rafmacalaba/datause-encoder-data huggingface dataset:
departure (with the worker intercepted upon arrival in New Zealand and interviewed separately from their household).

Given these conditions, we used a rolling sampling methodology, adding samples as we received updates of when, where, and who employers were recruiting. In both countries the baseline survey was conducted between October 2007 and April 2008. In Tonga our survey has near national coverage, covering the islands of Tongatapu, Vava’u and ‘Eua. These three islands contain 90 percent of the population and 92 percent of the RSE workers in the first year. We worked closely with the Tongan Labour Ministry to identify villages supplying workers, and within those the village town officers identified households with RSE workers and households with members of the RSE work-ready pool who had not been selected yet. We additionally surveyed randomly selected households in the same villages where no one had applied for the program. In each village we aimed for approximately five households with an RSE worker, three households with a member of the work-ready pool who was not selected, and four households with non-applicants. Our resulting baseline survey covered 448 households containing 2,335 individuals in 46 villages.<sup>9</sup> Vanuatu’s rugged geography and high transportation costs meant it was not feasible to survey in all islands, so a decision was made to limit the evaluation to three islands from which we believed there was a high ex ante chance of workers coming. These islands were Efate (population 50,000), where the capital city, Port Vila, is located; and Ambrym (population 10,000) and Tanna (population 20,000). These latter two islands were chosen due to Seasonal Solutions hiring from these islands. In contrast to Tonga, not all villages in Vanuatu were initially participating in the RSE, and in addition to sampling non-applicant households from within villages with participating RSE workers, we also sampled households from nearby villages or communities which had not participated in the RSE. Ultimately our baseline survey covered 456 households containing 2,173 individuals in 48 villages or communities.

Three rounds of follow-up surveys were then conducted. The first took place between April and July 2008, approximately six months after the baseline survey. This was intended to be a time when RSE workers were still in the midst of their 7 month stint abroad. However, as in practice many contracts were for shorter than 7 months (to be discussed below), approximately two-thirds of Tongan RSE workers and one-fifth of ni-Vanuatu RSE workers in our sample had returned by the time of this survey. The second follow-up survey took place between October 2008 and February 2009, approximately one year after the baseline, while the third and final follow-up survey took place between October 2009 and March 2010, two years after baseline.

> 9 Further details of the baseline sampling methodology for Tonga are contained in Gibson et al. (2008), while McKenzie et al. (2008) provides more details on the Vanuatu sampling methodology.

8
fixed-coefficient type (that is, the average input use is unresponsive to marginal relative price changes). In that case, the cost share of gross revenues that would otherwise H prevail without protection for an individual input, a gi , can be written in terms of the observed cost share, a gi , and the ad valorem protection rates: And so, from observed costs as shares of gross revenues, a gi , and ad valorem protection rates – usually observed tariff levels – one can estimate the hypothetical cost shares that would prevail without protection. Using the notation defined above, the hypothetical value added for industry g without protection and subsidies can be written (with the superscript indicating the hypothetical): The ERP is then estimated as the percent difference of the observed value added from the hypothetical (for details, see the Annex): Note, that without subsidies on the output side, the ERP calculation distills into a simpler, more familiar form found in other studies (e.g., the Nathan study and Al Iskandarani):<sup>6</sup> It is this last equation that is applied in Section 5 to the disaggregated industries used in the Nathan study. But in the case of Egypt there is another important source of intervention, energy subsidies, which interact with the tariff regime to impact the profitability of economic sectors.

_b. Adding input subsidies – reduced energy costs – to the ERP calculation_ In the case of Egypt energy subsidies are significant and can influence the effective rates of protection of particular industries, depending on their use of various energy sources. See for example, the 2009 World Bank report “Transforming Egypt: A Development Policy Review”; Lederman, 2005; and notably the April 2009 study by

> 6 At this point, one should note that this present study estimates the hypothetical costs as shares of gross revenue from observed cost shares and observed tariffs, a methodological strategy that relies on the assumption that input substitution elasticities are small. (See Sampson, 1974, for a critique of this assumption.) Alternatively one could estimate the hypothetical cost shares from other sources; one strategy in the literature has been to use benchmark cost shares from other countries with similar technologies but much less protection (e.g., Sampson and Yeats, 1979).

6
**Table** **<u>3: Determinants of educational expenses for generation III</u>**

||**To**|**tal**|**Books & s**|**tationary**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Female|-0.635|-0.628|-0.688*|-0.690|
||(0.344)|(0.465)|(0.195)|(0.289)|
|Female*Mother’s father died after 1994/alive|0.298|0.274|0.306|0.294|
||(0.336)|(0.353)|(0.191)|(0.207)|
|Female*Education gap mother-father||0.011||0.007|
|||(0.019)||(0.014)|
|Female*Share of mother’s gifts at marriage||0.289||0.127|
|||(0.865)||(0.621)|
|Reform*Female|0.452|0.464|-0.270|-0.147|
||(0.344)|(0.465)|(0.195)|(0.289)|
|Reform*Female*Mother’s father died after 1994/alive|1.178*|1.168*|0.965**|0.790*|
||(0.336)|(0.353)|(0.191)|(0.207)|
|Reform*Female*Education gap mother-father||-0.003||-0.062**|
|||(0.019)||(0.014)|
|Reform*Female*Share of mother’s gifts at marriage||-0.943||-0.042|
|||(0.865)||(0.621)|
|Observations|1,015|1,015|1,015|1,015|
|R-squared|0.806|0.807|0.794|0.795|

_Note:_ Total expenses on education include expenses on books/stationery, transport/hostel and private coaching/tuition. All regressions include gender and state specific year of birth fixed effects and household fixed effects. Robust standard errors in brackets are clustered by state. * significant at 10%; ** significant at 5%; *** significant at 1%.

18
Table 3: Descriptive Statistics

|S|ample with|Overall|F|Prob _>_ F|
|---|---|---|---|---|
||Attrition|Sample|||
|**Social and Economic Variables**|||||
|Household Size|5.491|5.365|3.361|0.067|
|Share of Dependent Members|0.432|0.433|0.038|0.845|
|Type of Settlement (Rural==1)<br>|0.400|0.409|0.535|0.465|
|HH Gender (1=Male)|0.851|0.843|0.888|0.346|
|HH Age|49.578|49.602|0.004|0.947|
|HH Years of Education|6.545|6.542|0.000|0.985|
|HH Employed|0.916|0.910|0.740|0.390|
|HH Married|0.805|0.790|2.520|0.112|
|HH Gender * HH Married|0.788|0.771|2.613|0.106|
|HH Language|.|.|.|.|
|Hausa|0.280|0.267|1.383|0.240|
|Igbo|0.174|0.173|0.028|0.867|
|Yoruba|0.238|0.252|1.549|0.213|
|Other|0.308|0.308|0.002|0.966|
|**Quality of Dwelling**|||||
|Number of Rooms|3.440|3.386|1.140|0.286|
|Quality of Roof|.|.|.|.|
|Low|0.176|0.184|0.781|0.377|
|Medium|0.808|0.799|0.867|0.352|
|High|0.016|0.017|0.061|0.805|
|Quality of Wall|.|.|.|.|
|Low|0.415|0.417|0.016|0.901|
|Medium|0.100|0.098|0.116|0.734|
|High|0.485|0.486|0.006|0.938|
|Quality of Floor|.|.|.|.|
|Low|0.103|0.108|0.491|0.483|
|Medium|0.191|0.189|0.078|0.780|
|High|0.706|0.703|0.054|0.816|
|Type of Toilet|.|.|.|.|
|No Facility|0.184|0.185|0.026|0.871|
|Flush Toilet|0.188|0.191|0.059|0.809|
|Improved Pit Latrine|0.026|0.026|0.001|0.972|
|Uncovered Pit Latrine|0.476|0.467|0.550|0.458|
|Other|0.126|0.131|0.474|0.491|
|Main Source of Lighting|.|.|.|.|
|Electricity/Gas|0.353|0.349|0.118|0.731|

29
**Table 7 Descriptive Regression (Pooled Cross-sectional Ordinary Least Squares)**

|Dependent<br>Variable|Log<br>(Sales)|Log<br>(Employment)|Log<br>(Productivity)|Sales<br>Growth|Prod<br>Growth|Employ<br>Growth|Log<br>(Wage)|Log(Invest<br>/Worker)|NonProd<br>/Prod|
|---|---|---|---|---|---|---|---|---|---|
|Export/Import|2.012|1.532|0.422|0.062|0.042|0.038|0.272|0.359|0.318|
||(0.055)***|(0.041)***|(0.036)***|(0.005)***|(0.003)***|(0.002)***|(0.026)***|(0.088)***|(0.049)***|
|Export Only|1.356|1.003|0.345|0.045|0.030|0.026|0.178|0.123|0.216|
||(0.069)***|(0.048)***|(0.038)***|(0.007)***|(0.005)***|(0.003)***|(0.032)***|(0.114)|(0.058)***|
|Import Only|0.513|0.330|0.180|0.022|0.010|0.016|0.091|0.060|0.107|
||(0.041)***|(0.026)***|(0.023)***|(0.005)***|(0.003)***|(0.002)***|(0.020)***|(0.071)|(0.040)***|
|Log(Labor)t|||0.034||||1.030|‐0.016|‐0.186|
||||(0.008)***||||(0.007)***|(0.022)|(0.015)***|
|Log(Sales)t-3||||‐0.008<br>(0.001)***||||||
|Log(Productivity|)t-3||||‐0.059<br>(0.002)***|||||
|Log(Labor)t-3||||||‐0.015<br>(0.001)***||||
|Observations|21507|27614|21530|16730|17624|23844|16251|6218|12745|
|R‐squared|0.328|0.227|0.457|0.068|0.184|0.069|0.815|0.271|0.263|

Robust standard errors clustered by country, industry, and year are in parentheses. In the regressions, I also control for 2-digit industry, survey year, and country fixed effects. *** p<0.01, ** p<0.05, * p<0.1.

15
We use corporate income tax records to confirm firms’ turnover and hence their eligibility for the POS rental subsidy.

Figure A.9 shows that the number of VAT filers has increased steadily over time, with a mild slowdown in the growth rate in 2014 and 2015. There is thus no indication that the introduction of the VAT rebates motivated previously informal firms to register. Table A.3 provides summary statistics for the full sample of VAT filers, and for retail and wholesale firms, the treated and control firms for part of our analysis. Retail firms are very similar to wholesale firms in terms of the distribution of their annual sales and VAT liability, except at the top of the distribution, where wholesale firms are larger. The key distinction between the two groups is that 52 percent of retail firms used a POS terminal even in 2013, before the reform, but only 16 percent of wholesale firms did. In both sectors, POS usage increases with firm size.

# **4 Use of Electronic Payment Technology**

We begin our analysis by evaluating the impact of VAT rebates on the use of electronic payment technology. As the rebates became available to all consumers nation-wide on the same day, we examine the effect of the rebates on aggregate outcomes. We use a regression discontinuity estimation in time around August 1, 2014, when the rebates became available. In the following sections, we present our empirical strategy, the results and robustness tests.

## **4.1 Empirical Strategy**

We use the following variables to measure the use of electronic payment technology on the extensive and intensive margin: the aggregate number of card transactions, the volume of card transactions, the number of POS in use, and the number of firms with at least one POS. Figure 1, Panel A, plots the raw time series of these outcomes between January 2010 and June 2016. Some of the series, especially the number and volume of transactions, exhibit seasonal variation with peaks in December and during the spring holiday season. We thus need to de-seasonalize the data while estimating the regression discontinuity. Concretely, we estimate where _Zt,m_ is the aggregate outcome in time period _t_ and month-of-year _m_ , _gm_ are monthof-year fixed effects, _t_<sup>_k_</sup> is a time trend, the _PostJuly_ 2014 dummy indicates months after July 2014 (i.e. post-reform months), _p_ is the degree of the polynomial we fit (either 1 or 2), and 11
In many contexts this would suggest a favored relationship between these two leaders. However, in the villages we study 50 percent of all households were related to the village head.

The bottom panel of table 4 further considers whether civil society leaders can realistically be viewed as a mechanism for checking the power of village heads by comparing the attitudes and legal knowledge of village heads and civil society leaders prior to the intervention (by looking at the statistics for control villages only). Importantly, a slight majority of civil society leaders favored checks on the power of the village head’s court. In addition, civil society leaders express significantly higher levels of support for individual rights than village heads.<sup>15</sup> Knowledge of the laws and procedures regulating village governance is approximately equal between village heads and civil society leaders.

This evidence suggests that the average civil society leader in these villages has both the incentive and the ability to check the power of village heads. However, it is noteworthy that the NGO depended on the village head to identify civil society leaders living in his or her village, and to select which one to invite. Our expectation was that this would reduce the probably of finding differential effects across the two training variants insofar as village heads should choose to invite civil society leaders over whom they have more influence. In table 6 in the appendix, we compare the traits of the trained civil society leaders to our random sample. We find village heads were more likely to invite female leaders and NGO officers to accompany them and less likely to invite religious leaders; these patterns are interesting insofar as they suggest village heads chose people who likely had an interest in reform but perhaps had weaker independent power bases.

# **5 Data Collection and Measurement of Key Variables**

We conducted surveys of village heads, a randomly selected civil society leader, and a random sample of eight households in each of the villages in the study three months following the completion of the training sessions, in late August and early September 2013. We contracted a local survey firm to conduct the survey after training from the research team. We were able to conduct household surveys in each of the 270 villages and to interview the village head in 91 percent of them. We also interviewed a randomly selected civil society leader in 96 percent of villages. Most questions on the questionnaires were closed-ended and retrospectively

> 15The differences in support for individual rights and beliefs about whether the dare should have more power are both statistically significant at the 95 percent confidence level.

17
**Figure 3.4. National poverty level in ECA countries and comparators**

<!-- Start of picture text --> 100<br>TJ<br>90<br>80<br>70 MD<br>KG<br>60 AZ<br>AM<br>50<br>40 TM<br>30 UZ RO<br>20 AL<br>BY BA<br>10<br>KZ BG<br>0<br>0 2000 4000 6000 8000 10000 12000<br>GDP per capita ppp 2001<br>2000)<br>National poverty rates (1995,<br><!-- End of picture text --> AL=Albania; AM=Armenia; AZ=Azerbaijan; BA=Bosnia-Herzegovina; BG=Bulgaria; BY=Belarus; KZ=Kazakhstan; KG=Kyrgyz; MD=Moldova; RO=Romania; TM=Turkmenistan; TJ=Tajikistan; UZ=Uzbekistan Source: WDI and staff calculations There is also a relationship (although not very strong) between the change in GDP and poverty levels, whereby ECA countries with greater declines in GDP over the past decade show the highest rates of poverty incidence (Figure 3.5).

**Figure 3.5. National poverty and GDP per capita growth**

<!-- Start of picture text --> 100<br>TJ<br>90<br>80<br>MD<br>70<br>KG<br>60<br>AM<br>AZ 50<br>UZ 4 0<br>TM<br>RO BY 30<br>KZ 2 0 AL<br>10<br>BG<br>0<br>-10 -8 -6 -4 -2 0 2 4 6 8 10<br>Annual GDP percapita growth rate between 1990 and 2000<br>National poverty rate<br><!-- End of picture text --> AL=Albania; AM=Armenia; AZ=Azerbaijan; BG=Bulgaria; BY=Belarus; KZ=Kazakhstan; KG=Kyrgyz; MD=Moldova; RO=Romania; TM=Turkmenistan; TJ=Tajikistan; UZ=Uzbekistan Source: WDI and staff calculations An equivalent relationship between urban poverty rates and GDP growth is shown in Figure 3.6 below. Countries where GDP has fallen more sharply tend to be those with higher rates of urban poverty. This suggests that the prolonged economic recession in these countries has had strong impacts on urban households.

15
**Figure 11: Percentage of cost explained by largest 2 cost ingredients**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>0 2 4 6 8 10 12<br><!-- End of picture text --> Source: Data adapted from J-PAL (2014a). Calculations by authors.

**Figure 12: Program cost per beneficiary student**

<!-- Start of picture text --> $500<br>$400<br>$300<br>$200<br>$100<br>$0<br>0 5,000 10,000 15,000 20,000 25,000 30,000 35,000<br>-$100<br>Total number of students<br>Cost/student 2011 USD<br><!-- End of picture text --> Source: Data adapted from J-PAL (2014a). Calculations by authors.

32
Table B11: Asset ownership, by wealth quintile

||(1)|(2)|(3)|(4)|(5)|Factor|
|---|---|---|---|---|---|---|
|||||||loadings|
|**Water source**|||||||
|Piped into dwelling|0.000|0.000|0.000|0.003|0.006|0.040|
|Piped into yard|0.003|0.006|0.003|0.003|0.003|-0.008|
|<br>Public tap|0.000|0.359|0.497|0.583|0.675|0.333|
|<br>Tube well|0.071|0.097|0.058|0.078|0.068|-0.026|
|Protected well|0.136|0.094|0.049|0.026|0.026|-0.121|
|Unprotected well|0.453|0.223|0.208|0.197|0.107|-0.187|
|<br>Protected spring|0.032|0.013|0.010|0.006|0.032|0.019|
|Unprotected spring|0.243|0.133|0.143|0.081|0.062|-0.124|
|<br>Other|0.061|0.074|0.032|0.023|0.019|-0.049|
|**Toilet type**|||||||
|Flush to PSS|0.000|0.000|0.000|0.000|0.006|0.044|
|Flush to septic tank|0.000|0.000|0.000|0.000|0.006|0.045|
|To other byte|0.000|0.000|0.000|0.000|0.010|0.042|
|<br>Pit latrine|0.000|0.000|0.000|0.006|0.032|0.118|
|Pit latrine slab|0.000|0.000|0.000|0.003|0.049|0.166|
|Pit latrine open|0.000|0.000|0.000|0.013|0.153|0.339|
|<br>Composting|0.000|0.000|0.000|0.003|0.000|-0.001|
|Bucket|0.000|0.000|0.000|0.000|0.003|0.046|
|Hanging|0.000|0.000|0.000|0.000|0.010|0.037|
|Bush|1.000|1.000|1.000|0.971|0.724|-0.406|
|Other|0.000|0.000|0.000|0.003|0.006|0.034|
|**Main cooking fuel**|||||||
|Electricity|0.000|0.000|0.000|0.003|0.003|0.012|
|Kerosene|0.000|0.000|0.000|0.000|0.023|0.181|
|Coal|0.000|0.000|0.000|0.000|0.019|0.143|
|Charcoal|0.000|0.000|0.000|0.065|0.198|0.312|
|Firewood|1.000|1.000|0.994|0.922|0.747|-0.399|
|Dung|0.000|0.000|0.006|0.010|0.000|-0.002|
|Other|0.000|0.000|0.000|0.000|0.010|0.205|
|**Electronics and Vehicles**|||||||
|Electricity|0.000|0.000|0.000|0.000|0.049|0.506|
|Radio|0.000|0.155|0.244|0.317|0.539|0.362|
|TV|0.000|0.000|0.000|0.000|0.023|0.486|
|Phone|0.000|0.000|0.000|0.000|0.023|0.393|
|Fridge|0.000|0.000|0.000|0.000|0.010|0.481|
|Bike|0.000|0.000|0.000|0.006|0.097|0.342|
|Motorbike|0.000|0.000|0.000|0.000|0.006|0.198|
|Car|0.000|0.000|0.000|0.000|0.010|0.165|
|Observations|309|309|308|309|308||

Note: Columns 1-5 shows the share of households owning the good or having access to the utility for each of the five wealth quintiles. Wealth quintiles are determined using the sample distribution of the asset index produced using the Filmer-Pritchett procedure (Filmer and Pritchett, 2001) and using all the variables presented in the Table. 23
- 2 -

Much has been written about the reasons for this changing strategy.2 First, there are technical reasons. The increasing complexity and international integration of economies--at all levels of development--has made it practically impossible to operate in alx overly centralized manner, either in terms of planning or of public sector ownership. Second, public sectors, particularly in developing countries, just have not had the managerial and administrative capabilities for carrying out all the functions expected from an interventionist state, continue to be heavily involved in the production of a large number of goods and services and in addition, for trying to fine tune the economy by conscious "positive distortions" aimed at correcting market failures, providing incentives to certain activities or regions, discouraging others, and achieving a large number of other specific social or political purposes. Third, the many functions, operations, and corrections which the state undertakes give openings for all types of political interventions, often resulting in consequences diametrically different from those originally intended by the politicians. This is often due to the confusion in which the activities of the public sector are often undertaken and to the inefficiency of the political process itself. Not to mention, of course, the many opportunities for private "rent seeking" emanating f:om such situations.

For all the consensus of the need to change, there continues to be substantial controversy as to the appropriate role of a reformed state. At

**2** See, for example, World Bank, World Development Report 1988. Oxford University Press.
||Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|---|---|
|Country fixed effects|Dummy variables (one for each country)<br>indicating the country where the firm is<br>located.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|GDP per capita (logs)|Log of GDP per capita (PPP adjusted and at<br>constant 2011 International Dollars). Values of<br>the variable used are for the fiscal year in<br>which the Enterprise Survey was conducted. In<br>cases where the Enterprise Survey spanned<br>more than one calendar year, the GDP per<br>capita value is the weighted average over the<br>calendar year values covered by Enterprise<br>Surveys, where the weights used are the<br>proportion of firms surveyed in the calendar<br>year.<br>Source: World Development Indicators, World<br>Bank.|
|Growth rate of GDP per capita|Annual percentage growth rate of real GDP per<br>capita. Values of the variable used are for the<br>fiscal year in which the Enterprise Survey was<br>conducted. In cases where the Enterprise<br>Survey spanned more than one calendar year,<br>the GDP per capita growth rate value is the<br>weighted average over the calendar year values<br>covered by Enterprise Surveys, where the<br>weights used are the proportion of firms<br>surveyed in the calendar year.<br>Source: World Development Indicators, World<br>Bank.|
|Courts: Obstacle Severity (CI avg.)|The ES asked firms if (lack of proper)<br>functioning of courts is no obstacle (0), minor<br>obstacle (1), moderate obstacle (2), major<br>obstacle (3), and a very severe obstacle (4) for<br>the firms’ day to day operations. We take the<br>average of the reported obstacle severity level<br>where the average if taken over all other firms<br>(that is, excluding the firm in question) in the<br>same country-industry cell. Industry grouping<br>is defined at the 2-digit ISIC Rev. 3.1 level.<br>There are 7 industries in our sample.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|

51