rafmacalaba/data-use-mentions-extended huggingface dataset:
this effect is not statistically different from zero. Likewise, we find no evidence of a RD effect on QAT takers in QAT 7. In QAT 8, we find a significant, negative RD effect on the number of QAT takers—the finding is however not robust to setting the bandwidth to twice the optimal bandwidth for the estimation.<sup>17</sup> The evidence collectively suggests that, despite their size, group-based teacher bonuses do not provide sufficiently strong incentives for marginal nonqualifiers to raise either QAT performance or participation in order to increase their chances for qualifying for bonuses in the next award round. It is plausible that given that PEF has revised the bonus eligibility criteria for every award round (without communicating the revisions well in advance), it may have generated uncertainty on the applicable criteria for the next award round, discouraging the effort of marginal disqualifiers to raise learning and test participation. Independent of this conjecture, it is plausible that gains in learning and test participation by marginal qualifiers may have been masked by gains in the corresponding measures among marginal nonqualifiers. Estimation issues may also be relevant: the inability to detect any effects at the composite score cutoff could be due to inadequate statistical power.

# **8. Conclusion**

Low student learning is a consistent finding in much of the developing world and the search for solutions is an active research and public policy agenda. This paper uses a relatively unique dataset of five semiannual rounds of standardized test data to characterize and explain the shortterm evolution of student learning in primary and secondary schools. The data are collected as part of the quality assurance system for a public-private partnership program which offers public subsidies conditional on minimum learning levels to low-cost private schools in Pakistan. Apart from a large positive distributional shift in learning between the first two test rounds, the learning distributions over test rounds show little progressive movement. School-level panel regressions essentially show that pre-program learning levels and the level of the school are associated with the level and evolution of conditional mean learning. Variance component
**Figure 4:  Shifting G7 Share of Global GDP (%)**

<!-- Start of picture text --> 55%<br>50%<br>45%<br>40% 50<br>years<br>35% 50 years<br>30%<br>1900 1950 1960 1970 1980 1990 2000 2008<br><!-- End of picture text --> Source: Data from Maddison (constant 1990 Geary-Khamis Dollars) _Low and Middle Income Country Growth ―Traps‖_ In principle, one would expect, and certainly hope, that the poorer countries in the world can catch up with the richer countries in the world. Unfortunately, few countries have experienced ―convergence‖ on a sustained basis. In fact, one famous paper that discusses the performance during the twentieth century is entitled: ―Divergence, Big Time‖.<sup>4</sup> One approach to measuring relative progress is to look at per capita GDP relative to the United States, which has been the symbol of advanced industrialized countries after WWII. Figure 5 shows that the shares of countries in each ratio range has been fairly stable, with some growth in the percentage of countries in the upper MIC range (say, roughly 0.3-0.7), but really not much expansion of the share of countries that are at 0.7 of the US level of per capita GDP. At the bottom end, the share at 0.1 or less of US levels remains stuck near 40 percent. Persistently, over 80 percent of the countries in the world have GDP per capita levels that are half or less than half of the level in the United States.

> 4 Pritchett (1997).

7
indexed by T (leaving the period after 2010 as reference). Exporter-time and importer-time fixed effects control for time-varying Multilateral Resistance Terms, while bilateral fixed effects control for timeinvariant unobserved characteristics of the country pair potentially leading to self-selection into PTAs (Baier & Bergstrand 2007). As an additional control, we also include the variable _TransitoryPTAsij,t_ , to control for agreements that are no longer in force.

The results presented in Table 5 provide the elasticity of bilateral trade to the different types of PTAS (as grouped by the clustering). This elasticity is here estimated within sample. As discussed in the Introduction, it will be introduced in a second step as a parameter in the estibration procedure.

> The first three columns exploit the raw UNIDO data we rely on. There are 99,000 observations for only 114 countries for which we observe internal flows. In order to expand internal flows coverage, in column 4 we rely

> 3 _Xij,t_ is in levels in columns 1 to 7 of Table 5, and in shares of absorption at destination – namely <u>�</u> _Xi_<sup>_X_</sup> _ij,_<sup>_ij,t_</sup> _t_<sup>–incolumn8.See</sup> discussion below.

> 4We tested the robustness of our findings over the period 1980-2015: the estimation results are not affected, see Table A2 in the Appendix.

> 5BACI is used to retrieve information on international trade flows from 1990 up to 2017 Gaulier & Zignago (2010), TradeProd provides data on both inter-national and intra-national trade from 1980 up to 2006 de Sousa, Mayer & Zignago (2012), whereas UNIDO INDSTAT2 2019 combined with BACI is used to derive domestic sales from 2007 to 2017. For the overlapping years we find a very high correlation (close to 99%) between TradeProd and the other sources for both inter-national and intra-national trade flows. When multiple sources are available for a given year we take the geometric average of
<mark>Prices belonging to the annual agriculture group are defined as follows:</mark>

1. **Cotton** (Cotlook “A” index): Middling 1-3/32 inch, traded in Far East, C/F.

2. **Maize** (U.S.): No. 2, yellow, f.o.b. U.S. Gulf ports.

3. **Rice** (Thailand): 5 percent broken, white rice (WR), milled, indicative price based on weekly surveys of export transactions, government standard, f.o.b. Bangkok.

4. **Soybean meal** : Brazilian pellets 48 percent protein, cif Rotterdam.

5. **Soybean oil** : Dutch crude, degummed, f.o.b. NW Europe.

6. **Wheat** (U.S.): No. 1, hard red winter (HRW), ordinary protein, export price delivered at the U.S. Gulf port for prompt or 30 days shipment.

# <mark>A.6. Perennial agriculture (5 series)</mark>

<mark>This group contains the following five commodities: Cocoa, coffee Arabica, coffee Robusta, natural rubber, and tea. These commodities are produced by trees, often termed tree crops, and therefore cannot be substituted on an annual basis. Thus, from a production perspective they resemble extractive commodities, such as metals. Coffee, consumed in a form of a beverage, comes in two main varieties, Arabica and Robusta—it is the only commodity in this study that includes more than one price indicator. Arabica, grown at high altitudes in Latin America (including Brazil) and northeastern Africa, accounts for two-thirds of total world output. It has a strong aroma and low level of caffeine. Robusta, with a much stronger taste than arabica, is grown in humid areas at low altitudes in
detailed dataset, we cannot directly control for potentially important variables such as demand and price differences. In principle, such endogeneity problems could be remedied by means of instrumental variable estimation but, unfortunately, credible instruments are not available in our data. However, using the same Ethiopian dataset as considered in this paper, but focusing only on small manufacturing firms Loening et al. (2008) argue that endogeneity bias is likely to be small. Using precipitation based indicators of local agricultural performance as a proxy for unobserved local demand, they find that while better local agricultural performance is strongly correlated with firm sales, their parameter estimates are not very sensitive to controlling for this variable, indicating that endogeneity bias is small. Moreover, firms do not frequently adjust factor inputs, despite facing frequent shocks, another reason why the impact of endogeneity bias may be limited.

Finally, this approach only focuses on the direct impact of gender and the investment climate on firm productivity. However, perhaps the most important gender and investment climate effects are _indirect._ For example, the investment climate may also impact on allocative efficiency (see, e.g. Mengistae and Honorati, 2009), investment and growth, a possibility which is investigated in Appendix F, which shows that, prima facie, gender differences in growth and investment performance are small. It could also induce women to sort into small-scale low productivity sectors. In short, we have to be cautious when interpreting the results from these models.

# **(c) Results**

The regressions are estimated separately for each country. Results are presented in Table 5. The dependent variable is the log of sales in US dollars. We present five different models. The first model only includes a gender dummy while the second model adds a control for firm size. Subsequent models add controls for sector (model 3), factor usage and differences in technology across sectors (model 4) as well as other firm, human capital and investment climate characteristics (model 5).<
# **2. Fiscal deficits and public debt in the medium term**

# **2.1 Fiscal deficits**

The fiscal position of the general government and medium term fiscal projections are shown in table 1. Romania ran a pro-cyclical fiscal policy during the economic boom of the mid 2000s, with a structural fiscal deficit of 8.5 percent of GDP in 2008, at the height of the boom. The severe recession which struck the economy in 2009 – real output contracted by 7.1 percent – exposed the fragility of the fiscal stance and forced the government to begin to implement a radical fiscal consolidation. The government has made tangible progress in reducing the structural fiscal deficit, to 5.1 percent of GDP in 2010 and to a projected 2.8 percent of GDP in 2011; if the latter target is achieved there will have been a fiscal consolidation amounting to 5.7 percent of GDP in the space of 3 years, one of the largest in the European Union (figure 1).

**Table 1: Key macroeconomic and fiscal data and projections (percent of GDP): 2008-2016**

||2008|2009|2010|2011|2012|2013|2014|2015|2016|
|---|---|---|---|---|---|---|---|---|---|
|Real<br>GDP<br>growth|7.3|-7.1|-1.3|2.0|1.8|3.5|4.0|4.0|4.0|
|Revenue and<br>grants|32.2|31.4|32.8|33.0|33.6|34.1|34.3|34.4|34.6|
|Expenditure|37.0|38.7|39.
the private sector and real GDP growth during the three years prior to the start of the crisis. We call this variable CREDIT GROWTH.

For the aggregate economic recovery, we construct proxies for economic output losses suffered due to a crisis. We use two approaches here. One way is comparing, in real terms, the pre-crisis GDP level of a certain country with the GDP level during the following years until the pre-crisis level is reached. This approach considers pre-crisis GDP growth rates to a country’s trend or potential growth rate. The pre-crisis, trend GDP growth is calculated as the average of GDP growth rates from year t-3 to t-1, where year t is the start of the crisis. Then, each GDP growth rate from year t onwards is compared to the trend until the trend growth is reached. The output loss is defined as the sum of the difference between the actual and the trend growth rate over all the years until trend growth is reached again. This approach follows the methodology used in IMF’s World Economic Outlook (1998), although we recalculate and update the data to include some recent crises. We call this variable OUTPUT LOSS (IMF).<sup>10</sup> The second version of the output loss variable results from using the Barro (1991) growth model to estimate a country’s GDP potential growth rates. We refer to this variable as OUTPUT LOSS (BARRO). Instead of using pre-crisis data to estimate the trend growth rate, we obtain predicted values of per capita GDP growth from equation (2) in Barro (1991, p. 410). The exact equation is: Average GDP per capita growth from 1960 to 1980 = 0.0302 - 0.0111 * GDP per capita in 1960 + 0.00051 * (GDP per capita in 1960)<sup>2</sup> + 0.0323 * Secondary school enrollment rate + 0.0270 * Primary school enrollment rate - 0.122 * Government consumption/GDP
**7 Summary and Conclusions**

Replacing one km of _kutcha_ track by one km of PMGSY road yields an estimated increase in the net price obtained for paddy of Rs. 7.95 per quintal. Given a typical stretch of 5 km to reach the next level of the road network, a household selling one tonne would therefore realize an increase in net revenue of almost Rs. 400, or about 5 percent of the price ruling in the main markets in _kharif_ 2009. To this gain must be added those arising from the marketing of other crops, especially cotton and vegetables, the estimated reductions in whose unit transport costs are, on average, larger than that of paddy. There are also the reductions in the farm gate prices of bought-in inputs such as fertilizers and agro-chemicals. Our measure of the net price relates to the village’s place in the road and marketing network, whatever be the actual location of the villagers’ transactions. Although rather noisy owing to the small number of observations in each village, it is conservative, in that the nearest alternative location to the farm gate is chosen when the sale is actually at the gate. The focus group interviews suggest that an all-weather connection leads to sharper competition among marketing intermediaries and more joint marketing by farmers to sell in their main market. We therefore suspect that the changes in behavior reported in the focus group interviews imply larger gains than those yielded by the cross-section econometric analysis.

All but two villages have their own primary school, and given that a village does so, the absence of an all-weather road will do little to hinder primary school children from getting to the school itself, even if its hamlets are a bit scattered. What its absence can do, however, is to prevent the teachers from doing so, especially during the monsoon; for they usually live elsewhere. This is confirmed by the survey data: in _kharif_ 2009, primary school children in villages with an all-weather road each lost, on average, 1.9 days involuntarily,
number of antidumping import restrictions. Such breaks remind us that these barriers are temporary as they reveal a sudden foreign market access liberalization facing the exporter – at least with respect to it confronting less TTB policy coverage of its _potential_ exports. Nevertheless, none of the indicators presented thus far address whether exports previously confronted by a TTB _actually_ resume. The next section turns to this question.

# **4 The Removal of Antidumping Import Restrictions: Do Exports Resume?**

The indicators of the previous two sections document at least two pieces of important evidence. First, major emerging markets have increased the scope of their imports covered by TTBs. Second, major emerging countries have a sizeable share of their total exports and exports sent to other emerging economies impacted by foreign-imposed TTBs. An important question yet to be addressed is what happens to exports when these _temporary_ trade barriers are finally removed? To what extent do exports resume?

# **4.1 Regression approach and data sample**

We use the _Temporary Trade Barriers Database_ to construct a baseline sample of TTBs that one of the fourteen member economies of the G20 (listed in Table 1) imposed and removed between 1992 and 2008. Let _t_ be the year that the TTB was first imposed and let _T_ be the year that the TTB was ultimately removed. Given the need for two years of data both prior to _t_ and subsequent to _T_ , we use the 19922008 period because of constraints imposed by the availability of the HS-06 import data. We also focus on antidumping alone (and not all TTB policies) as it is most frequently used TTB policy and one for which the decision to impose and remove barriers is undertaken on a trading-partner specific basis.<sup>9</sup> The approach is to investigate the trade impact associated with a sample of 746 antidumping cases pooled across fourteen policy-imposing G20 member economies. We take information from the _Temporary Trade Barriers Database_ regarding when the import restriction was imposed, when it was removed, and the product codes associated with
**Figure 3. Decomposition of GDP growth for 25 countries with the highest share of TFP growth with natural resources, ranked by GDP growth**

<!-- Start of picture text --> Contribution to GDP Growth<br>(long‐term averages, 1996‐2014)<br>Azerbaijan<br>China<br>Bosnia and Herzegovina<br>Mozambique<br>Rwanda<br>Chad<br>Iraq<br>Lao PDR<br>India<br>Mongolia<br>Nigeria<br>Armenia<br>Kazakhstan<br>Burkina Faso<br>Panama<br>Belarus<br>Tanzania<br>Georgia<br>Sierra Leone<br>Tajikistan<br>Sri Lanka<br>Dominican Republic<br>Malaysia<br>Botswana<br>Jordan<br>‐2.0% 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%<br>Labor Produced capital Natural capital TFP<br><!-- End of picture text --> Note that data on diamonds are not available for Botswana, which biases the TFP estimates 14
the empirical analysis is done separately for families residing in different areas such as rural vs. urban areas and relatively developed vs. less developed states. To discern any possible gender bias, we implement the empirical analysis separately for male and female samples. We use the mixed effects model to estimate the sibling correlation. An advantage of this approach is that both the family and community level covariates can be included in the analysis to examine their relative influence on sibling correlation (Mazumder (2008), Bjorklund et al. (2011)). We examine the influence of two sets of covariates on sibling and intergenerational correlations: the first set relates to caste and religion of the

> 14 There is evidence that geographic location may be important for economic opportunities faced by households in developing countries. For example, Jalan and Ravallion (1999) show that there are geographic poverty traps in China. Emran and Hou (forthcoming) find that better access to markets increases household consumption in rural China in a significant way. They also find that the effects of domestic market centers are much larger than that of international market access.

> 15 Our conclusions, however, do not depend on this particular age cohorts. In the robustness checks section, we provide evidence using an alternative age cohort sample.

5
Land is not rated as a major obstacle in large part because the respondents are managers of going concerns. Yet, in the regression analysis,<sup>22</sup> we find that lack of access to land has a negative effect on TFP and the other performance indicators. The entrepreneurs who want to set up firms but cannot because they cannot get access to land are not covered in the survey. Taxes are rated by managers as a major business obstacles, but their responses would be different if the question also identified what public services would have to be cut (or what the level of inflation would rise to) as a consequence of tax cuts. Indeed, in surveys such as PICS and BEEPS, taxes are usually identified by managers as a major obstacle to doing business, whatever the country. Interestingly, infrastructure, which was found to have a strong negative effect on productivity in the regression analysis, ranks last in the results of the factor analysis in Figure 6. A possible explanation is that although infrastructure constraints indeed have a negative impact on performance, we know that the better performing firms complain more about infrastructure. Since the highly productive firms are in the minority in a simple count of companies, their complaints about infrastructure weigh less in comparison to the complaints of lesser performing firms which complain more about lack of financing and taxation. (see Figure 6). In contrast, the regression analysis presented above, shows the importance of infrastructure in enhancing productivity and is more relevant to policy than the ranking of perceptions. We argue therefore that for prioritisation of policy measures, the effects of investment climate constraints on performance or productivity in a regression analysis – and not the ranking shown in this section - should be the guide.

**Figure 6: Combined investment climate measures**

Source: BEEPS II, PICS 2003 Serbia

# **Policy implications for entry, privatization and exit**

As presented above, the results from the international survey show that privately-owned firms, whether privatized or new private,
25 potential bias would also preclude estimating the village level spillover effect of social capital that is the most interesting. We can however reduce the plausibility of incidental association and omitted variable bias stories in two ways.

First, we can ask how bad it could possibly be. If an omitted variable were biasing the social capital coefficient upward, the magnitude of the bias would be worse the larger the effect of this omitted cluster specific variable. The importance of excluded cluster variables can be examined by comparing the R<sup>2</sup> of various regressions explaining household incomes. With only household characteristics the R<sup>2</sup> is .262 in the HRDS; adding social capital and cluster distance to markets and agroclimatic dummies raises it to .291, while adding the cluster averages of all the individual variables (education, assets, etc.) raises it further to .342. A full set of cluster dummy variables in addition to the household characteristics raises the R-squared to .462, so there is about 12 points of unexplained cluster variation. This relatively large variation in household incomes that is both cluster specific and unexplained by the included variables might suggest a potentially large omitted variables bias. However, the correlation of the cluster effect estimates across the two data sets is only .07. This suggests the unexplained cluster variation is mostly temporary random shocks or measurement error and not due to some time persistent excluded variable correlated with social capital which would significantly bias the resultsn2.

**'2** The alternative is that the lurking omitted variable is so highly correlated with social capital that its effect once controlling for social capital is very small. This however, begins to beg the question, as it is extremely unlikely the "omitted" variable and social capital are perfectly correlated unless both are in some sense a proxy for the same underlying social reality.