rafmacalaba/datause-encoder-data huggingface dataset:
new data on trade in value added in addition to more standard data on trade flows of parts and components to separately assess the impact of deeper trade agreements on goods and services and to investigate whether the relationship between trade agreements and GVC participation is heterogeneous across industries with different levels of value added shares in production. Second, it exploits new information on the content of PTAs and attempts to identify which type of provisions matter the most for GVC‐related trade. Third, it looks at how the effect of the content of deep PTAs changes depending on the level of development of the countries involved in trade agreements.

With this new approach, we are able to establish three main results.

1. The depth of trade agreements increases GVC‐related trade among participating countries. The positive impact of deep agreements on GVC integration is driven by value‐ added trade in intermediates rather than in final goods and services. Adding a policy area to a PTA increases domestic value added of intermediates (forward GVC linkages) and foreign value added of intermediates (backward GVC linkages) by 0.48 and 0.38 percent, respectively.

2. At the sectoral level, the positive impact of deep trade agreements is higher for industries with higher value‐added shares in total production, suggesting that deeper trade arrangements help countries to integrate in industries with higher levels of value added.

3. Provisions outside the current WTO mandate such as competition policy and investment are key drivers of the relationship between deep trade agreements and GVC‐related trade, particularly for North‐South PTAs. Border provisions are still an important driver of GVC trade for South‐South PTAs.

While these are important new findings, more work is needed in this area. There is little that we know on the detailed content of specific provisions in trade agreements and how this content may affect trade, GVC participation or other variables of interest. Future work could aim at collecting data for core policy areas and study how the detailed content of these provisions in deep trade agreements affects economic outcomes.

18
Regarding organizational arrangements, almost all of the banks interviewed in the World Bank surveys mention that they have separate, dedicated units to manage their relations with SMEs (Figure 8). That also seems to be a common feature among banks that are leaders in the SME business, since this characteristic is found in all the banks that the IFC interviewed in developed _and_ developing countries. Although not displayed in our graphs, the World Bank questionnaires indicate that many of the large domestic and foreign banks—particularly in Chile and, to a somewhat lesser extent, in Argentina— have separate, dedicated units for small enterprises (SEs) and medium enterprises (MEs). Most importantly, the units concerned with SMEs are different from consumer and corporate units, and in most cases also separate from the micro enterprise business, which banks tend to house either in a unit of its own or as part of the consumer lending unit, especially in Chile and Argentina.

The dedicated business units approach SMEs in an integrated way, offering them a wide array of products and services. Almost all of the banks interviewed offer both deposits and loan products (Figure 9). It is simply not the case that banks offer primarily deposits and cash management products, avoiding the risk of lending to SMEs. The number of products offered is also relatively large, with the average number of different deposit products ranging between 5.3 and 10.6 for developed and developing countries, respectively (Figure 10). The number of credit products ranges between 9.3 and 18.7, while payment and other transactional products are between 7.7 and 16.9 for developed and developing countries, respectively. Each SME client utilizes, on average, around 5 products if we consider both deposit and credit products (Figure 11).

28
_Round2_ and _Round3_ , respectively. On the other hand, coefficients χ _1 and_ χ _2_ tell us the additional impact of targeting both the male and female spouse in _Round2_ and _Round3_ , respectively.

Given this paper’s focus on estimating the differential impact of the intervention when it targeted the male spouse, the female spouse or both, we control for whether a household is female-headed. We do this because, first, such households may have been targeted by both WIF and FIF.<sup>13</sup> Second, these households may behave differently, as the woman will be the main decision-maker in the house and therefore decisions on how the resources from these interventions are used could be different.

Finally, we check the robustness of our results by rerunning our regressions with woreda<sup>14</sup> fixed effects to address any local differences that might affect the results. We also rerun the regressions by dropping observations with female household heads from our sample.

# **6. Results**

Based on our empirical specification, we test three groups of hypotheses. First, we explore the mechanisms that could explain how the interventions impact our productivity and revenue measures by looking at the male and female spouses’ uptake of extension services, technology adoption and off-farm activities. This enables us to better understand the channels through which these interventions have an impact. Second, we look at whether the interventions actually impact agricultural productivity and off-farm income. For this, we look at agricultural productivity and income at the household level, and off-farm activity both by gender and household. Finally, we look at whether there is any impact on household savings and welfare.

# **6.1 Mechanisms for Change**

The interventions mitigate information constraints on a household by facilitating access to extension providers and services, such as classroom training or the demonstration plot at the Farmer Training Center (FTC). As can be seen in Table 1, we find evidence that for women, as

> 13 Though of 25 female-headed households, only three received WIF (of which one received both WIF and FIF).

> 14 The woreda is one administrative level above the kebele (and two levels below the region).

13
**Figure 2: W hilst fast growers have used trade in services to drive growth, ESA countries have been less successful**

<!-- Start of picture text --> Exports of service as share of world services trade<br>Average HP 16 (% of US current dollars) Average ESA<br>0.9% 0.04%<br>0.8%<br>Average for HP 16 0.03%<br>0.7%<br>0.03%<br>0.6%<br>0.5% 0.02%<br>0.4% 0.02%<br>Average for ESA<br>0.3%<br>0.01%<br>0.2%<br>0.01%<br>0.1%<br>0.0% 0.00%<br>Source: Banks staff calculations, based on data from W orld Economic O utlook, Ethiopia and Eritrea counted as one country<br>19 80 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04<br><!-- End of picture text --> There are three key reasons why preferences have not been effective in stimulating a significant export response in ACP countries.<sup>6</sup> First, preferences have not dealt with, and may have distracted attention away from, the key supply side constraints that limit access to all markets. Brenton and Hoppe (2007), for example, find that whilst preferences for clothing have supported exports to the EU and US they have not overcome the negative impact of weak governance in Africa on the sourcing decisions of global buyers. The conclusions from a review of the range of diagnostic trade integration studies<sup>7</sup> that have been undertaken in least developed countries concludes that the principal constraints to trade are typically those that limit access to and raise the costs of the key backbone services that are critical for competitiveness (transport and logistics, energy, finance, telecommunications). These problems are compounded by poorly designed structures of incentives that constrain the flow of resources into their most productive uses and weak and ineffectual trade supporting institutions, such as customs, standards, export promotion agencies, trade ministries themselves, and the often existing fragmentation of authority with regard to trade related issues between a number of ministries, coupled with a lack of communication between stakeholders.

Second, and related, preferences margins for many of products currently exported by African countries are typically small and have, and will continue, to be eroded, by multilateral trade liberalization and the EU’s predilection to sign free trade agreements. It should be pointed out that 46 percent of exports from Africa to the EU are concentrated in

> 6 Ollareaga and Ozden (2005) suggest that preferences may actually have hindered integration of poor countries into global markets, showing that those countries that received preferences tended to have liberalised their own trade policies less than those developing countries that had not received preferential access to OECD markets.

> 7 See Biggs (2007)

6
**Notes:** Robust standard errors in parentheses (clustered on country pairs). *** p<0.01, ** p<0.05, * p<0.1.  The data cover 1995-2018. Estimation in all columns include country-pair fixed effects and importer and exporter time period interaction terms estimated with Stata’s _ppmlhdfe_ command (Correia et al. 2019a, 2019b).  Estimation in all columns also include controls for the regional trade agreement, tariff preference margins, and the seven “deep” clauses shown in Table 4.  Estimation in column 3 also includes the two subcategories for category 1 shown in Table 1.

pg. 33
Figure 2: Tax revenue (% of GDP) of Cambodia and ASEAN countries, 2019 <!-- Start of picture text --> Cambodia<br>Thailand<br>Philippines<br>Singapore<br>Malaysia<br>Indonesia<br>Myanmar<br>0 5 10 15 20<br>Source: WDI database.<br><!-- End of picture text --> Note: Data for Vietnam, Lao PDR and Brunei Darussalam were not available.

Cambodia’s improved fiscal space allowed the government to boost spending. <mark>Government expenditure increased from 20.2 percent of GDP in 2011 to 24 percent in 2019,</mark> largely driven by spending on social services and on defense and security (World Bank, 2023). However, Cambodia’s expenditure boost partly reflected salary increases for civil servants. <mark>Between 2011 and 2019, the public sector wage bill as a share of GDP almost doubled from 4.4 percent of GDP in 2011 to 7.1 percent of GDP in 2019</mark> (World Bank, 2023) <mark>and p</mark> ublic sector minimum wage rose from around US$ 50 a month in 2012 to US$ 250 in 2018 (World Bank, 2019). Social sector spending increased from 4.6 percent in 2011 to 6.8 percent in 2019 largely due to increased education spending, which almost doubled 1.5 percent of GDP in 2011 to 2.9 percent of GDP in 2019 (World Bank, 2023 and WDI). However, health spending only grew marginally in the years before the pandemic. Electricity subsidies to eligible households and businesses amounted to US$ 33 million (0.12 percent of GDP) in 2019.

# **Tax system in Cambodia**

Revenue collection in Cambodia is highly centralized with a small number of taxes collected at the municipal or provincial level. In total, central government revenue amounted to 23 percent of GDP in 2019, while provincial government revenue amounted to 2 percent (Table 1). Indirect taxes are the main source of government revenues (13 percent of GDP), followed by direct taxes (5 percent of GDP). Table 1 outlines Cambodia’s government revenue structure.

5
INTROCUCTION XIV Country, economy, or Life expectancy 1985-90 Infant mortality, 1985-90 Age-sex structure, mid-1985 territory U.N. 1989 (prelim. version) UNPVSR 10/88 (official est.) U.N. 1989 (prelim. version) Guadeloupe USBOC 1987 USBOC 1987 Bank est. Guam U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Guatemala U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Bank assessment Guinea Bank est. Bank est. Bank assessment Guinea-Bissau Based on official CDR Bank projection U.N. 1989 (prelim. version) Guyana U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Heiti **-** - * Noly See U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Honduras U.N. 1989 (prelim. versicn) U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Hong Kong U.N. 1989 (prellm. version) UNPVSR 10/88 (official est.) 1980 census Hurnary Based on UNPVSR 10/88 UNPVSR 10/88 (official est.) U.N. 1989 (prelim. version) Iceland (official est.) U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Bank projection based on India 1981 census Based on Contraceptive Based on Contraceptive Biro Pusat Statistik 1/88 Indonesia Prevalence Survey (DHS) 1987 Prevalence Survey (DHS) 1987 Bank est. Bank est. Bank est. Iran, Islamic Rep. of U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Iraq Based on Eurostat 1987 UNPVSR 10/88 (official est.) U.N. 1989 (prelim. version) Ireland *- **-** *- Isle of Han U.N. 1989 (pretim. version) U.N. 1989 (preLim. version) U.N. 1989 (prelim. version) Israel Based on Eurostat 1987 UNPVSR 10/88 (official est.) U.N. 1989 (prelim. version) Italy U.N. 1989 (preiim. version) U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Jamaica U.N. 1989 (prelim. version) Monnier 1988 U.N. 1989 (prelim. version) Japan Johnston Island U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Bank est. Jordan Bank assessment U.N. 1989 (prelim. version) Bank assessment Kenya USBOC 1987 USBOC 1987 Bank est. Kiribati USB0C 1987 USB0q 1987 U.N. 1989 (prelim. version) Korea. People's Dem. Rep. of U.N. 1989 tprelim. version) 'U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Korea. Rep. of U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Kuwait U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Lao People's Dem. Rep. U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Lebanon Based on UFS 1977 U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Lesotho U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Bank assessment Liberia U.N. 1989 (prelim. version) U.N. 1989 (pretim. version) U.N. 1989 (pretim. version) Libya Liechenstein U.N. 1989 (pretim. version) UNPVSR 10/88 (official est.) U.N. 1989 (pretim. version) Luxembourg Based on Pop. Ref. Bureau 1988 3ased on Pop. Ref. Bureau 1988 Bank est. Macao Bank assessment of 1980 and U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Madagascar 1984 surveys U.N. 1989 (prelim. version) U.N. 1989 (prelim. version) Bank assessment Matawi
24 It is still frequently claimed that any potential benefits of trade liberalization on overall growth come at the cost of an increased economic vulnerability to external shocks as a result of deindustrialization and concentration of exports in a few products. Table 4 shows that such claims are not supported by cross-country evidence. Trade reforms have been followed by only a very small decrease in industry valued added (on average of less than 1 percent of GDP) and in fact reforms were followed in average by a considerable decrease of 0.06 points in the Herfindahl-Hirschman Index of exports concentration. The move towards trade liberalization has been followed also by an industrialization of exports: an increase of 18 percentage points in the share of manufacturing exports in total merchandise exports<sup>27</sup> .

**Table 4: Impact of Trade Liberalization on Other Economic Variables in the - -** **<u>Periods [ 12, 5] and [2,9]</u>**

|Dependent<br>Variable:|Exports<br>to GDP|Investment<br>to GDP|Industry<br>Value<br>Added|Exports<br>Concentration|Share of<br>Manuf.<br>Exports|
|---|---|---|---|---|---|
|postlib|5.472|0.768|-0.839|-0.06|17.867|
||(11.44)**|(2.27)*|(2.68)**|(6.20)**|(18.96)**|
|Constant|23.972|20.443|27.522|0.364|24.872|
||(70.81)**|(85.45)**|(124.35)**|(46.97)**|(36.82)**|
|Observations|623|623|607|370|543|
|Number of<br>countries|39|39|38|38|39|
|R-squared|0.18|0.01|0.01|0.1|0.42|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1%

> 27 We do not present any results about the impact of trade liberalization on social indicators given the lack of such data for the period prior to the reforms.
MN Legacy revealed comparative advantage MO Entered revealed comparative advantage MO Exited revealed comparative advantage <!-- Start of picture text --> °<br>md<br>re<br>ir<br>o<br>Ke)<br>+<br>©<br>Do<br>© jo)<br>x<br>°<br>a<br>1970 1980 1990 2000 2010 2020<br><!-- End of picture text -->
Second, data from ICFES provide test scores for individuals and some student characteristics. The test scores used here are those for a general test that is administered to all students who are finishing their secondary education. Although the ICFES data provides test scores for various subjects, the analysis here focuses on mathematics and reading scores. Student characteristics, such as gender and current enrollment status are also provided. Other variables linked to the individual students include: a school code, the city where the test was taken, the semester (either A, which is from February to November, or B, which runs from September to June), the type of secondary school that the individual attended (mainly academic or technical), if the school is public or private and the location of the school within the city.<sup>8</sup> All ICFES data used in this analysis are for 2003.

Unfortunately, ICFES data on individual characteristics are limited. Presumably, fixed effects by location can capture some of the socio-economic characteristics of the individuals living in the area. In fact, the data identified nine localities in the city that are quite homogeneous in terms of income. In contrast, the information on the schools from the Ministry of Education is very rich, and I will exploit this as the source of variation in order to estimate the impacts.

In conclusion, in order to estimate the direct effect of the concession schools, this analysis uses the 2003 school and test score data. The panel data at the school level for 1999 and 2003 are used to estimate the indirect effect.

# **b. Descriptive statistics**

Table 1 presents data of the full sample of basic characteristics of public schools, divided into concession (close to 1,050 individuals in 17 schools) and non-concession schools (36,000 individuals in 2,790 schools). Eligibility requirements preclude the use of data on private schools, which may downplay the competition argument, though only slightly since there is still competition for resources among public schools.

> 8 The data identifies nine main geographical areas (subdivisions of location of the school in the north, south, west and east parts of the city).

14
Table 8: Labor supply (survey round 2)

|Dependent variable:|(1)<br>Indicator:<br>any HH<br>MASAF|(2)<br>Total<br>person-days<br>of MASAF|(3)<br>Indicator:<br>any HH<br>ganyu|(4)<br>Total HH<br>person-days<br>of ganyu|
|---|---|---|---|---|
|PWP|0.118***<br>|1.531***<br>|0.032<br>|0.329<br>|
||(0.026)|(0.348)|(0.032)|(0.632)|
|PWP * Top-up|0.374***<br>(0.025)|4.663***<br>(0.339)|-0.016<br>(0.019)|0.160<br>(0.421)|
|R-squared|0.26|0.21|0.03|0.04|
|P-value for non-zero efect on top-ups|0.00|0.00|0.62|0.43|
|Upper bound (total efect)|0.547|6.979|0.076|1.713|
|Lower bound (total efect)|0.437|5.410|-0.045|-0.735|
|Observations|2820|2836|2818|2836|
|Mean of dep. var.<br>in control group|0.04|0.51|0.41|4.43|

OLS estimates. Standard errors clustered at the EA level. All estimates include district and week-of-interview fixed effects. _∗p <_ 0 _._ 10 _<u>, ∗∗ p <</u>_ 0 _._ 05 _<u>, ∗∗∗p <</u>_ 0 _._ 001 41
# **A Decade of Declining Earnings Inequality in the Russian Federation**

Paula Andrea Calvo, Luis Felipe López-Calva and Josefina Posadas<sup>1</sup> The World Bank

**Keywords** : earnings, inequality, quantile decomposition, Russian Federation, wages, RIF **JEL Classification** : C21, D63, J21, J31

> 1 The authors would like to thank comments from Maurizio Bussolo, Carolina Sanchez-Paramo, Birgit Hansl, Michal Rutkowski, Mikhail Matytsin, Andrea Atencio, Pedro Olinto, Vladimir Gimpelson and participants at the Higher School of Economics April Conference 2015 that took place in Moscow. This paper was prepared as a background paper for the project “Deconstructing the Recent Achievements in Shared Prosperity in Russia”.
25 for SMEs to participate in export- markets would be substantially enhanced, with significant social returns.

--m:

Developing new products and acquiring new production capabilities, and improving upon those that a firm already has at its disposal, are crucial to success in export markets and in all four countries most of the SMEs surveyed reported substantial technical improvements. One important influence on technical effort and perfonnance is the extent to which policies expose firms to domestic or international competition, and thereby act as a spur to improve capabilities. Another important influence comprises education policy, including importantly education in science and engineering, and its impact on the supply of technologically-usefil human capital. Also influendtal are management processes within finns and their impact on onthe-job technological learning (including through reverse engineering).

The present study focuses on a fourth influence, namely the micro-environment of private, governmental and NGO market and institutional technological supports external to the finr, on which it can draw as it seeks to build technological capability. A central goal is to evaluate the impact of collective technical supports. In principle, their impac-t could be substantial: inter-firm spillovers of knowledge are pervasive, so individual firms do not capture for themselves all the benefits of their investments in knowledge, and in the absence of solutions to classic problems of collective action may rationally underinvest. However, the challenges of collective provision can also be substntial, since for information to be useful, ik may need to be tailored to the specific needs of an individual industry, if not an individual fum. Moroever, as with export marketing, there exists a variety of external private mechanisms which support a firm's technological effort. Consequently, we embed our analysis of collective technical support within the broader context of the range of private and collective mechanisms which can aid technological effort.

Three broad categories of external technological support can be distinguished.- The first category comprises technological learing that occurs as a byproduct of a firm's transactions categor copie:e-crn