rafmacalaba/datause-encoder-data huggingface dataset:
consumption data, its limited sample size means the survey is only representative at highly aggregated administrative levels; conversely, the population census has exactly the opposite strength and weakness, being nationally representative at a far more disaggregated administrative level but offering no consumption data. Applying the estimated model parameters of consumption from a household expenditure survey onto overlapping variables with the census, ELL can predict consumption data into the latter. These data can then be disaggregated to estimate poverty at lower administrative levels than are possible using the household survey alone. This method is sometimes referred to as the “poverty-mapping” approach owing to its extensive presentation of poverty estimates in a cartographic format. Kijima and Lanjouw (2003) then apply this method to provide survey-to-survey imputation-based poverty estimates for India.

Building on this approach, Stifel and Christiaensen (2007) combine household expenditure survey data with more recent rounds of the Demographic and Health Survey (DHS) in Kenya to impute household consumption into the latter. A more recent paper by Christiaensen et al. (2012) predicts consumption in the second round of a consumption survey using the estimated model parameters from the first round of the same survey for several countries. By generating consumption data in the second round that are more consistent with those in the first round, this study indicates that imputation methods can help obviate the need of updating expenditure data with problematic deflators over time. Using seven rounds of household survey data from

> household wealth based on household assets (Sahn and Stifel, 2000). This method’s greatest strength is perhaps that it is straightforward to implement in most contexts where information on household assets is available; however, the non-monetary nature of asset indices renders poverty estimates more difficult to interpret. Another branch of the (statistics and economics) literatures constructs weights to adjust estimates in the presence of missing data instead; for studies that follow this approach, see, e.g., Tarozzi (2007) and Bethlehem, Cobben, and Schouten (2011).

8
The consequences for income distribution are dramatic. Table 10 shows that this greater offfarm migration of workers would cause earnings of farm workers (mostly self-employed farmers) to rise because of WTO accession by 16.8 per cent, instead of falling by 0.7 per cent as in the core accession scenario. Even though the reduced demand for land would cause its rental value to fall more (9.7 instead of _5.5_ per cent), the farmers' overall earnings from agriculture would be 6.8 per cent greater in this scenario - in stark contrast to the core scenario where they fall. True, the earnings of all other factor owners fall in this scenario, particularly unskilled nonfarm labor (by 3.8 per cent). However, given that farmers are among the country's poorest households, this scenario suggests that WTO accession would reduce income inequality and poverty if the labor market distortion associated with the _hukou_ system were to be reformned. It also suggests that the boost to national economic welfare in aggregate could be greater if WTO accession were to be accompanied by labor market reform: $11 billion per year instead of just the $9.6 billion in the core accession scenario.

> Co¢ndilUAonM asd polcy iMmplczftioms

> The assumption in GTAP that all households are identical in their consumption patterns is of

> course a fiction. They differ as between urban and rural areas, between farm and non-farm households within rural areas, and within each of those groupings according to region and income level. Post-simulation analysis at that level of detail for the short run is provided in Chen and Ravallion (2002). But even without that detail, it is possible to draw some broad conclusions

> about the long run implications of WTO accession for agriculture and for income distribution.

Our initial analysis assuming no reform of the _hukou_ system affecting labor market suggests rural non-farm incomes will rise on average absolutely and possibly even relative to urban incomes in the case of households depending just on labor income (assuming urban laborers are more skilled). However, some farm households facing increased import competition may be worse off in this case, ceteris paribus, if they are:

- unable to send household members to jobs in expanding industrial and service industries;

- are too poorly served with infrastructure to attract such activities to their own region;

- are unable to diversify into producing farm goods whose relative price has risen; and/or

- do not have relatives able to repatriate non-farm earnings to them.

_15_
17 countries since the definition of what is included in the central government accounts differs greatly across countries -- see, for example, World Develovment Report 1988, p. 47, and Blejer and Chu (1988).

Table 4B displays the correlation coefficients between these two alternative measures of budget deficit, for different time periods. They are always positive and quite high at least over some time periods. On the other hand, the fact that in many cases the correlation coefficient is low highlights the measurement problems faced in this area of macroeconomics.

Most of the main conclusions obtained for the inflation tax are applicable to both indicators in Table 4A: we observe important differences across countries and across time, as well as across countries within a region. Moreover, there is a clear relation between Table 1 and Table 4A, tending to support the long maintained hypothesis that budget deficits are an important determinant of inflation. This suggestion is further strengthened by the evidence reported in Table 5. This table contains data on the proportion of the Central Bank's credit that goes to the (central) government. These data are quite striking, showing that while in some countries (mainly in Africa) the government gets as much of 80% of the credit, in others it obtains as little as 15 percent (e.g., Korea). These differences across countries are possibly capturing a number of characteristics of these countries, including the degree of development of the domestic capital market, the stage of development of the countries and their ability to borrow from the world capital market. However, the cross country differences appear to be too large to be explained by economic variables only. This indeed suggests that Institutional and political aspects play an important role in explaining these differences in behavior. Studying the role of these political and institutional variables is the main purpose of
- d. Has a business facebook page/account

- e. Has a business twitter profile

- f. Has a business instagram profile

- g. Has a business snapchat account

- h. Has a business Tumblr account

- i. Has a business Whatsapp account

- j. Has a business Youtube account

- k. Has a business Pinterest account

**Overall business practices index of all forty-one business practices** , averaging the binary outcomes, so that this aggregate index ranges between 0 and 1.

Note that the baseline survey conducted during the induction workshop had a different set of questions on business practices than the more detailed follow-up survey. These questions were used to construct a marketing index, accounting index, and HR index, as well as an overall score out of 10. Ancova estimation of the treatment effects on business practices will control for this baseline score out of 10, except for the marketing and sales practices index above, which will control for the baseline marketing measure; the accounting and finance index above, which will control for the baseline accounting index; and the HR and operations index above, which will control for the baseline HR index. An advantage of Ancova estimation is that it only controls for these proxy measures to the extent that they are correlated with outcomes in the follow-up survey.

# **Table 5:**

**Inverse hyperbolic Sine of Total sales in the last month** , in Naira, winsorized at the 99<sup>th</sup> percentile. For firms not answering the exact value, the midpoint of the sales range they give, for those answering sales are in range above 10,000,000, sales are coded as the median of firms with sales in this range, or 12,000,000 if no other firms are in this range.

**Inverse hyperbolic Sine of Total sales in the last year** , in Naira, winsorized at the 99<sup>th</sup> percentile. For firms not answering the exact value, the midpoint of the sales range they give, for those answering sales are in range above 10,000,000, sales are coded as the median of sales for other firms with sales in this range, or 12,000,000 if no other firms are in this range.

**Inverse hyperbolic Sine of Total profits in the last month** , in Naira, winsorized at the 99<sup>th</sup> percentile. This is asked via a direct question. For those not answering the exact value, the midpoint of the profits range they give, for those answering profits are in range above 10,000,000, profits are coded as the median of profits for other firms with profits in this range, or 12,000,000 if no other firms are in this range.

**Inverse hyperbolic Sine of Total profits in the last year** , in Naira, winsorized at the 99<sup>th</sup> percentile. This is asked via a direct question. For those not answering an exact value, the midpoint of the profits range they give, for those answering profits are in range above 10,000,000, profits are coded as the median of profits for other firms with profits in this range, or 12,000,000 if no other firms are in this range.

**Standardized profits and sales index** is obtained by aggregating these different effects as a standardized z-score.

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grade levels (grades 3-12) for at least 85% of school days. They also cannot receive a subsidy more than twice for the same grade.

Transfers increase with the grade, and they are higher for girls than for boys starting from the first grade of secondary school. Average monthly transfer amounts per child by education level are: 163 MX$ for primary, 355 MX$ (boys), 393 MX$ (girls) for lower secondary, and 603 MX$ (boys), 670 MX$ (girls) for upper secondary. In addition, children who are enrolled in school receive an additional monetary transfer for the acquisition of school supplies at the beginning of the academic year (on an annual basis: 220 MX$ for primary and 275 MX$ for secondary). No household can receive more than 1,055 MX$ per month from a combination of grants for different children. For households with children who are scholarship recipients at the upper secondary level, the transfer is capped at 1,785 MX$. During high school, students can further accumulate funds that are redeemable (under certain conditions) upon graduation from high school.<sup>8</sup> Although the structure of benefits is identical in rural and urban areas, the procedure by which families become beneficiaries differs. To identify eligible households, program’s officials visited every household in eligible rural localities and undertook a census, collecting information on a range of household socio-economic characteristics which would then directly determine the entitlement to the program benefits. In urban areas a comprehensive census was deemed to be too costly, so that an element of self-selection in the registration of beneficiaries was introduced. To determine initial eligibility for the urban program, three general steps are taken: (i) identification of high poverty areas to be served; (ii) information campaigns to promote the program; and (iii) identification of eligible families. If a household visits program centers, information is collected through a household questionnaire and these data are further verified during a home visit. A subset of these characteristics was used to apply a previously developed proxy-means targeting approach, whereby numerical weights were assigned to human capital and household assets, as well as characteristics of their communities to calculate a household poverty index (score), with a higher score denoting a higher level of poverty.<sup>9</sup>

> 8For students registered in the program since the last year of lower secondary school, this additional amount is about 3,000 MX$.

> 9The factors and weights are based on an analysis of the determinants of poverty conducted at the initiation of the program, which used a discriminant analysis to identify variables that discriminate best between poor and non-poor households and used the output of the analysis to develop an equation (discriminant score) for computing a poverty index [Skoufias et al., 2001].

7
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Consistent with the results in the literature, most of the disintegration losses stem from foregone services liberalization, whereas those from foregone agricultural liberalization and transport logistics reform are negligible, although the sectoral effects are sizable for some sectors, as discussed in the next section, and for landlocked Iraq which loses 1% from foregone transport logistics reform. The foregone services liberalization generates sizable welfare losses as barrier to entry hurt productivity and put upward pressure on production costs as well as the costs of importing services within the Levant. The results, however, differ across countries reflecting the different extent of productivity loss. As a productivity leader in the Levant, Turkey’s losses are small and stem mainly from failed liberalization of construction and business services.<sup>16</sup> By contrast, Iraq’s service sectors are among the most inefficient in the Levant so its welfare loss from failed services liberalization of 11% is largest. Syria and Egypt also lose to a substantial degree, reflecting the fact that their service sectors are more protected and less efficient than those of Jordan and Lebanon.<sup>17</sup>

# _4.2 Sectoral effects_

The direct sectoral effects of the conflict are negative and sizable across the board only in Syria and Iraq (Table 5), where the war has led to a productivity decline, and in the case of Syria considerable capital destruction and loss of labor. In fact, Jordan and Lebanon register sectoral expansions in response to the refugees’ effect on the demand for goods and services and supply of labor. The direct sectoral effects of conflict in Turkey and Egypt are negligible. Regional trade

> 16 According to the STRI data, Lebanon and Jordan have the least restrictive policies in terms of foreign presence in construction and business services in the Levant, respectively.

> 17 The inflow of refugees has created serious challenges, among which crime, congestion, and a strain on public systems for delivering basic services. This analysis does not factor in these challenges nor provides estimates of the financing needed to address them.

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differences in prices at one point in time may be just<sup>as important as the</sup> usual intertemporal differences associated with inflation (though the former have generally been ignored in past work.) The consumer<sup>price index (CPI)</sup> for Indonesia monitors price changes over time for bundles<sup>of goods which</sup> are predetermined in composition for each of a number of cities (spanning the archipellego), but are not, however, strictly comparable between cities.<sup>24</sup> Nor can it be convincingly argued that the differences in goods composition across regions solely reflect consumer substitution effects. It is quite likely that the bundle of goods is more "generous"<sup>in richer</sup> cities; the implicit reference utility level cannot be assumed to be constant.25 Without an ideal price deflator, we have based all distributional comparisons on two alternatives: i) the Ordinary CPI which<sup>ignores spatial</sup> price variability in the base period, and simply adjusts prices<sup>for January</sup> 1987 in each province to the corresponding February 1984 prices using the price index for its capital city (although with higher weights on food expenditure; see below), and ii) the Spatial CPI which uses the expenditure data by city underlying the CPI to construct an index which allows all 1987 consumptions to be expressed in 1984 Jakarta prices. The first index is likely to lead us to overestimate the regional disparities in living standards (since it ignores spatial price variability, which is likely to be positively correlated with nominal expenditures), while the second<sup>index is</sup> likely to underestimate those disparities (because<sup>of the aforementioned</sup> problem that the implicit bundle of goods is not spatially constant,<sup>and is</sup> likely to yield a higher standard of living in provinces with higher<sup>average</sup> living standards). It turns out, however, that the choice<sup>between these</sup> deflators has a negligible effect on our assessments of aggregate<sup>poverty.26</sup> Results are only reported here based on the ordinary CPI.
26

## **Table 4**

### Statignarity tests

### (a) Augmented Dickey-Fuller test statistics A

|Null**hy,pgXsis**|_kpr_|_kg_|z|_crpr_|_Pk_|
|---|---|---|---|---|---|
|I (1)|-0.392|-1.027|-0.469|-1.089|-0.745|
|1 (2)|-2.815*|-3.986**|-4.733**|-3.321**|-3.627**|

# (b) Kwiatkiowski-Phillips -Schmidt-Shin test statistics /

|NWI hypes||||||
|---|---|---|---|---|---|
||_kpr_|_kg_|z|_crpr_|_Pk_|
|I (0)|0.561**|0.546**|0.551**|0.529**|0.488**|
|1 (1)|0.125|0.415*|0.302|0.196|0.294|

Notes: W/ Third-order ADF test statistics

- k2 Computed setting the lag truncation parameter equal to 3.

- Significant at the 10 percent level.

** Significant at the 5 percent level.
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|Wish could have more respect for myself (Rosenberg)|1843|0.42|0.60|1435|0.40|0.57|-0.07|-0.13|0.00|
|---|---|---|---|---|---|---|---|---|---|
|Certainly feel useless at times (Rosenberg)|1843|1.33|1.10|1435|1.40|1.07|-0.03|-0.16|0.10|
|At times think I am no good at all (Rosenberg)|1843|1.32|1.09|1435|1.43|1.07|0.00|-0.12|0.12|
|Psychological well-being index|1843|0.00|1.00|1435|0.05|0.99|0.06|-0.06|0.17|
|Felt cheerful last 2 weeks (WHO)|1843|2.59|1.72|1435|2.64|1.64|0.05|-0.12|0.22|
|Felt calm & relaxed last 2 weeks (WHO)|1843|2.62|1.68|1435|2.68|1.59|0.06|-0.11|0.22|
|Felt active & vigorous last 2 weeks (WHO)|1843|2.57|1.64|1435|2.61|1.54|0.04|-0.13|0.21|
|Woke up fresh & rested last 2 weeks (WHO)|1843|2.58|1.62|1435|2.56|1.53|0.02|-0.14|0.18|
|Daily life filled with things that interest last 2 weeks (WHO)|1843|2.25|1.76|1435|2.30|1.70|0.12|-0.06|0.29|
|Felt unable to control important things last month (Cohen)|1843|2.95|1.26|1435|2.92|1.12|-0.06|-0.17|0.05|
|Felt confident about ability to handle personal problems last month (Cohen)|1843|3.09|1.27|1435|3.05|1.17|0.02|-0.08|0.13|
|Felt confident things were going your way last month (Cohen)|1843|2.81|1.33|1435|2.89|1.25|0.10|-0.02|0.23|
|Felt difficulties were piling up could not overcome them last month (Cohen)|1843|2.39|1.28|1435|2.56|1.20|0.10|-0.03|0.23|
|School attendance(dayspast week)|299|3.21|2.72|226|2.74|2.72|-0.38|-0.82|0.07|

ITT = intention-to-treat effect estimate; HH = household; FIB = fecal indicator bacteria; MPN = most probable number; IHS = inverse hyperbolic spline; WHO = World Health Organization; WVS = World Values Survey. Effects are estimated with models that include controls for randomization blocks based on province and number of villages per cluster. There were 121 clusters in total. The WASH governance perceptions index and the hygiene and behavior index were calculated by rescaling each variable in the index (e.g., satisfaction with water access) so that higher values imply better outcomes, then standardizing relative to the control group, following Kling _et al_ . Effects are in standard deviation units.
The upper- and lower- bound estimates for mobility derive from two extreme cases of the assumed relationship between the error terms. It is important to note that upper bounds of mobility correspond to the lower bound of immobility, i.e., remaining poor or remaining non-poor, and vice versa. Throughout the paper, upper-bound estimate and lower-bound estimate refer to upper and lower bounds of mobility.

of mobility. At one extreme, we consider the case of , which corresponds to the upper-bound mobility estimate. In this scenario, the errors are uncorrelated and mobility, given household characteristics, will ρρ= 0 be at a maximum, both out of poverty and into it. Because we assume that errors in fact are positively correlated, on average the upper bound will be strictly larger than actual mobility. Predicted upper-bound round 1 income for household surveyed in round 2 will be: ii 1′ �, 2UU ii1 ii2 ıı1 yy� = ββ�xx + εε̂ where � is a residual randomly drawn from the actual distribution of residuals resulting from estimating the income model on the round 1 data. Based on the predictions thus obtained, we can estimate the ıı1 εε̂ probability of each poverty transition for each observation. Aggregating these quantities at the population level, we obtain the country-level poverty dynamics.<sup>11</sup> Due to the random nature of this process, these steps are repeated R times, with R = 100 in this application, and averaged to obtain a robust estimate of the upper bound of mobility.

Analogous to the upper-bound scenario, but without the repetition, we obtain the lower-bound population-level poverty dynamics estimates.<sup>13</sup> Conditional probabilities are subsequently computed

> 11 In our analysis the regressions are at the household level taking log per capita household income as the dependent variable. Transition probabilities for each household are population-weighted when we aggregate, so that the country-level figures represent population proportions.

> 12 We use a scaling factor ⁄ to adjust for the difference in error variance between the two rounds ( is the standard error of the residuals). 13 εε1 εε2 εε Repetition is not necessary in this case because adding the scaled round 2 residual for the same unit is not a γγ= σσ� σσ� σσ� stochastic process.

11
# Figure 15 Impulse Response functions: DR-US Inflation Differential Shock

<!-- Start of picture text --> Response to Cholesky One S.D. Innovations ± 2 S.E.<br>Response of GAP_DR_BK to DLINFL_DUS_DR Response of GAP_HT_BK to DLINFL_DUS_DR<br>.008 .015<br>.004 .010<br>.000 .005<br>-.004 .000<br>-.008 -.005<br>-.012 -.010<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Response of DLINFL_DUS_DR to DLINFL_DUS_DR Response of DLINFL_DUS_HT to DLINFL_DUS_DR<br>.12 .06<br>.08 .04<br>.04 .02<br>.00 .00<br>-.04 -.02<br>-.08 -.04<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Response of RER_DR to DLINFL_DUS_DR Response of RER_HT to DLINFL_DUS_DR<br>.04 .06<br>.02 .04<br>.00 .02<br>-.02 .00<br>-.04 -.02<br>-.06 -.04<br>-.08 -.06<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text --> 44
- Karlan, Dean, and Jonathan Zinman. 2011. “Microcredit in Theory and Practice: Using Randomized Credit Scoring for Impact Evaluation.” _Science_ 332 (6035) (June 10): 1278–1284.

- King, R. G., and R. Levine. 1993. “Finance and Growth: Schumpeter Might Be Right.” _The Quarterly Journal of Economics_ 108 (3) (August 1): 717–737. doi:10.2307/2118406.

- Kling, Jeffrey, Jeffrey Liebman, and Lawrence Katz. 2007. “Experimental Analysis of Neighborhood Effects.” _Econometrica_ 75 (1) (January): 83–120.

- de Mel, Suresh, David J. McKenzie, and Christopher Woodruff. 2009. “Measuring Microenterprise Profits: Must We Ask How the Sausage Is Made?” _Journal of Development Economics_ 88 (1) (January): 19–31. doi:10.1016/j.jdeveco.2008.01.007.

- de Mel, Suresh, David McKenzie, and Christopher Woodruff. 2008. “Returns to Capital in

- Microenterprises: Evidence from a Field Experiment.” _Quarterly Journal of Economics_ 123 (4): 1329–1372.

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