rafmacalaba/datause-encoder-data huggingface dataset:
methodological choice. The preferred estimator derived from a combination of DDD and RDD empirical strategies suggests that the average program impact between 2003 and 2005 was an increase of 6 girl students per school in terms of absolute change and an increase of 9 percent in terms of relative change. These are modest but statistically significant program effects. We also make use of data from repeated cross-section household surveys (usually thought to be more independent and objective relative to administrative data such as the school census). Given that these household surveys are statistically representative only to the provincial level, any districtlevel analysis should be tempered with statistical caution. However, for what it is worth, we find an average treatment effect on proportion of school attendance for 10-14 year old girls ranging from 10 to 13 percentage points. Taken together, these results, strongly suggest that the stipend is attaining its objective of increasing female enrollment in public schools in Punjab. The evidence from the household survey data also points that the stipend program is helping children from poorer households to attend schools. Incidence of stipend subsidy also shows more equal distribution across rich-poor population groups.

Additional sources of data will help to test the robustness of our results (which essentially hinge on the veracity of the school census data), and more importantly, help address other pertinent issues besides enrollment increases. For example, Andrabi, Das, and Khwaja (2006) point out that the majority of Punjabi villages do not have middle/high schools. Sathar, Lloyd, Mete, and Haque (2000) have shown that access to school is one of the key determinants of enrollment (and other subsequent non-schooling outcomes such as contraceptive use) in rural Punjab. An immediate policy-relevant question then is what happens in villages in stipend districts which do not have a public middle/high school? To address this and other relevant questions we mentioned earlier, we are in the process of collecting relevant household and schooling data via primary surveys (instead of relying upon existing administrative data and limited household data).

23
|Douentza - Gao(rehabilitation)|67'000'000'000|67'000'000'000|
|---|---|---|
|Gossi - Gourma - Rharous|36'750'000'000|36'750'000'000|
|Dirtroad Boré - Korientzé - Aka|6'500'000'000|6'500'000'000|
|Dirtroad lndelimane - frontière Niger|4'000'000'000|4'000'000'000|
|Léré - Fassala|3'500'000'000|3'500'000'000|
|**Aerial infrastructure**|**178'950'000'000**|**178'700'000'000**|
|Rehabilitation of Kidal airport|32'250'000'000|32'250'000'000|
|Rehabilitation of Tessalit airport|35'000'000'000|35'000'000'000|
|Rehabilitation of Taoudeni airport|32'250'000'000|32'250'000'000|
|Rehabilitation of Menaka airport|32'250'000'000|32'250'000'000|
|Rehabilitation of Gao airport|14'700'000'000|14'700'000'000|
|Construction of Goundam airport|32'500'000'000|32'250'000'000|

_Source:_ Accord pour la Paix et la Réconciliation au Mali and World Bank (2016) The subcomponent agriculture (food security, productive capacity and livelihoods) has been allocated 24% of the total budget, 37% even of the budget for years 1-2. This is important because rural development and food security is the priority intervention for 13% of households and 15% of authorities ranking it as first and second priority initiative (respectively) in its importance to promoting peace in the north.

Developing access to basic services is the priority initiative for 23% of leaders and 9% of households, ranking it as number one priority for authorities and number four for households. In the MIEC report the two subcomponents of rehabilitating social service infrastructure and improving the quality of basic service delivery are very much in line with the preferences of households and authorities. It would be worth rethinking budget allocation over time on the other hand, as it seems that too little budget is being allocated to the short-term. Only 14% of the budget allocated for rehabilitating social service infrastructure has been reserved for Years 1-2. It looks a bit better with regard to improving the quality of basic service delivery where 38% are allocated to Years 1-2. Given the importance of access to basic services for peace it may be advisable to allocate a greater share of the budget to the short to medium term interventions.

Overall, the budget allocation raises questions whose priorities are reflected in the Peace Accord and its operationalization in the MIEC costing. Road infrastructure receives the lions’ share of the budget allocation while very little is dedicated to employment creation. The economic rationale is already relatively weak for the road to Timbuktu while the road to Kidal seems mostly informed by political and security objectives. It is noteworthy however, that the activities proposed to be financed during the first two years are much closer to popular preferences, while the MIEC has shifted those elements in the Peace Accord that have less popular support to the future. In short, both the MIEC Strategy and 29
# **4. Quantifying the trade and income effects of the China-U.S. trade agreement**

In this section, we quantify the impact of import targets in the China-U.S. trade agreement on the trade and income of these two countries as well as on third countries under different scenarios.<sup>13</sup> The simulations are based on a global dynamic computable general equilibrium model, Linkage, which uses the GTAP database (Freund, Ferrantino, Maliszewska, and Ruta, 2018). The model tracks historical GDP growth, trade balances and investment up to 2019 and then projects the developments in the global economy up to 2025. The commitments of China to buy more U.S. goods and services as per the text of the agreement are aggregated to the CGE model sectors (Figure 3).<sup>14</sup>

**Figure 3** : Chinese imports of goods from the United States ($billion) in 2017-2019 and assumed targets under the China-U.S. agreement in 2020-2021.

<!-- Start of picture text --> Chinese imports of goods from US ($billion)<br> 60<br> 50<br> 40<br> 30<br> 20<br> 10<br> -<br>Agriculture Natural Food Textiles, Chemicals Metals Transport Electronic Machinery Other<br>resources wearing equip. equip. manuf.<br>apparel<br>2017 2018 2019 2020 2021<br><!-- End of picture text --> Source: WITS and authors’ assumptions based on China-U.S. trade agreement. Note: 2019 has been estimated based on Jan-Nov 2019 data.

13 The import targets in 2020 may be less feasible in light of the negative demand shock from the Coronavirus. As the focus of the note is on the trade agreement and the extent and duration of the Coronavirus shock is uncertain, the scenarios assume demand in 2020 is not affected by the virus--though that does not preclude a temporary shock. To the extent demand is lower for the year in China, the attempt to reach the import targets will lead to more trade diversion and a larger loss in income for China and the ROW.

14 “For the category of manufactured goods identified in Annex 6.1, no less than $32.9 billion above the corresponding 2017 baseline amount is purchased and imported into China from the United States in calendar year 2020, and no less than $44.8 billion above the corresponding 2017 baseline amount is purchased and imported into China from the United States in calendar year 2021.” 11
# INTRODUCTION

A major objective of Viet-Nam's transition to market economy is to reactivate the private sector within a "multi-component economy" or mixed economy. Several new laws have been introduced in the past five years to implement this policy and create an enabling environment for the private sector to formally engage in business in many areas.

Viet-Nam's private sector has considerable potential for rapid growth that has been realized in successes in agriculture, trade and services, and is only beginning to make its presence felt in some light industry subsectors. Factors crucial to this success have been Vietnamese entrepreneurship, a literate population, and a comparative cost advantage in labor intensive industries. Unlike other transitional economies in Eastern Europe or China, the - southern half of Viet-Nam is still familiar with the workings of a market economy clearly an added advantage for easier transition. However, considerable impediments remain for further development of the private sector. Private investment remains quite low; less than 10 percent of GDP in 1993 and only slightly improved in 1994. A deficient legal framework made up of complicated regulatory laws and uncertain substantive laws is perceived by the private sector as one of the most serious obstacles to its expansion.<sup>1</sup> This paper will review some of the more important laws and regulations in Viet-Nam - affecting private sector activities including the laws on real property, intellectual property, companies law, domestic investment, foreign investment, bankruptcy, contracts, and dispute resolution. Although anti-monopoly law has not yet been introduced in Viet-Nam, the issue of competition will be addressed in the larger context of current trade law, the relative roles of state sector versus private sector, and the restrictions in the company law. The above areas establish the foundation of a legal framework for a market economy. The paper will also discuss Viet-Nam's judicial institutions, including its civil code tradition and socialist orientation, the capacity of its court system, and the efficiency of law enforcement. As in China, Viet-Nam's legal framework is still influenced by ideology, which causes problems in several areas, such as private ownership of real property, and can affect fundamental legal concepts such as "due process of law".

# I. The Emerging Private Sector

Viet-Nam's transition to a market economy begin in 1989 with a reform program called Renovation or Doi-Moi. Initially, the program focused on price reform including exchange rate liberalization and abolition of subsidies to state-owned enterprises (SOE).

l Viet Nam: Private Sector's perceptions of Impediments to Industrial DeveloRment - A survey done by the Center for Investment and Foreign Trade Development, for World Bank's Financial Sector Mission. Ho Chi Minh City, April 1994.
A rich person will clearly be able to afford to spend more to live longer than a poor person, and will typically do so. But should we build such inequalities into our assessment of a country‘s progress in ―human development‖? Does the HDR really want to suggest that, in the interests of promoting ―human development,‖ Zimbabwe should not be willing to implement a policy change that increases life expectancy by (say) one year if it lowers national income per capita by more than $0.51—barely 0.3% of the country‘s income? Most likely not. Rather it was just that the construction of the HDI did not properly consider what tradeoffs were acceptable.

**Figure 1: Implicit monetary values of an extra year of life in Human Development Index**

<!-- Start of picture text --> 9,000<br>8,000<br>7,000<br>6,000<br>5,000 HDI in<br>UNDP<br>4,000 (2010)<br>3,000 Alternative<br>HDI based on<br>2,000 Chakravarty<br>index<br>1,000<br>0<br>5 6 7 8 9 10 11 12<br>Gross national income ($ per person per year; log scale)<br>($ per person per year)<br>Valuation on an extra year of life<br><!-- End of picture text --> <u>Source: Author‘s calculation from data provided in UNDP (2010). The fitted lines are locally</u> smoothed (nonparametric) regressions.

The MPI and the Newsweek index also have implicit values of life, though it is hard to figure out what they are from the documentation.<sup>22</sup> In a recent comment on the HDI, Segura and Moya (2009) argue against imposing any tradeoff between its components, so that a country‘s progress in human development should be judged by the weakest (minimum) of its scaled components.

> 22  In the case of the Newsweek index, scaled life expectancy gets the same weight as (say) scaled test scores for education. I will return to the MPI.

14
**Table 1** . Impacts of the managed trade scenario as compared to the trade policy status quo scenario (percent)

||Income||Total expo|rts<br>|Total impor|ts|
|---|---|---|---|---|---|---|
||2020|2021|2020<br>|2021|2020|2021|
|USA|0.65|0.91|2.17|3.07|1.68|2.33|
|CHN|-0.29|-0.38|1.47|2.04|1.59|2.24|
|EAP excl. China|-0.25|-0.32|-0.36|-0.52|-0.34|-0.47|
|SAR|-0.07|-0.11|-0.09|-0.15|-0.07|-0.13|
|Mexico|0.09|0.11|-0.14|-0.21|-0.13|-0.19|
|LAC|-0.17|-0.27|-0.44|-0.68|-0.33|-0.54|
|AFR|-0.10|-0.17|-0.19|-0.33|-0.15|-0.29|
|ECA|-0.08|-0.11|-0.15|-0.21|-0.13|-0.17|
|MENA(Egypt,Arab Rep.)|-0.06|-0.09|-0.11|-0.17|-0.10|-0.15|
|HICs|-0.15|-0.21|-0.29|-0.43|-0.27|-0.39|
|ROW|-0.11|-0.17|-0.23|-0.34|-0.20|-0.30|
|Global|0.03|0.03|0.20|0.28|0.21|0.30|
|World excl. USA and CHN|**-0.11**|**-0.16**|**-0.21**|**-0.31**|**-0.19**|**-0.27**|

Source: Linkage model simulations.

_Managed trade is better than trade war for the United States and China but makes everyone else worse off (Table 2)._ Relative to an escalation of the trade war, managed trade improves the outcome for both the United States and China. The impact on the United States is significant, with income higher by 1.2 percent and total exports higher by 5 percent in 2021. The impact on Chinese income is smaller but positive. The impact on total Chinese exports is positive, at 4 percent, due to a combination of the positive income effect of higher growth in the United States and the assumption of the total trade balance as a share of GDP being the same in both scenarios.

The impact on the rest of the world is negative, with income lower by 0.2 percent and exports lower by 0.45 percent in 2021. The biggest relative loss of income and exports is expected in East Asia and the Pacific excluding China (-0.43 and -0.8 percent, respectively, in 2021), followed by Latin America (-0.21 and -0.6 percent, respectively, in 2021). The negative impact on the rest of the world is predominantly due to trade diversion, as China imports less from other partners. Losses are more extensive than in the previous scenario because in the trade war scenario exporters from the rest of the world benefit from higher effective preferences in both markets. The negative impact on Latin America is driven by agricultural goods and in East Asia and the Pacific by manufacturing goods. This effect is only in part driven by the reversal of the tariff preferences that China implicitly granted to the rest of the world as it raised tariffs on goods from the United States in 2018 and 2019. In fact, Annex 2 shows that a managed trade scenario imposed in the pre-trade war setting would have reduced income for third countries (although less than when the starting point is the high tariffs imposed during 2018 and 2019 on bilateral trade between China and the United States).

13
the formal sector in Harare undertakes these same activities. The city’s formal establishments tend to be involved in non-food retail. To test if these sector composition effects are driving the observed productivity differences, survey-weighted regressions accounting for these sector fixed effects were also run. OLS and quantile regression results confirm that on average, informal sector businesses are 88% or 86% less productive, respectively.<sup>13</sup>

**Table 1: Formal and Informal Sector Activities, Share**

||Formal|Informal|
|---|---|---|
|Manufacturing|18%|17%|
|Food provision|10%|50%|
|Retail|42%|17%|
|Other services|30%|16%|

Note: weighted estimates. Food provision includes retail sale of food, restaurants, bakeries, and beverage production

# _<u>Bribery in Zimbabwe</u>_

Using globally comparable indices, Zimbabwe fares poorly in terms of corruption. Transparency International’s Corruption Perceptions Index for 2017 ranked Zimbabwe 157<sup>th</sup> out of 180 countries, tied with Burundi, Haiti, and Uzbekistan. Further surveys by Transparency International, from 2015, report that roughly one in five respondents report encountering a bribe request when soliciting public services. According to the same Transparency International survey, nearly one in four respondents encounter a bribe request when dealing with police. What is more, Harare itself fares poorly within the country: the 2017 ES report that, at 19%, the rate of formal firms encountering at least one bribe request is the highest in the capital (World Bank, 2016).

A brief look at news articles from the first six months of 2017 provides anecdotal flavor to these data.<sup>14</sup> In the first half of the year, the economy was stagnant, facing widespread cash shortages, as a result of government-introduced bond notes pegged to the US dollar the previous year.<sup>15</sup> Reports of corruption were pervasive in every-day life, from encounters with gas station attendants to

> 13 Informal-sector dummy coefficient values of -2.14 [.166] and -1.93 [.242], respectively. Robust standard errors in brackets.

> 14 Subsequently cited news articles are based on a Factiva search of (“Harare” OR “Zimbabwe”) AND (“BRIBE” OR “CORRUPTION”), October 2018.

> 15 Note that by the end of 2017 the economy again had entered all-out crisis, including the ouster of President Robert Mugabe on November 19.

(11)
**-** **<u>Appendix table 1: Distribution of in situ data and accuracy of prediction by land cover type</u>**

|<br>**Land cover type**|<br> <br>|<br>**Field numbe**<br>|<br>**r**<br>|<br>**Accu**<br>|<br>**racy of predi**<br>|<br>**ction**<br>|
|---|---|---|---|---|---|---|
||**2016**|**2017**|**2018**|**2016**|**2017**|**2018**|
|Artificial|135|135|328|75|70.3|76.3|
|Bare land|114|108|161|60|60.8|76.9|
|Cereals|1819|1172|2292|60.4|96.9|92.9|
|Forest|676|683|1331|98.1|98.7|99.1|
|Grassland|798|544|1301|80.2|84.3|84.7|
|Maize|1027|688|972|93|92.1|60.6|
|Peas|32|49|76|70.9|91.8|91.8|
|Soybeans|545|381|649|82.5|81.6|31|
|Sugar beet|59|39|138|93.6|98.6|63.8|
|Sunflower|1645|1191|1751|94.3|95.6|78.6|
|Water|176|192|480|88.6|99.2|99.4|
|Wetland|48|70|132|77.3|62.5|78.7|
|Winter rape|188|213|391|83.6|97|95.2|
|Total|7,989|5,465|10,210|88.3|91|85|

26
collected on a monthly basis from each household. The list of non-food items is large as well, with 364 items being covered. In all, the survey collects information on 610 consumption items. Besides consumption data, the survey collected data on dwelling conditions, assets, education and employment as well at the beginning of the survey year.

In 2012, the Government of Jordan produced poverty estimates updating its previous methodology. The 2010 HEIS (Household Expenditure and Income Survey) was used to estimate a new national poverty line, following a ‘cost of basic needs’ (CBN) approach.<sup>8</sup> The poverty line was based on a common national caloric requirement of 2,347 calories per capita per day, and with a common food and non-food basket for all households. Since consumption habits of rich and poor households may differ greatly, the poverty line was based on the revealed consumption and expenditure patterns of the bottom 30 percent of the population (regarded as poor or near-poor) as reflected in the 2010 HEIS (World Bank, 2012). The estimated poverty line to meet basic needs was set at 813.7 JD per individual in 2010,<sup>9</sup> and the official estimated poverty rate based on this line was 14.4 percent.<sup>10</sup>

# **III. Measuring Poverty with Repeat-Visit Data**

The previous section describes the measure of welfare (i.e. per capita consumption) and poverty line used to identify basic needs in Jordan. The next step is simply to identify who is poor by examining whether their individual measure of welfare falls below or above the poverty line. The

> 8 See Ravallion (1998) for discussion of CBN and other methods.

> 9 This line translates to 3.42 US Dollars per day in 2005 PPP terms, as measured by the World Bank. 10 The 2010 poverty line was also ‘back-casted’ to 2008, based on official CPI deflators to maintain the real value of the line. The poverty rate in 2008 under this line was estimated at19.5 percent (World Bank, 2012).

6
# **_Participation in school functioning_**

Perhaps the most important indicator of parents’ participation in their child’s education is to ensure child attendance in school. We asked parents how many days of school their child had missed in the last 14 calendar days. We find that 42.55 percent of parents say their child missed 4 days or less, but an almost equal number (40 percent) say their child was absent from school for 5 days or more. This suggests there is a problem of regular attendance by children in schools. There is no significant difference between the number of days missed by children in public and private schools.

There are negligible instances of parents contributing funds or their time to school functioning. Almost all parents interviewed (98 percent) report not knowing how much money is provided to their public school from the government for its maintenance.

When asked if they thought teachers attended school regularly, 62 percent of parents responded yes, while 33 percent said they did not know. This response is at odds with independent survey estimates that find that teachers are absent for no official reason from public schools in UP 26 percent of the time, and with anecdotal evidence of parents complaining that teachers are frequently absent from schools (Chaudhury and others 2004; PROBE team 1999)<sup>6</sup> . From direct observation of the physical presence and activity of teachers in schools in our survey, we found 69 percent of teachers actually present in the school, and of those present in schools only 55 percent were observed to be actually engaged in teaching at the time of the unannounced visit.<sup>7</sup>

# **_Public school characteristics, financing and the SSA_**

Almost all schools have heard of the SSA (313 out of 316 interviews), and know that they are supposed to be entitled to resources from the program. We tried to measure how much resources schools have received from the SSA and separately from the state

> 6 It is possible that the response to this question is biased since respondents often assumed that surveyors were sent by the government, despite informing them that the surveyors were indeed independent. People might be hesitant of being critical of the government out of fear that the service providers will retaliate by withholding various entitlements. Chaudhury, N., J. Hammer, M. Kremer, K. Muralidharan, and F. H. Rogers "Missing in Action: Teacher and Health Worker Absence in Developing Countries." _Journal of Economic Perspectives_ 20(1):  Spring 2006. PROBE Team. 1999. _Public Report on Basic Education in India_ . Oxford University Press: New Delhi

> 7 Most of the survey team visits (77 percent) were made between 9 a.m. and 1p.m. on a day schools are supposed to be open. All the visits happened between 8 a.m. and 4 p.m.

16