexico’s trade, the last four columns of Table 1 display the absolute numbers for Mexico, as well as its ratio from the minimum and maximum values. It can be seen that only in two variables Mexico is an average 7
European Committee for Standardization (CEN) to create an original database of EU product standards applied to textiles and clothing. Our data distinguish between standards that are equivalent to ISO standards—a proxy for international harmonization—and those that are not. This allows us to address a different policy question from the one examined by, for example, Moenius (2004, 2006). In these papers, the author focuses on the extent to which bilaterallyshared standards promote trade.

Second, we examine the impact of voluntary standards, since they represent an important, but under-analyzed, part of the standards landscape in Europe. Our results are therefore complementary to existing work dealing exclusively with mandatory standards, such as Brenton et al. (2001), Henry de Frahan and Vancauteren (2006), Fontagné et al. (2005), Chen and Mattoo (2004), Disdier et al. (2007), and Baller (2007). These studies generally find some evidence that product standards impact negatively on trade with “outsiders”—i.e., those countries outside the harmonization zone. The effect is not uniform, however, and tends to vary from one sector to another (see also Moenius, 2004).

Third, we examine the trade impacts of product standards both at the extensive and intensive margins. Our results therefore complement existing analyses on mandatory standards (Chen and Mattoo, 2006; Baller, 2007) and firm-level survey data (Chen et al., 2006). Moreover, in specifying an over-identified version of our sample selection gravity model, we also produce evidence corroborating recent theoretical work on the importance of credit constraints in trade models with fixed costs. Our results can be interpreted as supporting the view that standards create fixed product adaptation costs, which need to be financed (e.g., Chaney, 2005; Manova, 2006).

- 4 -
- 19 -

# **Table 7. Estimated** **<u>technical inefficiency by country</u>**

||Pooled|Industry-sp|ecific regre<br>|ssion:<br>||
|---|---|---|---|---|---|
||||Aro-|Chemical||
||model|Textile|g<br>business|<br>& Metals|Service|
|All|0.015|0.010|0.005|0.004|0.052|
|Burundi|0.055|0.026|0.012|0.003|0.132|
|Kenya|0.008|0.004|0.005|0.004|0.027|
|Rwanda|0.016|0.010|0.002|0.001|0.033|
|Tanzania|0.008|0.013|0.004|0.001|0.027|
|Uganda|0.006|0.006|0.005|0.006|0.013|

Another issue of particular interest may be firm agglomeration. Theory suggests that firms prefer to be located close to each other to take advantage of agglomeration economies. The existing evidence is supportive. Procher (2011) finds agglomeration economies for French firms in Europe. Lee et al. (2012) find that there are positive externalities to Korean manufacturers in the United States. Mare and Graham (2013) show that the average agglomeration elasticity in New Zealand is significant and estimated at 0.06.

To examine how economies of agglomeration affect firm costs in East Africa, an additional specification is examined with two types of agglomeration economies considered: localization economies and urbanization economies. While the former is measured by the total number of existing firms in the same industry at the same city (denoted by _N_ 1), the latter is measured by the number of firms in the same city ( _N_ 2). The log of _N_ 1 and _N_ 2 and their squared terms are added to Equation (2). To avoid further complication related to endogeneity, the lagged values are used. Recall that our BEEPS data are for 2005 and 2006. Unfortunately, historical business register data are not always available in the sample countries. The relevant data are available only for three cities in Tanzania (Dar es Salaam, Arusha and Mbeya) and Kampala, Uganda.

The results suggest that economies of agglomeration matter to firm production. Especially, economies of localization are statistically significant. The estimated cost elasticity is about - 0.03 to -0.04 (Table 8). Thus, if the number of total firms in the same industry at the same locality increases by 10 percent, the firm costs would decline by 0.3–0.4 percent. This can be
measure poverty status per se, our approach is to be more inclusive rather than less so. Using the Planning Commission’s estimates, we assume 28% and 16% poverty rates for the rural and urban sectors respectively in 1993 and 14% and 15% in 2004. We adopt this relatively high cutoff to better capture households receiving BPL benefits. In our robustness checks, we test this assumption using an alternative, less inclusive scenario based on Deaton’s (2003) criteria. This assumes poverty rates of 17% and 11% for the rural and urban sectors in 1993 and 6% and 5% in 1999.

# **3.2.2. Which Households Are Eligible Under the SC/OBC Rule?**

Under the program rules, all households from a disadvantaged caste background are eligible unless they are extremely wealthy, i.e. all SC and OBC households, excluding those in which the household heads are gazetted government employees or pay income taxes. The major challenge in identifying SC and OBC households in this data is that SC and OBC status are reported as separate mutually exclusive categories in NFHS-2 and NFHS-3, but in NFHS-1 only SC is reported.

In order to match SC and OBC classifications across all surveys, we first identify the caste of the household head in NFHS-1. We then categorize that household as OBC if households of that caste are categorized as OBC in NFHS-2. This results in the recategorization of some castes that fall into SC in NFHS-1 as now OBC. In general, to avoid confusion, for analytical purposes we conflate SC and OBC together into a single category.

While we have no means of identifying top ranking government employees, this is likely to be an insignificant fraction of this sample. We do however address the income-tax qualification. In India, income tax-paying households are estimated to be the top 1-4% of the national income distribution (Banerjee and Piketty, 2006). We therefore also exclude SC or OBC households whose asset holdings place them in the top 4% of the rural or urban asset index distribution for that survey wave.

Consistent with the program rules, “Scheduled Tribe” (ST) members are not considered eligible even though ST issues are often discussed in conjunction with SC/OBC- policies. We note that there are no officially designated Scheduled Tribes in Haryana, and individuals reporting ST affiliations are likely to be misclassifications or migrants from other states (less than 5 individuals per wave).

# **3.2.3 Which Women and Children Are Eligible within an Eligible Household?**

Within any eligible household, women who bear daughters and who have fewer than two living children at the program’s start in October 1994 are eligible. Likewise, girls are eligible if they were born in or after October 1994, and had two or fewer living siblings at the time of their birth.

The NFHS collects a complete birth history from every woman interviewed, including the age of birth and death for each child. For women interviewed after October 1994, i.e.

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Table A6: The Complementary Effect of Internet and Roads by Sector of Occupation

||(1)|(2)<br>**DH**|(3)<br>**S**|(4)|(5)<br>**WB**|(6)<br>**ES**|
|---|---|---|---|---|---|---|
||**Low**<br>**skilled**<br>**worker**|**Services**|**Skilled**<br>**worker**|**High**<br>**skilled**<br>**worker**|**Number of full time**<br>**skilled employee**<br>**at end of last fscal year**|**Number of full time**<br>**unskilled employee**<br>**at end of last fscal year**|
|Submarine internet|-0.131***<br>(0.0195)|0.168***<br>(0.0236)|0.129***<br>(0.0189)|0.0962***<br>(0.0143)|1.070***<br>(0.268)|1.201***<br>(0.3430)|
|Proximity to road (KM)|0.00524***|0.00126***|0.000939***|0.000988|0.980***|0.447***|
||(0.000411)|(0.000283)|(0.000233)|(0.00176)|(0.250)|(0.227)|
|Submarine internet * road|-0.0194***<br>(0.00422)|0.0294***<br>(0.00529)|0.0218***<br>(0.00417)|0.0156***<br>(0.00314)|0.0629***<br>(0.0201)|0.1010***<br>(0.0382)|
|N|724778|717891|752131|767327|12276|3306|
|ymean|0.528|0.226|0.187|0.121|15.731|8.584|
|R-sq|0.139|0.0956|0.0734|0.0471|0.145|0.1360|
|R-s~~q~~<br>~~a~~|0.139|0.0955|0.0733|0.0471|0.140|0.1190|
|Kleibergen-Paap F-statistic|81.24|81.55|80.89|81.15|1.103|1.1300|
|_P > F_|0|0|0|0|0|0|
|Cragg-Donald Wald F-statistic|811.4|808.3|885.1|850.6|1202.1|227.6|
|Windmeijer F-statistic|17.52|20.01|26.87|16.71|29.11|21.14|
|Kleibergen-Paap Wald F-statistic|19.18|19.23|18.99|19.14|25.88|66.88|
|Age|✓|✓|✓|✓|✓|✓|
|Gender|✓|✓|✓|✓|✗|✗|
|Place of residence|✓|✓|✓|✓|✗|✗|
|Education|✓|✓|✓|✓|✗|✗|
|Year FE|✓|✓|✓|✓|✓|✓|
|Country FE|✓|✓|✓|✓|✓|✓|
|Grid (20*20) connectivity FE|✓|✓|✓|✓|✓|✓|

_Robust standard errors in parentheses clustered at the grid level. ∗p < ._ 10 _∗∗p < ._ 05 _∗∗∗ p <_ 0 _._ 01 _. We use a strategy that combines difference-in-differences and instrumental variables techniques. The causal effect of Internet is estimated using difference-in-differences, while the effects of the road and its interaction with the Internet are estimated using an instrumental variables approach. Our instruments are: the distance to the hypothetical road, the interaction between the distance to the hypothetical road and an indicator for whether an individual (or a firm) is connected to submarine Internet, lightning, and terrain elevation._ 57
# **4 Data**

## **4.1 Data Collection**

Figure A1 presents a timeline for data collection. Baseline data collection took place between September and December 2016. Endline data collection took place approximately three years after the baseline surveys, from December 2019 to April 2020. In both rounds, we followed best practices in survey protocols, and the study was approved by the National Bioethical Committee for Health in Mozambique (CNBS). In-person surveys were conducted in all data collection rounds. The instrument was before con- survey piloted ducting the baseline survey.

At baseline, we contacted all 4,687 caregivers whose children were eligible to participate in the intervention and consented to enroll in the study to collect their baseline information. Follow-up data was collected from caregivers three years after the baseline survey was conducted. We visited the 4,687 households that took the baseline survey and met the eligibility criteria. Three years after the baseline surveys, we collected follow-up data from 3,765 caregivers and their children (an 80.3% response rate). The follow-up survey included modules to measure child’s preschool and primary school enrollment, cognitive and social-emotional skills, and executive function. We also collected information on parental practices and household labor supply. Section 4.2 describes the information that we collected in more detail.

As noted in Section 2, for the communities in the treatment group, we collected endline information on child attendance, preschool infrastructure, and the educator’s characteristics, such as their educational background. In addition, we conducted a qualitative study designed to assess the quality of the different components of the DICIPE program, to use these results as suggestive evidence for the potential mechanisms of the intervention. Between June and July 2019, we collected data from a randomly selected sample of 40 communities across the provinces participating in the DICIPE program.<sup>9</sup> We inter-

> 9The qualitative study was conducted in the five provinces as this information was also used for admin-

11
# 3.1.5 Electricity imports

One modeling simplification entails simplifying the import module. Essentially, we drop the imports by fuel type and only add a category for total electricity imports.

We note that total energy consumed is either from local production or imports:

> _E_ = i.e., we import electricity if consumption exceeds domestic production capacity (∆ln �� _Qt_ � ∆ln �� _YtE_ �).

In the CCDR example, we can import infinite amounts at international prices if consumption exceeds local production. Technically it is possible to impose a cap on the amount of energy that can be imported (e.g., due to constraints that our trading partners are facing, or local distribution constraints). Let us call this constraint _M_<sup> ̄</sup> _t_ . This implies that consumption must be constrained to be the minimum of electricity consumption demand, or capacity ( _Y_<sup>�</sup> _t_<sup>_E_+</sup> _M_<sup> ̄</sup> _t_ ): This ensures that consumption can never exceed generation capacity, while imports cannot exceed import capacity, and should always be zero if local demand is met by local production.

# 3.1.6 How the model embeds desired energy transition paths

In some simulations the models are tasked with finding the carbon taxes associated with an emissions path conditional on the model reacting to the price path. The approach boils down to a goal-seek where we have a vector of control variables ( **x** ), in this case carbon taxes, for a vector of target variables ( **y** ), in this case emissions, to reach a desired path ( **y**<sup>_∗_</sup> ), e.g., net zero. The modeling approach requires that we find values of **x** that minimize the following quadratic function: Note that the weighting matrix ( **W** ) is symmetric ( _mT × mT_ ), where _m_ is the number of target variables (e.g., coal, oil and gas) and _T_ is the number of periods. The values of the control variables that minimizes the loss function will be a function of the gradient and the 13
**Table A5: Effects of a Workerʹs Unexpected Illness on the Labor Supply of Other Household Members, with Enumeration Area X Week controls**

|Dependent variable|1(W|ork)|
|---|---|---|
|Other worker ill|0.089**|0.141***|
||[0.036]|[0.052]|
|Other worker ill X Female||‐0.095<br>[0.069]|
|Mean Dependent Variable|0.807|0.807|
|Observations|3,951|3,951|
|R‐squared|0.592|0.592|

_Notes: Other worker ill = 1 if another household member reported missing work for a day in which s/he was planning to work and the reason for missing work was injury/illness. Sample includes respondents employed at baseline. All specifications include individual fixed effects and gender interacted with week of interview, enumeration area X week, and gender interacted with a control for the number of household members planning to work in that round. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._