rafmacalaba/datause-encoder-data huggingface dataset:
If a group of countries find themselves on a fundamentally different development trajectory than the rest, as we argue is the case of countries of the bottom billion, then global aggregate analysis may struggle to discern this policy-relevant pattern. Indeed, when highlight populous countries such as China are not in such a group, they may get statistically ‘drowned out’ from such global analysis. The problem is especially acute when major economies such as China are below key averages, such as median income. This is due to the way global aggregates and trends are calculated; fast-growing China when below the average contributes to a better score for catch-up, meanwhile once they cross this important threshold, which is only occurring recently, the divergence picture will suddenly begin to look much gloomier.

We argue this outcome is arbitrary and one that researchers and policy makers can anticipate. We therefore favor heterogeneity analysis alongside global trend analysis to draw out these important distinctions. Even under global results of unconditional converge many counties continue to fall behind. Such average results risk being misinterpreted to imply these countries will catch up on current trends; we argue that on the contrary, without significant changes in policy approaches these countries will not just languish but fall ever further behind.

3. Variation in the availability of data on countries, years and time periods GDP per capita data can typically be tracked for a large sample of countries since 1960. Most convergence analysis begins here for this reason. Since that year, the world has gone through economic booms, crises, and recessions that have impacted growth rates. For example, after the early 1990 recession, when the global GDP per capita suffered a contraction of -0.2 percent, the world lived in a decade of growth until a new recession came in the early 2000s. A few years later, as the Golden Decade evolved, several countries, especially those relatively more dependent on natural resources, started seeing unprecedented growth rates.

But these years of abundance ended in 2015 and were followed instead by a period of slower growth rates that have prevailed until the present post-COVID-19 era. Patel et al (2021) show unconditional convergence after 2000 when low-income countries’ growth rates were faster than the richer. However, the heterogeneity in the following years comprising the Golden Decade and the New Normal show contrasting trends. The Golden Decade shows a period of indisputable convergence that ends with the prelude of the New Normal when the relationship between income and growth shifts positive (Figure 20).

# **Figure 20. The divergence pattern shifted during the Golden Decade until the New Normal took place.**

26
_-_ 16 (see Box 1).

# _Bo x: Projects Wthout a Critical Analysis of Constraints_

In some countrie **s** imple expansion of student places may not result in an inctease woman's access if low - _jtsehotd ftgk.for higher education is weak. In such a case, identifing constraints on female education and introducing specifc policies to overcome them arc key to having a successful project. .: . . n rhi esponse to the observed shortage of female teachers (i.e. favoable demand situation in the labor market), Pakistan's Third Education **Poect (1977)** attempted to increase output of female teachcrs by construction and expansion of female teacher taning institutes. Feamal tea-chertraining capacity was incread by **50%.** But the PAR (1990) found .Xi:although the expansion objective was ultimately achieved, project facilities had been consistently ta underutilized and Moracc&s Pourth Education Poject also sough **t** o nrease the output of both male and female silled teacher; by expand.ing ive.differnt postseconday education institutions. All programs would be open to women, with a target .. -20.%.female nrliient. .The PCR found that the five higher education institutions assisted under the project tenain une-enrolled. Only one third of the target enrollment figurea was achieved. Similarly, **_Maoo's_** .Fibh duction Roject assisted with the contruction ind equipping.of four sendary, Xhet t~~"6tid stitOsA solene and.'mathematios with- j,860:Asudn pies(fwiha.u-on6 third .wo.ul be:..-w.o.men) However, the PCR found.tt enrolments are well blow te spciii obectives :. **set** at the appraisal. -A rcel mki*itdpoetin ~~~~~~~~~~~~~~~~~~. .... .. ..d Nea 'p6Aded dor' facilities' and' 'esrd studen~sae o rs :Ho: vH., th p.r.eli.n. rsult.s ofthe proje.ct indcte tha teobjectives of the projectsetatth.e. .appr.sa halve notbee 10! . ..0 t iow prtvae demand f. r d .. ion. .... ... ..xmp ese e of underutization.. of t existing capit. . suest e: expanio p i nQ .w... .m::.l s o.ia d/}. o o abrn........ ..... ii Purt-f. a and gender constraints affecting women's access to higher education. _This_ mismatch between identified constraints and interventions is the prmary reason for the observed low enrollment at the tertiary level notwithstanding expansion of higher education institutions.

In comparison with the EMENA and Africa regions, projects in S.Asia and E.Asia analyzed constraints inhibiting female participation in higher education more closely. In India, for example, three recent projects identified the lack of programs in non-traditional fields as one of the major constraints on female participation in higher education and in the labor force.
t <!-- Start of picture text --> Polley Research|<br>International Trade<br><!-- End of picture text -->

WPS 1024 This paper-aproduct of the Intemational Trade Division, Intemational Economics Department-is part of a larger departmental study of how distortions in commodity markets affect the benefits front, and incentives for undertaking, agricultural research and development projects in developing countries. The study was funded by the Bank's Research Support Budget under research project "Agricultural Policy Reform for Developing Countries" (RPO 676-1 1). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Dawn Gustafson, room S7-044, extension 33714 (November 1992, 31 pages).

It is commonly believed that taxing agricultural commodities in developing countries, and subsidizing agricultural commodities in industrial countries, reduces incentives in the develop- **ing** countries for both current production and longer-term investments in capital, knowledge, technology, and infrastructure. It is argued that distortions in agricultural markets have kept investments in research and development, and productivity rates, low in agriculture in developing countries.

Martin and Alston lay the theoretical foundation for empirical studies of how such distortions affect returns to agricultural research and development in developing countries. Earlier studies of the benefits from technological change have typicaUy used partial equilibrium models with MarshaUian welfare measures. Such models have not allowed for a general set of market distortions and market interactions.

models better measure the implications of tradedistorting policies. Martin and Alston describe how to hamess these approaches to evaluate the benefits and costs of technological changes.

They show that a modified _trade expenditure function_ can be used to measure welfare changes exactly, with a model consistent with the optimizing behavior of both producers and consumers. They do so in a general setting that allows for multiple market distortions and multiple paths of general equilibrium feedback.

They illustrate this approach using a quadratic form for a profit function that is a component of the trade expenditure function. They spell out, in principle, how to apply this approach with minimal requirements for additional information, using the results from a computable general equilibrium model. They provide a diagram to illustrate the application of the technique.

Techniques recently developed for evaluating welfare in the context of general equilibrium The Policy Research Working Paper Scries disseminates **the** findings of work under way in the Bank. An objective of the series is to get **these** findings out quickly, even if presentations are less **than** fuDy polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
# V. CONCLUSIONS

Though the paper does not delve empirically into the exact mechanisms and processes through which openness affects economic performance in Sub-Saharan Africa, it does provide persuasive evidence that differences in openness (both its level and change) do account for cross country differences in economic performance of countries in that region. Ihe results indicate that countries in the Sub-Saharan African region which enhanced the openness of their trade regimes have, on average, tended to perform better than those that have not. This cross-country evidence, developed in this paper, on a positive openness-performance relationship in Sub-Saharan Africa is surprisingly robust. Our results not only hold across different measures of openness, and different time periods, but also when a macro-economic policy variable is included in the regressions.

What is more surprising is that the estimated openness-performance link in Sub-Saharan Africa is not very different from that in Non-Sub-Saharan Africa over the period studies. Most of the coefficients on openness are statistically not significantly different for the two groups. Where they are different, the coefficient for Non-Sub-Saharan Africa is greater.

Further empirical research on the openness-performance relationship for Sub-Saharan Africa could proceed along the following two directions. First, more aggregative cross-country aggregative analysis of openness-performance link should be carried out, by developing other measures of openness. Second, more microeconomic analysis of the openness-performance relationship should be undertaken. Aggregative cross-country work cannot sharply discriminate among different hypotheses about the mechanisms and processes through which openness affects economic performance, as well as about the non-trade policies or factors that impede or facilitate those mechanisms and processes 24
Table 8 reports the marginal coefficients from the iv-ordered probit and ivcensored ordered probit models. These effects demonstrate that living in a migrant household has different effects at different levels of schooling, and it is not the case that the effects are linear. For example, for 12 to 15 year-old males, the instrumented censored order probit model shows that living in a migrant household lowers the probability of having 9 years of completed education by 22.5 percent, and lowers the probability of having 10 or 11 years of education (or more) by 12 percent. These effects are substantially larger than in the ordered probit model which does not allow for censoring, reflecting the fact that many 12 to 15 year-olds are still in school. Similarly for 15 to 18 year-old males and females, allowing for censoring shows a much larger effect of migration on lowering the probability of completing 12 years or more education. Overall these marginal effects show migration having very little effect on the probability of completing 7 to 8 years of education, while lowering the probabilities of receiving more years than this, and increasing the probability of receiving less years than this.

# **5.3. Allowing for heterogeneous effects**

As discussed in Section 4, the impact of migration on education may possibly vary with household wealth, since households with lower wealth may be more likely to be liquidity constrained when making their education decisions. In such cases, the remittance effect of migration is likely to relax these liquidity constraints and therefore potentially increase education, or at least not reduce it as much as one sees for those with higher wealth levels. Unfortunately the ENADID contains only limited information on household wealth, in the form of current asset indicators, which are themselves affected by a household’s migration decision. We therefore instead use 22
# **Table 3: Panel B – Multinomial Regressions**

Log Odds ratio between Outright Owners and Mortgage holders (Marginal Effects)

||||||||||**Hausman F**|**-**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**parttime**|**unemp**|**retired**|**urban**|**lage*secedu**|**lage*teredu**|**inc*secedu**|**inc*teredu**|**Statistic**|**Pseudo r**<sup>**2**</sup>|
|_New EU Countries_|||||||||||
|Czech Republic|0.0808|-0.0458|-0.0061|-0.0390 ***||-0.0140||-0.0544|9.12 ***|0.31|
|Estonia|-0.0322|0.0430|-0.0436|-0.0153||0.1138 **||-0.0179|1.22|0.20|
|Hungary|-0.0696|0.0322|0.0291|-0.0815 ***|0.0663|0.0308|-0.0082|-0.0471|1.59 **|0.11|
|Latvia|0.0108|0.0070|-0.0202|-0.1145 ***|0.0470|0.1136|0.1485 *|0.1550 *|0.71|0.10|
|Lithuania||-0.1535||-0.0038||0.0872 **||-0.0197|1.73 **|0.23|
|Poland|-0.0565|-0.1095 **|-0.0149|-0.2015 ***|-0.1409 **|-0.1619 **|-0.0194|-0.0236|2.01 ***|0.32|
|Slovak Republic|0.0202|0.1297|0.0478|-0.1780 ***|-0.2994|-0.3168|0.1911|0.1653|2.49 ***|0.30|
|Slovenia|-0.3018 ***|-0.2449 ***|-0.1937 ***||0.0812|0.1762|0.0300|0.0148|1.67 **|0.11|
|_EU-15 and Cyprus_|||||||||||
|Austria|-0.0988 **|-0.0880|-0.0460|-0.0637 ***|-0.4656|-0.3653|0.0979|0.0518|3.09 ***|0.25|
|Belgium|0.0112|0.1021|0.1878 ***|-0.0642 ***|0.2008|0.3102 **|0.0948 *|0.1322 **|2.29 ***|0.34|
|Cyprus|-0.0298|-0.1728|-0.1028|-0.1157 ***|0.3496 ***|0.2398 **|0.1422 ***|0.0838 *|0.92|0.19|
|Finland|0.0833 *|-0.0143|0.0795 **|-0.0300|-0.0375|0.1658|-0.0294|0.0256|2.58 ***|0.32|
|France|-0.0109|-0.0918 ***|-0.0184|-0.0053|0.0571|0.0898|-0.0214|0.0402|3.36 ***|0.32|
|Greece|-0.1398 *|-0.0122|0.0079|-0.0363 *|-0.2091 ***|-0.3359 ***|0.0882 ***|0.0489|1.51 *|0.17|
|Ireland|-0.0436|-0.1165|0.1337 ***|-0.1609 ***|-0.2227|0.1792|0.0110|0.0558|1.78 **|0.31|
|Italy|-0.1103 ***|-0.1067 **|-0.0131|-0.0771 ***|-0.0594|-0.1074 *|0.0666 ***|0.1108 ***|4.23 ***|0.13|
|Luxembourg|-0.0352|0.0483|0.0076|-0.0016|-0.1425|-0.0529|-0.0994|-0.0861|1.08|0.35|
|Portugal|-0.0969|-0.0987|-0.0520|-0.1339 ***|-0.1042|-0.1532|0.0411|0.0999|2.98 ***|0.16|
|Spain|-0.1251 **|-0.0097|0.0076|-0.0071|-0.1453 **|-0.1051|0.0333|0.0810 ***|2.10 ***|0.14|
|Sweden|0.0040|-0.0004|-0.0030|-0.0011|-0.0450 **|-0.0553 **|0.0052|0.0072|2.01 ***|0.31|
|United Kingdom|0.1237 ***|-0.0789|0.2263 ***|-0.0161|-0.2704 ***|||0.0703 ***|15.02 ***|0.32|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

Source: EU Statistics on Income and Living Conditions (SILC) database, 2007 6
Against this background, our paper proceeds as follows. The next Section describes the EU’s approach to product standardization and harmonization, and its interactions with WTO disciplines. We then outline the World Bank EU Standards Database in Section 3, contrast it with previous data collection efforts, and present some descriptive results. Section 4 contains our gravity model specification and empirical results. We draw some policy conclusions in Section 5, and sketch a number of possible directions for future research.

# **2 Product Standards and Harmonization in the European Union**

It is easy to get lost in the terminology that has grown up around product standards. As such, it is important to outline, in brief, the concepts we address here. In this Section, we also outline the legal regime governing product standards and harmonization in the EU, as well as its interactions with WTO disciplines.<sup>4</sup> The WTO distinguishes between technical regulations (which are mandatory) and product standards (which are voluntary). The principal legal obligations in the _Agreement on Technical Barriers to Trade_ (TBTs) do not apply to voluntary standards. They only address mandatory technical regulations. For instance, only draft technical regulations need be notified to the WTO Secretariat under Article 2.9. In addition, Article 2.4 requires that Members generally use “international standards”—which are most often voluntary—as the basis for their mandatory technical regulations. Moreover, Annex 3 of the Agreement sets out a Code of Good Practice in regard to voluntary product standards—which Members need take “reasonable measures” to ensure is accepted by national standard setting bodies. In sum, both mandatory technical

> 4 This discussion draws heavily on EC (2000), Trebilcock and Howse (1999, Chapter 6), and Brenton et al. (2001).

- 5 -
pursue (for example, outreach to the poor, women, rural nonagricultural activities, growth in lending in a post conflict region, etc.).

The integration of the SDI and OI concepts assists in evaluating the cost of providing subsidies to DFIs with the benefit of achieving their social objectives (for example, lending to low-income borrowers), thus more effectively measuring the social benefits of the subsidy.<sup>12</sup>

# **Computation of the SDI**

The amount of the annual subsidy received by a DFI is defined as where _S_ = Annual subsidy received by the DFI _A_ = DFI concessionary borrowed funds outstanding (annual average) _m_ = Social opportunity costs, or the interest rate that the DFI would be assumed to pay for borrowed funds without access to borrowed concessionary funds _C_ = Interest rate on DFI’s average annual (concessionary) borrowed funds outstanding _E_ = Average annual equity _P_ = Reported annual profit before tax (adjusted, when necessary, for loan loss provisions, inflation, and so on) _K_ = The sum of all other annual subsidies received by the (DFI)—such as partial or complete coverage of its operational costs by the state

> 12 Achieving DFIs’ social objective through subsidies makes the subsidy more socially desirable or less costly to society (for example, serving more poor clients, or a subsidy per unit of OLP, issued to poor farmers is more valuable to society than the same subsidy per $ OLP to rich farmers).

9
explanatory variables used. For our present topic the important result to note is that the "net" effect of state ownership is to raise the level of earnings by 32-35 per cent over the level of private sector firms with African ownership. This effect is larger than foreign ownership-which is also significant but raises earnings by only 17 to 18 per cent compared to the African-owned firms.

# **3.3.3 The Distribution of Earnings**

It is not enough to consider merely the difference in mean earnings between the public and private sectors, even if we are able to control for differences in the quality of labor to some extent. The wage structure in the public sector, as it has evolved during the large changes in nominal wages in the last decade is of major interest. On the one hand, we would want to see if the earnings of workers at the bottom of the distribution have fallen so low, relative to the other sectors, that efficiency of performance is impaired. On the other hand, it is desirable that the structure of earnings should be wide enough to allow for the possibility that higher skills and responsibilities are adequately rewarded.

We refer back to the comparative distributions of per capita incomes in different sectors, including public employment, produced by Appleton and Collier from the data set of the Social Dimensions of Adjustment Survey conducted in 1988, and presented above in Table 1.2 above. It will be recalled that the researchers allowed for multiple occupations. The statistics reported are the per capita household income, pooling the income of earners of members from all sources-and not the distribution of earnings in the particular sector.

The data presented show that while mean and median per capita incomes of households in the "government" group were significantly higher than those in the other groups, they also had a smaller proportion of households in the "poor" or bottom three deciles, and a larger proportion of households in the top deciles than the over-all sample of households surveyed. We can conclude from these distributions that workers depending partly on government work had higher income levels at the bottom of the distribution, and had also a wider distribution of income than the other socio-economic groups distinguished.

Between 1988 and 1992, the government tried to follow an incomes policy according to which average real earnings were held more or less constant, while salary differentials were permitted to widen across grade levels and occupational categories. This was tantamount to widening the earnings distribution further. However, in July 1992 the government announced an across the board increase of public sector wages by 80 per cent-which clearly had the effect of squeezing the earnings structure at the same time as it pushed up the levels of income of public sector employees at the bottom of the distribution further.

The finding from the statistics presented so far that public sector workers in the late eighties enjoyed a higher level of income at the lower end of the distribution than workers in other sectors is corroborated by some work on the earnings of workers laid 18
from the DCRTA database), and it is equal to zero otherwise. Thus, by construction, the observations that take a value of one in the _DTA_ variable are a subset of the observations _ij,t_ that are equal to one in the _PTAij,t_ dummy from column (1). The main nding from column (2) of Table 1 is encouraging and expected. Specically, we obtain a positive and statistically signicant estimate on _DTAij,t_ , which suggests that, on average, the deep trade agreements in our sample have led to a 16.1% (std.err. 3.184) increase in bilateral trade among member countries. This result is consistent with and reinforces the general message from Fernandes et al., eds (2021) that DTAs have been eective in stimulating international trade.

Finally, in column (3) of Table 1, we use the DTA database to construct a continuous variable ( _DEPTHij,t_ ), which counts the number of provisions within each of the DTAs in our sample. The number of provisions across the DTAs in our sample varies between 12 and 432. The main result from column (3) is that, on average, the deeper the agreement, the more it would promote trade among its members. Specically, we obtain a positive and statistically signicant estimate on _DEPTHij,t_ ( 0 _._ 00047 , std.err. 0 _._ 00023 ), which is consistent with the ndings from Osnago et al. (n.d.). Our estimate suggests that, depending on the number of provisions that they include, the DTAs in our sample have led to trade increases between 0.576% (std.err. 0.289) and 23.012% (std.err. 12.744). We capitalize on this variation in Section 4.2, where we obtain corresponding GE eects on FDI.

Our estimates of the eects of DTAs on FDI are presented in Table 2. As discussed earlier, and similar to our trade specication, all estimates are obtained with the PPML estimator with three-way xed eects, including origin-time, destination-time, and directional pair xed eects. In addition, all specications include indicator variables for WTO membership ( _WTOij,t_ ), bilateral investment treaties ( _BITij,t_ ), and economic sanctions ( _SANCTij,t_ ). Similar to our approach with trade ows, in order to highlight the importance of DTAs and their provisions for FDI, we develop the estimation analysis sequentially, in four steps.

The estimates in column (1) of Table 2 include a single indicator variable, _PTAij,t_ , that reects the presence of a trade agreement of any type (e.g., deep or shallow) between _i_ and _j_ 24