rafmacalaba/data-use-mentions-extended huggingface dataset:
# TRADE POLICY AND MARKET ACCESS ISSUES FOR DEVELOPING COUNTRIES: **Implications** for **the Millenium Round**

# SUMMARY

Following the Uruguay Round Agreements (URA) developing countries increased their integration into the world economy. As the World Trade Organization (WTO) is about to embark on a new Round of multilateral trade negotiations, this study analyses two important dimensions of developing countries' integration in the international trading system: their own trade policies and problems of market access for their merchandise exports. The results of the analysis are then used to recommend an agenda of topics and developing country positions for the upcoming WTO negotiations.

The major innovation of this study is that it is based on a systematic review of the detailed information contained in 61 Trade Policy Reviews (CPRs) of 42 developing countries prepared for GA'fT/WTO over the period 1989-1998. The TPRs contain a great deal of detailed and authoritative information on countries' trade policies and institutions on a consistent basis and over time which has not been hitherto systematically analysed. The group of developing countries on which the study is based includes thirteen developing economies from Latin America and the Caribbean, thirteen from Asia and the Pacific, eleven from Sub-Sahara Africa and five from Europe, Middle-East and North Africa which together account for the bulk of the trade of developing members of the WTO

# The main findings and recommendations are as follows:

1. The integration of the developing countries into the multilateral trading system has been especially impressive for a group of perhaps 15-20 middle and higher income developing countries in Latin America and Asia. For many others, progress has been much slower. Following the URA, protection both through tariff and non-tariff measures appears to be greater in low- income than in middle- and higher- income developing countries. While this conclusion is subject to a number of methodological caveats, it suggests the variety of challenges and opportunities different developing countries will face in the context of future WTO negotiations.

2. The URA resulted in a major step forward by bringing
Figure 28. Fiscal Spending - Local <!-- Start of picture text --> Rest Anatolian Tigers<br>2006 20072008 2009 2010 20112012 2013 20062007 2008 2009 2010 20112012 2013<br>Developed West<br>2006 20072008 2009 2010 20112012 2013<br>Graphs by Reg<br>East West<br>2006 20072008 2009 2010 20112012 2013 20062007 2008 2009 2010 20112012 2013<br>Personnel Social Security<br>Purchases Interest Payments<br>Current Transfers<br>Graphs by IWest<br>2<br>1.5<br>1<br>.5<br>0<br>2<br>1.5<br>1<br>.5<br>Per Capita Local Expenditures 2005PPP, (thousands)<br>0<br>2<br>1.5<br>1<br>.5<br>0<br>Per Capita Local Expenditures 2005PPP, (thousands)<br><!-- End of picture text --> _Notes_ : Monetary values are in thousands and 2005PPP _Source_ : Ministry of Finance– General Directorate of Public Accounts 41
**<u>Table 4. Relationship Between Alternative Measures of Payment Systems Development and Credit</u>**

**Dependent variable: Annual Credit over Annual GDP ratio**

||(1)|(2)|
|---|---|---|
|Credit Transfers per Inhabitant|0.0046***<br>(0.0013)||
|All Non-Cash Payments per Inhabitant (a)||0.0022*<br>(0.0009)|
|Year Fixed effects|x|x|
|One year Lagged Control Variables (b)|x|x|
|R-squared|0.89|0.79|
|Nbr. obser.|35|38|

Note: Robust clustered standard errors in parentheses. The sample includes all countries covered previously except Bulgaria. (a) Including direct debits and cheques. (**), (***) Stands for significance at 5 per cent and 1 per cent level, respectively. (b) All control variables as listed in Table 2

**<u>Table 5. Difference-in-Difference Estimates</u>**

# **Dependent variable: Annual Credit over Annual GDP ratio**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||**_two lags excluding_**<br>**_non-reform_**<br>**_countries_**||**_two lags excluding_**<br>**_non-reform_**<br>**_countries_**|
|**Post-Reform Trend Break**|**0.073**<br>**(0.010)**|**0.069**<br>**(0.006)**|
||||Inc|ome|||
|---|---|---|---|---|---|---|
||Rual|Urban|Rural|Urban|Rural|Urban|
|Households Reporting Access to Electricity<br>lane<br>y<br>(% ofhouseholds)|37|84|30|90|8|54|
|Access to Improved Water Sources<br>P<br>(%o ofpopulation)|73|98|85|96|54|83|
|Access to Improved Sanitation<br>P<br>(% ofpopulation)|44|86|76|91|28|54|
|Households with Own Telephone<br>I<br>(%ofhouseholds)|6|43|1|17|1|10|

Note: Data used isfrom ihe fatest observation available between 1997 and 2603, Source: Estache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643, Infrastructure Network, Office of the Vice President, June 2005.
designed for use by one person, (3) internet users in percentage of population, (4) number of ATMs per million capita (5) number of payments by credit and debit cards per million capita, (6) tractors used in agriculture per million capita, and (7) gross output of electric energy per million capita. Data are mainly from the CHAT database (Comin and Hobjin, 2010), except that bandwidth and internet users in percentage of population are from World Telecommunication Database (ITU).

For MENA, the pace of technology adoption for all technologies as income rises is slower _compared to other countries with the same income_ . The results are shown in Table 3 and illustrated graphically in Figure 6. The coefficients associated with the interaction between the MENA regional dummy and relative income are all negative and statistically significant, which translates into downward-sloping lines for MENA (in red) in Figure 6. For EAP, the coefficients associated with the interaction between the EAP regional dummy and relative income are also negative and significant, but the magnitudes are much smaller than those for MENA. That implies the speed of technology adoption as income rises in EAP is larger than that in MENA. This is shown by the gaps between the blue lines (EAP) and the red lines (MENA) in Figure 6.

It is noteworthy that both MENA and EAP have positive and significant coefficients for the region fixed effects (translating into positive intercepts of the blue and red lines in Figure 7). That suggests that at (very) low levels of income, EAP and MENA countries have a faster pace of technology adoption relative 14
# ideologies; and (iii) electoral competition.<sup>12</sup>

We classify the ideology of mayoral and presidential candidates as left, center, or right, following the methodology proposed by Fergusson et al. (2018) and Rozo and Vargas (2021), both published in leading political science and economic journals. The credibility of this classification is supported by Fergusson et al. (2020), who demonstrate that electoral victories of left-wing parties are associated with increased right-wing paramilitary violence, a pattern consistent with theoretical expectations from Acemoglu and Robinson (2008). Similarly, Rozo and Vargas (2021) apply this methodology to classify candidates in Colombian local elections, further validating its relevance.<sup>13</sup> Building on these prior efforts, we extend and refine the classification by manually coding an additional 1,343 candidates from the 2019 local elections. This expansion ensures consistency and comparability with previous work while addressing the complexities posed by Colombia’s highly fragmented party system. Unlike in Europe, Colombia’s electoral framework facilitates the of coalitions and new move- proliferation temporary political ments, complicating ideological classification.<sup>14</sup> Our classification process relies on electoral data from Colombia’s National Civil Registry and follows a structured three-step approach. First, for mayoral elections, we identify the political party or movement that endorsed each candidate. The initial classification considers party names, mottos, and slogans for ideological markers (e.g., terms like “socialist” suggesting a left-wing orientation or “conservative” indicating right-wing alignment). When this step does not yield a definitive classification, we analyze party statutes

> 12Calculated following Chac ́on et al. (2006) as: 1 _−_ (%1st _Candidate −_ %2nd _Candidate_ ). When the margin of victory among candidates is close to zero, the elections were competitive and the variable takes a value close to one.

> 13As part of their classification, they manually assigned ideological
2011).

The depth of African financial systems has improved within the past two decades, but the gap with other developing economies still remains. In the following two sections we investigate what is behind the macroeconomic indicators and examine financial inclusion from the perspective of the users of these financial services.

# **3. Individuals’ Access to Finance**

Until now, in Africa and elsewhere, little had been known about the reach of the financial sector—the extent of financial inclusion and the degree to which such groups as the poor, 3
Learning from Power Sector Reform: The Case of The Philippines Robert Bacon<sup>1</sup> <u>Keywords: power sector reform, electric utility, power generation, energy access, state-owned</u> enterprise, regulation, electricity pricing.

> 1 This paper is based on data collected and interviews conducted in the Philippines by Rauf A. Tan and Eric Groom. Valuable guidance was provided by Yuriy Myroshnychenko of the World Bank Country Team for the Philippines.
be responsible for the following activities: earn money for the family; have a higher level of education; do washing, cleaning and cooking; fetch water if there is no water pump or tap; feeding and bathing children; help the children in their studies at home; look after ill persons. The index is the share of questions to which the answer was both/same. Therefore, higher values represent more egalitarian gender norms. Time allocation data was collected both at baseline and endline. Respondents were provided a set of 25 beads and a board with six circles representing: "Education", "IGA", "Leisure", "Household Chores", "Sleep" and "Other". The Education category includes schooling, vocational training and study time. "IGA" includes paid and unpaid work of any kind. Respondents were then asked to allocate beads into each circle in a way that represents time allocation in an average week. Data on leisure time allocation was collected in a similar way. The recorded categories for leisure are: "Friends", "Men", "Alone", "Church", "Volunteer" and "Other". The exact phrasing for the "Men" category is "With boys or men you have a sexual relationship with". Respondents were then asked to allocate beads into each circle in a way that represents time allocation in an average week. The data points were later converted into weekly hours using recorded total leisure time from the first exercise. Unwanted Sex is defined as, "Been involved in any sexual intercourse that you were not willing to do". Transactional Sex is defined as, "Receiving anything such as money, gifts, help with schoolwork or something else in exchange for sexual intercourse". Literacy is assessed based on respondent's ability to read simple things like labels on containers (basic), and reading comprehension and writing of complete sentence (advanced). The score representing respondent's proficiency with these tasks is then rescaled
**Figure 7:** Deviations from Predicted Electricity Consumption in Dhaka **Deviation of electricity consumption from model prediction** Percent

<!-- Start of picture text --> 2020 2021<br>10<br>5 1 st Lockdown<br>0<br>-5<br>-10<br>-15<br>Deviations below the line<br>-20<br>are statistically significant<br>-25 at the 5 percent level<br>-30<br>-35 2 nd 3 rd & 4 th<br>-40 Ripple Lockdown Lockdown<br>-45<br>January February March April May June July August September October November December January February March April May June July August September October<br><!-- End of picture text --> However, unlike during the first lockdown, electricity consumption was not below the model prediction during the second one. Importantly, and in contrast to the first lockdown, the readymade garments factories remained open. In July 2021, during the third and fourth lockdowns, electricity consumption declined by 20 percent, with the deviation being significant at the 1 percent level. While this was a large decline, it was much lower than during the first lockdown. The wave was contained by mid-August, after which electricity consumption gradually recovered.

For the other divisions, the picture is mixed. In Chattogram, the deviations of actual electricity consumption from the model predictions follow a very similar pattern as in Dhaka, although the decline in April 2020 was somewhat smaller. The resemblance is expected since the two divisions have similar structural characteristics. However, in the other divisions, the decline was much weaker, suggesting that electricity consumption there is measured with huge errors or economic activity in these divisions was impacted less.<sup>10</sup> The declines in electricity consumption in Dhaka and Chattogram are very much in line with the decline in mobility. The Google mobility data
able to replicate the findings of Udry (1996) by focusing on a subset of villages that are in close proximity to the areas underlying Udry’s analysis, Akresh cannot recover the same relationships based on the data collected in other parts of the country. This discrepancy highlights the importance of revisiting the body of evidence on the gender gap in sub-Saharan Africa by using nationally-representative data.

Another limitation observed in the second strand of the relevant literature is the disproportionate focus on West Africa. It is important to investigate the extent and correlates of the gender gap in alternative sub-Saharan African settings with different sets of rights and obligations that differently affect the distribution of productive resources across men and women. Finally, in the case of empirical studies that document statistically insignificant differences in agricultural productivity between female-managed and male-managed plots, conditional on plot-level observable and household-level unobservable attributes, the analytical framework is not set up to isolate relative contributions of relevant attributes towards the observed gender gap for the purpose of prioritizing areas for policy interventions.

# 3. MALAWI: AGRICUTURAL PRODUCTIVITY AND GENDER

# 3.1.  THE COUNTRY CONTEXT<sup>13</sup>

Malawi is a small, population-dense, land-locked country in Southern Africa, with 94,080 square kilometers of land. The 2010 mid-year population projection and annual population growth rate stand at 14.5 million persons and 3.25 percent, respectively, and 85 percent of the population reside in rural areas (NSO, 2012). Agriculture is not only the backbone of Malawi’s economy but also an essential part of its social fabric. The sector accounts for 30 percent of the Gross Domestic Product (GDP), and 84 percent of Malawian households own and/or cultivate land<sup>14</sup> . The production system is overwhelmingly rainfed, characterized by limited access to irrigation and diminishing average land holding sizes due to population pressures. The rainfall is unimodal, and maize is the main staple crop, grown by nearly 100 percent of the farming household population.<sup
37|39|48|51|45|61|62|59|
|**B. Other FTA**|43|41|40|32|35|35|36|44|1.0|1.0|0.9|0.7|0.6|0.6|0.6|0.5|
|**TOTAL(A+B)**|2,035|2,070|2,534|3,027|3,476|4,218|4,683|6,530|47|49|59|63|59|77|78|77|
|Non-preferentialpartners|2,335|2,192|1,766|1,810|2,413|1,274|1,286|1,936|53|51|41|37|41|23|22|23|
|TOTAL IMPOORTS|4,370|4,262|4,299|4,836|5,889|5,492|5,969|8,466|100|100|100|100|100|100|100|100|

Note: Pan-European partners include EU-25, Romania, Turkey, and EFTA. Other FTA partners include SEE-5 and Israel. Industrial products are defined as SITC 3+5+6+7+8+27+28 in Revision 2 or Harmonized System 25 and above. Source: Derived from UN COMTRADE database as reported by Bulgaria Second and more important, there is no relationship between the shares of preferential imports in total imports of pan-European transition economies and their respective average MFN rates on manufactured
1. _Land and production policies for the poor._ The government estirnates that

close to 6 percent of farming households now live in poverty due to a lack of land. Efforts will be made to redistribute unused or inappropriately distributed commune land, or to move willing households to new economic zones. Provincial and district governments are responsible for ensuring that all households are adequately endowed with land.

2. _Micro-credit for income-generation at concessional rates._ Approximately 30 percent of poor households have already been provided with credit from various sources. However, the poorest households were often denied access to these loans. The goal is for 90 to 95 percent of households identified as poor to have unconditional access to loans to improve their production. Responsibility for providing financial services to the poor lies with the newly established Bank for the Poor.

13. _Vocational training and technology transfer for poor households and their children._ Voluntary groups will be dispatched to help the poor with business undertakings and technology transfer in production activities in poverty areas. Scholarships and other education stipends are planned for talented children from poor households.

4. _Infrastructure in poor communes. A._ survey by the Ministry of Construction identified 1,160 out of 10,000 communes as suffering particularly acutely from a lack of infrastructure. The HEPR program plans to develop infrastructure in these communes, focusingr on roads, electricity, safe water supply, primary and secondary schools, health care centers and communal market places.

5. _Education and health care for the poor._ Programs are planned for extending fee exernptions for poor children in primary and secondary school along with help with 20
2 sample in Tajikistan identified “improving extension services and providing seeds more resilient to climate extremes” (64 percent of respondents), “providing clean drinking water” (63 percent of respondents), and “investing in increasing availability of electricity” (57 percent) as important priorities for government spending (Figure 9a).

28. As one would expect, peoples’ past experiences with adverse climate change-related impacts play an important role in influencing their views regarding what future course of action should be taken to combat climate change: the P-LITS data clearly show that respondents that reported having experienced unpredictable and extreme weather that harmed agricultural production, or a decline in fresh water available for drinking or irrigation are much more likely to favor increased government spending on improving extension services and providing seeds more resilient to climate change, as well as investing in clean drinking water and irrigation infrastructure respectively, compared to respondents that did not experience such climate change-related hazards.

29. When asked to choose the single-most important priority for additional government spending related to climate change, (i) investing in increasing availability of electricity (22 percent), (ii) improving extension services and providing seeds more resilient to climate extremes (18 percent), and (iii) providing clean drinking water (14 percent) received the highest share of votes from respondents (Figure 9b); (iv) providing more help and relief after natural disasters occur (10 percent), (v) reforestation to prevent soil erosion (8 percent), (vi) providing more reliable weather forecasts (7 percent), and (vii) investing in health facilities and provision of health services (7 percent) received the next highest share of votes.

> 16 For instance, as documented in the two countries’ respective National Communications prepared recently under the United Nation’s Framework Convention for Climate Change (UNFCCC).

15
Figure 4: The likelihood of declaring foreign or domestic assets by asset type

# (a) Foreign assets

<!-- Start of picture text --> Number of<br>tax returns<br>(2015=100)<br>2016<br>Amnesty<br>600 Stocks<br>Real Estate<br>500<br>Deposits &<br>400 Currency<br>Real Rights<br>Accounts<br>300<br>Receivable<br>200 Vehicles<br>100 Other Asse ts<br>0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text --> (b) Domestic assets <!-- Start of picture text --> Number of<br>tax returns<br>(2015=100) 2016<br>Amnesty<br>600<br>500<br>400<br>300<br>200 Real Righ ts<br>Stocks<br>100<br>0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text --> _Notes:_ this figure decomposes Figure 2 by asset type and plots the number of taxpayers declaring different types of foreign or domestic assets in panels (a) and (b), respectively (2015 = 100). The number of people reporting foreign stocks and real estate increased by nearly 500% in 2016 relative to 2015. _Source:_ authors’ calculations using data from AFIP statistical yearbooks.

27
has during the past three decades grown very fast, converging on the rich world. Until recently, it was China alone that had been preventing a rise in global inequality as measured by Concept 2. But now it has “support” from India which is also registering high rates of growth, and is also starting from a very low baseline. High rates of growth of these two countries are thus the major factor underlying the downward trend of Inequality 2.

Inequality 3 can be calculated, as mentioned before, only from the mid-1980s because we do not have household surveys going further back in time. Figure 2 shows that Inequality 3 is higher than Inequality 2. This is true by definition because in Inequality 3 people enter the calculations with their actual incomes, not with country averages. A quick glance at Figure 1 shows that the variability of heights is greater in the third row than in the second. Averaging-out reduces measured inequality.

To calculate “true” global inequality or Concept 3 inequality, we have to adjust people’s incomes with the price levels they face and which, of course, differ between countries. We are interested in real welfare of people and those living in “cheaper” countries will get a boost in their incomes compared to what they make in nominal dollar terms. The currency we use is international (or “PPP” for purchasing power parity) dollar with which, in principle, one can buy the same amount of goods and services in any country of the world. Indeed, if we were not to adjust for the differences in price levels, and were to use nominal dollars, global inequality would have been even higher. This is because price levels tend to be lower in poorer countries, and income of people living in poorer countries thus gets a significant “boost” when we use PPP dollars.

Often, a key issue of concern regarding global inequality is not only its level, but its trend: has it been going up or down during the globalization era? Global inequality