rafmacalaba/datause-encoder-data huggingface dataset:
extension + video treatment; єij represents an individual level error term that is clustered at the mws level to allow for arbitrary correlation of households and individuals within clusters.

Although treatment indicators should be orthogonal to further explanatory variables due to randomization, all models are re-estimated including additional covariates in order to increase precision of the estimates and to control for small-sample imbalances: In these models, Xij0 represents a vector of control variables related to farmer and household characteristics captured at baseline, while Wj, adds community-level indicators for rainfall, temperature and remoteness. When available, the inclusion of the baseline level of an outcome Yij0 in the equation should reduce the overall variance, since some degree of path dependency on previously gained experience with a technology is assumed. This treatment effect model is appropriate in this case, since for some outcomes, baseline and endline measures are not completely identical, or baseline data is not available at all.<sup>17</sup> In addition, this specification has been shown to be more powerful than the difference-in-difference estimator in the presence of relatively low autocorrelation (McKenzie 2012a; De Brauw et al. 2018), which can at least be stated for some of the outcome variables. The main estimators of interest are _β_ and _λ,_ capturing the intent-to-treat effect (ITT). One-sided equality tests evaluate the additional impact of the video intervention, by testing whether the coefficient of T2 is significantly larger than that of T1.

> 17 Baseline data is available for adoption of compost, blended fertilizer, improved seeds and lime. Regarding blended fertilizer, we additionally control for ex-ante use of any inorganic fertilizer, since during time of baseline, blended fertilizer was largely unavailable; instead, farmers used the widely available DAP fertilizer (cp. Table 1). In the two years between baseline and endline, NPS/NPK fertilizer blends largely replaced other inorganic fertilizer types. Line seeding can only be proxied, since it was assessed on a more general level during baseline, asking farmers how they _usually_ plant crops, but not at the plot level. Knowledge variables were not measured in the baseline survey.

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**Figure 7. Carbon Tax Revenues**

<!-- Start of picture text --> 300<br>200<br>USD millions<br>100<br>0<br><!-- End of picture text --> Note: The plot shows in $ millions the total carbon tax revenues generated by a $110/tCO2 tax applied to the firms’ combined fuels and electricity bills sorted from the least polluting to the most polluting sector. Data refer to 2018.

other commercials, and hotels, bars, and restaurants, respectively. We estimate that the cumulative tax revenues amount to $920 millions. This corresponds to 20 percent of the firms’ total earnings and about 3 percent of their reported total income. A $52/tCO2 carbon tax would generate, on the other hand, cumulative revenues of about $434 millions. Documenting such figures is important to provide a sense of the impact of the regulation. However, as discussed, it is important take into account firm-level adjustments to the policy, as well as equilibrium adjustments in the input markets. We now perform counterfactual exercises on our model to derive the effect of the tax on our outcomes of interest, which are firm-level emissions intensities across fuel sources, and TFP across sectors.

**Effect of the tax on emissions intensities.** Following Proposition 2, the new emissions intensities become:

with _ξ_<sup>ˆ</sup> _xi_ and _ξ_<sup>ˆ</sup> _fi_ the historical emissions intensities, summarized in the Data Section, and _τ_ ˆ _ix_ and _τ_ ˆ _if_ the estimated historical price wedges in the electricity and fuel markets, summarized in the calibration Section.<sup>16</sup>

> 16Expression (17) holds true taking energy prices _zx_ and _zf_ as fixed at the historical benchmark. Lower (higher) prices would result in an overestimation (underestimation) of the tax effect respectively.

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24

# relatively more transfers, and therefore creates perverse incentives for regional governments.

This result is unexpected when evaluated based on common sense: it seems controversial for the federal government to run a major budget program whose major beneficiaries are the main offenders of the federal policy. At the same time, the results seems to be similar to those received by Treisman (1997): the allocation of federal transfers in Russia is heavily biased towards the regions which are in deep political confrontation with the federal government. The main recipients of federal funds are the most politically conservative regions. Because political conservatism in Russia is closely related with conservative economic policies, then it is less surprising that we found transfers being concentrated in regions with extensive subsidy programs.

The concentration of transfers in regions with the most distortive policies could increase potential gains for the reform process through the introduction of conditionality in federal transfer mechanisms. More dependent regions could not afford to lose federal transfers, and thus it may be expected that such regions could become more sensitive to federal guidance and could be eager to adjust their policies if non-compliance were to carry the risk of a fiscal loss.

_Table 12. /-Coefficients for the Variable "Total Federal Transfers as Percent of Total Regional Expenditures". Controlledfor the level of pre-transfer budget revenues. Significant results only._

||1992|1993|1994|1995|
|---|---|---|---|---|
|National Economy (total)|4.071|7.05|10.40|4.46|
|Housing|n.a.|5.52|8.01|3.59|
|Agriculture|n. a.|--|--|--|
|Transportation|n.a.|--|--|-0.369|
|Other (93/94)/Industry (95)|n.a.|1.22|1.79|1.56|
|Food|--|--|0.113|n. a.|
|Total Investments|2.04|2.27|1.46|--|
|Budget Loans|--|--|2.68|4.09|

# V.3. Impact of federal transfers on the structure of regional budget spending.

This section addresses the question which could be considered symmetrical to the problem in the section V.2: what is the impact of the level of federal transfers on the share of subsidies in total regional expenditures? Again, we control for the variation in own budget revenues.

The results for this set of regressions are presented in Table 13. They suggest that the level of transfers has a sustainable negative impact on the shares of the following expenditure items: agriculture, transportation, and investments. The share of housing expenditure is not correlated with the level of transfers received, and for the total expenditures on national economy a statistically significant impact could be identified only for 1992-93.
The OECD’s FDI Regulatory Restrictiveness Index reflects these developments: the Communications sub-index fell significantly from 0.595 to 0.120 between 1997 and 2003 as a result of the 1999 reforms, but increased again to 0.410 between 2006 and 2010 as a result of the increasing restrictiveness described above.

# **4. Data & Methodology**

What are the productivity losses that manufacturers face due to policy restrictiveness toward FDI in the service sector? Are some manufacturers more affected than others? If so, what determines these effects? Are there any service sectors for which policy restrictiveness towards FDI has particularly large effects on manufacturers, and are there specific restrictions that matter more than others?

In what follows we first describe the data used to answer these questions, as well as the empirical strategy.

# **4.1 Data**

There are four main sources of data used in this paper: (1) Manufacturing census data at the firm-level for about 20,000 firms (the census collects information on those firms that are registered and employ at least 20 workers), (2) OECD FDI regulatory restrictiveness index both for services and for manufacturing, (3) input-output tables constructed by the Indonesian statistical office (BPS), and (4) the World Bank Enterprise Surveys for Indonesia (2003 and 2009)..

From the manufacturing census, TFP was calculated using a multilateral index following Aw, Chen and Roberts (2001). The index is an extension of that derived by Caves et al. (1982), and allows for consistent comparison of TFP in plant-level data with a panel structure.<sup>10</sup> The index expresses each individual plant‘s output and inputs (capital, labor, materials and energy) as deviations from a single reference point. As the reference point, the index uses a hypothetical plant operating in the base time period (the first year of the data being 1983) and having average input cost shares, average logarithm of inputs and average logarithm of output. The index is calculated separately for each 4-digit ISIC sector.

The key input for our indicator of services sector policy restrictiveness is obtained from the OECD FDI regulatory restrictiveness index, disaggregated by type of restriction and by sector of activity. Input-output data allows us to generate an index of restrictiveness that varies at the level of each manufacturing sector, defined at the 2-digit ISIC 3 disaggregation (a precise description of the method is presented in the subsection that follows).

Data on tariffs were obtained from the TRAINS database of the United Nations, and here again, input-output tables were used to calculate input tariffs and output tariffs.

> 10 We thank Beata Javorcik from sharing the STATA program to calculate this index.

9
Payments in the study districts were facilitated by the research team for the purposes of the evaluation. This was intended to ensure that payments were made without delay, on specific schedules. Administrative payment records confirm that there are no differences in time lag between work and payment across the districts.

The cross-randomization is designed to test interaction effects between the two design variants, since the payment schedule may have a differential effect depending on the season. While a lump-sum payment may facilitate investment in a lumpy input in December, split payments may help smooth consumption during the lean season. A lump sum in February may be used for staples as well as temptation goods; divided payments can act as a form of commitment savings that will lead to smoother consumption of staples if people otherwise have high temptation to spend or high discount rates even over very short periods of time.

We study a total of 182 villages (EAs) across our five treatment groups (Figure 1). Twenty eight districts are included in the PWP program. For the evaluation, we randomly selected 12 districts, stratifying by the country’s three geographic regions. The 12 districts are Blanytyre, Chikwawa, Dowa, Karonga, Lilongwe, Mangochi, Mchinji, Mzimba, Nsanje, Ntchisi, Phalombe, and Zomba. Within selected districts, the list of PWP-eligible villages (prescreened) villages from the District Council and Traditional Authorities was compared to nationally representative survey data from the Integrated Household Survey (IHS3) collected in 2010 and 2011. We identify the overlap between the sampled enumeration areas (EAs) in the IHS3 and the list of communities pre-selected for PWP projects in our 12 districts. For the villages selected for treatment, we randomly chose one project in the in the event that the village are mapped to two, in order to have unique village-project pairs. The geographical targeting of the program is reflected in the regional breakdown of the sample (see Table 1), with about one-half of the sample drawn from the Southern region, which has a higher incidence of poverty and food insecurity (Machinjili & Kanyanda 2012). Random assignment was stratified by region.

# **2.1.2 Household randomization**

The second level of randomization is at the household level. This level of randomization improves statistical power and provides a mechanism for testing for the program’s indirect effects on non-participating households. Under the highly decentralized MASAF program, the GVH identifies households that are offered PWP employment. The intention of the program is to target poor households with able-bodied adults. As discussed below, we use to test the proof of concept of whether the timing of the payment would alter the pattern of consumption and investments in this context. Under discussion for the scale-up was an e-payments which would have entailed a small marginal cost of delivery.

7
Following Levine, Loayza, Beck (2000) and Dollar and Kraay (2004), the generalized method of moments (GMM) estimators developed for dynamic models of panel data introduced by Holtz-Eakin, Newey, and Rosen (1988), Arellano and Bond (1991), and Arellano and Bover (1995) are used to control for country-specific effects and joint endogeneity in this dynamic panel growth regression model. These estimators are based, first, on differencing regressions to control for (time invariant) unobserved effects and, second, on using previous observations of explanatory and lagged-dependent variables as instruments (which are called internal instruments).

After accounting for time-specific effects, equation 3.1 can be rewritten as: with Xi,t including CVi,t and NDi,t. To eliminate the country-specific effect, take first differences of equation 3.2: Instruments are required to deal with the likely endogeneity of the explanatory variables and the problem that, by construction, the new error term,  _i,t_ –  _i,t_ –1, is correlated with the lagged dependent variable, _yi,t_ –1 – _yi,t_ –2. The instruments take advantage of the panel nature of the data set and consist of previous observations of the explanatory and lagged-dependent variables. Conceptually, this assumes that shocks to economic growth (that is, the regression error term) are unpredictable given past values of the explanatory variables. The method allows, however, for current and future values of the explanatory variables to be affected by growth shocks. It is this type of endogeneity that the method is devised to handle.

Under the assumptions that the error term,  , is not serially correlated, and that the explanatory variables are weakly exogenous (that is, the explanatory variables are assumed to be uncorrelated with future realizations of the error term), the following moment conditions emerge: 10
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Policy Research Working Paper 5231

# **Abstract**

The authors use data from the National Family Health Survey 2005 to present age-specific patterns of child mortality among India’s tribal (Adivasi) population. The analysis shows three clear findings. First, a disproportionately high number of child deaths are concentrated among Adivasis, especially in the 1–5 age group and in those states and districts where there is a high concentration of Adivasis. Any effort to reduce child morality in the aggregate will have to focus more squarely on lowering mortality among the Adivasis. Second, the gap in mortality between Adivasi children and the rest really appears after the age of one. In fact, before the age of one, tribal children face more or less similar odds of dying as other children. However, these odds significantly reverse later. This calls for a shift in attention from infant mortality or in general under-five mortality to factors that cause a wedge between tribal children and the rest between the ages of one and five. Third, the analysis goes contrary to the conventional narrative of poverty being the primary factor driving differences between mortality outcomes. Instead, the authors find that breaking down child mortality by age leads to a much more refined picture. Tribal status is significant even after controlling for wealth.

This paper—a product of the Human Development Department, South Asia Region—is part of a larger effort in the department to undertake empirical analysis on the human development outcomes of the most excluded populations. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mdas@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
followed in 2004 by a second wave of liberalization, whereby tariff rates were simplified and further reduced. See Table 1 for a list of Egypt’s Regional Trade Agreements currently in force. Reforms to improve the business environment and exchange rate accompanied these changes (Zaki 2016; Zaki et al. 2018). In November 2016, the Central Bank of Egypt announced the floatation of the Egyptian pound, which depreciated by 45 percent against the US dollar, reaching EGP 13, at the decision and reached EGP 18 soon thereafter (Zaki et al. 2018).

Following these reforms, Egypt’s weighted mean applied tariffs fell from 20.0 percent to 6.6 percent between 2002 and 2016, before rebounding to 10.4 percent in 2019 (Figure 1). Egypt’s trade rose significantly, almost tripling between 2000 and 2020, with exports increasing by 196 percent while imports soared 231 percent (Figure 2). The increase in trade flows coincided with the depreciation of the Egyptian pound, which made exports relatively less expensive for foreign customers (Figure 2). Despite this increase in trade volume, the export sector remained a relatively small share of the Egyptian economy. In 2020, Egypt’s export sector accounted for a mere 13 percent of its GDP, as compared to 35 percent in Morocco, 38 percent in Tunisia, 61 percent in Malaysia, and 52 percent in Thailand (World Development Indicators).

Labor market outcomes tend to be better for workers in exporting firms compared to those in non-exporting firms. Consistent with both economic theory and results from other countries, Egypt’s exporting firms tend to pay higher wages, provide better jobs, demand more skilled workers, deliver higher job security, and hire more women (Saad 2020). They also tend to be few and relatively larger relative to non-exporting firms (ES data). According to the Egyptian Enterprise Census in 2017, which contains all enterprises, only 1 percent of firms export. The average sizes of exporting and non-exporting firms are 18.9 and 3.2 employees, respectively.<sup>5</sup>

> 5 The Enterprise Survey, which targets larger firms (5 employees and larger) similarly finds that exporters are on average considerably larger than non-exporters (313 versus 65, respectively, in 2020).

6
thus a substantial share of the papers adopt Heckman-type methods to assess the effects of adjustment lending. A smaller subset uses control group matching techniques and/or before and after analysis. We employ all three types of analysis here.

There have also been recent attempts to assess the effects of developing country policy reform on trade using similar selection techniques (see for example, Lederman and Ozden, 2004 and Jinjarik, Salinas, and Tsikata, 2004). As in trade, where specific indicators exist to assess outcomes such as the ratios of imports and exports to GDP, outcomes in the financial sector can be assessed via standard indicators such as the ratio of M2 or private credit to GDP. However, in the trade literature it has been easier to pinpoint specific reform episodes tied to trade agreements and legislative actions.<sup>2</sup> In the financial sector, where reform efforts often were carried out under broader multi-sector adjustment projects, and thus are harder to identify, little empirical work has been done.<sup>3</sup> We therefore rely on a comprehensive database of financial sector reforms, including both financial sector adjustment loans and multi-sector adjustment loans with financial sector components, that was recently put together by the World Bank’s Operations Evaluation Department (OED).

The remainder of the paper is organized as follows. Section 2 describes our data, including the indicators that we use to assess outcomes and the variables that summarize World Bank lending to support financial sector reform from 1992 to 2003. Section 3 describes the basic regression models and section 4 summarizes the base results. Section 5 presents results from two methods that address self-selection problems that could be affecting the base results. Section 6 offers additional robustness checks, including regional regressions and models that test whether improvements in financial indicators came before or after World Bank loans were received. Section 7 concludes with a brief summary of the results from the various methods.

> (1997), Summers and Pritchett (1993), Przeworski and Vreeland (2000), Svensson (2003), and Van de Walle (2001).

> 2 In addition to Lederman and Ozden (2004) and Jinjarik _et al._ (2004), see Santos-Paulino and Thirlwell (2004) and Wacziarg and Welch (2003).

> 3 Cull (2000) uses before and after analysis, but only for countries that received financial adjustment loans.

3