rafmacalaba/datause-encoder-data huggingface dataset:
We will use this framework, the interaction of shocks and institutions, to explain the recent evolution of the labor market in the ECA countries.

# **_The regression model_**

To capture the interaction between shocks and institutions, the simplest model is as follows: where _u_ is the unemployment or employment rate in country _i_ at time _t_ , _c_ are the country dummies, _d_ are time dummies, that is, unidentified shocks assumed to be equal across countries<sup>11</sup> , and _X_ is the time-invariant value over the period of the institution _j_ in country _i_ .

What matters in the estimation is not the value of the shock or the institution but the interaction between both of them. This is the most general specification since no specific shocks are imposed; we only assume that a number of shocks over time, of equal dimension across countries, have hit the economies. Hence, it allows isolating the impact of the (different) institutions from that of the shocks.

The next step in the analysis will be to identify the shock, that is, to allow the shock to be specific to each country. Transition economies have been hit by a similar shock over the last decade, namely, a transition shock. But their labor markets have reacted differently. Some countries have managed to go through with low unemployment while others have ended up with a poor employment score. Why is that? The answer is that the impact of the shock depends on whether the institutions of the country are more or less friendly to employment creation.

Substituting the time dummy in (1) by the transition shock, which differs across countries, we can estimate how the different institutional framework of each country has facilitated or obstructed the creation of a new private sector after the fall of the communism.

# **_Data_**

To estimate the coefficients corresponding to each of the institutions in model (1), we use an unbalanced panel of 28 ECA countries over 14 years: from 1988 to 2002. Unfortunately we have only one year of information for most of the institutions introduced in section 2. It is unfortunate because it would be interesting to explore how the reforms introduced in the business environment over the last decade have affected the capacity of the economy to respond to shocks. Given the data constraint, we have to assume that the institutional framework in which firms have operated

> 11 The first period is left out so it becomes the constant. Therefore the country dummies can be interpreted as the unemployment in the first period.

13
- 33 -

Figure 10.

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_Source:_ Bank of Thailand.

_Performance of Banks'_ **_and_** _Finance Companies._ Commercial banks outgrew their weak performance of the 1980s during 1988 to 1995. Profitability as measured in return on assets and equity (See Table 9) increased considerably in the early 1990s because: (i) of a restructuring of the typical balance sheet (as a ratio to total assets, earning assets increased from 79 percent in 1988 to 85 percent by 1995), (ii) lower effective tax rates (BIBF loans were taxed at 10 percent instead of 30 percent), and (iii)
**<u>Table 3. Whether Self-Assessed is Food Security Explained by Relative Food Prices</u>**

|Regression No.|1|2|3|
|---|---|---|---|
||Food price|Food|Food|
|Dependent variable|level|insecurity|insecurity|
|No. ofobservations|99|91|91|
|Constant|61.74***|17.0**|31.1**|
|GDP per capita ($1,000s)|2.80***|−3.1***|−2.3***|
|GDP per capita, squared||0.04***|0.03***|
|Food price ratio||63.8***|48.7|
|Food price ratio, squared||−19.4***|−9.2|
|Africa dummy|30.4||18.6|
|Latin America dummy|−12.3||10.5|
|Asia dummy|5.0||4.6|
|Europe-plus dummy|−12.5||5.9|
|R-squared|0.65|0.73|0.76|
|Adjusted R-squared|0.63|0.72|0.75|

_Source_ : “Food insecurity” is from the GWP (Gallup 2011) and is described in the text. GDP per capita is from the World Bank (2010) and is measured in constant purchasing power parity dollars. “Relative food prices” are measured as the purchasing power parity of food and nonalcoholic beverages relative to the nominal exchange rate for the year 2005. Information is from the World Bank (2008b). _Note_ : *, **, and *** indicate significant at the 10 percent, 5 percent, and 1 percent levels, respectively. “Europe-plus” includes Eastern European countries plus North America and Australasia. Note that self-reported food insecurity data are measured in 2005 or 2006, whereas the food price ratio is measured in 2005.

Overall, the results reported above present a mixed picture of the validity of cross-country patterns in the Gallup data. On the one hand, there are certainly some worrying outliers in the GWP indicator (particularly in the 2005–06 round). On the other hand, the data as a whole are plausibly patterned across countries and strongly correlated with other welfare indicators and relative food prices. However, we acknowledge that many social scientists are wary of subjective 14
(Bernanke, 1983; Dixit and Pindyck, 1994). Matching grants may help firms overcome this reluctance and spur new activity, or conversely, these other factors may limit the demand for such programs only to the few firms planning on undertaking these activities anyway, in which case the subsidy would not have any additionality. To date there is no evidence from fragile states as to which channel dominates.

This paper reports on a randomized experiment testing the impact of matching grants for business development services in the Republic of Yemen. The program provided firms with a matching grant of up to $10,000 as a 50 percent subsidy towards the cost of business services like finance and accounting systems, website creation, training, marketing, participation in exhibitions, and some associated goods such as office and IT equipment. The program was intended to run in two annual calls, but the second year was canceled due to the outbreak of civil conflict in 2015. We therefore report on the randomized experiment run on the first round.

The results show that such programs can be designed in a way that results in many firms applying, randomization is feasible, and that the program does appear to result in additional innovation and firm growth. Building on lessons from past matching grant programs, the program was designed to make it easy for firms to apply, and approximately four times as many eligible firms ended up applying as there was funding available. A public randomization ceremony proved an effective and transparent way of choosing among eligible applicants. A follow-up survey was conducted in March 2015 just as civil conflict was beginning to break out. Although this survey had relatively high attrition (45%), the sample interviewed remain balanced between treatment and control on observed characteristics, and the results show that the matching grant led to firms being 37 percentage points more likely to engage in innovation, introducing more new products, carrying out more marketing, being more likely to introduce a new accounting system, training more workers, making more capital investments, and being 48 percentage points more likely to say their sales grew over the past year. These results suggest that additionality was a result of this matching grant program.

The remainder of the paper is structured as follows: Section 2 discusses the context and details of the matching grant program; Section 3 describes the randomization process and take-up of the program; Section 4 provides the results of the impact evaluation, and Section 5 concludes.

3
# **Table 1: Marital Status of Recent Mexican Immigrants in the U.S. Census**

||Males<br>|Females<br>%|
|---|---|---|
|Married, with spouse present|14.4|48.3|
|Married, with spouse absent|26.9|8.7|
|Separated|1.8|3.7|
|Divorced|1.5|2.4|
|Widowed|0.2|0.7|
|Never married/Single|55.2|36.1|

Source: US Census 5% public use sample (Ruggles et al, 2004) Immigrants born in Mexico who migrated to the U.S. in last two years

**Table 2: Summary Statistics of Key Variables**

||Number of<br>|All mal|<br>es 15-49|Mi|<br>grants|
|---|---|---|---|---|---|
|_Individual level variables_|Observations|Mean|Std dev.|Mean|Std dev.|
|_ALL COMMUNITIES_||||||
|Proportion migrating in the last two years|55848|0.0099||1||
|Age|55848|28.6|9.7|28.4|7.9|
|Years of Education|55848|8.7|4.4|7.0|3.6|
|Proportion married|55848|0.55||0.75||
|Proportion who are household heads|55848|0.50||0.65||
|Years of Education*Community migration prevalence|55848|1.26|1.3|1.90|1.4|
|_COMMUNITIES WITH 100,000 OR LESS POPULATION_||||||
|Proportion migrating in the last two years|25531|0.014||||
|Age|25531|28.2|9.8|28.0|8.0|
|Years of Education|25531|7.3|4.1|6.3|3.2|
|Proportion married|25531|0.55||0.74||
|Proportion who are household heads|25531|0.49||0.67||
|Years of Education*Community migration prevalence|25531|1.31|1.55|2.21|1.55|
|_Community level variables_|Number of|All com|munities|> median|prevalence|
|(communitieswith 100,000 or less population)|Communities|Mean|Std dev.|Mean|Std dev.|
|Community migration prevalence|254|0.205|0.191|0.355|0.155|
|State migration rate in 1924|254|0.0068|0.0083|0.010|0.008|
|Percent of rural households owning land in 1910|254|2.89|2.17|3.41|2.29|
|Number of schools per 1000 population in 1930|254|1.22|0.43|1.14|0.36|
|Male school attendance in 1930 (% of 6 to 10 year olds)|254|44.64|11.60|44.61|11.23|
|Gini of schooling years for males 15-20 in 1960|254|0.51|0.10|0.51|0.10|
|Average years of schooling of males in 1960|254|2.92|0.81|3.01|0.83|
|Gini of household income in 1960|254|0.76|0.09|0.75|0.08|

Source: 15-49 year old males in ENADID 1997, non-migrants and those migrating in last 2 years for the first time only.
- Ravallion, M. 1992. Poverty Comparisons. A Guide to Concepts and Methods. World Bank, Living Standards Measurement Study Working Paper No. 88.

- Sahn, David E., and Harold Alderman. 1995. “Incentive Effects on Labor Supply of Sri Lanka’s Rice Subsidy.” In van de Walle, D., and Nead, K. 1995. Public Spending and the Poor: Theory and Evidence. Baltimore: The Johns Hopkins University Press.

- Salisbury, L.N. 1992. Enhancing Development Sustainability and Eliminating Food Aid Dependency: Lessons from the World Food Programme. Ithaca, NY, City and Regional Planning, Cornell University.

- Thielke T. 2006. Kenya's Deadly Dependency on Food Aid. Der Spiegel, 01/19/2006, available at <u>http://www.spiegel.de/international/spiegel/0,1518,396031,00.html</u>

- Tucker, J.B. 1982. The politics of refugees in Somalia. Horn of Africa, vol. 5, No. 3.

- Van de Walle, D. 1998. Targeting Revisited. The World Bank Research Observer, year 1998, volume 13, issue2, page 231-249.

20
# **I. INTRODUCTION**

# **Overview of Current Tax Structure**

1.1 Like most countries, Russia collects tax revenues from three bases - labor, capital and natural resources. Table 1 gives the shares of the main revenue categories in the 1999 consolidated and federal budgets.'

Table 1. Russia's 1999 Budget Revenues

||Conso|lidatedbud|get|Fe|deralbudge|t|
|---|---|---|---|---|---|---|
||Share|<br>mnRUB|<br>mnUS$|Share|mnRUB|mnUS$|
|Totalrevenues|100%|1,197,454|48,284|100%|611,710|24,666|
|Taxrevenues|83.7%|1,002,606|40,428|83.3%|509,507|20,545|
|CPT|18.4%|220,207|8,879|13.3°/o|81,201|3,274|
|PIT|9.8%|116,940|4,715|3.3%|19,928|804|
|VAT|24.0%|286,894|11,568|36.1%|221,031|8,913|
|Excise taxes|9.1%|108,416|4,372|13.8%|84,212|3,396|
|Licenses|0.1%|1,620|65|0.1%|689|28|
|Foreigncurrencytax|0.2%|2,467|99|0.2%|1,450|58|
|Salestax|1.6%|19,293|778|0.0%|0|0|
|Othertaxes|0.0%|11|0|0.0%|1|0|
|Imputedincometax|0.6%|6,936|280|0.2%|1,259|51|
|Propertytaxes|4.4%|52,600|2,121|0.1%|749|30|
|"Greentaxes"|3.7%|44,575|1,797|1.7%|10,496|423|
|Foreigntradetaxes|7.2%|86,262|3,478|14.1%|86,262|3,478|
|Other taxes|4.7%|56,386|2,274|0.4%|2,228|90|
|Non-taxrevenues|6.9%|82,583|3,330|7.7%|47,010|1,896|
|Transfers|1.3%|15,051|607|0.0%|9|0|
|Budgetfunds|8.1%|97,214|3,920|9.0%|55,183|2,225|

Source: Ministry of Finance.

1.2 Taxes on income (including corporate profits and personal income), goods and services (including VAT and excises), and foreign trade (import and export duties) together make up about 70% of consolidated revenues. The concentration of the revenue capacity in a few taxes is even more marked at the federal level, where the same five taxes provide some 80% of revenues.

1.3 Table 2 breaks down the total revenues from natural resource and environmental taxes into the various constituent parts. Green taxes, i.e. natural resource user fees and pollution charges, make up less than 4% of consolidated revenues, and less than 2% of total federal revenues. They provide more at the sub-national (regional and local) level, namely

> ' The consolidated budget is the sum of the federal and sub-national budgets of the members of the Russian Federation (provinces, autonomous republics, _etc.)_
# **A Heterogeneous Effects**

Table A.1. Heterogeneous effects on Safety-related Outcomes

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|_A. Heterogeneous Effects b_|_y Wealth In_|_dex_|||||
||||Experie|nced or witness|ed||
|Indicator Variables|Serious<br>Injury|Combat<br>Situation|Rape or<br>sexual abuse|Being close<br>to death|Murder of family<br>or friend|Murder of<br>stranger|
|SID_×_I( High Wealth)|-0.019**|-0.000|0.001|-0.009|-0.001|-0.013**|
||(0.008)|(0.007)|(0.003)|(0.011)|(0.003)|(0.004)|
|SID|0.037***|0.021*|-0.000|0.016|0.002|0.018***|
||(0.005)|(0.009)|(0.002)|(0.009)|(0.001)|(0.001)|
|I(High Wealth)|0.002|-0.000|0.008**|0.002|0.008*|-0.000|
||(0.006)|(0.002)|(0.003)|(0.007)|(0.004)|(0.004)|
|R-squared|0.028|0.027|0.008|0.013|0.006|0.016|
|Observations|13,605|13,605|13,605|13,605|13,605|13,605|
|_B. Heterogeneous Effects b_|_y Gender_||||||
|SID_×_I(Male)|0.015**|0.006*|-0.003|0.004|-0.001|-0.002|
||(0.004)|(0.003)|(0.002)|(0.003)|(0.001)|(0.002)|
|SID|0.019***|0.018***|0.001|0.009*|0.002|0.012**|
||(0.004)|(0.004)|(0.001)|(0.004)|(0.002)|(0.003)|
|I(Male)|0.009|0.008|0.001|-0.004|0.003|0.006|
||(0.011)|(0.005)|(0.003)|(0.005)|(0.004)|(0.004)|
|R-squared|0.028|0.027|0.007|0.013|0.006|0.015|
|Observations|13,701|13,701|13,701|13,701|13,701|13,701|
|_C. Heterogeneous Effects b_|_y Age_||||||
|SID_×_I(Young)|0.0003**|0.0006***|0.0002**|0.0001|0.0001**|0.0001*|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|SID|0.0161**|0.0008|-0.0064*|0.0072*|-0.0026|0.0064*|
||(0.007)|(0.004)|(0.003)|(0.003)|(0.002)|(0.003)|
|I(Young)|0.0313**|0.0289***|0.0059|0.0146|0.0043|0.0066**|
||(0.009)|(0.006)|(0.004)|(0.008)|(0.005)|(0.002)|
|R-squared|0.029|0.028|0.008|0.013|0.006|0.015|
|Observations|13,701|13,701|13,701|13,701|13,701|13,701|

_Notes:_ The table presents results from equation 3 with estimates examining the interaction between SID and indicator variables of wealth, gender, and age. SID represents standardized values of the inverse distance from surveyor households to refugee camps. The estimates incorporate population weights. Dependent variables are available for both the 2019 and 2023 waves, and detailed explanations for their construction are provided in Appendix B. Panel A illustrates the heterogeneous effects for high wealth, where _I_ ( _High Wealth_ ) is an indicator set to one if the respondent’s wealth asset index exceeds the median of the overall sample. Panel B presents the heterogeneous effects for male respondents, where _I_ ( _Male_ ) is an indicator variable set to one if the respondent’s gender is male. Panel C showcases the heterogeneous effects for age, with _I_ ( _Y oung_ ) as an indicator set to one if the respondent is between 0 and 23 years old. Estimates control for individual characteristics including male=1, age, household size in 2019, household head=1, married=1, and household income sufficiency in 2017. Wave fixed effects are included and clustered standard errors at the upazila level (i.e., subdistrict) are presented in parentheses. Significance levels: _∗∗∗_ at 1%, _∗∗_ at 5%, _∗_ at 10%.

52
Table 7: Selected product densities, aggregated categories, 2007<sup>_†_</sup>

|HS<br>|Product|RCA<br>|Change<br>|Density|
|---|---|---|---|---|
|code||(2007)|(%)||
|6|Live trees|0.07|-87|0.429|
|5|Animal products|0.83|-31|0.333|
|12<br>|Oil seed<br>|0.34<br>|-17<br>|0.371<br>|
|23|Food residue & waste|0.15|29|0.422|
|24|Tobacco|0.10|35|0.344|
|56|Wadding yarns, and twine|0.27|83|0.387|
|68|Stone, plaster, and cement|0.51|241|0.257|
|46|Straw|0.56|304|0.410|
|18|Cocoa|0.81|473|0.369|
|74|Copper|0.18|533|0.193|
|21|Miscellaneous edible preparations|0.44|613|0.392|
|49|Books, newspapers, and pictures|0.68|617|0.197|
|51|Wool and animal hair|0.67|666|0.254|
|33|Essential oils|0.48|848|0.197|
|83|Miscellaneous base metals|0.16|1,283|0.297|
|42|Leather|0.42|1,502|0.312|
|76|Aluminium|0.51|1,511|0.322|
|38|Miscellaneous chemical products|0.17|2,980|0.358|
|95|Toys, games, and sports equipment|0.09|3,116|0.236|
|84|Nuclear reactors|0.18|3,582|0.343|
|48|Paper and paperboard|0.32|3,786|0.193|
|44|Wood|0.09|3,811|0.294|
|39|Plastics|0.65|3,865|0.294|
|73|Iron or steel articles|0.58|4,508|0.419|
|30|Pharmaceuticals|0.37|4,661|0.290|
|94|Furniture|0.52|10,612|0.355|
|87|Vehicles|0.01|22,546|0.370|
|96|Miscellaneous manufactured articles|0.81|123,780|0.320|
|35|Albuminoidal substitutes|0.88|_‡_|0.322|
|50|Silk|0.70|_‡_<br>|0.236|
|78|Lead|0.57|_‡_|0.220|
|43|Furskins|0.47|_‡_|0.322|
|69|Ceramics|0.44|_‡_<br>|0.402|
|36|Explosives|0.43|_‡_|0.314|
|92|Musical instruments|0.28|_‡_<br>|0.406|
|32|Tanning/dyeing extract|0.21|_‡_|0.331|
|59|Coated fabrics|0.19|_‡_<br>|0.220|
|85|Electrical machinery|0.17|_‡_|0.287|
|72|Iron and steel|0.11|_‡_|0.329|

> _†_ Calculations applied at the 2-digit level. Change in RCA calculated between 2001 and 2007. For lines with incalculable RCA changes, only those with RCA in 2007 above 0.1 were reported.

> _‡_ Indicates nonexistence of exports in 2001.

21
Following the completion of training and assessment, peer adherence supporters were assigned eight FEATS study participants each, four in each intervention arm. An original randomization list was employed for this purpose, with an additional, rank-ordered list of reserve cases, per intervention group. During recruitment, PAS explained the intervention to each client and obtained written, informed consent from FEATS study participants who volunteered to partake in the experiment. As illustrated in Figure 5, a relatively large proportion of patients randomized to the two treatment arms of the study (almost one-third) could not be traced and contacted to be offered the relevant intervention. As a result, one in four study participants recruited into the two experiments represented so-called reserve cases originally included in the control group. A small proportion only (6.1% or 22 cases) of contacted study participants refused the offered adherence and nutritional support, while due to logistical issues a small number of participants assigned to a treatment crossed over to the other treatment arm at implementation (13 cases or 3.9%). (For a detailed discussion of Figure 5 refer to the CONSORT statement.) During the study, a brief survey instrument was used to collect key information from the 52 peer adherence supporters (PAS) working in the project at the time. (Initially, a total of 57 peer adherence supporters was recruited, trained and employed. By this time, five PAS had resigned due to not being able to fulfil their responsibilities, with patients being re-assigned to other PAS.) The peer adherence supporters can be described as follows:

- Predominantly female (98%) – only one male PAS remains in the study

- Mainly holds higher secondary education levels: grade 10 (20%), 11 (34%), 12 (38%)

- Aged mid to higher 30’s: mean age 35.8 years, [median 34.5 years, IQR 32.0 – 39.5]

- Majority walk when visiting clients: 56% walk, 38% taxi/bus, 6% own transport

- Single trip to closest client on average takes 39.1 minutes [median 30 min, IQR 15–60]

- Single trip to furthest client on average takes 94.5 minutes [median 90 min, IQR 45–120]

- Majority (98%) meets their client at the client’s home

5
- 2 -

# **I. INTRODUCTION**

Aid effectiveness largely rests on efficiency in the public procurement systems of recipient countries. Our companion paper (Estache and Iimi, 2008) suggests that promoting competition at auctions is a key to reduce procurement costs. The potential benefits that the developing world could receive are estimated at as much as 8.2 percent of total annual investment in infrastructure, including electricity, water and sewage, and roads.

An important policy question is how to intensify competition in procurement auctions for development projects. Joint bidding may be one of the solutions. In theory, however, it is expected to have both effects for and against competition at the auction level. Whether coalitional bidding practices have a pro- or anti-competitive effect defers to empirical analyses. It is potentially affected by underlying sectoral characteristics. This paper, using procurement data from official development assistance projects, explores the impact of joint bidding on the equilibrium bid function in three infrastructure sectors. It is shown that in a few segments a particular type of bidding coalition would be strongly pro-competitive, whence curbing project procurement costs.

The OECD Development Assistance Committee (DAC) member countries are spending about 12 billion U.S. dollars for assisting infrastructure development in developing countries every year. But this is far below the estimated financial requirements—such as 470 billion U.S. dollars by Fay and Yepes (2003), and about 360 billion U.S. dollars by our companion paper (Estache and Iimi, 2008).<sup>1</sup> Hence, it is essential to utilize the available aid resources more effectively and most efficiently (Iimi, 2006). It is indicated that to take full advantage of the positive competition effect, at least seven firms are required in the road and water sectors, and perhaps a fewer bidders for electricity projects.

> 1 The underlying assumptions and estimation methods for these figures are different. For instance, Fay and Yepes (2003) estimates the demand for infrastructure including electricity, water, roads, railways and telecommunications and assumes that the individual unit costs are constant. On the other hand, our companion paper focuses on only electricity, water and roads but accounts for the potential efficiency gains from enhanced procurement competition.