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Table 3: Poaching Elasticity with Respect to Prices in China

|LHS = Ln(PIKE)||O|LS||2|SLS, IV =|Gold Pric|e|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Ln(Median Price), China|0.118|0.132|0.132|0.163|0.402|0.395|0.407|0.347|
||(0.061)|(0.053)|(0.053)|(0.057)|(0.066)|(0.066)|(0.066)|(0.067)|
|Ln(Total Carcass Found)||||||0.010<br>(0.028)|||
|Number of Conficts|||0.024|-0.052|||-0.003|-0.110|
||||(0.069)|(0.105)|||(0.067)|(0.104)|
|GDP Growth Rate|||-0.003|0.009|||-0.005|0.007|
||||(0.005)|(0.008)|||(0.005)|(0.008)|
|Total Seizure||||0.028||||-0.007|
|||||(0.047)||||(0.046)|
|Constant|-0.626|0.970|1.077|-0.299|0.629|0.678|0.628|-0.229|
||(0.043)|(2.187)|(2.229)|(0.541)|(1.430)|(1.437)|(1.460)|(0.395)|
|||||||First|Stage||
|Ln(Gold Price)|||||0.869|0.911|0.873|1.145|
||||||(0.332)|(0.345)|(0.335)|(0.429)|
|N|422|422|422|248|422|422|422|248|
|First Stage F statistic|||||153.899|155.102|150.511|140.367|
|p-val (_H_0 :_β_ == 1)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Site FE|No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean, Ln(PIKE)|-0.603|-0.603|-0.603|-0.696|-0.603|-0.603|-0.603|-0.696|
|SD, Ln(PIKE)|0.715|0.715|0.715|0.739|0.715|0.715|0.715|0.739|

Note: Standard errors clustered at the year level with Moulton correction in parentheses. This table reports the results of estimating equation 11 with OLS and 2SLS. The dependent variable variable is the log of PIKE at the site level. “Ln(Median Price)” refers to the price in China. Variable definitions and sources are described in detail in the text.

preferred local price coefficient (Table 1, column 6) with the post-2002 pass-through coefficient (Table 2, column 6) we get 0 _._ 431 _×_ 0 _._ 900 = 0 _._ 39, roughly equal to the measured elasticity with respect to global prices.

How informative are our estimates about the impact of policies in consuming countries such as China? In particular, China’s recent decision to ban its domestic market for ivory is considered the single most significant step towards the conservation of elephants since the 1989 CITES ban on the international trade of elephant specimens. Vigne and Martin (2017) document a drop in raw ivory prices in China from US$2,100 per kg in early 2014 down to US$730 per kg in early 2017, i.e. a price drop of as much as 65.2 percent. The estimated elasticities imply that poaching is expected to fall by an average of 34.3 percent. A 0.63 PIKE that prevailed in 2014 suggests a fall in PIKE to 0.41 in 2017.<sup>11</sup>

> 11The number of elephants poached declines by 1 _−_ (1 _−_ 0 _._ 652)0 _._ 398 _≈_ 34 _._ 3 percent, where 0.398 is the supply elasticity from column 6, Table 3. To infer the changes in PIKE from the number of elephants poached, we assume a constant total number of carcasses found within a short-span of time. This implies that PIKE will decline by 34.3 percent as well, and thus 0 _._ 63 _×_ (1 _−_ 0 _._ 343) _≈_ 0 _._ 41.

21
One would hope that MGNREGS—the country’s (and possibly the world’s) largest antipoverty program—worked well in India’s poorest states, where it is presumably needed most. Bihar is one of the poorest two or three of India’s larger states if not the poorest. In 2009/10, 55% of its rural population of 90 million lived below the poverty line.<sup>6</sup> The state has had one of the lowest long-run trend rates of poverty reduction in India; indeed, there was virtually no longrun trend over 1960-2000 (Datt and Ravallion, 2002). Yet Bihar has the lowest participation rate in MGNREGS of any state. Figure 1 plots survey-based participation rates (the share of the population participating in the scheme) against poverty rates (the share of the population living below the poverty line) across states. Overall, there is virtually no correlation between the two variables. And Bihar stands out as having one of the lowest participation rates and highest poverty rates. Bihar’s participation rate is 0.22 below the regression line in Figure 1, making it a statistical outlier.<sup>7</sup> So MGNREGS in Bihar poses a puzzle. (When referring to MGNREGS in Bihar we will abbreviate to “BREGS.”) The state has one of the highest poverty rates, but lowest participation rates in this massive antipoverty program. Also, Bihar’s participation rate is low conditional on its poverty rate. Essentially, the central government is willing to fund more employment on the scheme in Bihar, where it is surely needed, but the money is not flowing to workers.

One possibility is that a large share of the central government’s disbursements is being siphoned off somehow, before the money reaches workers. Dutta et al. (2013) address this question by using a household survey representative of rural Bihar (the same survey used in this paper and described further below) to estimate the gross wages and employment received by households. They then compare this with the total central disbursements to the state recorded in the official administrative data. The survey aggregates accounted for 80% of the employment claimed in the administrative data for 2008/09, rising to 86% in 2009/10. (The survey aggregates account for a slightly lower share of the administrative data on wages paid, namely 75% in the first year and 80% in the second.) So, while there are signs of leakage, this does not explain the low participation rate of workers based on survey data. It appears that the center’s money is not reaching Bihar’s poor for some other reason. It may well be that people are too ill-informed about their rights, or of how to

> 6  Based on official Planning Commission poverty lines for 2009/10. 7  The t-statistic is -3.16 (using a White standard error).

4
raise adjustment costs for firms, since they have a higher fraction of firms in the range of activity.

Second, Panel B of Figure 3 considers the case of two sectors with the same variance of productivity shocks; however sector 2 has higher adjustment costs and hence a wider range of inactivity (which is illustrated by _z◦_ 2 _< z◦_ 1 and _z_ 2<sup>_◦>z_</sup> 1<sup>_◦_).Ifadjustmentcosts</sup> in a country increase for both sectors because of more stringent regulations - for example, stricter employment protection legislation - the thresholds will be pushed to _z◦_ 1 _∗ < z◦_ 1, _z_ 1<sup>_◦_</sup> _∗_<sup>_<z_</sup> 1<sup>_◦_,</sup><sup>_z◦_2</sup><sup>_∗<z◦_2and</sup><sup>_z_</sup> 1<sup>_◦_</sup> _∗_<sup>_<z_</sup> 1<sup>_◦_.Sectorswithoriginallyloweradjustmentcostswillbe</sup> more adversely affected, since increase in adjustment costs will push a higher fraction of firms from the range of adjustment (reallocation) to the range of inactivity (no reallocation).

# **5.2 The Estimation Model**

We explore the links between the regulatory environment in which firms operate and job turnover by exploiting the observed industry/size variations through a difference-indifference approach (see Rajan and Zingales [1998]).<sup>10</sup> The test is constructed as follows: we identify an industry/size propensity for job reallocation from the United States data. Under the assumption that regulations in the labor and goods markets in the United States are among the least restrictive in our sample, variation in job reallocation across industry/size cells in the United States should proxy for the technological and market driven differences in job reallocation in the absence of policy induced adjustment costs. Under the additional assumption that these technological and market driven differences in the demand for job reallocation carry over to other countries, we assess whether industry/size cells that have a greater propensity for job reallocation are disproportionally affected by regulations that raise adjustment costs. This would imply that, ceteris paribus, industry/size cells with more volatile idiosyncratic profit shocks and more frequent adjustment of factors should be more strongly affected by regulations raising adjustment costs than those industry/size cells with less volatile idiosyncratic profit shocks and less frequent adjustment. The advantage of our approach compared to standard cross-country/cross-industry empirical studies is that we exploit within country differences between industry/size cells based on the interaction between country and industry/size characteristics. Thus, we can also control for country and industry/size effects, thereby minimizing problems of omitted variable bias and other misspecifications.

> 10The difference-in-difference approach has already been used in the corporate literature (e.g., Classens and Laeven [2003]), in the analysis of firm dynamics (Klapper et al. [2004]) and in the analysis of job flows (Micco and Pages [2004]).

26
shocks. CCP cannot be used as starting point for the NPM algorithm when the sample size is very small (when we simulated 2000 agents the CCP method did not converge to finite numbers). Naturally, it is possible to use PPML estimates as a starting point for the NPM algorithm. The row labeled as “PPML-NPM” shows estimates of a variation of NPM method that uses PPML as a starting point rather than CCP. The extra NPM-loop after the PPML regression reduces the standard errors, but it is difficult to implement when there are aggregate shocks.

# **5 Conclusion**

We present a novel and computationally efficient method for estimating dynamic discrete choice models with heterogeneity and time-varying resistance (i.e moving cost) parameters. The method performs well with large number of choices, sparse decision transition matrices (caused by small sample size) and aggregate shocks. All expectations of agents are fully accounted for in the first step regression, which allows us to be agnostic about agents’ expectations and distribution of aggregate shocks. Therefore the method can be used for estimation out of steady state. Potential applications are migration, sectoral and occupational labor mobility models with large number of discrete choices, macroeconomic shocks and limited heterogeneity.

20
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exports flows, as well as measurement error associated with the use of eBay cross border flows as a proxy for online exports. We address this by instrumenting online and total trade flows using three exogenous sources of variation. First, we use the time-varying distance determinant of online and total exports as in Feyrer (2009). We expect the impact of distance to vary over time due to innovations in the transport sector that has shifted shipments towards air transport, as well as technological progress in the information and telecommunication sector. We expect the impact of distance to be different for online and total trade because technological improvements, which reduce information barriers and that we proxy using geographic distance, are likely to be stronger for online transactions given the information providing mechanisms in online platforms such as the top rated seller or buyer information in online markets (Lendle et al., 2016). Thus the variation in the use of these instruments comes from the differential impact of distance across time and across online and The second set of instruments capture foreign demand shocks for online and total exports in importing countries. In the case of online trade, domestic demand is computed as the sum of domestic online transactions and online imports. For total trade, we simply use the GDP of the importing country. We expect demand in the importing country to be positively correlated with online and total export flows of exporting countries, and uncorrelated with other determinants of the wage gap in the exporting country.

Finally, we use the average tariff imposed by the importing country. To differentiate the tariff imposed on online and total trade, we use the average import tariff on manufacturing products for online trade and the average tariff on all products (including raw materials and agricultural products) for total trade. There is no reason to believe that the average tariff imposed by the importing country is correlated with the wage gap in the exporting country other than through its impact on bilateral trade, which we expect to be negative.

We estimate the following six equations: 5
**<u>Figure 13. CGE trade liberalization scenarios</u>**

||“Community reforms” at<br>Mercosur|Mercosur-EU|Mercosur- Pacific Alliance|
|---|---|---|---|
|Tariffs|Tariffs in all Mercosur countries<br>reduced by 50 percent|Bilateral tariffs: in Mercosur from 10.5<br>to 3.1percent; in the EU from 2.5 to<br>0.7percent|<br>Bilateral tariffs: in Mercosur from 1.3 to<br>0.3 percent; in the PA from 2.6 to<br>0.6percent|
|NTMs|Reduced by 15 percent intra-<br>Mercosur; export controls<br>eliminated|Reduced by 15 percent among parties;<br>export controls eliminated|Reduced by 15 percent among parties;<br>export controls eliminated|

The largest economywide gains would come from coordinated trade reforms within Mercosur. The second largest economywide gains would come a reciprocal preferential trade agreement between Mercosur and the EU. Coordinated trade reforms within Mercosur would boost exports and imports by 7.5 and 6.6 percent, respectively, while real GDP would experience a 0.93 percent increase (above baseline projections in 2030). The effects on output would be different across sectors. Figure 15 shows the output percentage deviations from the baseline in 2030. The largest negative effects are seen for manufacturing (-0.8 percent), led by retrenchments of the textiles and apparel sector (-$2.37 billion) and vehicles (-$2.17 billion). All other broad sectors would expand relative to the baseline as resources shift to other activities. On a dollar basis, the services sector expands the most (by $16.6 billion), followed by natural resources and energy products ($10.3 billion), and agriculture ($8.1 billion). The second largest economywide gains would come from a reciprocal preferential trade agreement between Mercosur and the EU. Exports and imports would increase by 5.5 and 4.9 percent, respectively, while GDP would experience a 0.58 percent increase (above baseline projections in 2030). All big sectoral aggregates would experience positive output variations (above baseline projections in 2030): manufacturing would experience the highest expansion (2.2 percent), led mainly by expansion of the footwear sector.

**Figure 14. Economy wide effects - deviations Figure 15. Sectoral output deviations from the from the baseline, 2030 (%) baseline, 2030 (%)**

<!-- Start of picture text --> 4.0 3.4<br>3.5<br>Mercosur-Pacific Alliance 3.0 2.2<br>0.41 2.5<br>2.0<br>1.5<br>1.0<br>Mercosur-EU 0.5<br>0.58 0.0<br>-0.5<br>-1.0 ‐0.8<br>Coordinated trade reforms<br>Coordinated trade Mercosur-EU Mercosur-Pacific<br>within Mercosur 0.93 reforms within Alliance<br>Mercosur<br>0.00 2.00 4.00 6.00 8.00<br>Agri. And food Manufacturing<br>Imports Exports Real GDP NR and energy Services<br>Source: World Bank staff elaboration using GTAP-Linkage Source: World Bank staff elaboration using GTAP-Linkage<br>model model<br><!-- End of picture text --> 11
agricultural and food tariffs are more substantial, being nearly halved on average (15.9 percent to 8.2 percent), as are those for textiles and clothing (from 7.3 percent to 4.1 percent).

The implied tariff reductions for developing countries are similarly modest, but for different reasons. The average tariff drops from 8.4 percent to 7.5 percent in the case of the central Doha scenario, and further to 6.8 percent when developing-country cuts (including those of LDCs) are fully reciprocal (Doha-all). The principal reason for the modest reductions lies in the huge gap between applied tariffs and tariff bindings. Average bound tariffs in agriculture are 2.4 times as high as applied tariffs, and this ratio is 5 in the case of the LDCs.<sup>16</sup> Thus, when bound tariffs are reduced, there is often no real liberalization because they remain above the currently applied tariffs.<sup>17</sup>

# **4. Global Merchandise Trade Reform**

## **4.1 Welfare Impact**

Table 1 presents the standard welfare impact, or change in real income.<sup>18</sup> Global merchandise trade reform that includes elimination of domestic support would generate US$287 billion in additional income in 2015 at the global level compared with the baseline scenario.

Almost 70 percent of the global gains would accrue to the high-income countries. However, as a share of income, developing countries do somewhat better, with an average increase of 0.8 percent compared to 0.6 percent for high-income countries. The results vary widely across

> 16. The scenarios, though assessed at an aggregate level, are based on formula cuts at the six-digit level thanks to the MAcMaps database that also incorporates the existing tariff bindings.

> 17. Francois and Martin (2004) have explored potential gains from setting bindings at applied levels even in the absence of actual liberalization. It could encourage imports by reducing the risk of reversal in tariff policies. 18. Technically, it is a measure of Hicksian equivalent variation.

10
16 Figure 4: Trade restrictive NTMs faced by EU exporters, by type of measure <!-- Start of picture text --> Subsidies Competition Issues Government<br>3% 2% Procurement<br>Standards and Other<br>10%<br>Technical<br>Non Tariff Barriers<br>Requirements<br>3%<br>15%<br>Other Non‐Tariff<br>Measures<br>7%<br>Quantitative<br>Restrictions and<br>Related Measures<br>8%<br>Sanitary and<br>Phytosanitary Registration,<br>measures Documentation,<br>37% Customs Procedures<br>15%<br><!-- End of picture text --> Source: Authors's calculations based on EU Market Access database.

A difficult question to answer concerns the supposed proliferation over time of NTMs. There is little doubt that from the perspective of exporters, non-tariff measures are an increasing concern, particularly at times of economic crisis. Figure 5, which again is based on barriers reported to the EU Commission, shows this increase over time, with a peak around the global financial crisis. Official data, however, are not sufficiently detailed to subject this question to a systematic investigation. For instance, SPS and TBT notifications at the WTO show an increase up to the early 2000s and a flattening of the number of notifications afterwards (Figure 6, left panels). One suspects that these data may reflect lack of reporting rather than the absence of new measures, as governments may face incentives to under-report NTMs at times of economic crisis. Data on specific trade concerns partially address this problem, but still the evidence concerning a surge in NTMs is weak. The right panels in Figure 6 show a marked increase in new and cumulative (i.e. new minus resolved) concerns for technical barriers, however the number of new and cumulative SPS concerns appear relatively stable in recent years and do not show a change in
meaningful. For example, the Nigerian sample of highly educated young entrepreneurs who were selected through a nationwide business plan competition averages much higher scores than the other samples. However, we do see a large dispersion in business practices within each country. This echoes the large spread of management practices found by Bloom and Van Reenen (2007) across larger firms within countries. The dispersion is evident even within samples that screened the firms to be similar in terms of size and industry. For example, the Kenyan sample consists of female-owned firms largely operating retail trade businesses in small markets, with less than 3 employees and profits below 4,000 KSH per week (US$47). The standard deviation of the business practice score is still 0.19 relative to a mean of 0.52 even in this relatively homogenous sample. Thus there are large differences in the way even very small firms operate.

# **2.5 Stability of Business Practices over Time**

The data from Ghana, Kenya, Nigeria, and Sri Lanka enable us to examine how stable these practices are over a one year period. Figure 2 plots the business practices score in a one-year follow-up survey against that in the baseline.<sup>4</sup> The mass lies close to a 45 degree line, but there are also firms that change their practices dramatically from one year to the next. The Pearson (Spearman) correlation is 0.591 (0.593). There is thus a strong persistent component of these scores. It is unclear how much of the variation over time reflects genuine change in practices versus measurement error. By way of comparison, Bloom and Van Reenen (2007) report that for a sample of 64 firms for which repeat interviews were done by different interviewers of different managers in a plant, but at the same point in time, the correlation was 0.73, while Bloom et al. (2012) report that their correlation in re-interviews was 0.51 when done for 222 re-interviews.

# **2.6 Reporting Issues and Audit Results**

Our measures of business practices come from survey self-reports. This raises three potential concerns. The first is that the survey responses may not be informative if everyone claims to be employing good practices. The second is that there is a systematic bias in reporting after a training intervention, with individuals who have gone through training claiming to employ more practices

> 4 We exclude the firms in Sri Lanka and Kenya who were assigned to receive business training treatments for this part of the analysis in order to examine the stability of practices in the absence of an intervention intended to change these practices: the correlation falls slightly to 0.56 if we include treated firms.

7