rafmacalaba/datause-encoder-data huggingface dataset:
and additional empirical and theoretical justification for the supply elasticities is provided in Jensen and Tarr (2010).

# **5. Results for East African Customs Union Preferential and Multilateral**

# **Policies to Reduce Trade Costs**

# **5.1 Deep Preferential Integration Within the East African Customs Union (EACU)**

We execute several scenarios in our multi-region trade model to assess the impacts of the reduction in trade costs by the East African Customs Union on Kenya and Tanzania, as well as on Uganda and Rwanda.<sup>22</sup> We decompose the trade costs into three categories: excessive trade facilitation costs; non-tariff barriers; and barriers on foreign providers of services. In the case of trade facilitation, as we have explained in more detail in appendix C, there are several reasons to take modest cuts in these barriers. These include that the most efficient countries in the world, such as Singapore, the Republic of Korea and Hong Kong SAR, China, have not cut the time cost of trade to zero; and part of the costs are due to infrastructure deficiencies which can’t be addressed through policy alone. There are, however, some collaborative projects and plans among members of the EACU (see East African Community Secretariat, 2011), such as common customs posts, designed to cut the time costs of trade. Consequently, we assume a 20 percent cut in the ad valorem equivalents of the time cost of trade within EACU. Since there is likely a spillover benefit of these measures that will cut the time costs of trade outside of the EACU, we take a 5 percent cut in these costs for trade with countries outside of the EACU.

Similarly, under the auspices of the East African Community, the member countries are undertaking collaborative efforts to reduce non-tariff barriers (see, for example, East African Community, 2012). Non-tariff measures, however, have become much more subtle in the postUruguay Round world. Most measures have a legitimate regulatory function and distinguishing the legitimate regulations from protective or inefficient regulations is complicated. Consequently, we take a more modest 20 percent reduction in the ad valorem equivalent of these barriers.

> 22 Burundi is a fifth member of the EACU.  Since Burundi is not represented as a separate region in the GTAP 8.1 data set, it is not a separate region of our model and we do not have separate results for Burundi.

19
17 expect, adjust the efficiency scores to take into account the effects of the exogenous constraints. This ex post regression approach has in any case been criticized on the grounds that it makes no allowance for the way the scores are generated (cf. e.g. Burgess 2006).

# **Methods**

A more fundamental objection to the post-hoc analysis of the influence of exogenous constraints on efficiency scores is that such constraints ought to be allowed for _during_ the computation of the efficiency scores. In our hybrid approach we do this by allowing different groups of units (defined in terms of constraints) to have different frontiers.<sup>15</sup> This allows for considerable flexibility—much more than would be obtained than by, for example, including a constraint variable in a stochastic frontier. Our approach allows the location and shape of the frontier to vary across the groups. For each group, having generated the frontier, we compute an inefficiency score relative to the relevant frontier.

# **Empirical example**

We illustrate the idea of allowing for exogenous constraints using the California schools data. We stratify school districts by poverty, distinguishing between districts in the bottom half and top half of the poverty headcount distribution. The rationale is that pupils from more affluent families may face more favorable home environments than pupils from poor families: the time available for homework may be greater; the degree of parental input and oversight may be greater; the pupils may come to school better nourished; and so on. School inputs may be comparable, but home inputs that are beyond the control of the school may be smaller in schools in poorer catchment areas.

The school district estimates of school age students (aged 5 to 17) living in households under poverty were sourced from the U.S. Census Bureau‟s Small Area Income and Poverty Estimates (SAIPE).<sup>16</sup> According to the U.S. Census Bureau‟s website, the estimates were computed using data from administrative records, intercensal population estimates, and the decennial census with direct estimates from the American Community Survey.

> 15 This principle could also be applied to other approaches to efficiency measurement, of course. One could, for example, estimate separate stochastic frontiers for different groups. 16 The data are available at http://www.census.gov/did/www/saipe/data/schools/index.html
statistics demonstrate the depth of the crisis: in 2009, world GDP fell by 1.9 percent. In high income countries it fell by 3.4 percent (2.6 percent in the US, 5.2 percent in Japan, 4.9 percent in the U.K. and 4.3 percent in the EU). In emerging markets, GDP growth fell to about 0.5 percent, while developing countries maintained a reasonably good growth performance of above 4 percent. World trade in 2009 was around 65 percent lower than in the previous year (see Figure 15). Equity markets collapsed (as shown in Figure 3). Unemployment around the world rose sharply (Figure 16). It surpassed 15 percent in countries like Spain and Greece that already had high levels of unemployment before the crisis (Figure 17).

<!-- Start of picture text --> Figure 15: World Imports and Exports<br>18 40%<br>16 30%<br>20%<br>14<br>10%<br>12<br>0%<br>10<br>-10%<br>8<br>-20%<br>6 -30%<br>4 -40%<br>World Exports Percent Change (right axis)<br>Current US$, trillions<br>Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10<br><!-- End of picture text --> Source: International Monetary Fund, Direction of Trade Statistics.

19
to another survey with the same design, or to another survey with a different design. We also provide a critical review of recent studies in the economics and statistics literatures that use imputation. Our estimation results combining the HEISs and the HEISs with the LFSs, are quite encouraging, with imputation-based poverty estimates not showing statistically significant differences from the true poverty rates. We also provide step-by-step estimation procedures that can facilitate the implementation of our proposed methods.

Even though we provide an illustration with data from a middle-income country like Jordan, our method is more general and can be applied in other contexts where household consumption surveys are not frequently or consistently collected, while other surveys that can be benchmarked to these household surveys exist. We thus provide support to the growing assessment that survey-to-survey imputation methods can comprise a valuable tool for poverty tracking purposes in developing countries where financial and technical constraints on fielding (expensive and high-quality) consumption surveys can be particularly binding.

34
heinous crime with a minimum sentence of 12 years. Despite this new law, the female homicide rate is still rising. In 2018, the male homicide rate fell, but the female homicide rate increased by more than 4 percent. One reason may be the insufficient resources allocated for the enforcement of restraining orders and the establishment of safe houses, which now exist only in the large cities (IRB 2017).

The effort to improve the reporting of abuse started in 2006, when the Watch Registry of Accidents and Violent Incidents system (Vigilância de Violências e Acidentes, VIVA) of continuous reporting was created. However, the system was implemented only in a subset of health establishments.<sup>4</sup> In 2011, the registration of incidents of violence became mandatory at all types of health establishments. Under the legislation, employees of health establishments must submit documentation whenever they suspect or are able to confirm that violence has occurred. However, though acts of violence are recorded, there is not necessarily any follow-up because notification to authorities who may intervene is only required if children or adolescents ages 19 or less or elderly individuals ages 60 or more are involved. Research has shown that intervention without appropriate precautions may put women at risk of further violence (Kendall 2020). The analysis concluded that, among cases involving women ages 15–49, the group covered in this study, only 20 percent were reported to the police; 30 percent were reported elsewhere (for instance, social services); and half were not associated with information on any follow-up.

# **Methods**

# **Measures of Violence**

The measures of violence used in the study have been developed by the Department of Informatics of the Unified Health System (Departamento de Informática do Sistema Único de Saúde, DATASUS). They consist of indicators on female homicides, female overnight hospitalizations for assault, and mandatory reports by health workers of physical and sexual violence against women (VIVA). For each of these outcomes, the study examined female victims in the 15–49 age range, the focal demographic group in the IPV literature (Vyas and Watts 2009). The study panel spans 2011–16, the years on which data are available on all outcome variables. Aggregate trends across the sample are fairly constant over this time period (figure 1).

The analysis aggregated individual reports for each outcome at the municipalityyear level. If no observations could be made within a municipality-year for a particular

> 4 Kendall (2020) provides excellent guidance for best practices and important considerations in developing administrative systems for these purposes.

7
Table 1: Summary Statistics and Balance ( _Continued_ )

||(0.018)|(0.021)|(0.025)|(0.021)|
|---|---|---|---|---|
|Has informal savings|0.215|0.039|0.067|-0.028|
||(0.016)|(0.019)|(0.025)|(0.021)|
|Log total savings|4.782<br>|-0.030<br>|0.055<br>|-0.085<br>|
||(0.194)|(0.229)|(0.280)|(0.237)|
|Log formal savings|2.835|-0.312|-0.535|0.223|
||(0.178)|(0.208)|(0.248)|(0.204)|
|Log informal savings|2.541|0.272|0.588|-0.316|
||(0.163)|(0.195)|(0.245)|(0.212)|
|Has loans|0.513|0.009|-0.014|0.023|
||(0.020)|(0.023)|(0.028)|(0.024)|
|Has formal loans|0.058|-0.005|-0.004|-0.001|
||(0.009)|(0.011)|(0.013)|(0.011)|
|Has informal loans|0.498<br>|-0.001<br>|-0.019<br>|0.018<br>|
||(0.020)|(0.023)|(0.028)|(0.024)|
|Log total loans|4.447|-0.019|-0.177|0.158|
||(0.174)|(0.205)|(0.251)|(0.210)|
|Log formal loans|0.093|-0.041|-0.044|0.003|
||(0.038)|(0.042)|(0.047)|(0.034)|
|Log informal loans|4.244|-0.100|-0.215|0.115|
||(0.171)|(0.202)|(0.246)|(0.207)|
|Sent remittances|0.810|-0.029|-0.049|0.020|
||(0.015)|(0.019)|(0.023)|(0.020)|
|Log remittances|8.691|-0.272|-0.507|0.235|
||(0.170)|(0.205)|(0.258)|(0.225)|
||**Panel **|**D: consu**|**mption smo**|**othing**|
|Need to borrow|0.470|0.034|0.068|-0.034|
||(0.020)|(0.023)|(0.028)|(0.024)|
|Able to save BDT 5,000|0.782|-0.005|0.016|-0.022|
||(0.016)|(0.019)|(0.023)|(0.020)|
|Trouble sticking with fnancial plans|0.639|0.022|0.018|0.004|
||(0.019)|(0.022)|(0.027)|(0.023)|

Notes: This table reports statistics for the baseline sample and tests of randomization balance * ** *** across the treatment groups. _p <_ 0.1, _p <_ 0.05, _p <_ 0.01.

41
on it. It pays taxes, as well as interest on its foreign debt, and receives unrequited transfers from abroad. Thus, the household’s disposable income in nominal terms, Ydisp, can be defined as

# Ydisp = PY·Ys - TAX - RP*·ER·FdebtP-1 + RD·DdebtG-1 + ER*UTR$,       (12)

where TAX denotes total (direct and indirect) tax revenue,<sup>6</sup> RP* the interest rate on private foreign borrowing, FdebtP the stock of private foreign debt, DdebtG the stock of domestic public debt, RD the interest rate on that debt, and $UTR the foreigncurrency value of private unrequited transfers (assumed exogenous).

Total private consumption in real terms, CP, is defined as a function of disposable income and lagged consumption:

# where PQ is the composite market price.

To allocate domestic demand between domestic and imported goods, we use the standard Armington assumption.<sup>7</sup> Total demand for goods sold on the domestic market (which includes both imports and domestically-produced goods), Qd, is defined as the sum of private and public spending on consumption and investment: where CG and IG denote real government spending on consumption and investment and IP private investment.

> 6In principle, of course, only direct taxes should appear in the definition of disposable income. We nevertheless use a broader definition here, given that we do not model fully the composition of tax revenues. As a result, we also do not account for the effect of indirect taxes on the price of sales on the domestic market.

> 7See Winters (1984) for a discussion of the limitations of the Armington specification.

15
**Appendix** I.A.18 **Historical Demographic Data, Italy** 1861-1981

|Year|1861|1881|1901|1911|1921|
|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years ol|14.72|12.22|9.82|9.16|9.20|
|Population aged 20 to 59/<br>population over 60 years ol|7.60|5.54|4.87|4.56|4.63|
|Percentage of population<br>over 60 years old|6.56|8.96|9.60|10.19|10.45|
|Percentage of population<br>under 15 years old|34.19|32.18|34.36|33.96|31.21|
|Women over 60 years old/<br>Men over 60 years old|0.95|0.98|1.03|1.03|1.04|

|Year|1931|1936|1951|1961|1971|1981|
|---|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|8.62|8.32|8.01|6.92|5.70|5.78|
|Population aged 20 to59/<br>population over60 years old|4.60|4.67|4.38|3.89|3.11|3.41|
|Percentage of population<br>over 60 years old|10.79|10.97|12.15|13.92|16.65|15.76|
|Percentage of population<br>under 15 years old|29.74|30.59|26.14|24.50|24.44|21.94|
|Women over 60 years old/<br>Menover60yearsold|1.09|1.10|1.22|1.31|1.30||

Ratios calculated using data found in Mitchell, B. R. 1990 "Intemational Historical Statistics: Europe 1750-1988".
# **I. Background and Main Objectives**

The official poverty figures announced by the Government of Tanzania in November 2013 revealed a decline in the basic needs poverty rate from around 34 percent to 28.2 percent between 2007 and 2012—this is the first significant decline the country has experienced during the last 20 years. This reduction in poverty has been confirmed in the recently published Poverty Assessment report for Mainland Tanzania. The report examined the recent trends in poverty and inequality and their determinants and explored the responsiveness of poverty reduction to economic growth using the Household Budget Survey (HBS) collected in 2011/12. The report shows that poverty dropped by approximately 1 percentage point per year between 2007 and 2011/12, and that inequality, measured by the Gini coefficient of real per capita monthly consumption, declined from around 39 to 36 during the same period. The report also found emerging signs of pro‐poor growth, despite a persistently high number of people living in poverty.

However, the declining trend in poverty revealed by the HBS data is in contrast to the increasing trend that is observed using the National Panel Survey (NPS) data, which show an increase in poverty from 14.6 percent to 18.1 percent and subsequently to 21.2 percent between 2008/09, 2010/11 and 2012/13. The data also show a slight increase in inequality across the three rounds and do not support the pro‐poor growth pattern revealed by HBS data. Although HBS is the official source for official poverty numbers, this mismatch in poverty levels and trends between the two surveys is puzzling.

The NPS is a national level longitudinal survey designed to collect data from the same households over time in an attempt to better track the progress of the National Strategy for Growth and Reduction of Poverty (MKUKUTA), understand the poverty dynamics and evaluate policy impacts. NPS is conducted every two years by the National Bureau of Statistics (NBS). Four waves have been conducted for the periods 2008/09, 2010/11, 2012/13 and 2014/15. While the official poverty numbers are based on HBS data, NPS remains very important for policy analysis of poverty reduction due to the longitudinal nature of the data that makes it a particularly attractive survey for studying poverty dynamics and transitions.

This study aims to carry out a thorough investigation of the potential sources of mismatch in poverty and inequality levels and trends between the NPS and HBS. The investigation will focus on the key candidate sources for the divergence between the two surveys. These include the methodological differences in the construction of the consumption aggregates and the estimation of the poverty lines, 2
level and entry barriers (table 4). Yao (2002) believes that the WTO accession will likely accelerate the negative impact on rural employment and income, at least in the short and medium terms. 18

|**Table 4. Matrix of the Impact of WTO Accession on Chinese Industry**|
|---|

|_Market structure_|_Ownership_<br>_structure_|_Entry barriers (tariff_<br>_or non-tariff)_|_Government_<br>_subsidy_|_Status & impact of the_<br>_accession_|
|---|---|---|---|---|
|**_Globally_**<br>**_integrated/competitive:_**<br>Competition in both<br>global and domestic<br>markets (e.g.,<br>shipbuilding, textiles,<br>clothing, electronics)|State-<br>owned,<br>collective,<br>TVEs,<br>joint<br>ventures,<br>or private|Low or zero|Low or zero|**_(a) Economic:_**<br>•Higher efficiency;<br>•Better market access.<br>**_(b) Social:_**<br>•Increase in employment<br>through increase in<br>exports.|
|**_Segmented/protected:_**<br>Domestic and export<br>markets are segmented<br>as the former is<br>protected (e.g.,<br>machinery and<br>chemicals, and<br>automobiles)|State-<br>owned,<br>collective,<br>TVEs,<br>joint<br>ventures<br>or private|Medium or high|Low or<br>medium|**_(a) Economic:_**<br>•Improved efficiency;<br>•Better market access.<br>**_(b) Social:_**<br>Loss of employment in<br>inefficient enterprises;<br>increase in employment in<br>efficient ones.|
|**_Insulated:_**Only<br>domestic competition,<br>mostly closed to<br>foreign entry (e.g.,<br>telecom, banking &<br>financial services, and<br>petrochemicals)|State-<br>owned|High|Medium or<br>high|**_(a) Economic:_**<br>•Scope for raising<br>efficiency;<br>•Better market access.<br>**_(b) Social:_**loss in<br>employment due to import<br>competition; increase in<br>employment through better<br>market access.|

Source: Bhalla, A. S., and S. Qiu, 2002.

Despite the different effects on specific industries, the overall employment situation after the WTO accession will be very challenging. According to the author’s estimation, China’s labor force will grow by 80 million people over the next decade—from 742 million in 2000 to 822 million in 2010. A World Bank study (which the author participated in) shows that, considering the new entrants, the continuing employment shake-offs from agriculture, state-owned enterprises (SOEs), and township and village enterprises (TVEs), as well as the displaced and early retirees, it is expected that the Chinese economy needs to create about 8 to 9 million new employment opportunities annually. 19 This is much higher than China’s current job-creation capacity. Furthermore, other projections estimate the number of new jobs to be created at a much higher level –

> 18 Yao, Shujie, “China’s Rural Economy in the First Decade of the 21st Century: Problems and Growth Constraints”, China Economic Review, V13, 2002, pp. 354-360.

> 19 Dahlman, C. and J. Aubert, _China & Knowledge Economy: Seizing the 21st Century_ , 2001, the World Bank, Washington, DC.

8
|Table 3: Regr|ession Re|sults for Ot|her Develop|ment Outcome|s|
|---|---|---|---|---|---|
||2S<br>|LS<br>T<br>|P-Value for<br>est of Overidentifyi<br>|F-statistic<br>ng from first-stage<br>|Number of<br>|
||-<br>p|s.e.|Restrictions|Rearession|Observations|
||<br>Dep|endent Variable|is Infant Mortality|Per 1000 Live Birth|s|
|Full Sample||||||
|Voice and Accountability|-0.847|0.118|0.324|23.025|173|
|Political Instability and Violence|-1.408|0.212|0.610|6.301|155|
|<br>Govemment Effectiveness|-1.124|0.165|0.283|12.080|156|
|Regulatory Burden|-1.101|0.142|0.816|18.413|166|
|Rule of Law|-1.361|0.218|0.126|7.983|166|
|Graft|-1.025|0.140|0.013|12.069|155|
|Non-OECD Sample||||||
|Voice and Accountability|-0.636|0.169|0.309|14.259|149|
|<br>Political Instability and Violence|-1.842|0.722|0.966|1.190|131|
|Govemment Effectiveness|-1.573|0.590|0.853|2.552|132|
|Regulatory Burden|-0.812|0.186|0.790|11.250|142|
|<br>Rule of Law|-2.211|0.977|0.445|1.227|142|
|Graft|-1.495|0.533|0.149|2.227|131|
||D|ependent Varia|ble is Adult Literac|y Rate In Percent||
|Full Sample||||||
|Voice and Accountability|'15.102|1.690|0.164|23.025|173|
|Political Instability and Violence|26.140|5.242|0.611|6.301|155|
|Govemment Effectiveness|20.445|3.911|0.336|12.080|156|
|Regulatory Burden|20.902|2.809|0.932|18.413|166|
|Rule of Law|25.167|5.354|0.220|7.983|166|
|Graft|18.602|3.544|0.080|12.069|155|
|Non-OECD Sample||||||
|Voice and Accountability|15.857|2.688|0.103|14.259|149|
|<br>Political Instability and Violence|!55.701|20.129|0.791|1.190|131|
|Govemment Effectiveness|44.342|13.794|0.987|2.552|132|
|Regulatory Burden|22.281|4.224|0.672|11.250|142|
|Rule of Law|.54.679|21.820|0.473|1.227|142|
|Graft|:36.789|8.252|0.269|2.227|131|

27
# **5.3. Heterogeneity Analysis**

_Vulnerable migrant households_ We check whether our estimation results still hold for different groups of migrant households, particularly the vulnerable and disadvantaged groups (as predicted by Corollary 3.2). For this, we stratify the sample in various ways and estimate our main specification (column 3 from Table 3) on each subsample. Table 6 reports the impacts of the instrumented median school fee on the migrant worker’s decision to bring the child for (some of) these subsamples.

We first stratify the sample by income, defining as poor those who fall in the lower half of the household income distribution, and non-poor the remaining households. Table 6 (row 1) shows that higher school fees indeed deter poor migrants from bringing their children with them. These results, however, do not hold for the non-poor group. We then stratify the sample by insurance (or social benefits) status, and find that the same results hold for the migrants who do not have any access to these benefits (row 2).<sup>41</sup> The impacts of school fees are statistically significant for households who migrated to noncoastal cities: Zhengzhou, Luoyang, Hefei, Bengbu, Chongqing, Wuhan and Chengdu (row 3). For households that migrated to coastal cities (Guangzhou, Dongguan, Shenzhen, Shanghai, Nanjing, Wuxi, Hangzhou and Ningbo), the impacts are only significant after controlling for housing prices (not shown).

We then stratify the migrant workers sample into two groups according to their work status: those with a permanent (or long-term) contract (one year or more) and those with a short-term contract (less than one year) or without a contract (including the self-employed, family business helpers, part-timers, workers in a probationary period or interns, apprentices or hourly workers).

> 41 For each migrant worker, RUMiC records the enrollment status of four major social insurances/benefits: unemployment insurance, pension insurance, work injury insurance, and housing fund ( _San Xian Yi Jin_ ), which are mandated by the Social Insurance Law. We define a migrant worker as insured if he/she has access to at least one of these insurances/benefits.

30