rafmacalaba/datause-encoder-data huggingface dataset:
chapters reported in the data: SPS measures, TBTs, PSIs, quantity control measures, and price control measures.

We include a variety of control variables in equation 1. First, we isolate the impact of tariffs from NTMs. _ln_ ( 1 + _τijpt_ ) controls for the applied ad valorem tariff rate imposed by destination markets _j_ on imports from Colombia _i_ in HS 6-digit product _p_ in year _t_ . To include zero tariffs, we add 1 before taking the natural log.

Second, we control for product-level demand conditions in destination markets, such as time-varying business cycles or import-demand shocks. These are proxied by the natural log of Imports _f pjt_ , which represents imports by destination market _j_ of product _p_ in year _t_ . Note that we subtract firm _f_ ’s exports from these imports to avoid correlation by construction for large firms, hence the subscript _f_ in Imports _f pjt_ .

Moreover, we exploit the granularity of the data to control for three types of interacted fixed effects. This strongly reduces concerns about alternative explanations. First, firm-destination-HS 6-digit product fixed effects _ωf jp_ account for unobserved heterogeneity at the panel level and allow us to identify our coefficients of interest based on within firm-destination-product changes in exports as NTMs enter into force over the sample period. Second, firm-product-year fixed effects _ω f pt_ capture product-specific productivity differences of Colombian firms and supply shocks. Third, destination-year fixed effects _ωjt_ account for time-variant demand and macroeconomic shocks in destination markets.<sup>9</sup> Taken together, these fixed effects control for multilateral resistance terms in a structural gravity equation (Baier and Bergstrand, 2007; Felbermayr et al., 2020). Our final control variables are the Inverse Mills Ratios of each NTM type l _I M_<sup>ˆ</sup> _Rljpt_ , discussed in detail in the next section.

Our coefficients of interest _β_ 2, _l_ measure the percentage change in export value, probability of participation, and exit following the introduction of an NTM of type l for Colombian exporters to a destination-product market relative to product-destination markets without NTMs.

> 9Note that we cannot control for HS 6-digit product-destination-year fixed effects as they would absorb all variation in our variable of interest _NTM_<sup>_l_</sup> . _pjt_

19
There also appears to be no evident association of wages to either economy-wide productivity or even firm level productivity changes. This can be traced to weak managerial bargaining power and, to some extent, the associated attachment of t1he govenment to social peace. We then have the result that even if unions are not that powerful, managers prefer 'the easy life'. The bill may ultimately end up with the fisc and/or the banking system 19.

# Unemployment

Unemployment rose rapidly after 1989 from a negligible base, driven by the sharply asymmetric dynamics of state and private sector employment. Both registered unemployment and the ILO-based rate began to fall in 1993 after a peak reached in February 1993. The registered rate then was 13.6% and regional rates varied between c7-22% 2.

First, wve can sum up net shifts between labor market states between 1.1.1990 and 1.1.1993. using the classification of the CSO's "Munkaeromerleg',and between 1.1. 1993 and 1.1. 1994., using the Labor Force Survey **21.** In constructing Table 14 we calculate the stocks for 1 January by takdng the average of the preceding and following quarters, assume that employment abroad stayed on its 1993 level (45,000) and add it to the employment stock. As indicated above, the decline in employment is large and continuing. At the same time, movements out of the labor force have indeed been large.

The recent fall of unemployment cannot be attributed to a rise **OI** employment. In 1992.Q4-1993.Q4. unemployment rose by 30,000 while employment declined by 232,000; since then both unemployment and employment have been falling. The inflow to unemployment decreased slightly relative to 1992. On the other side, after controlling for seasonal effects, we find that the rate of registered unemployed finding a job remained in the range of 3-4 per cent per month since early 1992. A net shift towards non-participation can be attributed to a "Note that managers in state firms may have autonomy in decision-maldng but not nocesarily large relative bargaining power with respect to workers. In so far as managers have acquired or maintained their positions through political connections their bargainig power with respec **t** o government (and henice for subsidies) may be large.

2DThe range was larger - 7-46% - when using labor office areas as the reference.

2a The latte is more stringent in classifying people out of work as unemployed so its figure for 1.1.1993. is 498,000 as opposed to 680,000 in the 'Munkaeromerleg".

33
# **3. Survey Content: When Data Are Too Rich**

# **3.1 Income and Expenditure Aggregates**

The first question one must ask about the CMte d'Ivoire Living Standards Survey is whether it achieved its prime objective of measuring households' levels of living. The answer is a clear "yes." The survey contains all necessary information to construct a complete set of current accounts for the household. Both income and expenditure are collected in great detail, and savings can be derived as the residual. With respect to the capital account, the survey collects a fair amount of information on household assets and liabilities, although no attempt was made to be comprehensive in coverage (mainly because of expected respondent resistance to detailed inquiries about financial assets and liabilities).

However, in order to achieve its objective, the CILSS can.e equipped with some extraordinarily lengthy and complex questionnaire modules on income and expenditure. In total, there are about a thousand questions in the survey and the questionnaire takes an average of six hours of interview time in the field, spread over two visits. The questionnaire is a highly non-linear design, with complex skip patterns, yielding data with a multi-level hierarchical structure (households, individuals, household enterprises, crops, expenditure categories, loans, recipients of transfers, etc.) All this not only represents a significant challenge for the data collection in the field, but it adds tremendously to the analytical complexity and cost. One overwhelming lesson learned in the course of analyzing the C **LSS** data is the difficulty and time-consuming nature of constructing the household accounts from this survey. It speaks for itself that it took more than three years, after the end of the field work, before the first analysis which used all four years of CILSS data on income and expenditure was completed (see Grootaert, 1992).

In view of the complexity and detail of the different income and expenditure modules, it is not surprising that there is more than one way to build the relevant income

**3**
|**TABLE 19: OVERALL QUALITY SCORE O**|**F CHILD HEALTH† AND ANC CONSULTATION**<br>(1)<br>Child health consultations|**S‡**<br>(2)<br>ANC consultations|
|---|---|---|
|Post indicator|0.030|0.129***|
||[0.041]|[0.029]|
|PBF/Post interact|-0.021|-0.056|
||[0.055]|[0.042]|
|Control 1/Post interact|0.044|0.015|
||[0.056]|[0.045]|
|Control 2/Post interact|-0.029|-0.042|
||[0.058]|[0.042]|
|p-value PBF vs. C1|0.230|0.131|
|p-value PBF vs. C2|0.888|0.742|
|p-value PBF vs. C3|0.705|0.191|
|Baseline mean in C3|0.511|0.592|
|N|575|729|

* = p < 0.10, ** p < 0.05, *** p< 0.01

<mark>† Results from difference-in-differences regression models examining the effect of PBF on the overall quality score from child health consultations from direct observation of child health consultations. Regression model adjusted for individual child-level variables (age, sex), maternal variables (marital status, education level) and facility-level control variables (type of health facility public/private/religious, urban/rural). ‡Results from difference-in-differences regression models examining the effect of PBF on the overall quality score from antenatal care consultations from direct observation. Regression model adjusted for individual variables (age, literacy, education, marital status), and facility-level control variables (type of health facility public/private/religious, urban/rural). In addition to the standard controls, also controlled for whether it is the first pregnancy (Y/N) and where in the pregnancy the woman is (<32 weeks, 32-35 weeks, >35 weeks). Standard errors were clustered at the health facility level.</mark> 52
to continue emitting large amounts of CO2 in the future in the absence of any policy intervention; this finding is consistent with Lee et al. [38].

**Table 1.** System GMM estimation results for **full sample (1980-2015)** .

|Dependent variab<br>|le:<br>2<br>ln_CO_<br>|||||
|---|---|---|---|---|---|
|Variable|(1)|(2)|(3)|(4)|(5)|
||Global panel|HI countries|UMI countries|LMI countries|LI countries|
|2<br>1<br>ln<br>_it_<br>_CO_<br>|0.718<sup>***</sup>|0.445<sup>***</sup>|0.584<sup>***</sup>|0.640<sup>***</sup>|0.085<sup>**</sup>|
||(35.898)|(15.847)|(16.592)|(16.156)|(2.246)|
|ln<br>_it_<br>_EC_|0.057<sup>***</sup>|0.594<sup>***</sup>|0.059<sup>**</sup>|0.027<sup>***</sup>|0.050<sup>*</sup>|
||(2.960)|(16.444)|(2.275)|(7.270)|(1.780)|
|ln<br>_it_<br>_ECS_|0.331<sup>***</sup>|0.052<sup>**</sup>|0.475<sup>***</sup>|0.507<sup>***</sup>|0.976<sup>***</sup>|
||(14.089)|(2.323)|(11.507)|(9.463)|(11.504)|
|ln<br>_it_<br>_EI_|0.445<sup>***</sup>|0.619<sup>***</sup>|0.509<sup>***</sup>|0.471<sup>***</sup>|0.308<sup>***</sup>|
||(13.891)|(17.098)|(9.586)|(7.369)|(11.927)|
|ln<br>_it_<br>_PCG_|0.290<sup>***</sup>|1.504<sup>***</sup>|0.768<sup>***</sup>|0.334<sup>***</sup>|0.267<sup>***</sup>|
||(9.283)|(18.572)|(7.756)|(6.267)|(3.722)|
|2<br>ln<br>_it_<br>_PCG_|-0.045<sup>***</sup>|-0.201<sup>***</sup>|-0.148<sup>***</sup>|0.049|-0.185<sup>**</sup>|
||(-3.649)|(-14.844)|(-3.163)|(1.647)|(-2.054)|
|ln<br>_it_<sup>_P_</sup>|0.270<sup>***</sup>|0.534<sup>***</sup>|0.838<sup>***</sup>|0.886<sup>***</sup>|0.865<sup>***</sup>|
||(13.040)|(18.528)|(12.071)|(9.131)|(13.223)|
|Constant|2.605<sup>***</sup>|1.253<sup>***</sup>|1.186<sup>***</sup>|0.985<sup>***</sup>|0.844<sup>***</sup>|
||(9.292)|(4.201)|(3.570)|(7.648)|(3.311)|
|Hansen test|0.103|0.194|0.649|0.582|0.412|
|AR(2) test|0.229|0.152|0.172|0.202|0.188|
|Turning point|25,084|42,152|13,391|-|-|

**_Note:_** ***, **, and * denote statistical significance at 1%, 5%, and 10%, respectively; the values in

parentheses represent t-statistics; the values of Hansen and AR(2) tests represent p-values; and the unit of turning point is 2010 US$.

16
Finally, even in an environment where local capture is limited or controlled, local capacity to make informed decisions and effectively implement them is crucial to the success of decentralization policies. In high-income countries such as the United States, conventional wisdom suggests that institutional arrangements that favor and foster accountability, competition, and autonomy are the most effective in improving schools (Hanushek and Woessman, 2007 & 2009). Differences between the high and low-income countries, and even between India and The Gambia, render extrapolation from existing evidence to poor country settings difficult. The interaction effects reported earlier suggest that baseline local capacity may constrain the benefits from local empowerment. We conclude that a combination of low baseline local capability, the complexity of the intervention, and the low quality of other educational inputs are the main factors explaining the limited impact of the intervention. School-based management models will need to be appropriately adapted to the needs of local communities.

# **2. The context**

This section combines administrative data with our baseline data to describe the education system in The Gambia. Basic education in The Gambia lasts nine years. The first six years are called Lower Basic and the following three years are Upper Basic. Upon completion of basic education, students take a national exam (9th grade exam) that determines admission to high school. High school lasts an additional three years.

The education sector in The Gambia has been growing rapidly in recent years. The total number of students enrolled in the formal education system doubled between 1998 and 2010. Nearly every community has its own lower basic school or has one within a fivekilometer radius. The basic infrastructure (classrooms, tables, chairs, water) is in general sufficient even in rural areas. However, due to the increased enrollment, many schools have adopted a double shift system where one group of students comes in the morning and the other group in the afternoon.

In terms of organization, there is a Ministry of Basic and Secondary Education (MoBSE) in charge of the education system up to 12th grade. The country is organized in six administrative regions: five regions outside the capital plus the district of Banjul (the capital city). Each of the regions has a regional educational office with a regional director. The regional directors are the key liaisons between the schools in their region and the ministry. They ensure the monitoring of activities at the school level and collect key indicators on a regular basis.

The baseline data from this research (gathered in 2008) include specific information about Gambian schools (Adebimpe, Blimpo, and Evans, 2009). Those data demonstrate that overall the basic infrastructure of schools was in good condition.<sup>4</sup> The main buildings 4 These assessments are based on visual observation by the enumerators. We limited self-reported information whenever possible. For example, when inquiring about management practices such as 5
# References:

Almec Corporation - Pacific Consultants International, 2000. "The<sup>StudyoftheNational</sup> Transport Development Strategy in the Social Republic of Vietnam<sup>(VITRANSS)." Final</sup> Report. Prepared by Japan International Cooperation Agency, Ministry of<sup>Transport, Socialist</sup> Republic of Vietnam and the Transport Development and Strategy Institute<sup>(July 2000).</sup> Athukorala, Prema-Chantra. 2002. "Trade Policy Reforms, Export<sup>Strategies,andthe</sup> Incentive Structure." Background Paper to the World Bank study "Vietnam<sup>Exports:Policy</sup> and Prospects" (September 2002).

Centre for International Economics (CE). 1999. "Non-Tariff<sup>Barriers inVietnam:A</sup> Framework for Developing a Phase Out Strategy". (September<sup>1999)</sup> Centre for International Economics (CE). 1998. "Vietnam's Trade<sup>Policies1998".(July</sup> 1998).

Centre for International Economics (CIE). 1997. "Vietnam's Trade<sup>Policies1997".(August</sup> 1997).

Fukase, Emiko and Martin, Will. 1999. "A Quantitative<sup>Evaluationof Vietnam'sAccession</sup> to the ASEAN Free Trade Area." Policy Research Working Paper<sup># 2220.TheWorld Bank.</sup> (November 1999).

Hopkins, Ben. 2002. "The Impact of Vietnan's Infrastructure on<sup>Exporters:ASurvey."</sup> Background Paper to the World Bank study "Vietnam Exports: Policy<sup>and Prospects" (March</sup> 2002).

Ianchovichina, Elena. 2001. "Vietnam's Duty Drawback System: Current State and Implications". World Bank Policy Note. (November 2001).

Ianchovichina, Elena and Martin, Will. 2001. "Trade Liberalization<sup>in China's Accessionto</sup> WTO". Journal of Economic Integration 16(4), pp. 421-444.

Ianchovichina, Elena and Martin, Will. 2002. "Economic Impacts of China's<sup>Accessionto</sup> the WTO". Paper presented at the seminar on WTO Accession, Policy<sup>ReformandPoverty</sup> Reduction in China. World Bank. Beijing. June 28-29, 2002.

Ianchovichina, Elena; Suthiwart-Narueput, Sethaput and Zhao,<sup>Min.</sup> 2002. "Regional Impact of China WTO Accession". Paper presented at the Regional<sup>Workshop on East Asia</sup> Trade and Poverty. Singapore. (September 2002) International Monetary Fund (2002). Vietnam Country Report No 02/151.<sup>(July 2002).</sup> Liqun, Jin. 2002. "China: One Year into the WTO Process." Speech<sup>madespoke thisweek</sup> at the World Bank on October 22, 2002. See: http://www.worldbank.org/wbi/B-SPAN/ or http://www.worldbank.org/wbi/B-SPAN/sub_china_seminar.htm Lord, Montague. 2002. "Vietnam's Export Competitiveness:<sup>Tradeand Macroeconomic</sup> Policy Linkages." Background Paper to the World Bank study "Vietnam<sup>Exports:Policy and</sup> Prospects" (March 2002).

15
Figure 1: Natural Resources, the Environment, and Trade <!-- Start of picture text --> EnvironmentallyPreferableProducts (EPPs) Goods for Environmental Management (GEMs)<br>TradableEnvironment-RelatedProducts OtherProducts<br>Production by-product<br>externalities<br>•Local/Regional:( SO2) X = F(K,V, NRP, θX)...(a)<br>•Global:( GHGs,CFCs)<br>E =G(X,T,Y,θE) (b)<br>•Transport emissions<br>•Resource depletion T =H(NRC,NRP, E(T),θT) (c)<br>•disease/Invasive Species<br>/ecological diversity<br>Natural Resources in consumption (NRC) Natural Resources in production (NRP)<br>Species, genetic resources, scenery Non renewableRenewable<br>Fuels, Forestryproducts,<br>Fresh water<br>Mineralproducts<br><!-- End of picture text --> (a) X= Output; K= physical and human capital; V=Intermediate inputs, NRP= Natural resources in production; θX=Output-related policies (e.g. taxes or subsidies on fossil fuels) (b) E= output-related emissions; Y=per capita income; θE= caps/taxes on emissions (c) T= environment-related Trade; NRC=natural resources in consumption; E(T)=Emission related to transport; θT=border taxes GHG= Green-house gases; CFC=chlorofluorocarbons;SO= Sulfuric oxides; 6
on the data.

# 3.1 Demand and technology parameters

Our main parameters of interest are the capital adjustment cost and labor mobility cost parameters. We denote them with the vector Γ = ( _γ_ 1 _, γ_ 2 _, γ_ 3; _Ce, Cu_ ) . We estimate Γ by simulated method of moments (SMM, McFadden, 1989; Pakes and Pollard, 1989). Prior to the estimation of Γ we dene or estimate values for the other model parameters.

Table 1 provides a list of the parameters that are predened or estimated prior to the SMM. We set the discount factor _β_ to 0.95. The depreciation rate _δ_ is 0.0991. Due to lack of data for Argentina, we compute it as a weighted average of sectoral depreciation rates for the U.S. reported by the Bureau of Economic Analysis, using number of rms as weights. From National Accounts, we recover the consumption weights for each sector _φj_ in order to compute sector demand. Because demand is assumed to be Cobb-Douglas, a constant fraction given by the CPI weights is spent on each product. Argentine families spend on average 31.3 percent of the budget on food and beverages, 37.4 percent on non-tradables and 21.1 percent on other manufactures; textiles and apparel account of 5.2 percent, and minerals and metals for about 2.5 percent each.

The technology parameters, including coecients of the production function and the stochastic processes for the sector-level and rm-level shocks, are estimated with the rm panel and reported in Table 1 as well. We estimate the Cobb-Douglas output elasticities using the regression method of Ackerberg, Caves and Frazer (2015) with data on revenue, employment and capital stock. The estimates for _αL_ and _αK_ are 0.619 and 0.283, both statistically signicant. The input requirement for materials _vm_ is computed as a weighted average of the share of expenditure in materials on total value of production. The estimate is 0.41.

We assume that the sector-level and rm-level shocks, _bjt_ and _Aijt_ follow independent AR(1) processes with correlation coecients _ρb_ , _ρa_ , variance of the innovation terms _σb_<sup>2and</sup><sup>_σ_</sup> _a_<sup>2,constant</sup> _b_ for the aggregate shocks and, without loss of generality, a constant of zero for the idiosyncratic shocks. Since the shocks are not observable we rst infer rm-level prot shocks from data on prots and inputs, the functional form assumption of the prot function, and the estimates of the production function parameters, as in Cooper and Haltiwanger (2006). We split rm-level prot shocks into _b_ and _A_ by computing sector-year means and subtracting them from rm-level shocks. We then run two separate AR(1) regressions to estimate the correlation coecients, variance and 15
the performance of the sample: (i) RSEs of less than 10 percent from the mean nominal per capita expenditure for each district and division (urban/rural), as well as for each quarter at the national level; (ii) RSEs of less than 10 percent from the mean nominal household expenditure for each district and division (urban/rural), and for each quarter at the national level; and (iii) SEs of less than 5 percent of the poverty headcount ratio for each district and division (urban/rural), and for each quarter at the national level. Final sample size determination, described in the next section, is based on the nominal per capita expenditure target variable, but we evaluate performance of the sample in terms of these three accuracy statistics to confirm that the final sample provides reliable poverty and welfare estimates at all these levels.

It is important to highlight that RSEs are not used to evaluate the performance of the sample in terms of poverty rates. Instead, we use SEs for poverty rates. This responds to two reasons. First, the value of the RSE can go up very quickly when the poverty rates are small as it is the case in several districts in Bangladesh. For instance, in 2010 poverty rates of Kushtia and Noakhali were as low as 3.6 percent and 9.6 percent, while the poverty rates of the poorest districts of Kurigram, Barisal, and Shariatpur were as high as 63.7 percent, 54.8 percent, and 52.6 percent respectively. This large heterogeneity across districts in terms of poverty rates results in misleading results when interpreting RSEs. The poorest districts with elevated poverty rates will have relatively low RSEs, while better-off districts with low poverty rates will have relatively large RSEs. Therefore, the RSE is not a good accuracy statistic to use for target variables whose distributions show high levels of dispersion. Second, poverty measurement has traditionally focused on SEs to evaluate the precision of the poverty rates, which aligns more closely with the standard literature on poverty measurement and its practical applications.

# **3.6. Estimation of the sample size**

The HIES 2016/17 uses a stratified, two-stage clustered sampling design. In the first stage, PSUs are equally allocated across the 64 districts in Bangladesh: 36 PSUs are allocated to each of the 64 Bangladesh districts, bringing the total number of PSUs in the sample to 2,304 (36 PSUs*64 districts). An equal number of PSUs are allocated across districts because the HIES 13