rafmacalaba/datause-encoder-data huggingface dataset:
_Yi Chang_ 7 function and performs a grid search to find multiple solutions. The results are reported in a data set format that includes the values of the evaluated objective functions, the resulting parameters of each optimization iteration and the initial parameters that yielded that result.

To install the command on your system, open Stata and type: `. ssc install scale transformation` .

You can also find the latest version of the program at: `https://github.com/andresyichang/scale transformation` .

# **3.4 Syntax**

`scale transformation ,` <u>`type(`</u> _integer_ `)` <u>`score1(`</u> _varname_ `)` <u>`score2(`</u> _varname_ `)` � <u>`compgroup(`</u> _varname_ `)` <u>`controls(`</u> _varlist_ `)` <u>`controls1(`</u> _varlist_ `)` <u>`controls2(`</u> _varlist_ `)` <u>`weights(`</u> _varname_ `)` <u>`iterations(`</u> _integer_ `)` <u>`maxoptiterations(`</u> _integer_ `)` <u>`singhmethod(`</u> _integer_ `)` <u>`bounddown(`</u> _integer_ `)` <u>`boundup(`</u> _integer_ `)` <u>`monotonicity(`</u> _integer_ `)` <u>`monofile(`</u> _filename_ `)` <u>`timeroff save(`</u> _filename_ `)` <u>`seed(`</u> _integer_ `)` <u>`robust(`</u> _integer_ `)` <u>�</u>

# **3.5 Main Options (Required)**

`type(` _integer)_ indicates the type of optimization and the objective function to be performed. The options are as follows:

1. Gap Growth Maximization

2. Gap Growth Minimization 3. Correlation Maximization 4. Correlation Minimization 5. R-squared Maximization 6. R-squared Minimization 7. Controls Explanation Maximization `score1(` _varname_ `)` specifies the variable that contains scores at (initial) period 1. In order to improve processing speed, this variable is automatically scaled to be between 0 and 1 prior to running the program. The transformation used for this process is the same for variables score1 and score2, and does not affect the resulting estimations in any way.
To Sew or Not to Sew? Assessing the Welfare Effects of the Garment Industry in Cambodia<sup>_∗_</sup> Carolina Mej ́ıa-Mantilla<sup>_†_</sup> Martha Tesfaye Woldemichael<sup>_‡_</sup>

# **JEL Classification** : I3, F6, O12, O14, F24, R23.

**Keywords** : Cambodia, manufacturing, garment, welfare, poverty, remittances, propensity score matching, instrumental variables.

> _∗_ We are grateful to Simone Bertoli, Olivier Cadot, Julian Clarke, Samuel Freije-Rodriguez, Sekou Keita, Pierre Mandon, Moctar Ndiaye, Patrick Plane, Carlos Sobrado and Salman Zaidi for their useful comments and suggestions. We also thank participants at CERDI PhD Seminar and the GDRI Conference on International Development Economics at ClermontFerrand for their helpful comments. All remaining errors are ours.

> _†_ East Asia Pacific Region, Poverty and Equity Practice, World Bank. Email: `cmejiamantilla@worldbank.org`

> _‡_ CERDI, Universit ́e Clermont Auvergne. Email: `martha.t.woldemichael@gmail.com`
**-28 -**

Figure 9. Africa's food imports million metric tons <!-- Start of picture text --> 16<br>14 - . .....<br>13 - . - Y<br>121<br>1 1 .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. .. ..... ..... . .... .... . ... .......... ,,<br>10 ,- .......... - -- ~,, ---<br>7-<br>2 . ... . .. s; f._A ... _Y_._. _ _ _ ... ..~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.. .<br>1 4 . ................... .. .. .... ........... ... ..... ... .. . .... . ._.. . ...-.. . .. .. .... _ _ _<br>2<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988<br>- Total 6 Total less Nigeria -4- Food aid<br><!-- End of picture text --> Figure 10, Africa's import capacity (exports, borrowing, aid, investment) <!-- Start of picture text --> billions of $US<br>70<br>65 .. .. - ---- Total -- -- --- l Nigeria<br>6 0 . .. ... ... ... ..... .. ..... .. .. . \ .. .. .....<br>50 -\ < L_<br>4 5 . . .... -. ... ....... \. ...<br>4 0 ----- . .. .. ...<br>1970 1972 1974 1976 1978 1980 1982 19B4 1986<br>- Total °Total less Nigeria<br><!-- End of picture text -->
# **13.4 Effectiveness of government institutions**

102. The quality of government institutions also matters for fighting undeclared work. If institutions remain weak, a tax reform aimed at reducing undeclared work may not achieve its objective.<sup>14</sup> According to the corruption perception index of Transparency International, among the NM-EU countries, Slovenia and Estonia have the lowest corruption while in Bulgaria and Romania corruption is most pervasive (Table 15). This is confirmed by the Control of Corruption index of the World Bank, which can also be taken as a proxy for the quality of government institutions among the NM-EU countries. According to this indicator, Slovenia and Estonia have the strongest and Bulgaria and Romania the weakest government intitutions in the region (Table 16).

**Table 15: Perception of Corruption**

|Country|Score|Ranking among<br>180countries|
|---|---|---|
|Slovenia|6.7|26|
|Estonia|6.6|27|
|Czech Republic|5.2|45|
|Hungary|5.1|47|
|Latvia|5.0|52|
|Slovakia|5.0|52|
|Lithuania|4.6|58|
|Poland|4.6|58|
|Croatia|4.4|62|
|Romania|3.8|70|
|Bulgaria|3.6|72|

_Source:_ Transparency International, Global Corruption Report 2009.

> 14 In the literature there is a controversal discussion about the relationship between institutional quality, corruption and the size of the shadow economy. In theory, the relationship between corruption and the size of the shadow economy is ambigous and can be substitutive or complementary, depending on the circumtances (see Dreher et al. 2005 and Dreher and Schneider 2006 and the literature mentioned in these papers).  If corruption (i.e. the use of public power by politicians and civil servants) is used to circumvent overly strict regulations (so-called greasing the wheels) it helps firms to survive in the formal sector so that it is a substitute to working informally. If instead, corruption allows firms or workers to hide economic activity, government officials benefit from the shadow economy and corruption and undeclared work are complementary. Corruption in the form of bribing tax collectors to keep economic activity fully or partially underground is always complementary to tax evasion so that reducing this form of corruption helps reducing evasion.

44
wetness also directly affect food security, likely by reducing production risk within a given crop. Both mechanisms operate in tandem.

# **Conclusion**

We revisit the economic consequences of land fragmentation. We take seriously concerns regarding the exogeneity of fragmentation, its measurement and the importance of considering impacts in terms of welfare metrics. We argue that our Ethiopian data are well-suited to address these concerns. Continued land redistribution over a long period, the ban on land sales and mortgaging, limitations on land rentals and customary land inheritance practices mean that land access and fragmentation in Ethiopia are conditioned by history, location and demography. Our data allow us to construct multiple complementary fragmentation measures, reflecting number of parcels, size and geographical dispersion. Unlike most studies, we have information on household food security outcomes allowing us to assess the impact of land fragmentation on welfare metrics in terms of food security.

We find that in Ethiopia, land fragmentation reduces food insecurity. This result is robust to how we measure fragmentation and to how we address exogeneity concerns. Consistent with Shaw’s (1963) conjecture, land fragmentation mitigates the adverse effects of low rainfall on food security. Increased land fragmentation means households are endowed with a more diverse set of parcels in terms of walking distance, slope, elevation and wetness. The level of these characteristics has no effect on food security, but a higher standard deviation translates to improved food security outcomes. In part, this is because a farmer with multiple parcels can cater the crops she grows to her parcel’s characteristics. Farmers who grow more crop types are more food secure. This suggests that consideration of efforts to consolidate holdings should account for the possi23
importing regime of the economy. As an example, capping all tariffs at a maximum rate of 25% is projected to result in moderate tariff (overall import) revenue losses of 0.5% (0.1%), 3.8% (1.1%) and 10.1% (4.6%), in Zambia, Tanzania and Ethiopia respectively<sup>8</sup> .

The level of detail of the data used in TRIST as well as both the transparency and the flexibility of the model have contributed to the widespread use of TRIST for the analysis of policy relevant questions in client countries and ensured country ownership of the results. TRISTs have to date been developed for Albania, Bolivia, Ethiopia, Jordan, Kenya, Madagascar, Malawi, Mauritius, Morocco, Mozambique, Nigeria, Seychelles, Syria, Tanzania, Tunisia and Zambia and a range of TRISTs are under construction.

In many circumstances, TRIST based simulations have contributed actively to policy making. In Madagascar, policymakers decided to substantially reduce tariffs on capital goods which had been high compared to regional competitors. This happened only after a careful assessment of the revenue implications of the reform using TRIST, which demonstrated that revenue losses would be much lower than initially expected. TRIST is also the tool of choice for the COMESA secretariat to project revenue shares and to estimate revenue losses emanating from trade reforms for which countries can subsequently be compensated. In Nigeria, an analysis of the revenue implications of an EPA with the EU (Andriamananjara et al, 2009) has found strong interest among high level policymakers involved in the decision whether or not to engage in such an agreement.

# **3. The Methodology Used in TRIST**

TRIST is an Excel based tool that predicts the impact of tariff reform scenarios on the basis of a simple partial equilibrium model. It consists of two Excel files: the first is the _Data Aggregation Tool_ which organizes and appropriately formats the data to be imported into the second, the _Simulation Tool_ . The _Data Aggregation Tool_ allows the user to create country and product groups that are relevant to the formulation of trade policy scenarios in the country specific context. For example, the analysis of the revenue implications of joining a free trade agreement (FTA) requires defining the members of the FTA as a separate trading partner group. In the _Simulation Tool_ the user defines the tariff reform scenarios, can choose the parameters of the trade model underlying the calculations, and reviews the simulation results both at the aggregate, the sectoral and at the tariff line level.

In order to implement a TRIST for a given country, detailed and complete data on import transactions for the most recent year is required (data averaged across a number of years can also be used). For each import transaction, the data must identify the type of product (tariff line level, typically HS 8 digit), the country of origin of the trade flow, the customs procedure code (CPC) defining the customs regime under which the good enters the

> 8 For the simulations underlying the results presented in this paragraph a demand elasticity of 0.5 and an exporter substitution elasticity of 1.5 were used.

7
||Table 3a. Correlation|Coefficient|s Significant|at the1%|Level,Mex|ico|||
|---|---|---|---|---|---|---|---|---|
|||Not<br>inactive|School<br>completion|Older<br>age<br>when<br>started<br>working|Safe<br>Sexual<br>behavior|Older age<br>at first<br>pregnancy/<br>parenthood|Participate<br>in<br>activities|Attitude<br>towards<br>alcohol|
||No early school<br>dropout|0.42|||||||
||Older age when<br>started working|-0.04|0.24||||||
|es|Safe Sexual behavior|0.21|0.35|0.14|||||
|utcom|Older age at first<br>pregnancy/child|0.35|0.32|0.07|0.53||||
|aviors/O|Participate in<br>extracurricular<br>activities|0.12|0.15|-0.06|0.10|0.13|||
|Beh|Negative attitude<br>towards alcohol|-0.10|-0.04|0.06|0.04|-0.06|-0.05||
||Low parental<br>education level|-0.10|-0.21|-0.11|0.03|-0.03|-0.12|0.08|
|s|Rural residence|-0.12|-0.08|-0.02|--|-0.04|-0.06|0.11|
|factor|Negative parental<br>response to|-0.06|-0.08|-0.05|-0.13|-0.15|-0.03|-0.02|
|sk|misbehavior||||||||
|Ri|Social exclusion|--|0.11|0.11|0.18|0.05|--|0.09|
||Living with both<br>parents|0.27|0.35|0.13|0.49|0.49|0.08|-0.03|
||Positive relationship<br>with father|0.13|0.12|0.02|0.09|0.15|0.16|-0.03|
||Positive relationship<br>with mother|0.08|0.09|0.03|0.09|0.12|0.15||
||Church attendance|-0.02|0.06|0.06|0.10|0.05|0.12|0.08|
||Spiritual influence in<br>beliefs, opinions and<br>attitudes|--|--|-0.07|0.03|0.04|0.15|--|
|actors|Trust in governmental<br>institutions|--|--|--|0.02|--|0.03|0.02|
|ctive f|Trust in community<br>institutions|--|--|0.02|--|--|0.02|0.02|
|ote|Schoolquality|0.05|0.06|0.05|0.04|0.04|--|0.04|
|Pr|Well-being|0.03|0.05|0.05|-0.03|--|0.04|0.02|

-- not significant at the 1% level
education (10 years of schooling in Bangladesh and India) are also the children who leave the village household, because the “colleges” (for grades 11 and 12) and universities (for three-four year undergraduate, and graduate study) are located in the cities and large towns.

Our estimation sample consists of household heads and spouses, and their children, including those from other marriages in the case of Bangladesh. Our main empirical analysis is based on a sample of children aged 13-60 years. To test the sensitivity of our conclusions with respect to the specific age cutoffs, we estimate the rank correlation and absolute mobility at 25th percentile for two alternative age ranges; 16-60 and 13-50 years.

Table 2 reports the summary statistics for both the Bangladesh and India data for our main estimation sample (children in the age range 13-60 years). Several interesting observations and patterns emerge. The average schooling attainment is low in rural areas of both of the countries at the time of the survey years. The mean and median years of schooling for children are 4.97 and 5.00 respectively for Bangladesh, and 6.23 and 7 for India. The relatively lower education attainments in Bangladesh compared with India are also observed for the parent’s generation: median years of father’s schooling was 2 years in Bangladesh compared with 2.50 years in India. The age distribution of children also differs: the median age for the Bangladesh data is 30 years compared with 33 years for India. The gender gap in education between boys and girls is about 1 year in Bangladesh in contrast with 2.42 years in India.

In a study of intergenerational schooling persistence, one can define parental schooling in a number of different ways: some researchers use average schooling of father and mother as the relevant indicator, while others use the maximum of parents education, which in most cases amounts to father’s schooling in many developing countries. Also, many existing studies focus on the sub-sample of sons, and use father’s schooling as the relevant indicator of parental human capital. We use three indicators of parental education: father’s years of schooling, mother’s years of schooling, and the average years of schooling for father and mother. The percentile rank for father is estimated from the distribution of 10
18 is critical to the development of a reliable legal framework for copyright protection. Add4tional technical assistance, training, and equipment in both the Industrial Property and the Copyright Agencies could help those agencies meet the growing demand from foreign investors and domestic private entrepreneurs for viable intellectual property protection.

# International Conventions

Yugoslavia has ratifiad the major conventions in the field of intellectual property, including the 1883 Paris Convention for the Protection of Industrial Property (1967 Stockholm text)," t1- 1891 Madrid Agreement for the International Registration of Tradomarks (19 **t** 'tockholm text), and the Berne Copyright Convention (Paris text of 1971). lovenia has indicated its intention to be a signatory to these conventions in its own right. However, whether Slovenia is assumed to inherit Yugoslavia's treaty rights and obligations or must begin the accession process anew is still an open question."7

# ComDany Law

# Back2round

For some 30 years prior to 1989, Yugoslav companies operated under the unique Yugoslav concepts of social ownersh:.p of the means of production and worker self-management. These principles, enshrined in successive constitutions and laid out in greatest detail in the Law on Associated Labor of 1976,78 gave no one true ownership rights over enterprise assets but gave ultimate managerial power (at least formally) to workers' councils elected by the workers' assembly (Articles 490-495). Separate enterprises with legal personality--called "basic organizations of associated labor" or "BOALs"-could be formed by any group of workers, whether or not these enterprises constituted logically separate economic entities. The only three conditions " Yugoslav Official Gazette, No 5 (1974), most recent amendment Yugoslav Official Gazette, No 7 (1986).

76 Yugoslav Official Gazette, No 14 (1975). Several more recent conventions, such as the Patent Cooperation Treaty and the Hague Agreement for the Protection of Industrial Property, are in the process of ratification.

" The International Court of Justice in the Hague recently ruled that Yugoslavia's disintegration is a case of succession, not secession. This would imply that Slovenia would automatically be considered signatories to any conventions previously ratified by Yugoslavia, including those on intellectual property, and would--together with other successor states--be bound by previous Yugoslav obligations and entitled to all its rights.

78 Zakon o zdrutenem delu (Law on Associated labor], Yugoslav Official Gazette, No 53 (1976), most recent amendment Yugoslav Official Gazette, No 40 (1990).
3) Data on daily infections and deaths from COVID-19, by country, from Our World in Data. The period covered is January 1, 2020, to October 1, 2020.

4) Data on trust in different institutions, by country, from the 9<sup>th</sup> round of the European Social Survey (2018) and the third round of the Life in Transition Survey (2016).

# **5. Results**

Table 2 summarizes the analysis of the impact of reopening sequencing on economic activity based on equation (2). The first column of Table 2 shows the FE estimations of a specification with reopening sequence dummies only. In columns (2) and (3) we control for the epidemiological situation in the country and the potentially endogenous nature of the pandemic. The coefficients in columns (1-3) on the group of dummies reflecting the sequence of coming out of the full national lockdowns indicate that gradual exit results in better economic outcomes.

Moving from full lockdown to no work or business restrictions only increases relative electricity consumption by 1.9 percent and the coefficient loses significance once we properly control for the impact of the pandemic in column 3. At the same time, a progression from full lockdown to partial lockdown results in about 5 percentage points higher relative electricity consumption, and the following relaxation from partial lockdown to no restrictions results in approximately an additional 6 percentage points. In total, a country that moved from a full lockdown to a partial one and later removed the remaining restrictions sees a cumulative increase of about 11 percentage points in economic activity relative to predicted. Given that the average gap between actual and predicted electricity consumption in the first week of reopening is about 8 percentage points, a gradual reopening process appears to entirely close that gap, while a move from full restrictions to no restrictions yields a marginal increase of less than 2 percentage points at best. This may be because a gradual reopening process may give firms and individuals more confidence to increase their levels of investment and consumption than a fast reopening process, which may not restore confidence at the same pace.

We see a similar qualitative effect with school reopening – partial school reopening appears to be beneficial for the economy, while the jump to a complete school reopening has no significant economic impact. Removing restrictions on public events both partially and completely -probably correlated with a wider reopening of the entertainment and massive retail sectors- are associated 16
.

## Malnutrition: Targets and challenges

# Box Sb. MDG Goal Four: Reduce Child Mortality by two thirds by 2015

{ Vietnam Development Goal: reduce child mortality, child malnutrition and reduce the birth rate Target: Reduce under five malnutrition to 25 percent by 2005 and 20 percent by 2010 Source: Socialist Republic of Vietnam (2002) Ambitious targets are set - In addition to the two targets for mortality rates, it is proposed that the child malnutrition indicator — often included as an indicator under poverty targets — be identified as an important strategic target for the next decade within the health sector. Efforts for improving nutrition will directly help in achieving the targets for infant and child mortality. The National Nutrition Strategy 20012010 puts significant focus on improving the nutritional status of the Vietnamese population, particularly, that of children and mothers and ethnic minority groups. The Government of Vietnam’s own national goals for reducing malnutrition, which imply a reduction of child malnutrition levels of 60 percent between 1990 and 2010, are more ambitious than the MDGs, which call for a halving of malnutrition between 1990 and 2015 (Figure 12). Projections suggest that these ambitious rates will be achievable if the rapid rates of decline over the 1990’s continue.

Figure 12. Malnutrition: progress made during 1990-1998 and projections for 2010 and 2015 <!-- Start of picture text --> 50Pe<br>= 30 = ;<br>5 ‘ . : ~ — ay '<br>s ‘ — — ;<br>10 —+ ——<br>1990 2000 2010 2015<br><!-- End of picture text --> Source: ADB and WHO (2002) Poverty reduction alone will not solve the malnutrition problem - In Vietnam, increased income and health services coverage—the factors contributing to the declines in infant and child mortality and improvements in reproductive health—have not operated to reduce child malnutrition. The VLSS93 and VLSS98 generally show only weak relationships between consumption expenditure and malnutrition indicators (Glewwe, Koch and Bui Linh Nguyen 2001). This implies that other factors such as feeding and childcare practices have a more dominant effect and addressing these factors as well as the use of safe water and sanitation will be important.

Malnutrition varies quite considerably across regions. However, the variation is not as large as in the case of other health sector indicators, such as infant mortality and access to reproductive health services. Even 27